Line data Source code
1 : /*-------------------------------------------------------------------------
2 : *
3 : * analyze.c
4 : * the Postgres statistics generator
5 : *
6 : * Portions Copyright (c) 1996-2024, PostgreSQL Global Development Group
7 : * Portions Copyright (c) 1994, Regents of the University of California
8 : *
9 : *
10 : * IDENTIFICATION
11 : * src/backend/commands/analyze.c
12 : *
13 : *-------------------------------------------------------------------------
14 : */
15 : #include "postgres.h"
16 :
17 : #include <math.h>
18 :
19 : #include "access/detoast.h"
20 : #include "access/genam.h"
21 : #include "access/multixact.h"
22 : #include "access/relation.h"
23 : #include "access/table.h"
24 : #include "access/tableam.h"
25 : #include "access/transam.h"
26 : #include "access/tupconvert.h"
27 : #include "access/visibilitymap.h"
28 : #include "access/xact.h"
29 : #include "catalog/index.h"
30 : #include "catalog/indexing.h"
31 : #include "catalog/pg_inherits.h"
32 : #include "commands/dbcommands.h"
33 : #include "commands/progress.h"
34 : #include "commands/tablecmds.h"
35 : #include "commands/vacuum.h"
36 : #include "common/pg_prng.h"
37 : #include "executor/executor.h"
38 : #include "foreign/fdwapi.h"
39 : #include "miscadmin.h"
40 : #include "nodes/nodeFuncs.h"
41 : #include "parser/parse_oper.h"
42 : #include "parser/parse_relation.h"
43 : #include "pgstat.h"
44 : #include "postmaster/autovacuum.h"
45 : #include "statistics/extended_stats_internal.h"
46 : #include "statistics/statistics.h"
47 : #include "storage/bufmgr.h"
48 : #include "storage/procarray.h"
49 : #include "utils/attoptcache.h"
50 : #include "utils/datum.h"
51 : #include "utils/guc.h"
52 : #include "utils/lsyscache.h"
53 : #include "utils/memutils.h"
54 : #include "utils/pg_rusage.h"
55 : #include "utils/sampling.h"
56 : #include "utils/sortsupport.h"
57 : #include "utils/spccache.h"
58 : #include "utils/syscache.h"
59 : #include "utils/timestamp.h"
60 :
61 :
62 : /* Per-index data for ANALYZE */
63 : typedef struct AnlIndexData
64 : {
65 : IndexInfo *indexInfo; /* BuildIndexInfo result */
66 : double tupleFract; /* fraction of rows for partial index */
67 : VacAttrStats **vacattrstats; /* index attrs to analyze */
68 : int attr_cnt;
69 : } AnlIndexData;
70 :
71 :
72 : /* Default statistics target (GUC parameter) */
73 : int default_statistics_target = 100;
74 :
75 : /* A few variables that don't seem worth passing around as parameters */
76 : static MemoryContext anl_context = NULL;
77 : static BufferAccessStrategy vac_strategy;
78 :
79 :
80 : static void do_analyze_rel(Relation onerel,
81 : VacuumParams *params, List *va_cols,
82 : AcquireSampleRowsFunc acquirefunc, BlockNumber relpages,
83 : bool inh, bool in_outer_xact, int elevel);
84 : static void compute_index_stats(Relation onerel, double totalrows,
85 : AnlIndexData *indexdata, int nindexes,
86 : HeapTuple *rows, int numrows,
87 : MemoryContext col_context);
88 : static VacAttrStats *examine_attribute(Relation onerel, int attnum,
89 : Node *index_expr);
90 : static int acquire_sample_rows(Relation onerel, int elevel,
91 : HeapTuple *rows, int targrows,
92 : double *totalrows, double *totaldeadrows);
93 : static int compare_rows(const void *a, const void *b, void *arg);
94 : static int acquire_inherited_sample_rows(Relation onerel, int elevel,
95 : HeapTuple *rows, int targrows,
96 : double *totalrows, double *totaldeadrows);
97 : static void update_attstats(Oid relid, bool inh,
98 : int natts, VacAttrStats **vacattrstats);
99 : static Datum std_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull);
100 : static Datum ind_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull);
101 :
102 :
103 : /*
104 : * analyze_rel() -- analyze one relation
105 : *
106 : * relid identifies the relation to analyze. If relation is supplied, use
107 : * the name therein for reporting any failure to open/lock the rel; do not
108 : * use it once we've successfully opened the rel, since it might be stale.
109 : */
110 : void
111 12602 : analyze_rel(Oid relid, RangeVar *relation,
112 : VacuumParams *params, List *va_cols, bool in_outer_xact,
113 : BufferAccessStrategy bstrategy)
114 : {
115 : Relation onerel;
116 : int elevel;
117 12602 : AcquireSampleRowsFunc acquirefunc = NULL;
118 12602 : BlockNumber relpages = 0;
119 :
120 : /* Select logging level */
121 12602 : if (params->options & VACOPT_VERBOSE)
122 0 : elevel = INFO;
123 : else
124 12602 : elevel = DEBUG2;
125 :
126 : /* Set up static variables */
127 12602 : vac_strategy = bstrategy;
128 :
129 : /*
130 : * Check for user-requested abort.
131 : */
132 12602 : CHECK_FOR_INTERRUPTS();
133 :
134 : /*
135 : * Open the relation, getting ShareUpdateExclusiveLock to ensure that two
136 : * ANALYZEs don't run on it concurrently. (This also locks out a
137 : * concurrent VACUUM, which doesn't matter much at the moment but might
138 : * matter if we ever try to accumulate stats on dead tuples.) If the rel
139 : * has been dropped since we last saw it, we don't need to process it.
140 : *
141 : * Make sure to generate only logs for ANALYZE in this case.
142 : */
143 12602 : onerel = vacuum_open_relation(relid, relation, params->options & ~(VACOPT_VACUUM),
144 12602 : params->log_min_duration >= 0,
145 : ShareUpdateExclusiveLock);
146 :
147 : /* leave if relation could not be opened or locked */
148 12602 : if (!onerel)
149 170 : return;
150 :
151 : /*
152 : * Check if relation needs to be skipped based on privileges. This check
153 : * happens also when building the relation list to analyze for a manual
154 : * operation, and needs to be done additionally here as ANALYZE could
155 : * happen across multiple transactions where privileges could have changed
156 : * in-between. Make sure to generate only logs for ANALYZE in this case.
157 : */
158 12594 : if (!vacuum_is_permitted_for_relation(RelationGetRelid(onerel),
159 : onerel->rd_rel,
160 12594 : params->options & ~VACOPT_VACUUM))
161 : {
162 36 : relation_close(onerel, ShareUpdateExclusiveLock);
163 36 : return;
164 : }
165 :
166 : /*
167 : * Silently ignore tables that are temp tables of other backends ---
168 : * trying to analyze these is rather pointless, since their contents are
169 : * probably not up-to-date on disk. (We don't throw a warning here; it
170 : * would just lead to chatter during a database-wide ANALYZE.)
171 : */
172 12558 : if (RELATION_IS_OTHER_TEMP(onerel))
173 : {
174 0 : relation_close(onerel, ShareUpdateExclusiveLock);
175 0 : return;
176 : }
177 :
178 : /*
179 : * We can ANALYZE any table except pg_statistic. See update_attstats
180 : */
181 12558 : if (RelationGetRelid(onerel) == StatisticRelationId)
182 : {
183 126 : relation_close(onerel, ShareUpdateExclusiveLock);
184 126 : return;
185 : }
186 :
187 : /*
188 : * Check that it's of an analyzable relkind, and set up appropriately.
189 : */
190 12432 : if (onerel->rd_rel->relkind == RELKIND_RELATION ||
191 708 : onerel->rd_rel->relkind == RELKIND_MATVIEW)
192 : {
193 : /* Regular table, so we'll use the regular row acquisition function */
194 11724 : acquirefunc = acquire_sample_rows;
195 : /* Also get regular table's size */
196 11724 : relpages = RelationGetNumberOfBlocks(onerel);
197 : }
198 708 : else if (onerel->rd_rel->relkind == RELKIND_FOREIGN_TABLE)
199 : {
200 : /*
201 : * For a foreign table, call the FDW's hook function to see whether it
202 : * supports analysis.
203 : */
204 : FdwRoutine *fdwroutine;
205 52 : bool ok = false;
206 :
207 52 : fdwroutine = GetFdwRoutineForRelation(onerel, false);
208 :
209 52 : if (fdwroutine->AnalyzeForeignTable != NULL)
210 52 : ok = fdwroutine->AnalyzeForeignTable(onerel,
211 : &acquirefunc,
212 : &relpages);
213 :
214 52 : if (!ok)
215 : {
216 0 : ereport(WARNING,
217 : (errmsg("skipping \"%s\" --- cannot analyze this foreign table",
218 : RelationGetRelationName(onerel))));
219 0 : relation_close(onerel, ShareUpdateExclusiveLock);
220 0 : return;
221 : }
222 : }
223 656 : else if (onerel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE)
224 : {
225 : /*
226 : * For partitioned tables, we want to do the recursive ANALYZE below.
227 : */
228 : }
229 : else
230 : {
231 : /* No need for a WARNING if we already complained during VACUUM */
232 0 : if (!(params->options & VACOPT_VACUUM))
233 0 : ereport(WARNING,
234 : (errmsg("skipping \"%s\" --- cannot analyze non-tables or special system tables",
235 : RelationGetRelationName(onerel))));
236 0 : relation_close(onerel, ShareUpdateExclusiveLock);
237 0 : return;
238 : }
239 :
240 : /*
241 : * OK, let's do it. First, initialize progress reporting.
242 : */
243 12432 : pgstat_progress_start_command(PROGRESS_COMMAND_ANALYZE,
244 : RelationGetRelid(onerel));
245 :
246 : /*
247 : * Do the normal non-recursive ANALYZE. We can skip this for partitioned
248 : * tables, which don't contain any rows.
249 : */
250 12432 : if (onerel->rd_rel->relkind != RELKIND_PARTITIONED_TABLE)
251 11776 : do_analyze_rel(onerel, params, va_cols, acquirefunc,
252 : relpages, false, in_outer_xact, elevel);
253 :
254 : /*
255 : * If there are child tables, do recursive ANALYZE.
256 : */
257 12392 : if (onerel->rd_rel->relhassubclass)
258 724 : do_analyze_rel(onerel, params, va_cols, acquirefunc, relpages,
259 : true, in_outer_xact, elevel);
260 :
261 : /*
262 : * Close source relation now, but keep lock so that no one deletes it
263 : * before we commit. (If someone did, they'd fail to clean up the entries
264 : * we made in pg_statistic. Also, releasing the lock before commit would
265 : * expose us to concurrent-update failures in update_attstats.)
266 : */
267 12374 : relation_close(onerel, NoLock);
268 :
269 12374 : pgstat_progress_end_command();
270 : }
271 :
272 : /*
273 : * do_analyze_rel() -- analyze one relation, recursively or not
274 : *
275 : * Note that "acquirefunc" is only relevant for the non-inherited case.
276 : * For the inherited case, acquire_inherited_sample_rows() determines the
277 : * appropriate acquirefunc for each child table.
278 : */
279 : static void
280 12500 : do_analyze_rel(Relation onerel, VacuumParams *params,
281 : List *va_cols, AcquireSampleRowsFunc acquirefunc,
282 : BlockNumber relpages, bool inh, bool in_outer_xact,
283 : int elevel)
284 : {
285 : int attr_cnt,
286 : tcnt,
287 : i,
288 : ind;
289 : Relation *Irel;
290 : int nindexes;
291 : bool hasindex;
292 : VacAttrStats **vacattrstats;
293 : AnlIndexData *indexdata;
294 : int targrows,
295 : numrows,
296 : minrows;
297 : double totalrows,
298 : totaldeadrows;
299 : HeapTuple *rows;
300 : PGRUsage ru0;
301 12500 : TimestampTz starttime = 0;
302 : MemoryContext caller_context;
303 : Oid save_userid;
304 : int save_sec_context;
305 : int save_nestlevel;
306 12500 : int64 AnalyzePageHit = VacuumPageHit;
307 12500 : int64 AnalyzePageMiss = VacuumPageMiss;
308 12500 : int64 AnalyzePageDirty = VacuumPageDirty;
309 12500 : PgStat_Counter startreadtime = 0;
310 12500 : PgStat_Counter startwritetime = 0;
311 :
312 12500 : if (inh)
313 724 : ereport(elevel,
314 : (errmsg("analyzing \"%s.%s\" inheritance tree",
315 : get_namespace_name(RelationGetNamespace(onerel)),
316 : RelationGetRelationName(onerel))));
317 : else
318 11776 : ereport(elevel,
319 : (errmsg("analyzing \"%s.%s\"",
320 : get_namespace_name(RelationGetNamespace(onerel)),
321 : RelationGetRelationName(onerel))));
322 :
323 : /*
324 : * Set up a working context so that we can easily free whatever junk gets
325 : * created.
326 : */
327 12500 : anl_context = AllocSetContextCreate(CurrentMemoryContext,
328 : "Analyze",
329 : ALLOCSET_DEFAULT_SIZES);
330 12500 : caller_context = MemoryContextSwitchTo(anl_context);
331 :
332 : /*
333 : * Switch to the table owner's userid, so that any index functions are run
334 : * as that user. Also lock down security-restricted operations and
335 : * arrange to make GUC variable changes local to this command.
336 : */
337 12500 : GetUserIdAndSecContext(&save_userid, &save_sec_context);
338 12500 : SetUserIdAndSecContext(onerel->rd_rel->relowner,
339 : save_sec_context | SECURITY_RESTRICTED_OPERATION);
340 12500 : save_nestlevel = NewGUCNestLevel();
341 12500 : RestrictSearchPath();
342 :
343 : /* measure elapsed time iff autovacuum logging requires it */
344 12500 : if (AmAutoVacuumWorkerProcess() && params->log_min_duration >= 0)
345 : {
346 174 : if (track_io_timing)
347 : {
348 0 : startreadtime = pgStatBlockReadTime;
349 0 : startwritetime = pgStatBlockWriteTime;
350 : }
351 :
352 174 : pg_rusage_init(&ru0);
353 174 : starttime = GetCurrentTimestamp();
354 : }
355 :
356 : /*
357 : * Determine which columns to analyze
358 : *
359 : * Note that system attributes are never analyzed, so we just reject them
360 : * at the lookup stage. We also reject duplicate column mentions. (We
361 : * could alternatively ignore duplicates, but analyzing a column twice
362 : * won't work; we'd end up making a conflicting update in pg_statistic.)
363 : */
364 12500 : if (va_cols != NIL)
365 : {
366 94 : Bitmapset *unique_cols = NULL;
367 : ListCell *le;
368 :
369 94 : vacattrstats = (VacAttrStats **) palloc(list_length(va_cols) *
370 : sizeof(VacAttrStats *));
371 94 : tcnt = 0;
372 164 : foreach(le, va_cols)
373 : {
374 120 : char *col = strVal(lfirst(le));
375 :
376 120 : i = attnameAttNum(onerel, col, false);
377 120 : if (i == InvalidAttrNumber)
378 38 : ereport(ERROR,
379 : (errcode(ERRCODE_UNDEFINED_COLUMN),
380 : errmsg("column \"%s\" of relation \"%s\" does not exist",
381 : col, RelationGetRelationName(onerel))));
382 82 : if (bms_is_member(i, unique_cols))
383 12 : ereport(ERROR,
384 : (errcode(ERRCODE_DUPLICATE_COLUMN),
385 : errmsg("column \"%s\" of relation \"%s\" appears more than once",
386 : col, RelationGetRelationName(onerel))));
387 70 : unique_cols = bms_add_member(unique_cols, i);
388 :
389 70 : vacattrstats[tcnt] = examine_attribute(onerel, i, NULL);
390 70 : if (vacattrstats[tcnt] != NULL)
391 70 : tcnt++;
392 : }
393 44 : attr_cnt = tcnt;
394 : }
395 : else
396 : {
397 12406 : attr_cnt = onerel->rd_att->natts;
398 : vacattrstats = (VacAttrStats **)
399 12406 : palloc(attr_cnt * sizeof(VacAttrStats *));
400 12406 : tcnt = 0;
401 99436 : for (i = 1; i <= attr_cnt; i++)
402 : {
403 87030 : vacattrstats[tcnt] = examine_attribute(onerel, i, NULL);
404 87030 : if (vacattrstats[tcnt] != NULL)
405 87018 : tcnt++;
406 : }
407 12406 : attr_cnt = tcnt;
408 : }
409 :
410 : /*
411 : * Open all indexes of the relation, and see if there are any analyzable
412 : * columns in the indexes. We do not analyze index columns if there was
413 : * an explicit column list in the ANALYZE command, however.
414 : *
415 : * If we are doing a recursive scan, we don't want to touch the parent's
416 : * indexes at all. If we're processing a partitioned table, we need to
417 : * know if there are any indexes, but we don't want to process them.
418 : */
419 12450 : if (onerel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE)
420 : {
421 638 : List *idxs = RelationGetIndexList(onerel);
422 :
423 638 : Irel = NULL;
424 638 : nindexes = 0;
425 638 : hasindex = idxs != NIL;
426 638 : list_free(idxs);
427 : }
428 11812 : else if (!inh)
429 : {
430 11744 : vac_open_indexes(onerel, AccessShareLock, &nindexes, &Irel);
431 11744 : hasindex = nindexes > 0;
432 : }
433 : else
434 : {
435 68 : Irel = NULL;
436 68 : nindexes = 0;
437 68 : hasindex = false;
438 : }
439 12450 : indexdata = NULL;
440 12450 : if (nindexes > 0)
441 : {
442 8994 : indexdata = (AnlIndexData *) palloc0(nindexes * sizeof(AnlIndexData));
443 25858 : for (ind = 0; ind < nindexes; ind++)
444 : {
445 16864 : AnlIndexData *thisdata = &indexdata[ind];
446 : IndexInfo *indexInfo;
447 :
448 16864 : thisdata->indexInfo = indexInfo = BuildIndexInfo(Irel[ind]);
449 16864 : thisdata->tupleFract = 1.0; /* fix later if partial */
450 16864 : if (indexInfo->ii_Expressions != NIL && va_cols == NIL)
451 : {
452 66 : ListCell *indexpr_item = list_head(indexInfo->ii_Expressions);
453 :
454 66 : thisdata->vacattrstats = (VacAttrStats **)
455 66 : palloc(indexInfo->ii_NumIndexAttrs * sizeof(VacAttrStats *));
456 66 : tcnt = 0;
457 132 : for (i = 0; i < indexInfo->ii_NumIndexAttrs; i++)
458 : {
459 66 : int keycol = indexInfo->ii_IndexAttrNumbers[i];
460 :
461 66 : if (keycol == 0)
462 : {
463 : /* Found an index expression */
464 : Node *indexkey;
465 :
466 66 : if (indexpr_item == NULL) /* shouldn't happen */
467 0 : elog(ERROR, "too few entries in indexprs list");
468 66 : indexkey = (Node *) lfirst(indexpr_item);
469 66 : indexpr_item = lnext(indexInfo->ii_Expressions,
470 : indexpr_item);
471 132 : thisdata->vacattrstats[tcnt] =
472 66 : examine_attribute(Irel[ind], i + 1, indexkey);
473 66 : if (thisdata->vacattrstats[tcnt] != NULL)
474 66 : tcnt++;
475 : }
476 : }
477 66 : thisdata->attr_cnt = tcnt;
478 : }
479 : }
480 : }
481 :
482 : /*
483 : * Determine how many rows we need to sample, using the worst case from
484 : * all analyzable columns. We use a lower bound of 100 rows to avoid
485 : * possible overflow in Vitter's algorithm. (Note: that will also be the
486 : * target in the corner case where there are no analyzable columns.)
487 : */
488 12450 : targrows = 100;
489 99514 : for (i = 0; i < attr_cnt; i++)
490 : {
491 87064 : if (targrows < vacattrstats[i]->minrows)
492 12420 : targrows = vacattrstats[i]->minrows;
493 : }
494 29314 : for (ind = 0; ind < nindexes; ind++)
495 : {
496 16864 : AnlIndexData *thisdata = &indexdata[ind];
497 :
498 16930 : for (i = 0; i < thisdata->attr_cnt; i++)
499 : {
500 66 : if (targrows < thisdata->vacattrstats[i]->minrows)
501 0 : targrows = thisdata->vacattrstats[i]->minrows;
502 : }
503 : }
504 :
505 : /*
506 : * Look at extended statistics objects too, as those may define custom
507 : * statistics target. So we may need to sample more rows and then build
508 : * the statistics with enough detail.
509 : */
510 12450 : minrows = ComputeExtStatisticsRows(onerel, attr_cnt, vacattrstats);
511 :
512 12450 : if (targrows < minrows)
513 0 : targrows = minrows;
514 :
515 : /*
516 : * Acquire the sample rows
517 : */
518 12450 : rows = (HeapTuple *) palloc(targrows * sizeof(HeapTuple));
519 12450 : pgstat_progress_update_param(PROGRESS_ANALYZE_PHASE,
520 : inh ? PROGRESS_ANALYZE_PHASE_ACQUIRE_SAMPLE_ROWS_INH :
521 : PROGRESS_ANALYZE_PHASE_ACQUIRE_SAMPLE_ROWS);
522 12450 : if (inh)
523 706 : numrows = acquire_inherited_sample_rows(onerel, elevel,
524 : rows, targrows,
525 : &totalrows, &totaldeadrows);
526 : else
527 11744 : numrows = (*acquirefunc) (onerel, elevel,
528 : rows, targrows,
529 : &totalrows, &totaldeadrows);
530 :
531 : /*
532 : * Compute the statistics. Temporary results during the calculations for
533 : * each column are stored in a child context. The calc routines are
534 : * responsible to make sure that whatever they store into the VacAttrStats
535 : * structure is allocated in anl_context.
536 : */
537 12448 : if (numrows > 0)
538 : {
539 : MemoryContext col_context,
540 : old_context;
541 :
542 8488 : pgstat_progress_update_param(PROGRESS_ANALYZE_PHASE,
543 : PROGRESS_ANALYZE_PHASE_COMPUTE_STATS);
544 :
545 8488 : col_context = AllocSetContextCreate(anl_context,
546 : "Analyze Column",
547 : ALLOCSET_DEFAULT_SIZES);
548 8488 : old_context = MemoryContextSwitchTo(col_context);
549 :
550 72490 : for (i = 0; i < attr_cnt; i++)
551 : {
552 64002 : VacAttrStats *stats = vacattrstats[i];
553 : AttributeOpts *aopt;
554 :
555 64002 : stats->rows = rows;
556 64002 : stats->tupDesc = onerel->rd_att;
557 64002 : stats->compute_stats(stats,
558 : std_fetch_func,
559 : numrows,
560 : totalrows);
561 :
562 : /*
563 : * If the appropriate flavor of the n_distinct option is
564 : * specified, override with the corresponding value.
565 : */
566 64002 : aopt = get_attribute_options(onerel->rd_id, stats->tupattnum);
567 64002 : if (aopt != NULL)
568 : {
569 : float8 n_distinct;
570 :
571 6 : n_distinct = inh ? aopt->n_distinct_inherited : aopt->n_distinct;
572 6 : if (n_distinct != 0.0)
573 6 : stats->stadistinct = n_distinct;
574 : }
575 :
576 64002 : MemoryContextReset(col_context);
577 : }
578 :
579 8488 : if (nindexes > 0)
580 5282 : compute_index_stats(onerel, totalrows,
581 : indexdata, nindexes,
582 : rows, numrows,
583 : col_context);
584 :
585 8482 : MemoryContextSwitchTo(old_context);
586 8482 : MemoryContextDelete(col_context);
587 :
588 : /*
589 : * Emit the completed stats rows into pg_statistic, replacing any
590 : * previous statistics for the target columns. (If there are stats in
591 : * pg_statistic for columns we didn't process, we leave them alone.)
592 : */
593 8482 : update_attstats(RelationGetRelid(onerel), inh,
594 : attr_cnt, vacattrstats);
595 :
596 18710 : for (ind = 0; ind < nindexes; ind++)
597 : {
598 10228 : AnlIndexData *thisdata = &indexdata[ind];
599 :
600 10228 : update_attstats(RelationGetRelid(Irel[ind]), false,
601 : thisdata->attr_cnt, thisdata->vacattrstats);
602 : }
603 :
604 : /* Build extended statistics (if there are any). */
605 8482 : BuildRelationExtStatistics(onerel, inh, totalrows, numrows, rows,
606 : attr_cnt, vacattrstats);
607 : }
608 :
609 12442 : pgstat_progress_update_param(PROGRESS_ANALYZE_PHASE,
610 : PROGRESS_ANALYZE_PHASE_FINALIZE_ANALYZE);
611 :
612 : /*
613 : * Update pages/tuples stats in pg_class ... but not if we're doing
614 : * inherited stats.
615 : *
616 : * We assume that VACUUM hasn't set pg_class.reltuples already, even
617 : * during a VACUUM ANALYZE. Although VACUUM often updates pg_class,
618 : * exceptions exist. A "VACUUM (ANALYZE, INDEX_CLEANUP OFF)" command will
619 : * never update pg_class entries for index relations. It's also possible
620 : * that an individual index's pg_class entry won't be updated during
621 : * VACUUM if the index AM returns NULL from its amvacuumcleanup() routine.
622 : */
623 12442 : if (!inh)
624 : {
625 : BlockNumber relallvisible;
626 :
627 11736 : if (RELKIND_HAS_STORAGE(onerel->rd_rel->relkind))
628 11686 : visibilitymap_count(onerel, &relallvisible, NULL);
629 : else
630 50 : relallvisible = 0;
631 :
632 : /*
633 : * Update pg_class for table relation. CCI first, in case acquirefunc
634 : * updated pg_class.
635 : */
636 11736 : CommandCounterIncrement();
637 11736 : vac_update_relstats(onerel,
638 : relpages,
639 : totalrows,
640 : relallvisible,
641 : hasindex,
642 : InvalidTransactionId,
643 : InvalidMultiXactId,
644 : NULL, NULL,
645 : in_outer_xact);
646 :
647 : /* Same for indexes */
648 28588 : for (ind = 0; ind < nindexes; ind++)
649 : {
650 16852 : AnlIndexData *thisdata = &indexdata[ind];
651 : double totalindexrows;
652 :
653 16852 : totalindexrows = ceil(thisdata->tupleFract * totalrows);
654 16852 : vac_update_relstats(Irel[ind],
655 16852 : RelationGetNumberOfBlocks(Irel[ind]),
656 : totalindexrows,
657 : 0,
658 : false,
659 : InvalidTransactionId,
660 : InvalidMultiXactId,
661 : NULL, NULL,
662 : in_outer_xact);
663 : }
664 : }
665 706 : else if (onerel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE)
666 : {
667 : /*
668 : * Partitioned tables don't have storage, so we don't set any fields
669 : * in their pg_class entries except for reltuples and relhasindex.
670 : */
671 638 : CommandCounterIncrement();
672 638 : vac_update_relstats(onerel, -1, totalrows,
673 : 0, hasindex, InvalidTransactionId,
674 : InvalidMultiXactId,
675 : NULL, NULL,
676 : in_outer_xact);
677 : }
678 :
679 : /*
680 : * Now report ANALYZE to the cumulative stats system. For regular tables,
681 : * we do it only if not doing inherited stats. For partitioned tables, we
682 : * only do it for inherited stats. (We're never called for not-inherited
683 : * stats on partitioned tables anyway.)
684 : *
685 : * Reset the changes_since_analyze counter only if we analyzed all
686 : * columns; otherwise, there is still work for auto-analyze to do.
687 : */
688 12442 : if (!inh)
689 11736 : pgstat_report_analyze(onerel, totalrows, totaldeadrows,
690 : (va_cols == NIL));
691 706 : else if (onerel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE)
692 638 : pgstat_report_analyze(onerel, 0, 0, (va_cols == NIL));
693 :
694 : /*
695 : * If this isn't part of VACUUM ANALYZE, let index AMs do cleanup.
696 : *
697 : * Note that most index AMs perform a no-op as a matter of policy for
698 : * amvacuumcleanup() when called in ANALYZE-only mode. The only exception
699 : * among core index AMs is GIN/ginvacuumcleanup().
700 : */
701 12442 : if (!(params->options & VACOPT_VACUUM))
702 : {
703 25882 : for (ind = 0; ind < nindexes; ind++)
704 : {
705 : IndexBulkDeleteResult *stats;
706 : IndexVacuumInfo ivinfo;
707 :
708 14836 : ivinfo.index = Irel[ind];
709 14836 : ivinfo.heaprel = onerel;
710 14836 : ivinfo.analyze_only = true;
711 14836 : ivinfo.estimated_count = true;
712 14836 : ivinfo.message_level = elevel;
713 14836 : ivinfo.num_heap_tuples = onerel->rd_rel->reltuples;
714 14836 : ivinfo.strategy = vac_strategy;
715 :
716 14836 : stats = index_vacuum_cleanup(&ivinfo, NULL);
717 :
718 14836 : if (stats)
719 0 : pfree(stats);
720 : }
721 : }
722 :
723 : /* Done with indexes */
724 12442 : vac_close_indexes(nindexes, Irel, NoLock);
725 :
726 : /* Log the action if appropriate */
727 12442 : if (AmAutoVacuumWorkerProcess() && params->log_min_duration >= 0)
728 : {
729 174 : TimestampTz endtime = GetCurrentTimestamp();
730 :
731 208 : if (params->log_min_duration == 0 ||
732 34 : TimestampDifferenceExceeds(starttime, endtime,
733 : params->log_min_duration))
734 : {
735 : long delay_in_ms;
736 140 : double read_rate = 0;
737 140 : double write_rate = 0;
738 : StringInfoData buf;
739 :
740 : /*
741 : * Calculate the difference in the Page Hit/Miss/Dirty that
742 : * happened as part of the analyze by subtracting out the
743 : * pre-analyze values which we saved above.
744 : */
745 140 : AnalyzePageHit = VacuumPageHit - AnalyzePageHit;
746 140 : AnalyzePageMiss = VacuumPageMiss - AnalyzePageMiss;
747 140 : AnalyzePageDirty = VacuumPageDirty - AnalyzePageDirty;
748 :
749 : /*
750 : * We do not expect an analyze to take > 25 days and it simplifies
751 : * things a bit to use TimestampDifferenceMilliseconds.
752 : */
753 140 : delay_in_ms = TimestampDifferenceMilliseconds(starttime, endtime);
754 :
755 : /*
756 : * Note that we are reporting these read/write rates in the same
757 : * manner as VACUUM does, which means that while the 'average read
758 : * rate' here actually corresponds to page misses and resulting
759 : * reads which are also picked up by track_io_timing, if enabled,
760 : * the 'average write rate' is actually talking about the rate of
761 : * pages being dirtied, not being written out, so it's typical to
762 : * have a non-zero 'avg write rate' while I/O timings only reports
763 : * reads.
764 : *
765 : * It's not clear that an ANALYZE will ever result in
766 : * FlushBuffer() being called, but we track and support reporting
767 : * on I/O write time in case that changes as it's practically free
768 : * to do so anyway.
769 : */
770 :
771 140 : if (delay_in_ms > 0)
772 : {
773 140 : read_rate = (double) BLCKSZ * AnalyzePageMiss / (1024 * 1024) /
774 140 : (delay_in_ms / 1000.0);
775 140 : write_rate = (double) BLCKSZ * AnalyzePageDirty / (1024 * 1024) /
776 140 : (delay_in_ms / 1000.0);
777 : }
778 :
779 : /*
780 : * We split this up so we don't emit empty I/O timing values when
781 : * track_io_timing isn't enabled.
782 : */
783 :
784 140 : initStringInfo(&buf);
785 140 : appendStringInfo(&buf, _("automatic analyze of table \"%s.%s.%s\"\n"),
786 : get_database_name(MyDatabaseId),
787 140 : get_namespace_name(RelationGetNamespace(onerel)),
788 140 : RelationGetRelationName(onerel));
789 140 : if (track_io_timing)
790 : {
791 0 : double read_ms = (double) (pgStatBlockReadTime - startreadtime) / 1000;
792 0 : double write_ms = (double) (pgStatBlockWriteTime - startwritetime) / 1000;
793 :
794 0 : appendStringInfo(&buf, _("I/O timings: read: %.3f ms, write: %.3f ms\n"),
795 : read_ms, write_ms);
796 : }
797 140 : appendStringInfo(&buf, _("avg read rate: %.3f MB/s, avg write rate: %.3f MB/s\n"),
798 : read_rate, write_rate);
799 140 : appendStringInfo(&buf, _("buffer usage: %lld hits, %lld misses, %lld dirtied\n"),
800 : (long long) AnalyzePageHit,
801 : (long long) AnalyzePageMiss,
802 : (long long) AnalyzePageDirty);
803 140 : appendStringInfo(&buf, _("system usage: %s"), pg_rusage_show(&ru0));
804 :
805 140 : ereport(LOG,
806 : (errmsg_internal("%s", buf.data)));
807 :
808 140 : pfree(buf.data);
809 : }
810 : }
811 :
812 : /* Roll back any GUC changes executed by index functions */
813 12442 : AtEOXact_GUC(false, save_nestlevel);
814 :
815 : /* Restore userid and security context */
816 12442 : SetUserIdAndSecContext(save_userid, save_sec_context);
817 :
818 : /* Restore current context and release memory */
819 12442 : MemoryContextSwitchTo(caller_context);
820 12442 : MemoryContextDelete(anl_context);
821 12442 : anl_context = NULL;
822 12442 : }
823 :
824 : /*
825 : * Compute statistics about indexes of a relation
826 : */
827 : static void
828 5282 : compute_index_stats(Relation onerel, double totalrows,
829 : AnlIndexData *indexdata, int nindexes,
830 : HeapTuple *rows, int numrows,
831 : MemoryContext col_context)
832 : {
833 : MemoryContext ind_context,
834 : old_context;
835 : Datum values[INDEX_MAX_KEYS];
836 : bool isnull[INDEX_MAX_KEYS];
837 : int ind,
838 : i;
839 :
840 5282 : ind_context = AllocSetContextCreate(anl_context,
841 : "Analyze Index",
842 : ALLOCSET_DEFAULT_SIZES);
843 5282 : old_context = MemoryContextSwitchTo(ind_context);
844 :
845 15516 : for (ind = 0; ind < nindexes; ind++)
846 : {
847 10240 : AnlIndexData *thisdata = &indexdata[ind];
848 10240 : IndexInfo *indexInfo = thisdata->indexInfo;
849 10240 : int attr_cnt = thisdata->attr_cnt;
850 : TupleTableSlot *slot;
851 : EState *estate;
852 : ExprContext *econtext;
853 : ExprState *predicate;
854 : Datum *exprvals;
855 : bool *exprnulls;
856 : int numindexrows,
857 : tcnt,
858 : rowno;
859 : double totalindexrows;
860 :
861 : /* Ignore index if no columns to analyze and not partial */
862 10240 : if (attr_cnt == 0 && indexInfo->ii_Predicate == NIL)
863 10136 : continue;
864 :
865 : /*
866 : * Need an EState for evaluation of index expressions and
867 : * partial-index predicates. Create it in the per-index context to be
868 : * sure it gets cleaned up at the bottom of the loop.
869 : */
870 104 : estate = CreateExecutorState();
871 104 : econtext = GetPerTupleExprContext(estate);
872 : /* Need a slot to hold the current heap tuple, too */
873 104 : slot = MakeSingleTupleTableSlot(RelationGetDescr(onerel),
874 : &TTSOpsHeapTuple);
875 :
876 : /* Arrange for econtext's scan tuple to be the tuple under test */
877 104 : econtext->ecxt_scantuple = slot;
878 :
879 : /* Set up execution state for predicate. */
880 104 : predicate = ExecPrepareQual(indexInfo->ii_Predicate, estate);
881 :
882 : /* Compute and save index expression values */
883 104 : exprvals = (Datum *) palloc(numrows * attr_cnt * sizeof(Datum));
884 104 : exprnulls = (bool *) palloc(numrows * attr_cnt * sizeof(bool));
885 104 : numindexrows = 0;
886 104 : tcnt = 0;
887 94630 : for (rowno = 0; rowno < numrows; rowno++)
888 : {
889 94532 : HeapTuple heapTuple = rows[rowno];
890 :
891 94532 : vacuum_delay_point();
892 :
893 : /*
894 : * Reset the per-tuple context each time, to reclaim any cruft
895 : * left behind by evaluating the predicate or index expressions.
896 : */
897 94532 : ResetExprContext(econtext);
898 :
899 : /* Set up for predicate or expression evaluation */
900 94532 : ExecStoreHeapTuple(heapTuple, slot, false);
901 :
902 : /* If index is partial, check predicate */
903 94532 : if (predicate != NULL)
904 : {
905 20066 : if (!ExecQual(predicate, econtext))
906 19328 : continue;
907 : }
908 75204 : numindexrows++;
909 :
910 75204 : if (attr_cnt > 0)
911 : {
912 : /*
913 : * Evaluate the index row to compute expression values. We
914 : * could do this by hand, but FormIndexDatum is convenient.
915 : */
916 74466 : FormIndexDatum(indexInfo,
917 : slot,
918 : estate,
919 : values,
920 : isnull);
921 :
922 : /*
923 : * Save just the columns we care about. We copy the values
924 : * into ind_context from the estate's per-tuple context.
925 : */
926 148920 : for (i = 0; i < attr_cnt; i++)
927 : {
928 74460 : VacAttrStats *stats = thisdata->vacattrstats[i];
929 74460 : int attnum = stats->tupattnum;
930 :
931 74460 : if (isnull[attnum - 1])
932 : {
933 0 : exprvals[tcnt] = (Datum) 0;
934 0 : exprnulls[tcnt] = true;
935 : }
936 : else
937 : {
938 148920 : exprvals[tcnt] = datumCopy(values[attnum - 1],
939 74460 : stats->attrtype->typbyval,
940 74460 : stats->attrtype->typlen);
941 74460 : exprnulls[tcnt] = false;
942 : }
943 74460 : tcnt++;
944 : }
945 : }
946 : }
947 :
948 : /*
949 : * Having counted the number of rows that pass the predicate in the
950 : * sample, we can estimate the total number of rows in the index.
951 : */
952 98 : thisdata->tupleFract = (double) numindexrows / (double) numrows;
953 98 : totalindexrows = ceil(thisdata->tupleFract * totalrows);
954 :
955 : /*
956 : * Now we can compute the statistics for the expression columns.
957 : */
958 98 : if (numindexrows > 0)
959 : {
960 90 : MemoryContextSwitchTo(col_context);
961 144 : for (i = 0; i < attr_cnt; i++)
962 : {
963 54 : VacAttrStats *stats = thisdata->vacattrstats[i];
964 :
965 54 : stats->exprvals = exprvals + i;
966 54 : stats->exprnulls = exprnulls + i;
967 54 : stats->rowstride = attr_cnt;
968 54 : stats->compute_stats(stats,
969 : ind_fetch_func,
970 : numindexrows,
971 : totalindexrows);
972 :
973 54 : MemoryContextReset(col_context);
974 : }
975 : }
976 :
977 : /* And clean up */
978 98 : MemoryContextSwitchTo(ind_context);
979 :
980 98 : ExecDropSingleTupleTableSlot(slot);
981 98 : FreeExecutorState(estate);
982 98 : MemoryContextReset(ind_context);
983 : }
984 :
985 5276 : MemoryContextSwitchTo(old_context);
986 5276 : MemoryContextDelete(ind_context);
987 5276 : }
988 :
989 : /*
990 : * examine_attribute -- pre-analysis of a single column
991 : *
992 : * Determine whether the column is analyzable; if so, create and initialize
993 : * a VacAttrStats struct for it. If not, return NULL.
994 : *
995 : * If index_expr isn't NULL, then we're trying to analyze an expression index,
996 : * and index_expr is the expression tree representing the column's data.
997 : */
998 : static VacAttrStats *
999 87166 : examine_attribute(Relation onerel, int attnum, Node *index_expr)
1000 : {
1001 87166 : Form_pg_attribute attr = TupleDescAttr(onerel->rd_att, attnum - 1);
1002 : int attstattarget;
1003 : HeapTuple atttuple;
1004 : Datum dat;
1005 : bool isnull;
1006 : HeapTuple typtuple;
1007 : VacAttrStats *stats;
1008 : int i;
1009 : bool ok;
1010 :
1011 : /* Never analyze dropped columns */
1012 87166 : if (attr->attisdropped)
1013 6 : return NULL;
1014 :
1015 : /*
1016 : * Get attstattarget value. Set to -1 if null. (Analyze functions expect
1017 : * -1 to mean use default_statistics_target; see for example
1018 : * std_typanalyze.)
1019 : */
1020 87160 : atttuple = SearchSysCache2(ATTNUM, ObjectIdGetDatum(RelationGetRelid(onerel)), Int16GetDatum(attnum));
1021 87160 : if (!HeapTupleIsValid(atttuple))
1022 0 : elog(ERROR, "cache lookup failed for attribute %d of relation %u",
1023 : attnum, RelationGetRelid(onerel));
1024 87160 : dat = SysCacheGetAttr(ATTNUM, atttuple, Anum_pg_attribute_attstattarget, &isnull);
1025 87160 : attstattarget = isnull ? -1 : DatumGetInt16(dat);
1026 87160 : ReleaseSysCache(atttuple);
1027 :
1028 : /* Don't analyze column if user has specified not to */
1029 87160 : if (attstattarget == 0)
1030 6 : return NULL;
1031 :
1032 : /*
1033 : * Create the VacAttrStats struct.
1034 : */
1035 87154 : stats = (VacAttrStats *) palloc0(sizeof(VacAttrStats));
1036 87154 : stats->attstattarget = attstattarget;
1037 :
1038 : /*
1039 : * When analyzing an expression index, believe the expression tree's type
1040 : * not the column datatype --- the latter might be the opckeytype storage
1041 : * type of the opclass, which is not interesting for our purposes. (Note:
1042 : * if we did anything with non-expression index columns, we'd need to
1043 : * figure out where to get the correct type info from, but for now that's
1044 : * not a problem.) It's not clear whether anyone will care about the
1045 : * typmod, but we store that too just in case.
1046 : */
1047 87154 : if (index_expr)
1048 : {
1049 66 : stats->attrtypid = exprType(index_expr);
1050 66 : stats->attrtypmod = exprTypmod(index_expr);
1051 :
1052 : /*
1053 : * If a collation has been specified for the index column, use that in
1054 : * preference to anything else; but if not, fall back to whatever we
1055 : * can get from the expression.
1056 : */
1057 66 : if (OidIsValid(onerel->rd_indcollation[attnum - 1]))
1058 12 : stats->attrcollid = onerel->rd_indcollation[attnum - 1];
1059 : else
1060 54 : stats->attrcollid = exprCollation(index_expr);
1061 : }
1062 : else
1063 : {
1064 87088 : stats->attrtypid = attr->atttypid;
1065 87088 : stats->attrtypmod = attr->atttypmod;
1066 87088 : stats->attrcollid = attr->attcollation;
1067 : }
1068 :
1069 87154 : typtuple = SearchSysCacheCopy1(TYPEOID,
1070 : ObjectIdGetDatum(stats->attrtypid));
1071 87154 : if (!HeapTupleIsValid(typtuple))
1072 0 : elog(ERROR, "cache lookup failed for type %u", stats->attrtypid);
1073 87154 : stats->attrtype = (Form_pg_type) GETSTRUCT(typtuple);
1074 87154 : stats->anl_context = anl_context;
1075 87154 : stats->tupattnum = attnum;
1076 :
1077 : /*
1078 : * The fields describing the stats->stavalues[n] element types default to
1079 : * the type of the data being analyzed, but the type-specific typanalyze
1080 : * function can change them if it wants to store something else.
1081 : */
1082 522924 : for (i = 0; i < STATISTIC_NUM_SLOTS; i++)
1083 : {
1084 435770 : stats->statypid[i] = stats->attrtypid;
1085 435770 : stats->statyplen[i] = stats->attrtype->typlen;
1086 435770 : stats->statypbyval[i] = stats->attrtype->typbyval;
1087 435770 : stats->statypalign[i] = stats->attrtype->typalign;
1088 : }
1089 :
1090 : /*
1091 : * Call the type-specific typanalyze function. If none is specified, use
1092 : * std_typanalyze().
1093 : */
1094 87154 : if (OidIsValid(stats->attrtype->typanalyze))
1095 5580 : ok = DatumGetBool(OidFunctionCall1(stats->attrtype->typanalyze,
1096 : PointerGetDatum(stats)));
1097 : else
1098 81574 : ok = std_typanalyze(stats);
1099 :
1100 87154 : if (!ok || stats->compute_stats == NULL || stats->minrows <= 0)
1101 : {
1102 0 : heap_freetuple(typtuple);
1103 0 : pfree(stats);
1104 0 : return NULL;
1105 : }
1106 :
1107 87154 : return stats;
1108 : }
1109 :
1110 : /*
1111 : * Read stream callback returning the next BlockNumber as chosen by the
1112 : * BlockSampling algorithm.
1113 : */
1114 : static BlockNumber
1115 110034 : block_sampling_read_stream_next(ReadStream *stream,
1116 : void *callback_private_data,
1117 : void *per_buffer_data)
1118 : {
1119 110034 : BlockSamplerData *bs = callback_private_data;
1120 :
1121 110034 : return BlockSampler_HasMore(bs) ? BlockSampler_Next(bs) : InvalidBlockNumber;
1122 : }
1123 :
1124 : /*
1125 : * acquire_sample_rows -- acquire a random sample of rows from the table
1126 : *
1127 : * Selected rows are returned in the caller-allocated array rows[], which
1128 : * must have at least targrows entries.
1129 : * The actual number of rows selected is returned as the function result.
1130 : * We also estimate the total numbers of live and dead rows in the table,
1131 : * and return them into *totalrows and *totaldeadrows, respectively.
1132 : *
1133 : * The returned list of tuples is in order by physical position in the table.
1134 : * (We will rely on this later to derive correlation estimates.)
1135 : *
1136 : * As of May 2004 we use a new two-stage method: Stage one selects up
1137 : * to targrows random blocks (or all blocks, if there aren't so many).
1138 : * Stage two scans these blocks and uses the Vitter algorithm to create
1139 : * a random sample of targrows rows (or less, if there are less in the
1140 : * sample of blocks). The two stages are executed simultaneously: each
1141 : * block is processed as soon as stage one returns its number and while
1142 : * the rows are read stage two controls which ones are to be inserted
1143 : * into the sample.
1144 : *
1145 : * Although every row has an equal chance of ending up in the final
1146 : * sample, this sampling method is not perfect: not every possible
1147 : * sample has an equal chance of being selected. For large relations
1148 : * the number of different blocks represented by the sample tends to be
1149 : * too small. We can live with that for now. Improvements are welcome.
1150 : *
1151 : * An important property of this sampling method is that because we do
1152 : * look at a statistically unbiased set of blocks, we should get
1153 : * unbiased estimates of the average numbers of live and dead rows per
1154 : * block. The previous sampling method put too much credence in the row
1155 : * density near the start of the table.
1156 : */
1157 : static int
1158 13430 : acquire_sample_rows(Relation onerel, int elevel,
1159 : HeapTuple *rows, int targrows,
1160 : double *totalrows, double *totaldeadrows)
1161 : {
1162 13430 : int numrows = 0; /* # rows now in reservoir */
1163 13430 : double samplerows = 0; /* total # rows collected */
1164 13430 : double liverows = 0; /* # live rows seen */
1165 13430 : double deadrows = 0; /* # dead rows seen */
1166 13430 : double rowstoskip = -1; /* -1 means not set yet */
1167 : uint32 randseed; /* Seed for block sampler(s) */
1168 : BlockNumber totalblocks;
1169 : TransactionId OldestXmin;
1170 : BlockSamplerData bs;
1171 : ReservoirStateData rstate;
1172 : TupleTableSlot *slot;
1173 : TableScanDesc scan;
1174 : BlockNumber nblocks;
1175 13430 : BlockNumber blksdone = 0;
1176 : ReadStream *stream;
1177 :
1178 : Assert(targrows > 0);
1179 :
1180 13430 : totalblocks = RelationGetNumberOfBlocks(onerel);
1181 :
1182 : /* Need a cutoff xmin for HeapTupleSatisfiesVacuum */
1183 13430 : OldestXmin = GetOldestNonRemovableTransactionId(onerel);
1184 :
1185 : /* Prepare for sampling block numbers */
1186 13430 : randseed = pg_prng_uint32(&pg_global_prng_state);
1187 13430 : nblocks = BlockSampler_Init(&bs, totalblocks, targrows, randseed);
1188 :
1189 : /* Report sampling block numbers */
1190 13430 : pgstat_progress_update_param(PROGRESS_ANALYZE_BLOCKS_TOTAL,
1191 : nblocks);
1192 :
1193 : /* Prepare for sampling rows */
1194 13430 : reservoir_init_selection_state(&rstate, targrows);
1195 :
1196 13430 : scan = table_beginscan_analyze(onerel);
1197 13430 : slot = table_slot_create(onerel, NULL);
1198 :
1199 13430 : stream = read_stream_begin_relation(READ_STREAM_MAINTENANCE,
1200 : vac_strategy,
1201 : scan->rs_rd,
1202 : MAIN_FORKNUM,
1203 : block_sampling_read_stream_next,
1204 : &bs,
1205 : 0);
1206 :
1207 : /* Outer loop over blocks to sample */
1208 110034 : while (table_scan_analyze_next_block(scan, stream))
1209 : {
1210 96604 : vacuum_delay_point();
1211 :
1212 8390804 : while (table_scan_analyze_next_tuple(scan, OldestXmin, &liverows, &deadrows, slot))
1213 : {
1214 : /*
1215 : * The first targrows sample rows are simply copied into the
1216 : * reservoir. Then we start replacing tuples in the sample until
1217 : * we reach the end of the relation. This algorithm is from Jeff
1218 : * Vitter's paper (see full citation in utils/misc/sampling.c). It
1219 : * works by repeatedly computing the number of tuples to skip
1220 : * before selecting a tuple, which replaces a randomly chosen
1221 : * element of the reservoir (current set of tuples). At all times
1222 : * the reservoir is a true random sample of the tuples we've
1223 : * passed over so far, so when we fall off the end of the relation
1224 : * we're done.
1225 : */
1226 8294200 : if (numrows < targrows)
1227 8045072 : rows[numrows++] = ExecCopySlotHeapTuple(slot);
1228 : else
1229 : {
1230 : /*
1231 : * t in Vitter's paper is the number of records already
1232 : * processed. If we need to compute a new S value, we must
1233 : * use the not-yet-incremented value of samplerows as t.
1234 : */
1235 249128 : if (rowstoskip < 0)
1236 114060 : rowstoskip = reservoir_get_next_S(&rstate, samplerows, targrows);
1237 :
1238 249128 : if (rowstoskip <= 0)
1239 : {
1240 : /*
1241 : * Found a suitable tuple, so save it, replacing one old
1242 : * tuple at random
1243 : */
1244 114008 : int k = (int) (targrows * sampler_random_fract(&rstate.randstate));
1245 :
1246 : Assert(k >= 0 && k < targrows);
1247 114008 : heap_freetuple(rows[k]);
1248 114008 : rows[k] = ExecCopySlotHeapTuple(slot);
1249 : }
1250 :
1251 249128 : rowstoskip -= 1;
1252 : }
1253 :
1254 8294200 : samplerows += 1;
1255 : }
1256 :
1257 96604 : pgstat_progress_update_param(PROGRESS_ANALYZE_BLOCKS_DONE,
1258 : ++blksdone);
1259 : }
1260 :
1261 13430 : read_stream_end(stream);
1262 :
1263 13430 : ExecDropSingleTupleTableSlot(slot);
1264 13430 : table_endscan(scan);
1265 :
1266 : /*
1267 : * If we didn't find as many tuples as we wanted then we're done. No sort
1268 : * is needed, since they're already in order.
1269 : *
1270 : * Otherwise we need to sort the collected tuples by position
1271 : * (itempointer). It's not worth worrying about corner cases where the
1272 : * tuples are already sorted.
1273 : */
1274 13430 : if (numrows == targrows)
1275 158 : qsort_interruptible(rows, numrows, sizeof(HeapTuple),
1276 : compare_rows, NULL);
1277 :
1278 : /*
1279 : * Estimate total numbers of live and dead rows in relation, extrapolating
1280 : * on the assumption that the average tuple density in pages we didn't
1281 : * scan is the same as in the pages we did scan. Since what we scanned is
1282 : * a random sample of the pages in the relation, this should be a good
1283 : * assumption.
1284 : */
1285 13430 : if (bs.m > 0)
1286 : {
1287 9536 : *totalrows = floor((liverows / bs.m) * totalblocks + 0.5);
1288 9536 : *totaldeadrows = floor((deadrows / bs.m) * totalblocks + 0.5);
1289 : }
1290 : else
1291 : {
1292 3894 : *totalrows = 0.0;
1293 3894 : *totaldeadrows = 0.0;
1294 : }
1295 :
1296 : /*
1297 : * Emit some interesting relation info
1298 : */
1299 13430 : ereport(elevel,
1300 : (errmsg("\"%s\": scanned %d of %u pages, "
1301 : "containing %.0f live rows and %.0f dead rows; "
1302 : "%d rows in sample, %.0f estimated total rows",
1303 : RelationGetRelationName(onerel),
1304 : bs.m, totalblocks,
1305 : liverows, deadrows,
1306 : numrows, *totalrows)));
1307 :
1308 13430 : return numrows;
1309 : }
1310 :
1311 : /*
1312 : * Comparator for sorting rows[] array
1313 : */
1314 : static int
1315 3865180 : compare_rows(const void *a, const void *b, void *arg)
1316 : {
1317 3865180 : HeapTuple ha = *(const HeapTuple *) a;
1318 3865180 : HeapTuple hb = *(const HeapTuple *) b;
1319 3865180 : BlockNumber ba = ItemPointerGetBlockNumber(&ha->t_self);
1320 3865180 : OffsetNumber oa = ItemPointerGetOffsetNumber(&ha->t_self);
1321 3865180 : BlockNumber bb = ItemPointerGetBlockNumber(&hb->t_self);
1322 3865180 : OffsetNumber ob = ItemPointerGetOffsetNumber(&hb->t_self);
1323 :
1324 3865180 : if (ba < bb)
1325 853826 : return -1;
1326 3011354 : if (ba > bb)
1327 854294 : return 1;
1328 2157060 : if (oa < ob)
1329 1415606 : return -1;
1330 741454 : if (oa > ob)
1331 741454 : return 1;
1332 0 : return 0;
1333 : }
1334 :
1335 :
1336 : /*
1337 : * acquire_inherited_sample_rows -- acquire sample rows from inheritance tree
1338 : *
1339 : * This has the same API as acquire_sample_rows, except that rows are
1340 : * collected from all inheritance children as well as the specified table.
1341 : * We fail and return zero if there are no inheritance children, or if all
1342 : * children are foreign tables that don't support ANALYZE.
1343 : */
1344 : static int
1345 706 : acquire_inherited_sample_rows(Relation onerel, int elevel,
1346 : HeapTuple *rows, int targrows,
1347 : double *totalrows, double *totaldeadrows)
1348 : {
1349 : List *tableOIDs;
1350 : Relation *rels;
1351 : AcquireSampleRowsFunc *acquirefuncs;
1352 : double *relblocks;
1353 : double totalblocks;
1354 : int numrows,
1355 : nrels,
1356 : i;
1357 : ListCell *lc;
1358 : bool has_child;
1359 :
1360 : /* Initialize output parameters to zero now, in case we exit early */
1361 706 : *totalrows = 0;
1362 706 : *totaldeadrows = 0;
1363 :
1364 : /*
1365 : * Find all members of inheritance set. We only need AccessShareLock on
1366 : * the children.
1367 : */
1368 : tableOIDs =
1369 706 : find_all_inheritors(RelationGetRelid(onerel), AccessShareLock, NULL);
1370 :
1371 : /*
1372 : * Check that there's at least one descendant, else fail. This could
1373 : * happen despite analyze_rel's relhassubclass check, if table once had a
1374 : * child but no longer does. In that case, we can clear the
1375 : * relhassubclass field so as not to make the same mistake again later.
1376 : * (This is safe because we hold ShareUpdateExclusiveLock.)
1377 : */
1378 706 : if (list_length(tableOIDs) < 2)
1379 : {
1380 : /* CCI because we already updated the pg_class row in this command */
1381 12 : CommandCounterIncrement();
1382 12 : SetRelationHasSubclass(RelationGetRelid(onerel), false);
1383 12 : ereport(elevel,
1384 : (errmsg("skipping analyze of \"%s.%s\" inheritance tree --- this inheritance tree contains no child tables",
1385 : get_namespace_name(RelationGetNamespace(onerel)),
1386 : RelationGetRelationName(onerel))));
1387 12 : return 0;
1388 : }
1389 :
1390 : /*
1391 : * Identify acquirefuncs to use, and count blocks in all the relations.
1392 : * The result could overflow BlockNumber, so we use double arithmetic.
1393 : */
1394 694 : rels = (Relation *) palloc(list_length(tableOIDs) * sizeof(Relation));
1395 : acquirefuncs = (AcquireSampleRowsFunc *)
1396 694 : palloc(list_length(tableOIDs) * sizeof(AcquireSampleRowsFunc));
1397 694 : relblocks = (double *) palloc(list_length(tableOIDs) * sizeof(double));
1398 694 : totalblocks = 0;
1399 694 : nrels = 0;
1400 694 : has_child = false;
1401 3296 : foreach(lc, tableOIDs)
1402 : {
1403 2602 : Oid childOID = lfirst_oid(lc);
1404 : Relation childrel;
1405 2602 : AcquireSampleRowsFunc acquirefunc = NULL;
1406 2602 : BlockNumber relpages = 0;
1407 :
1408 : /* We already got the needed lock */
1409 2602 : childrel = table_open(childOID, NoLock);
1410 :
1411 : /* Ignore if temp table of another backend */
1412 2602 : if (RELATION_IS_OTHER_TEMP(childrel))
1413 : {
1414 : /* ... but release the lock on it */
1415 : Assert(childrel != onerel);
1416 0 : table_close(childrel, AccessShareLock);
1417 698 : continue;
1418 : }
1419 :
1420 : /* Check table type (MATVIEW can't happen, but might as well allow) */
1421 2602 : if (childrel->rd_rel->relkind == RELKIND_RELATION ||
1422 728 : childrel->rd_rel->relkind == RELKIND_MATVIEW)
1423 : {
1424 : /* Regular table, so use the regular row acquisition function */
1425 1874 : acquirefunc = acquire_sample_rows;
1426 1874 : relpages = RelationGetNumberOfBlocks(childrel);
1427 : }
1428 728 : else if (childrel->rd_rel->relkind == RELKIND_FOREIGN_TABLE)
1429 : {
1430 : /*
1431 : * For a foreign table, call the FDW's hook function to see
1432 : * whether it supports analysis.
1433 : */
1434 : FdwRoutine *fdwroutine;
1435 30 : bool ok = false;
1436 :
1437 30 : fdwroutine = GetFdwRoutineForRelation(childrel, false);
1438 :
1439 30 : if (fdwroutine->AnalyzeForeignTable != NULL)
1440 30 : ok = fdwroutine->AnalyzeForeignTable(childrel,
1441 : &acquirefunc,
1442 : &relpages);
1443 :
1444 30 : if (!ok)
1445 : {
1446 : /* ignore, but release the lock on it */
1447 : Assert(childrel != onerel);
1448 0 : table_close(childrel, AccessShareLock);
1449 0 : continue;
1450 : }
1451 : }
1452 : else
1453 : {
1454 : /*
1455 : * ignore, but release the lock on it. don't try to unlock the
1456 : * passed-in relation
1457 : */
1458 : Assert(childrel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE);
1459 698 : if (childrel != onerel)
1460 66 : table_close(childrel, AccessShareLock);
1461 : else
1462 632 : table_close(childrel, NoLock);
1463 698 : continue;
1464 : }
1465 :
1466 : /* OK, we'll process this child */
1467 1904 : has_child = true;
1468 1904 : rels[nrels] = childrel;
1469 1904 : acquirefuncs[nrels] = acquirefunc;
1470 1904 : relblocks[nrels] = (double) relpages;
1471 1904 : totalblocks += (double) relpages;
1472 1904 : nrels++;
1473 : }
1474 :
1475 : /*
1476 : * If we don't have at least one child table to consider, fail. If the
1477 : * relation is a partitioned table, it's not counted as a child table.
1478 : */
1479 694 : if (!has_child)
1480 : {
1481 0 : ereport(elevel,
1482 : (errmsg("skipping analyze of \"%s.%s\" inheritance tree --- this inheritance tree contains no analyzable child tables",
1483 : get_namespace_name(RelationGetNamespace(onerel)),
1484 : RelationGetRelationName(onerel))));
1485 0 : return 0;
1486 : }
1487 :
1488 : /*
1489 : * Now sample rows from each relation, proportionally to its fraction of
1490 : * the total block count. (This might be less than desirable if the child
1491 : * rels have radically different free-space percentages, but it's not
1492 : * clear that it's worth working harder.)
1493 : */
1494 694 : pgstat_progress_update_param(PROGRESS_ANALYZE_CHILD_TABLES_TOTAL,
1495 : nrels);
1496 694 : numrows = 0;
1497 2598 : for (i = 0; i < nrels; i++)
1498 : {
1499 1904 : Relation childrel = rels[i];
1500 1904 : AcquireSampleRowsFunc acquirefunc = acquirefuncs[i];
1501 1904 : double childblocks = relblocks[i];
1502 :
1503 : /*
1504 : * Report progress. The sampling function will normally report blocks
1505 : * done/total, but we need to reset them to 0 here, so that they don't
1506 : * show an old value until that.
1507 : */
1508 : {
1509 1904 : const int progress_index[] = {
1510 : PROGRESS_ANALYZE_CURRENT_CHILD_TABLE_RELID,
1511 : PROGRESS_ANALYZE_BLOCKS_DONE,
1512 : PROGRESS_ANALYZE_BLOCKS_TOTAL
1513 : };
1514 1904 : const int64 progress_vals[] = {
1515 1904 : RelationGetRelid(childrel),
1516 : 0,
1517 : 0,
1518 : };
1519 :
1520 1904 : pgstat_progress_update_multi_param(3, progress_index, progress_vals);
1521 : }
1522 :
1523 1904 : if (childblocks > 0)
1524 : {
1525 : int childtargrows;
1526 :
1527 1768 : childtargrows = (int) rint(targrows * childblocks / totalblocks);
1528 : /* Make sure we don't overrun due to roundoff error */
1529 1768 : childtargrows = Min(childtargrows, targrows - numrows);
1530 1768 : if (childtargrows > 0)
1531 : {
1532 : int childrows;
1533 : double trows,
1534 : tdrows;
1535 :
1536 : /* Fetch a random sample of the child's rows */
1537 1768 : childrows = (*acquirefunc) (childrel, elevel,
1538 1768 : rows + numrows, childtargrows,
1539 : &trows, &tdrows);
1540 :
1541 : /* We may need to convert from child's rowtype to parent's */
1542 1768 : if (childrows > 0 &&
1543 1768 : !equalRowTypes(RelationGetDescr(childrel),
1544 : RelationGetDescr(onerel)))
1545 : {
1546 : TupleConversionMap *map;
1547 :
1548 1712 : map = convert_tuples_by_name(RelationGetDescr(childrel),
1549 : RelationGetDescr(onerel));
1550 1712 : if (map != NULL)
1551 : {
1552 : int j;
1553 :
1554 105890 : for (j = 0; j < childrows; j++)
1555 : {
1556 : HeapTuple newtup;
1557 :
1558 105796 : newtup = execute_attr_map_tuple(rows[numrows + j], map);
1559 105796 : heap_freetuple(rows[numrows + j]);
1560 105796 : rows[numrows + j] = newtup;
1561 : }
1562 94 : free_conversion_map(map);
1563 : }
1564 : }
1565 :
1566 : /* And add to counts */
1567 1768 : numrows += childrows;
1568 1768 : *totalrows += trows;
1569 1768 : *totaldeadrows += tdrows;
1570 : }
1571 : }
1572 :
1573 : /*
1574 : * Note: we cannot release the child-table locks, since we may have
1575 : * pointers to their TOAST tables in the sampled rows.
1576 : */
1577 1904 : table_close(childrel, NoLock);
1578 1904 : pgstat_progress_update_param(PROGRESS_ANALYZE_CHILD_TABLES_DONE,
1579 1904 : i + 1);
1580 : }
1581 :
1582 694 : return numrows;
1583 : }
1584 :
1585 :
1586 : /*
1587 : * update_attstats() -- update attribute statistics for one relation
1588 : *
1589 : * Statistics are stored in several places: the pg_class row for the
1590 : * relation has stats about the whole relation, and there is a
1591 : * pg_statistic row for each (non-system) attribute that has ever
1592 : * been analyzed. The pg_class values are updated by VACUUM, not here.
1593 : *
1594 : * pg_statistic rows are just added or updated normally. This means
1595 : * that pg_statistic will probably contain some deleted rows at the
1596 : * completion of a vacuum cycle, unless it happens to get vacuumed last.
1597 : *
1598 : * To keep things simple, we punt for pg_statistic, and don't try
1599 : * to compute or store rows for pg_statistic itself in pg_statistic.
1600 : * This could possibly be made to work, but it's not worth the trouble.
1601 : * Note analyze_rel() has seen to it that we won't come here when
1602 : * vacuuming pg_statistic itself.
1603 : *
1604 : * Note: there would be a race condition here if two backends could
1605 : * ANALYZE the same table concurrently. Presently, we lock that out
1606 : * by taking a self-exclusive lock on the relation in analyze_rel().
1607 : */
1608 : static void
1609 18710 : update_attstats(Oid relid, bool inh, int natts, VacAttrStats **vacattrstats)
1610 : {
1611 : Relation sd;
1612 : int attno;
1613 18710 : CatalogIndexState indstate = NULL;
1614 :
1615 18710 : if (natts <= 0)
1616 10180 : return; /* nothing to do */
1617 :
1618 8530 : sd = table_open(StatisticRelationId, RowExclusiveLock);
1619 :
1620 72586 : for (attno = 0; attno < natts; attno++)
1621 : {
1622 64056 : VacAttrStats *stats = vacattrstats[attno];
1623 : HeapTuple stup,
1624 : oldtup;
1625 : int i,
1626 : k,
1627 : n;
1628 : Datum values[Natts_pg_statistic];
1629 : bool nulls[Natts_pg_statistic];
1630 : bool replaces[Natts_pg_statistic];
1631 :
1632 : /* Ignore attr if we weren't able to collect stats */
1633 64056 : if (!stats->stats_valid)
1634 6 : continue;
1635 :
1636 : /*
1637 : * Construct a new pg_statistic tuple
1638 : */
1639 2049600 : for (i = 0; i < Natts_pg_statistic; ++i)
1640 : {
1641 1985550 : nulls[i] = false;
1642 1985550 : replaces[i] = true;
1643 : }
1644 :
1645 64050 : values[Anum_pg_statistic_starelid - 1] = ObjectIdGetDatum(relid);
1646 64050 : values[Anum_pg_statistic_staattnum - 1] = Int16GetDatum(stats->tupattnum);
1647 64050 : values[Anum_pg_statistic_stainherit - 1] = BoolGetDatum(inh);
1648 64050 : values[Anum_pg_statistic_stanullfrac - 1] = Float4GetDatum(stats->stanullfrac);
1649 64050 : values[Anum_pg_statistic_stawidth - 1] = Int32GetDatum(stats->stawidth);
1650 64050 : values[Anum_pg_statistic_stadistinct - 1] = Float4GetDatum(stats->stadistinct);
1651 64050 : i = Anum_pg_statistic_stakind1 - 1;
1652 384300 : for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
1653 : {
1654 320250 : values[i++] = Int16GetDatum(stats->stakind[k]); /* stakindN */
1655 : }
1656 64050 : i = Anum_pg_statistic_staop1 - 1;
1657 384300 : for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
1658 : {
1659 320250 : values[i++] = ObjectIdGetDatum(stats->staop[k]); /* staopN */
1660 : }
1661 64050 : i = Anum_pg_statistic_stacoll1 - 1;
1662 384300 : for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
1663 : {
1664 320250 : values[i++] = ObjectIdGetDatum(stats->stacoll[k]); /* stacollN */
1665 : }
1666 64050 : i = Anum_pg_statistic_stanumbers1 - 1;
1667 384300 : for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
1668 : {
1669 320250 : int nnum = stats->numnumbers[k];
1670 :
1671 320250 : if (nnum > 0)
1672 : {
1673 99150 : Datum *numdatums = (Datum *) palloc(nnum * sizeof(Datum));
1674 : ArrayType *arry;
1675 :
1676 807606 : for (n = 0; n < nnum; n++)
1677 708456 : numdatums[n] = Float4GetDatum(stats->stanumbers[k][n]);
1678 99150 : arry = construct_array_builtin(numdatums, nnum, FLOAT4OID);
1679 99150 : values[i++] = PointerGetDatum(arry); /* stanumbersN */
1680 : }
1681 : else
1682 : {
1683 221100 : nulls[i] = true;
1684 221100 : values[i++] = (Datum) 0;
1685 : }
1686 : }
1687 64050 : i = Anum_pg_statistic_stavalues1 - 1;
1688 384300 : for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
1689 : {
1690 320250 : if (stats->numvalues[k] > 0)
1691 : {
1692 : ArrayType *arry;
1693 :
1694 70010 : arry = construct_array(stats->stavalues[k],
1695 : stats->numvalues[k],
1696 : stats->statypid[k],
1697 70010 : stats->statyplen[k],
1698 70010 : stats->statypbyval[k],
1699 70010 : stats->statypalign[k]);
1700 70010 : values[i++] = PointerGetDatum(arry); /* stavaluesN */
1701 : }
1702 : else
1703 : {
1704 250240 : nulls[i] = true;
1705 250240 : values[i++] = (Datum) 0;
1706 : }
1707 : }
1708 :
1709 : /* Is there already a pg_statistic tuple for this attribute? */
1710 128100 : oldtup = SearchSysCache3(STATRELATTINH,
1711 : ObjectIdGetDatum(relid),
1712 64050 : Int16GetDatum(stats->tupattnum),
1713 : BoolGetDatum(inh));
1714 :
1715 : /* Open index information when we know we need it */
1716 64050 : if (indstate == NULL)
1717 8524 : indstate = CatalogOpenIndexes(sd);
1718 :
1719 64050 : if (HeapTupleIsValid(oldtup))
1720 : {
1721 : /* Yes, replace it */
1722 26114 : stup = heap_modify_tuple(oldtup,
1723 : RelationGetDescr(sd),
1724 : values,
1725 : nulls,
1726 : replaces);
1727 26114 : ReleaseSysCache(oldtup);
1728 26114 : CatalogTupleUpdateWithInfo(sd, &stup->t_self, stup, indstate);
1729 : }
1730 : else
1731 : {
1732 : /* No, insert new tuple */
1733 37936 : stup = heap_form_tuple(RelationGetDescr(sd), values, nulls);
1734 37936 : CatalogTupleInsertWithInfo(sd, stup, indstate);
1735 : }
1736 :
1737 64050 : heap_freetuple(stup);
1738 : }
1739 :
1740 8530 : if (indstate != NULL)
1741 8524 : CatalogCloseIndexes(indstate);
1742 8530 : table_close(sd, RowExclusiveLock);
1743 : }
1744 :
1745 : /*
1746 : * Standard fetch function for use by compute_stats subroutines.
1747 : *
1748 : * This exists to provide some insulation between compute_stats routines
1749 : * and the actual storage of the sample data.
1750 : */
1751 : static Datum
1752 60231660 : std_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull)
1753 : {
1754 60231660 : int attnum = stats->tupattnum;
1755 60231660 : HeapTuple tuple = stats->rows[rownum];
1756 60231660 : TupleDesc tupDesc = stats->tupDesc;
1757 :
1758 60231660 : return heap_getattr(tuple, attnum, tupDesc, isNull);
1759 : }
1760 :
1761 : /*
1762 : * Fetch function for analyzing index expressions.
1763 : *
1764 : * We have not bothered to construct index tuples, instead the data is
1765 : * just in Datum arrays.
1766 : */
1767 : static Datum
1768 74460 : ind_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull)
1769 : {
1770 : int i;
1771 :
1772 : /* exprvals and exprnulls are already offset for proper column */
1773 74460 : i = rownum * stats->rowstride;
1774 74460 : *isNull = stats->exprnulls[i];
1775 74460 : return stats->exprvals[i];
1776 : }
1777 :
1778 :
1779 : /*==========================================================================
1780 : *
1781 : * Code below this point represents the "standard" type-specific statistics
1782 : * analysis algorithms. This code can be replaced on a per-data-type basis
1783 : * by setting a nonzero value in pg_type.typanalyze.
1784 : *
1785 : *==========================================================================
1786 : */
1787 :
1788 :
1789 : /*
1790 : * To avoid consuming too much memory during analysis and/or too much space
1791 : * in the resulting pg_statistic rows, we ignore varlena datums that are wider
1792 : * than WIDTH_THRESHOLD (after detoasting!). This is legitimate for MCV
1793 : * and distinct-value calculations since a wide value is unlikely to be
1794 : * duplicated at all, much less be a most-common value. For the same reason,
1795 : * ignoring wide values will not affect our estimates of histogram bin
1796 : * boundaries very much.
1797 : */
1798 : #define WIDTH_THRESHOLD 1024
1799 :
1800 : #define swapInt(a,b) do {int _tmp; _tmp=a; a=b; b=_tmp;} while(0)
1801 : #define swapDatum(a,b) do {Datum _tmp; _tmp=a; a=b; b=_tmp;} while(0)
1802 :
1803 : /*
1804 : * Extra information used by the default analysis routines
1805 : */
1806 : typedef struct
1807 : {
1808 : int count; /* # of duplicates */
1809 : int first; /* values[] index of first occurrence */
1810 : } ScalarMCVItem;
1811 :
1812 : typedef struct
1813 : {
1814 : SortSupport ssup;
1815 : int *tupnoLink;
1816 : } CompareScalarsContext;
1817 :
1818 :
1819 : static void compute_trivial_stats(VacAttrStatsP stats,
1820 : AnalyzeAttrFetchFunc fetchfunc,
1821 : int samplerows,
1822 : double totalrows);
1823 : static void compute_distinct_stats(VacAttrStatsP stats,
1824 : AnalyzeAttrFetchFunc fetchfunc,
1825 : int samplerows,
1826 : double totalrows);
1827 : static void compute_scalar_stats(VacAttrStatsP stats,
1828 : AnalyzeAttrFetchFunc fetchfunc,
1829 : int samplerows,
1830 : double totalrows);
1831 : static int compare_scalars(const void *a, const void *b, void *arg);
1832 : static int compare_mcvs(const void *a, const void *b, void *arg);
1833 : static int analyze_mcv_list(int *mcv_counts,
1834 : int num_mcv,
1835 : double stadistinct,
1836 : double stanullfrac,
1837 : int samplerows,
1838 : double totalrows);
1839 :
1840 :
1841 : /*
1842 : * std_typanalyze -- the default type-specific typanalyze function
1843 : */
1844 : bool
1845 88324 : std_typanalyze(VacAttrStats *stats)
1846 : {
1847 : Oid ltopr;
1848 : Oid eqopr;
1849 : StdAnalyzeData *mystats;
1850 :
1851 : /* If the attstattarget column is negative, use the default value */
1852 88324 : if (stats->attstattarget < 0)
1853 87718 : stats->attstattarget = default_statistics_target;
1854 :
1855 : /* Look for default "<" and "=" operators for column's type */
1856 88324 : get_sort_group_operators(stats->attrtypid,
1857 : false, false, false,
1858 : <opr, &eqopr, NULL,
1859 : NULL);
1860 :
1861 : /* Save the operator info for compute_stats routines */
1862 88324 : mystats = (StdAnalyzeData *) palloc(sizeof(StdAnalyzeData));
1863 88324 : mystats->eqopr = eqopr;
1864 88324 : mystats->eqfunc = OidIsValid(eqopr) ? get_opcode(eqopr) : InvalidOid;
1865 88324 : mystats->ltopr = ltopr;
1866 88324 : stats->extra_data = mystats;
1867 :
1868 : /*
1869 : * Determine which standard statistics algorithm to use
1870 : */
1871 88324 : if (OidIsValid(eqopr) && OidIsValid(ltopr))
1872 : {
1873 : /* Seems to be a scalar datatype */
1874 85648 : stats->compute_stats = compute_scalar_stats;
1875 : /*--------------------
1876 : * The following choice of minrows is based on the paper
1877 : * "Random sampling for histogram construction: how much is enough?"
1878 : * by Surajit Chaudhuri, Rajeev Motwani and Vivek Narasayya, in
1879 : * Proceedings of ACM SIGMOD International Conference on Management
1880 : * of Data, 1998, Pages 436-447. Their Corollary 1 to Theorem 5
1881 : * says that for table size n, histogram size k, maximum relative
1882 : * error in bin size f, and error probability gamma, the minimum
1883 : * random sample size is
1884 : * r = 4 * k * ln(2*n/gamma) / f^2
1885 : * Taking f = 0.5, gamma = 0.01, n = 10^6 rows, we obtain
1886 : * r = 305.82 * k
1887 : * Note that because of the log function, the dependence on n is
1888 : * quite weak; even at n = 10^12, a 300*k sample gives <= 0.66
1889 : * bin size error with probability 0.99. So there's no real need to
1890 : * scale for n, which is a good thing because we don't necessarily
1891 : * know it at this point.
1892 : *--------------------
1893 : */
1894 85648 : stats->minrows = 300 * stats->attstattarget;
1895 : }
1896 2676 : else if (OidIsValid(eqopr))
1897 : {
1898 : /* We can still recognize distinct values */
1899 2314 : stats->compute_stats = compute_distinct_stats;
1900 : /* Might as well use the same minrows as above */
1901 2314 : stats->minrows = 300 * stats->attstattarget;
1902 : }
1903 : else
1904 : {
1905 : /* Can't do much but the trivial stuff */
1906 362 : stats->compute_stats = compute_trivial_stats;
1907 : /* Might as well use the same minrows as above */
1908 362 : stats->minrows = 300 * stats->attstattarget;
1909 : }
1910 :
1911 88324 : return true;
1912 : }
1913 :
1914 :
1915 : /*
1916 : * compute_trivial_stats() -- compute very basic column statistics
1917 : *
1918 : * We use this when we cannot find a hash "=" operator for the datatype.
1919 : *
1920 : * We determine the fraction of non-null rows and the average datum width.
1921 : */
1922 : static void
1923 236 : compute_trivial_stats(VacAttrStatsP stats,
1924 : AnalyzeAttrFetchFunc fetchfunc,
1925 : int samplerows,
1926 : double totalrows)
1927 : {
1928 : int i;
1929 236 : int null_cnt = 0;
1930 236 : int nonnull_cnt = 0;
1931 236 : double total_width = 0;
1932 472 : bool is_varlena = (!stats->attrtype->typbyval &&
1933 236 : stats->attrtype->typlen == -1);
1934 472 : bool is_varwidth = (!stats->attrtype->typbyval &&
1935 236 : stats->attrtype->typlen < 0);
1936 :
1937 692442 : for (i = 0; i < samplerows; i++)
1938 : {
1939 : Datum value;
1940 : bool isnull;
1941 :
1942 692206 : vacuum_delay_point();
1943 :
1944 692206 : value = fetchfunc(stats, i, &isnull);
1945 :
1946 : /* Check for null/nonnull */
1947 692206 : if (isnull)
1948 : {
1949 425456 : null_cnt++;
1950 425456 : continue;
1951 : }
1952 266750 : nonnull_cnt++;
1953 :
1954 : /*
1955 : * If it's a variable-width field, add up widths for average width
1956 : * calculation. Note that if the value is toasted, we use the toasted
1957 : * width. We don't bother with this calculation if it's a fixed-width
1958 : * type.
1959 : */
1960 266750 : if (is_varlena)
1961 : {
1962 34166 : total_width += VARSIZE_ANY(DatumGetPointer(value));
1963 : }
1964 232584 : else if (is_varwidth)
1965 : {
1966 : /* must be cstring */
1967 0 : total_width += strlen(DatumGetCString(value)) + 1;
1968 : }
1969 : }
1970 :
1971 : /* We can only compute average width if we found some non-null values. */
1972 236 : if (nonnull_cnt > 0)
1973 : {
1974 104 : stats->stats_valid = true;
1975 : /* Do the simple null-frac and width stats */
1976 104 : stats->stanullfrac = (double) null_cnt / (double) samplerows;
1977 104 : if (is_varwidth)
1978 38 : stats->stawidth = total_width / (double) nonnull_cnt;
1979 : else
1980 66 : stats->stawidth = stats->attrtype->typlen;
1981 104 : stats->stadistinct = 0.0; /* "unknown" */
1982 : }
1983 132 : else if (null_cnt > 0)
1984 : {
1985 : /* We found only nulls; assume the column is entirely null */
1986 132 : stats->stats_valid = true;
1987 132 : stats->stanullfrac = 1.0;
1988 132 : if (is_varwidth)
1989 132 : stats->stawidth = 0; /* "unknown" */
1990 : else
1991 0 : stats->stawidth = stats->attrtype->typlen;
1992 132 : stats->stadistinct = 0.0; /* "unknown" */
1993 : }
1994 236 : }
1995 :
1996 :
1997 : /*
1998 : * compute_distinct_stats() -- compute column statistics including ndistinct
1999 : *
2000 : * We use this when we can find only an "=" operator for the datatype.
2001 : *
2002 : * We determine the fraction of non-null rows, the average width, the
2003 : * most common values, and the (estimated) number of distinct values.
2004 : *
2005 : * The most common values are determined by brute force: we keep a list
2006 : * of previously seen values, ordered by number of times seen, as we scan
2007 : * the samples. A newly seen value is inserted just after the last
2008 : * multiply-seen value, causing the bottommost (oldest) singly-seen value
2009 : * to drop off the list. The accuracy of this method, and also its cost,
2010 : * depend mainly on the length of the list we are willing to keep.
2011 : */
2012 : static void
2013 1684 : compute_distinct_stats(VacAttrStatsP stats,
2014 : AnalyzeAttrFetchFunc fetchfunc,
2015 : int samplerows,
2016 : double totalrows)
2017 : {
2018 : int i;
2019 1684 : int null_cnt = 0;
2020 1684 : int nonnull_cnt = 0;
2021 1684 : int toowide_cnt = 0;
2022 1684 : double total_width = 0;
2023 2848 : bool is_varlena = (!stats->attrtype->typbyval &&
2024 1164 : stats->attrtype->typlen == -1);
2025 2848 : bool is_varwidth = (!stats->attrtype->typbyval &&
2026 1164 : stats->attrtype->typlen < 0);
2027 : FmgrInfo f_cmpeq;
2028 : typedef struct
2029 : {
2030 : Datum value;
2031 : int count;
2032 : } TrackItem;
2033 : TrackItem *track;
2034 : int track_cnt,
2035 : track_max;
2036 1684 : int num_mcv = stats->attstattarget;
2037 1684 : StdAnalyzeData *mystats = (StdAnalyzeData *) stats->extra_data;
2038 :
2039 : /*
2040 : * We track up to 2*n values for an n-element MCV list; but at least 10
2041 : */
2042 1684 : track_max = 2 * num_mcv;
2043 1684 : if (track_max < 10)
2044 78 : track_max = 10;
2045 1684 : track = (TrackItem *) palloc(track_max * sizeof(TrackItem));
2046 1684 : track_cnt = 0;
2047 :
2048 1684 : fmgr_info(mystats->eqfunc, &f_cmpeq);
2049 :
2050 1112930 : for (i = 0; i < samplerows; i++)
2051 : {
2052 : Datum value;
2053 : bool isnull;
2054 : bool match;
2055 : int firstcount1,
2056 : j;
2057 :
2058 1111246 : vacuum_delay_point();
2059 :
2060 1111246 : value = fetchfunc(stats, i, &isnull);
2061 :
2062 : /* Check for null/nonnull */
2063 1111246 : if (isnull)
2064 : {
2065 928270 : null_cnt++;
2066 928270 : continue;
2067 : }
2068 182976 : nonnull_cnt++;
2069 :
2070 : /*
2071 : * If it's a variable-width field, add up widths for average width
2072 : * calculation. Note that if the value is toasted, we use the toasted
2073 : * width. We don't bother with this calculation if it's a fixed-width
2074 : * type.
2075 : */
2076 182976 : if (is_varlena)
2077 : {
2078 66552 : total_width += VARSIZE_ANY(DatumGetPointer(value));
2079 :
2080 : /*
2081 : * If the value is toasted, we want to detoast it just once to
2082 : * avoid repeated detoastings and resultant excess memory usage
2083 : * during the comparisons. Also, check to see if the value is
2084 : * excessively wide, and if so don't detoast at all --- just
2085 : * ignore the value.
2086 : */
2087 66552 : if (toast_raw_datum_size(value) > WIDTH_THRESHOLD)
2088 : {
2089 0 : toowide_cnt++;
2090 0 : continue;
2091 : }
2092 66552 : value = PointerGetDatum(PG_DETOAST_DATUM(value));
2093 : }
2094 116424 : else if (is_varwidth)
2095 : {
2096 : /* must be cstring */
2097 0 : total_width += strlen(DatumGetCString(value)) + 1;
2098 : }
2099 :
2100 : /*
2101 : * See if the value matches anything we're already tracking.
2102 : */
2103 182976 : match = false;
2104 182976 : firstcount1 = track_cnt;
2105 386194 : for (j = 0; j < track_cnt; j++)
2106 : {
2107 381482 : if (DatumGetBool(FunctionCall2Coll(&f_cmpeq,
2108 : stats->attrcollid,
2109 381482 : value, track[j].value)))
2110 : {
2111 178264 : match = true;
2112 178264 : break;
2113 : }
2114 203218 : if (j < firstcount1 && track[j].count == 1)
2115 2866 : firstcount1 = j;
2116 : }
2117 :
2118 182976 : if (match)
2119 : {
2120 : /* Found a match */
2121 178264 : track[j].count++;
2122 : /* This value may now need to "bubble up" in the track list */
2123 185398 : while (j > 0 && track[j].count > track[j - 1].count)
2124 : {
2125 7134 : swapDatum(track[j].value, track[j - 1].value);
2126 7134 : swapInt(track[j].count, track[j - 1].count);
2127 7134 : j--;
2128 : }
2129 : }
2130 : else
2131 : {
2132 : /* No match. Insert at head of count-1 list */
2133 4712 : if (track_cnt < track_max)
2134 4392 : track_cnt++;
2135 74210 : for (j = track_cnt - 1; j > firstcount1; j--)
2136 : {
2137 69498 : track[j].value = track[j - 1].value;
2138 69498 : track[j].count = track[j - 1].count;
2139 : }
2140 4712 : if (firstcount1 < track_cnt)
2141 : {
2142 4712 : track[firstcount1].value = value;
2143 4712 : track[firstcount1].count = 1;
2144 : }
2145 : }
2146 : }
2147 :
2148 : /* We can only compute real stats if we found some non-null values. */
2149 1684 : if (nonnull_cnt > 0)
2150 : {
2151 : int nmultiple,
2152 : summultiple;
2153 :
2154 1222 : stats->stats_valid = true;
2155 : /* Do the simple null-frac and width stats */
2156 1222 : stats->stanullfrac = (double) null_cnt / (double) samplerows;
2157 1222 : if (is_varwidth)
2158 702 : stats->stawidth = total_width / (double) nonnull_cnt;
2159 : else
2160 520 : stats->stawidth = stats->attrtype->typlen;
2161 :
2162 : /* Count the number of values we found multiple times */
2163 1222 : summultiple = 0;
2164 4520 : for (nmultiple = 0; nmultiple < track_cnt; nmultiple++)
2165 : {
2166 3914 : if (track[nmultiple].count == 1)
2167 616 : break;
2168 3298 : summultiple += track[nmultiple].count;
2169 : }
2170 :
2171 1222 : if (nmultiple == 0)
2172 : {
2173 : /*
2174 : * If we found no repeated non-null values, assume it's a unique
2175 : * column; but be sure to discount for any nulls we found.
2176 : */
2177 156 : stats->stadistinct = -1.0 * (1.0 - stats->stanullfrac);
2178 : }
2179 1066 : else if (track_cnt < track_max && toowide_cnt == 0 &&
2180 : nmultiple == track_cnt)
2181 : {
2182 : /*
2183 : * Our track list includes every value in the sample, and every
2184 : * value appeared more than once. Assume the column has just
2185 : * these values. (This case is meant to address columns with
2186 : * small, fixed sets of possible values, such as boolean or enum
2187 : * columns. If there are any values that appear just once in the
2188 : * sample, including too-wide values, we should assume that that's
2189 : * not what we're dealing with.)
2190 : */
2191 606 : stats->stadistinct = track_cnt;
2192 : }
2193 : else
2194 : {
2195 : /*----------
2196 : * Estimate the number of distinct values using the estimator
2197 : * proposed by Haas and Stokes in IBM Research Report RJ 10025:
2198 : * n*d / (n - f1 + f1*n/N)
2199 : * where f1 is the number of distinct values that occurred
2200 : * exactly once in our sample of n rows (from a total of N),
2201 : * and d is the total number of distinct values in the sample.
2202 : * This is their Duj1 estimator; the other estimators they
2203 : * recommend are considerably more complex, and are numerically
2204 : * very unstable when n is much smaller than N.
2205 : *
2206 : * In this calculation, we consider only non-nulls. We used to
2207 : * include rows with null values in the n and N counts, but that
2208 : * leads to inaccurate answers in columns with many nulls, and
2209 : * it's intuitively bogus anyway considering the desired result is
2210 : * the number of distinct non-null values.
2211 : *
2212 : * We assume (not very reliably!) that all the multiply-occurring
2213 : * values are reflected in the final track[] list, and the other
2214 : * nonnull values all appeared but once. (XXX this usually
2215 : * results in a drastic overestimate of ndistinct. Can we do
2216 : * any better?)
2217 : *----------
2218 : */
2219 460 : int f1 = nonnull_cnt - summultiple;
2220 460 : int d = f1 + nmultiple;
2221 460 : double n = samplerows - null_cnt;
2222 460 : double N = totalrows * (1.0 - stats->stanullfrac);
2223 : double stadistinct;
2224 :
2225 : /* N == 0 shouldn't happen, but just in case ... */
2226 460 : if (N > 0)
2227 460 : stadistinct = (n * d) / ((n - f1) + f1 * n / N);
2228 : else
2229 0 : stadistinct = 0;
2230 :
2231 : /* Clamp to sane range in case of roundoff error */
2232 460 : if (stadistinct < d)
2233 128 : stadistinct = d;
2234 460 : if (stadistinct > N)
2235 0 : stadistinct = N;
2236 : /* And round to integer */
2237 460 : stats->stadistinct = floor(stadistinct + 0.5);
2238 : }
2239 :
2240 : /*
2241 : * If we estimated the number of distinct values at more than 10% of
2242 : * the total row count (a very arbitrary limit), then assume that
2243 : * stadistinct should scale with the row count rather than be a fixed
2244 : * value.
2245 : */
2246 1222 : if (stats->stadistinct > 0.1 * totalrows)
2247 278 : stats->stadistinct = -(stats->stadistinct / totalrows);
2248 :
2249 : /*
2250 : * Decide how many values are worth storing as most-common values. If
2251 : * we are able to generate a complete MCV list (all the values in the
2252 : * sample will fit, and we think these are all the ones in the table),
2253 : * then do so. Otherwise, store only those values that are
2254 : * significantly more common than the values not in the list.
2255 : *
2256 : * Note: the first of these cases is meant to address columns with
2257 : * small, fixed sets of possible values, such as boolean or enum
2258 : * columns. If we can *completely* represent the column population by
2259 : * an MCV list that will fit into the stats target, then we should do
2260 : * so and thus provide the planner with complete information. But if
2261 : * the MCV list is not complete, it's generally worth being more
2262 : * selective, and not just filling it all the way up to the stats
2263 : * target.
2264 : */
2265 1222 : if (track_cnt < track_max && toowide_cnt == 0 &&
2266 1214 : stats->stadistinct > 0 &&
2267 : track_cnt <= num_mcv)
2268 : {
2269 : /* Track list includes all values seen, and all will fit */
2270 762 : num_mcv = track_cnt;
2271 : }
2272 : else
2273 : {
2274 : int *mcv_counts;
2275 :
2276 : /* Incomplete list; decide how many values are worth keeping */
2277 460 : if (num_mcv > track_cnt)
2278 404 : num_mcv = track_cnt;
2279 :
2280 460 : if (num_mcv > 0)
2281 : {
2282 460 : mcv_counts = (int *) palloc(num_mcv * sizeof(int));
2283 1250 : for (i = 0; i < num_mcv; i++)
2284 790 : mcv_counts[i] = track[i].count;
2285 :
2286 460 : num_mcv = analyze_mcv_list(mcv_counts, num_mcv,
2287 460 : stats->stadistinct,
2288 460 : stats->stanullfrac,
2289 : samplerows, totalrows);
2290 : }
2291 : }
2292 :
2293 : /* Generate MCV slot entry */
2294 1222 : if (num_mcv > 0)
2295 : {
2296 : MemoryContext old_context;
2297 : Datum *mcv_values;
2298 : float4 *mcv_freqs;
2299 :
2300 : /* Must copy the target values into anl_context */
2301 1216 : old_context = MemoryContextSwitchTo(stats->anl_context);
2302 1216 : mcv_values = (Datum *) palloc(num_mcv * sizeof(Datum));
2303 1216 : mcv_freqs = (float4 *) palloc(num_mcv * sizeof(float4));
2304 5304 : for (i = 0; i < num_mcv; i++)
2305 : {
2306 8176 : mcv_values[i] = datumCopy(track[i].value,
2307 4088 : stats->attrtype->typbyval,
2308 4088 : stats->attrtype->typlen);
2309 4088 : mcv_freqs[i] = (double) track[i].count / (double) samplerows;
2310 : }
2311 1216 : MemoryContextSwitchTo(old_context);
2312 :
2313 1216 : stats->stakind[0] = STATISTIC_KIND_MCV;
2314 1216 : stats->staop[0] = mystats->eqopr;
2315 1216 : stats->stacoll[0] = stats->attrcollid;
2316 1216 : stats->stanumbers[0] = mcv_freqs;
2317 1216 : stats->numnumbers[0] = num_mcv;
2318 1216 : stats->stavalues[0] = mcv_values;
2319 1216 : stats->numvalues[0] = num_mcv;
2320 :
2321 : /*
2322 : * Accept the defaults for stats->statypid and others. They have
2323 : * been set before we were called (see vacuum.h)
2324 : */
2325 : }
2326 : }
2327 462 : else if (null_cnt > 0)
2328 : {
2329 : /* We found only nulls; assume the column is entirely null */
2330 462 : stats->stats_valid = true;
2331 462 : stats->stanullfrac = 1.0;
2332 462 : if (is_varwidth)
2333 462 : stats->stawidth = 0; /* "unknown" */
2334 : else
2335 0 : stats->stawidth = stats->attrtype->typlen;
2336 462 : stats->stadistinct = 0.0; /* "unknown" */
2337 : }
2338 :
2339 : /* We don't need to bother cleaning up any of our temporary palloc's */
2340 1684 : }
2341 :
2342 :
2343 : /*
2344 : * compute_scalar_stats() -- compute column statistics
2345 : *
2346 : * We use this when we can find "=" and "<" operators for the datatype.
2347 : *
2348 : * We determine the fraction of non-null rows, the average width, the
2349 : * most common values, the (estimated) number of distinct values, the
2350 : * distribution histogram, and the correlation of physical to logical order.
2351 : *
2352 : * The desired stats can be determined fairly easily after sorting the
2353 : * data values into order.
2354 : */
2355 : static void
2356 62406 : compute_scalar_stats(VacAttrStatsP stats,
2357 : AnalyzeAttrFetchFunc fetchfunc,
2358 : int samplerows,
2359 : double totalrows)
2360 : {
2361 : int i;
2362 62406 : int null_cnt = 0;
2363 62406 : int nonnull_cnt = 0;
2364 62406 : int toowide_cnt = 0;
2365 62406 : double total_width = 0;
2366 77762 : bool is_varlena = (!stats->attrtype->typbyval &&
2367 15356 : stats->attrtype->typlen == -1);
2368 77762 : bool is_varwidth = (!stats->attrtype->typbyval &&
2369 15356 : stats->attrtype->typlen < 0);
2370 : double corr_xysum;
2371 : SortSupportData ssup;
2372 : ScalarItem *values;
2373 62406 : int values_cnt = 0;
2374 : int *tupnoLink;
2375 : ScalarMCVItem *track;
2376 62406 : int track_cnt = 0;
2377 62406 : int num_mcv = stats->attstattarget;
2378 62406 : int num_bins = stats->attstattarget;
2379 62406 : StdAnalyzeData *mystats = (StdAnalyzeData *) stats->extra_data;
2380 :
2381 62406 : values = (ScalarItem *) palloc(samplerows * sizeof(ScalarItem));
2382 62406 : tupnoLink = (int *) palloc(samplerows * sizeof(int));
2383 62406 : track = (ScalarMCVItem *) palloc(num_mcv * sizeof(ScalarMCVItem));
2384 :
2385 62406 : memset(&ssup, 0, sizeof(ssup));
2386 62406 : ssup.ssup_cxt = CurrentMemoryContext;
2387 62406 : ssup.ssup_collation = stats->attrcollid;
2388 62406 : ssup.ssup_nulls_first = false;
2389 :
2390 : /*
2391 : * For now, don't perform abbreviated key conversion, because full values
2392 : * are required for MCV slot generation. Supporting that optimization
2393 : * would necessitate teaching compare_scalars() to call a tie-breaker.
2394 : */
2395 62406 : ssup.abbreviate = false;
2396 :
2397 62406 : PrepareSortSupportFromOrderingOp(mystats->ltopr, &ssup);
2398 :
2399 : /* Initial scan to find sortable values */
2400 55239992 : for (i = 0; i < samplerows; i++)
2401 : {
2402 : Datum value;
2403 : bool isnull;
2404 :
2405 55177586 : vacuum_delay_point();
2406 :
2407 55177586 : value = fetchfunc(stats, i, &isnull);
2408 :
2409 : /* Check for null/nonnull */
2410 55177586 : if (isnull)
2411 : {
2412 7436014 : null_cnt++;
2413 7462108 : continue;
2414 : }
2415 47741572 : nonnull_cnt++;
2416 :
2417 : /*
2418 : * If it's a variable-width field, add up widths for average width
2419 : * calculation. Note that if the value is toasted, we use the toasted
2420 : * width. We don't bother with this calculation if it's a fixed-width
2421 : * type.
2422 : */
2423 47741572 : if (is_varlena)
2424 : {
2425 5479940 : total_width += VARSIZE_ANY(DatumGetPointer(value));
2426 :
2427 : /*
2428 : * If the value is toasted, we want to detoast it just once to
2429 : * avoid repeated detoastings and resultant excess memory usage
2430 : * during the comparisons. Also, check to see if the value is
2431 : * excessively wide, and if so don't detoast at all --- just
2432 : * ignore the value.
2433 : */
2434 5479940 : if (toast_raw_datum_size(value) > WIDTH_THRESHOLD)
2435 : {
2436 26094 : toowide_cnt++;
2437 26094 : continue;
2438 : }
2439 5453846 : value = PointerGetDatum(PG_DETOAST_DATUM(value));
2440 : }
2441 42261632 : else if (is_varwidth)
2442 : {
2443 : /* must be cstring */
2444 0 : total_width += strlen(DatumGetCString(value)) + 1;
2445 : }
2446 :
2447 : /* Add it to the list to be sorted */
2448 47715478 : values[values_cnt].value = value;
2449 47715478 : values[values_cnt].tupno = values_cnt;
2450 47715478 : tupnoLink[values_cnt] = values_cnt;
2451 47715478 : values_cnt++;
2452 : }
2453 :
2454 : /* We can only compute real stats if we found some sortable values. */
2455 62406 : if (values_cnt > 0)
2456 : {
2457 : int ndistinct, /* # distinct values in sample */
2458 : nmultiple, /* # that appear multiple times */
2459 : num_hist,
2460 : dups_cnt;
2461 58158 : int slot_idx = 0;
2462 : CompareScalarsContext cxt;
2463 :
2464 : /* Sort the collected values */
2465 58158 : cxt.ssup = &ssup;
2466 58158 : cxt.tupnoLink = tupnoLink;
2467 58158 : qsort_interruptible(values, values_cnt, sizeof(ScalarItem),
2468 : compare_scalars, &cxt);
2469 :
2470 : /*
2471 : * Now scan the values in order, find the most common ones, and also
2472 : * accumulate ordering-correlation statistics.
2473 : *
2474 : * To determine which are most common, we first have to count the
2475 : * number of duplicates of each value. The duplicates are adjacent in
2476 : * the sorted list, so a brute-force approach is to compare successive
2477 : * datum values until we find two that are not equal. However, that
2478 : * requires N-1 invocations of the datum comparison routine, which are
2479 : * completely redundant with work that was done during the sort. (The
2480 : * sort algorithm must at some point have compared each pair of items
2481 : * that are adjacent in the sorted order; otherwise it could not know
2482 : * that it's ordered the pair correctly.) We exploit this by having
2483 : * compare_scalars remember the highest tupno index that each
2484 : * ScalarItem has been found equal to. At the end of the sort, a
2485 : * ScalarItem's tupnoLink will still point to itself if and only if it
2486 : * is the last item of its group of duplicates (since the group will
2487 : * be ordered by tupno).
2488 : */
2489 58158 : corr_xysum = 0;
2490 58158 : ndistinct = 0;
2491 58158 : nmultiple = 0;
2492 58158 : dups_cnt = 0;
2493 47773636 : for (i = 0; i < values_cnt; i++)
2494 : {
2495 47715478 : int tupno = values[i].tupno;
2496 :
2497 47715478 : corr_xysum += ((double) i) * ((double) tupno);
2498 47715478 : dups_cnt++;
2499 47715478 : if (tupnoLink[tupno] == tupno)
2500 : {
2501 : /* Reached end of duplicates of this value */
2502 9562296 : ndistinct++;
2503 9562296 : if (dups_cnt > 1)
2504 : {
2505 848078 : nmultiple++;
2506 848078 : if (track_cnt < num_mcv ||
2507 349952 : dups_cnt > track[track_cnt - 1].count)
2508 : {
2509 : /*
2510 : * Found a new item for the mcv list; find its
2511 : * position, bubbling down old items if needed. Loop
2512 : * invariant is that j points at an empty/ replaceable
2513 : * slot.
2514 : */
2515 : int j;
2516 :
2517 569172 : if (track_cnt < num_mcv)
2518 498126 : track_cnt++;
2519 7306496 : for (j = track_cnt - 1; j > 0; j--)
2520 : {
2521 7242664 : if (dups_cnt <= track[j - 1].count)
2522 505340 : break;
2523 6737324 : track[j].count = track[j - 1].count;
2524 6737324 : track[j].first = track[j - 1].first;
2525 : }
2526 569172 : track[j].count = dups_cnt;
2527 569172 : track[j].first = i + 1 - dups_cnt;
2528 : }
2529 : }
2530 9562296 : dups_cnt = 0;
2531 : }
2532 : }
2533 :
2534 58158 : stats->stats_valid = true;
2535 : /* Do the simple null-frac and width stats */
2536 58158 : stats->stanullfrac = (double) null_cnt / (double) samplerows;
2537 58158 : if (is_varwidth)
2538 8564 : stats->stawidth = total_width / (double) nonnull_cnt;
2539 : else
2540 49594 : stats->stawidth = stats->attrtype->typlen;
2541 :
2542 58158 : if (nmultiple == 0)
2543 : {
2544 : /*
2545 : * If we found no repeated non-null values, assume it's a unique
2546 : * column; but be sure to discount for any nulls we found.
2547 : */
2548 15732 : stats->stadistinct = -1.0 * (1.0 - stats->stanullfrac);
2549 : }
2550 42426 : else if (toowide_cnt == 0 && nmultiple == ndistinct)
2551 : {
2552 : /*
2553 : * Every value in the sample appeared more than once. Assume the
2554 : * column has just these values. (This case is meant to address
2555 : * columns with small, fixed sets of possible values, such as
2556 : * boolean or enum columns. If there are any values that appear
2557 : * just once in the sample, including too-wide values, we should
2558 : * assume that that's not what we're dealing with.)
2559 : */
2560 26114 : stats->stadistinct = ndistinct;
2561 : }
2562 : else
2563 : {
2564 : /*----------
2565 : * Estimate the number of distinct values using the estimator
2566 : * proposed by Haas and Stokes in IBM Research Report RJ 10025:
2567 : * n*d / (n - f1 + f1*n/N)
2568 : * where f1 is the number of distinct values that occurred
2569 : * exactly once in our sample of n rows (from a total of N),
2570 : * and d is the total number of distinct values in the sample.
2571 : * This is their Duj1 estimator; the other estimators they
2572 : * recommend are considerably more complex, and are numerically
2573 : * very unstable when n is much smaller than N.
2574 : *
2575 : * In this calculation, we consider only non-nulls. We used to
2576 : * include rows with null values in the n and N counts, but that
2577 : * leads to inaccurate answers in columns with many nulls, and
2578 : * it's intuitively bogus anyway considering the desired result is
2579 : * the number of distinct non-null values.
2580 : *
2581 : * Overwidth values are assumed to have been distinct.
2582 : *----------
2583 : */
2584 16312 : int f1 = ndistinct - nmultiple + toowide_cnt;
2585 16312 : int d = f1 + nmultiple;
2586 16312 : double n = samplerows - null_cnt;
2587 16312 : double N = totalrows * (1.0 - stats->stanullfrac);
2588 : double stadistinct;
2589 :
2590 : /* N == 0 shouldn't happen, but just in case ... */
2591 16312 : if (N > 0)
2592 16312 : stadistinct = (n * d) / ((n - f1) + f1 * n / N);
2593 : else
2594 0 : stadistinct = 0;
2595 :
2596 : /* Clamp to sane range in case of roundoff error */
2597 16312 : if (stadistinct < d)
2598 410 : stadistinct = d;
2599 16312 : if (stadistinct > N)
2600 0 : stadistinct = N;
2601 : /* And round to integer */
2602 16312 : stats->stadistinct = floor(stadistinct + 0.5);
2603 : }
2604 :
2605 : /*
2606 : * If we estimated the number of distinct values at more than 10% of
2607 : * the total row count (a very arbitrary limit), then assume that
2608 : * stadistinct should scale with the row count rather than be a fixed
2609 : * value.
2610 : */
2611 58158 : if (stats->stadistinct > 0.1 * totalrows)
2612 12064 : stats->stadistinct = -(stats->stadistinct / totalrows);
2613 :
2614 : /*
2615 : * Decide how many values are worth storing as most-common values. If
2616 : * we are able to generate a complete MCV list (all the values in the
2617 : * sample will fit, and we think these are all the ones in the table),
2618 : * then do so. Otherwise, store only those values that are
2619 : * significantly more common than the values not in the list.
2620 : *
2621 : * Note: the first of these cases is meant to address columns with
2622 : * small, fixed sets of possible values, such as boolean or enum
2623 : * columns. If we can *completely* represent the column population by
2624 : * an MCV list that will fit into the stats target, then we should do
2625 : * so and thus provide the planner with complete information. But if
2626 : * the MCV list is not complete, it's generally worth being more
2627 : * selective, and not just filling it all the way up to the stats
2628 : * target.
2629 : */
2630 58158 : if (track_cnt == ndistinct && toowide_cnt == 0 &&
2631 25496 : stats->stadistinct > 0 &&
2632 : track_cnt <= num_mcv)
2633 : {
2634 : /* Track list includes all values seen, and all will fit */
2635 22962 : num_mcv = track_cnt;
2636 : }
2637 : else
2638 : {
2639 : int *mcv_counts;
2640 :
2641 : /* Incomplete list; decide how many values are worth keeping */
2642 35196 : if (num_mcv > track_cnt)
2643 31880 : num_mcv = track_cnt;
2644 :
2645 35196 : if (num_mcv > 0)
2646 : {
2647 19464 : mcv_counts = (int *) palloc(num_mcv * sizeof(int));
2648 390786 : for (i = 0; i < num_mcv; i++)
2649 371322 : mcv_counts[i] = track[i].count;
2650 :
2651 19464 : num_mcv = analyze_mcv_list(mcv_counts, num_mcv,
2652 19464 : stats->stadistinct,
2653 19464 : stats->stanullfrac,
2654 : samplerows, totalrows);
2655 : }
2656 : }
2657 :
2658 : /* Generate MCV slot entry */
2659 58158 : if (num_mcv > 0)
2660 : {
2661 : MemoryContext old_context;
2662 : Datum *mcv_values;
2663 : float4 *mcv_freqs;
2664 :
2665 : /* Must copy the target values into anl_context */
2666 42374 : old_context = MemoryContextSwitchTo(stats->anl_context);
2667 42374 : mcv_values = (Datum *) palloc(num_mcv * sizeof(Datum));
2668 42374 : mcv_freqs = (float4 *) palloc(num_mcv * sizeof(float4));
2669 540310 : for (i = 0; i < num_mcv; i++)
2670 : {
2671 995872 : mcv_values[i] = datumCopy(values[track[i].first].value,
2672 497936 : stats->attrtype->typbyval,
2673 497936 : stats->attrtype->typlen);
2674 497936 : mcv_freqs[i] = (double) track[i].count / (double) samplerows;
2675 : }
2676 42374 : MemoryContextSwitchTo(old_context);
2677 :
2678 42374 : stats->stakind[slot_idx] = STATISTIC_KIND_MCV;
2679 42374 : stats->staop[slot_idx] = mystats->eqopr;
2680 42374 : stats->stacoll[slot_idx] = stats->attrcollid;
2681 42374 : stats->stanumbers[slot_idx] = mcv_freqs;
2682 42374 : stats->numnumbers[slot_idx] = num_mcv;
2683 42374 : stats->stavalues[slot_idx] = mcv_values;
2684 42374 : stats->numvalues[slot_idx] = num_mcv;
2685 :
2686 : /*
2687 : * Accept the defaults for stats->statypid and others. They have
2688 : * been set before we were called (see vacuum.h)
2689 : */
2690 42374 : slot_idx++;
2691 : }
2692 :
2693 : /*
2694 : * Generate a histogram slot entry if there are at least two distinct
2695 : * values not accounted for in the MCV list. (This ensures the
2696 : * histogram won't collapse to empty or a singleton.)
2697 : */
2698 58158 : num_hist = ndistinct - num_mcv;
2699 58158 : if (num_hist > num_bins)
2700 9922 : num_hist = num_bins + 1;
2701 58158 : if (num_hist >= 2)
2702 : {
2703 : MemoryContext old_context;
2704 : Datum *hist_values;
2705 : int nvals;
2706 : int pos,
2707 : posfrac,
2708 : delta,
2709 : deltafrac;
2710 :
2711 : /* Sort the MCV items into position order to speed next loop */
2712 25950 : qsort_interruptible(track, num_mcv, sizeof(ScalarMCVItem),
2713 : compare_mcvs, NULL);
2714 :
2715 : /*
2716 : * Collapse out the MCV items from the values[] array.
2717 : *
2718 : * Note we destroy the values[] array here... but we don't need it
2719 : * for anything more. We do, however, still need values_cnt.
2720 : * nvals will be the number of remaining entries in values[].
2721 : */
2722 25950 : if (num_mcv > 0)
2723 : {
2724 : int src,
2725 : dest;
2726 : int j;
2727 :
2728 13680 : src = dest = 0;
2729 13680 : j = 0; /* index of next interesting MCV item */
2730 486910 : while (src < values_cnt)
2731 : {
2732 : int ncopy;
2733 :
2734 473230 : if (j < num_mcv)
2735 : {
2736 462798 : int first = track[j].first;
2737 :
2738 462798 : if (src >= first)
2739 : {
2740 : /* advance past this MCV item */
2741 338926 : src = first + track[j].count;
2742 338926 : j++;
2743 338926 : continue;
2744 : }
2745 123872 : ncopy = first - src;
2746 : }
2747 : else
2748 10432 : ncopy = values_cnt - src;
2749 134304 : memmove(&values[dest], &values[src],
2750 : ncopy * sizeof(ScalarItem));
2751 134304 : src += ncopy;
2752 134304 : dest += ncopy;
2753 : }
2754 13680 : nvals = dest;
2755 : }
2756 : else
2757 12270 : nvals = values_cnt;
2758 : Assert(nvals >= num_hist);
2759 :
2760 : /* Must copy the target values into anl_context */
2761 25950 : old_context = MemoryContextSwitchTo(stats->anl_context);
2762 25950 : hist_values = (Datum *) palloc(num_hist * sizeof(Datum));
2763 :
2764 : /*
2765 : * The object of this loop is to copy the first and last values[]
2766 : * entries along with evenly-spaced values in between. So the
2767 : * i'th value is values[(i * (nvals - 1)) / (num_hist - 1)]. But
2768 : * computing that subscript directly risks integer overflow when
2769 : * the stats target is more than a couple thousand. Instead we
2770 : * add (nvals - 1) / (num_hist - 1) to pos at each step, tracking
2771 : * the integral and fractional parts of the sum separately.
2772 : */
2773 25950 : delta = (nvals - 1) / (num_hist - 1);
2774 25950 : deltafrac = (nvals - 1) % (num_hist - 1);
2775 25950 : pos = posfrac = 0;
2776 :
2777 1399842 : for (i = 0; i < num_hist; i++)
2778 : {
2779 2747784 : hist_values[i] = datumCopy(values[pos].value,
2780 1373892 : stats->attrtype->typbyval,
2781 1373892 : stats->attrtype->typlen);
2782 1373892 : pos += delta;
2783 1373892 : posfrac += deltafrac;
2784 1373892 : if (posfrac >= (num_hist - 1))
2785 : {
2786 : /* fractional part exceeds 1, carry to integer part */
2787 438154 : pos++;
2788 438154 : posfrac -= (num_hist - 1);
2789 : }
2790 : }
2791 :
2792 25950 : MemoryContextSwitchTo(old_context);
2793 :
2794 25950 : stats->stakind[slot_idx] = STATISTIC_KIND_HISTOGRAM;
2795 25950 : stats->staop[slot_idx] = mystats->ltopr;
2796 25950 : stats->stacoll[slot_idx] = stats->attrcollid;
2797 25950 : stats->stavalues[slot_idx] = hist_values;
2798 25950 : stats->numvalues[slot_idx] = num_hist;
2799 :
2800 : /*
2801 : * Accept the defaults for stats->statypid and others. They have
2802 : * been set before we were called (see vacuum.h)
2803 : */
2804 25950 : slot_idx++;
2805 : }
2806 :
2807 : /* Generate a correlation entry if there are multiple values */
2808 58158 : if (values_cnt > 1)
2809 : {
2810 : MemoryContext old_context;
2811 : float4 *corrs;
2812 : double corr_xsum,
2813 : corr_x2sum;
2814 :
2815 : /* Must copy the target values into anl_context */
2816 54644 : old_context = MemoryContextSwitchTo(stats->anl_context);
2817 54644 : corrs = (float4 *) palloc(sizeof(float4));
2818 54644 : MemoryContextSwitchTo(old_context);
2819 :
2820 : /*----------
2821 : * Since we know the x and y value sets are both
2822 : * 0, 1, ..., values_cnt-1
2823 : * we have sum(x) = sum(y) =
2824 : * (values_cnt-1)*values_cnt / 2
2825 : * and sum(x^2) = sum(y^2) =
2826 : * (values_cnt-1)*values_cnt*(2*values_cnt-1) / 6.
2827 : *----------
2828 : */
2829 54644 : corr_xsum = ((double) (values_cnt - 1)) *
2830 54644 : ((double) values_cnt) / 2.0;
2831 54644 : corr_x2sum = ((double) (values_cnt - 1)) *
2832 54644 : ((double) values_cnt) * (double) (2 * values_cnt - 1) / 6.0;
2833 :
2834 : /* And the correlation coefficient reduces to */
2835 54644 : corrs[0] = (values_cnt * corr_xysum - corr_xsum * corr_xsum) /
2836 54644 : (values_cnt * corr_x2sum - corr_xsum * corr_xsum);
2837 :
2838 54644 : stats->stakind[slot_idx] = STATISTIC_KIND_CORRELATION;
2839 54644 : stats->staop[slot_idx] = mystats->ltopr;
2840 54644 : stats->stacoll[slot_idx] = stats->attrcollid;
2841 54644 : stats->stanumbers[slot_idx] = corrs;
2842 54644 : stats->numnumbers[slot_idx] = 1;
2843 54644 : slot_idx++;
2844 : }
2845 : }
2846 4248 : else if (nonnull_cnt > 0)
2847 : {
2848 : /* We found some non-null values, but they were all too wide */
2849 : Assert(nonnull_cnt == toowide_cnt);
2850 258 : stats->stats_valid = true;
2851 : /* Do the simple null-frac and width stats */
2852 258 : stats->stanullfrac = (double) null_cnt / (double) samplerows;
2853 258 : if (is_varwidth)
2854 258 : stats->stawidth = total_width / (double) nonnull_cnt;
2855 : else
2856 0 : stats->stawidth = stats->attrtype->typlen;
2857 : /* Assume all too-wide values are distinct, so it's a unique column */
2858 258 : stats->stadistinct = -1.0 * (1.0 - stats->stanullfrac);
2859 : }
2860 3990 : else if (null_cnt > 0)
2861 : {
2862 : /* We found only nulls; assume the column is entirely null */
2863 3990 : stats->stats_valid = true;
2864 3990 : stats->stanullfrac = 1.0;
2865 3990 : if (is_varwidth)
2866 3436 : stats->stawidth = 0; /* "unknown" */
2867 : else
2868 554 : stats->stawidth = stats->attrtype->typlen;
2869 3990 : stats->stadistinct = 0.0; /* "unknown" */
2870 : }
2871 :
2872 : /* We don't need to bother cleaning up any of our temporary palloc's */
2873 62406 : }
2874 :
2875 : /*
2876 : * Comparator for sorting ScalarItems
2877 : *
2878 : * Aside from sorting the items, we update the tupnoLink[] array
2879 : * whenever two ScalarItems are found to contain equal datums. The array
2880 : * is indexed by tupno; for each ScalarItem, it contains the highest
2881 : * tupno that that item's datum has been found to be equal to. This allows
2882 : * us to avoid additional comparisons in compute_scalar_stats().
2883 : */
2884 : static int
2885 440476986 : compare_scalars(const void *a, const void *b, void *arg)
2886 : {
2887 440476986 : Datum da = ((const ScalarItem *) a)->value;
2888 440476986 : int ta = ((const ScalarItem *) a)->tupno;
2889 440476986 : Datum db = ((const ScalarItem *) b)->value;
2890 440476986 : int tb = ((const ScalarItem *) b)->tupno;
2891 440476986 : CompareScalarsContext *cxt = (CompareScalarsContext *) arg;
2892 : int compare;
2893 :
2894 440476986 : compare = ApplySortComparator(da, false, db, false, cxt->ssup);
2895 440476986 : if (compare != 0)
2896 164047912 : return compare;
2897 :
2898 : /*
2899 : * The two datums are equal, so update cxt->tupnoLink[].
2900 : */
2901 276429074 : if (cxt->tupnoLink[ta] < tb)
2902 39709456 : cxt->tupnoLink[ta] = tb;
2903 276429074 : if (cxt->tupnoLink[tb] < ta)
2904 2867492 : cxt->tupnoLink[tb] = ta;
2905 :
2906 : /*
2907 : * For equal datums, sort by tupno
2908 : */
2909 276429074 : return ta - tb;
2910 : }
2911 :
2912 : /*
2913 : * Comparator for sorting ScalarMCVItems by position
2914 : */
2915 : static int
2916 1745664 : compare_mcvs(const void *a, const void *b, void *arg)
2917 : {
2918 1745664 : int da = ((const ScalarMCVItem *) a)->first;
2919 1745664 : int db = ((const ScalarMCVItem *) b)->first;
2920 :
2921 1745664 : return da - db;
2922 : }
2923 :
2924 : /*
2925 : * Analyze the list of common values in the sample and decide how many are
2926 : * worth storing in the table's MCV list.
2927 : *
2928 : * mcv_counts is assumed to be a list of the counts of the most common values
2929 : * seen in the sample, starting with the most common. The return value is the
2930 : * number that are significantly more common than the values not in the list,
2931 : * and which are therefore deemed worth storing in the table's MCV list.
2932 : */
2933 : static int
2934 19924 : analyze_mcv_list(int *mcv_counts,
2935 : int num_mcv,
2936 : double stadistinct,
2937 : double stanullfrac,
2938 : int samplerows,
2939 : double totalrows)
2940 : {
2941 : double ndistinct_table;
2942 : double sumcount;
2943 : int i;
2944 :
2945 : /*
2946 : * If the entire table was sampled, keep the whole list. This also
2947 : * protects us against division by zero in the code below.
2948 : */
2949 19924 : if (samplerows == totalrows || totalrows <= 1.0)
2950 19090 : return num_mcv;
2951 :
2952 : /* Re-extract the estimated number of distinct nonnull values in table */
2953 834 : ndistinct_table = stadistinct;
2954 834 : if (ndistinct_table < 0)
2955 160 : ndistinct_table = -ndistinct_table * totalrows;
2956 :
2957 : /*
2958 : * Exclude the least common values from the MCV list, if they are not
2959 : * significantly more common than the estimated selectivity they would
2960 : * have if they weren't in the list. All non-MCV values are assumed to be
2961 : * equally common, after taking into account the frequencies of all the
2962 : * values in the MCV list and the number of nulls (c.f. eqsel()).
2963 : *
2964 : * Here sumcount tracks the total count of all but the last (least common)
2965 : * value in the MCV list, allowing us to determine the effect of excluding
2966 : * that value from the list.
2967 : *
2968 : * Note that we deliberately do this by removing values from the full
2969 : * list, rather than starting with an empty list and adding values,
2970 : * because the latter approach can fail to add any values if all the most
2971 : * common values have around the same frequency and make up the majority
2972 : * of the table, so that the overall average frequency of all values is
2973 : * roughly the same as that of the common values. This would lead to any
2974 : * uncommon values being significantly overestimated.
2975 : */
2976 834 : sumcount = 0.0;
2977 1736 : for (i = 0; i < num_mcv - 1; i++)
2978 902 : sumcount += mcv_counts[i];
2979 :
2980 972 : while (num_mcv > 0)
2981 : {
2982 : double selec,
2983 : otherdistinct,
2984 : N,
2985 : n,
2986 : K,
2987 : variance,
2988 : stddev;
2989 :
2990 : /*
2991 : * Estimated selectivity the least common value would have if it
2992 : * wasn't in the MCV list (c.f. eqsel()).
2993 : */
2994 972 : selec = 1.0 - sumcount / samplerows - stanullfrac;
2995 972 : if (selec < 0.0)
2996 0 : selec = 0.0;
2997 972 : if (selec > 1.0)
2998 0 : selec = 1.0;
2999 972 : otherdistinct = ndistinct_table - (num_mcv - 1);
3000 972 : if (otherdistinct > 1)
3001 972 : selec /= otherdistinct;
3002 :
3003 : /*
3004 : * If the value is kept in the MCV list, its population frequency is
3005 : * assumed to equal its sample frequency. We use the lower end of a
3006 : * textbook continuity-corrected Wald-type confidence interval to
3007 : * determine if that is significantly more common than the non-MCV
3008 : * frequency --- specifically we assume the population frequency is
3009 : * highly likely to be within around 2 standard errors of the sample
3010 : * frequency, which equates to an interval of 2 standard deviations
3011 : * either side of the sample count, plus an additional 0.5 for the
3012 : * continuity correction. Since we are sampling without replacement,
3013 : * this is a hypergeometric distribution.
3014 : *
3015 : * XXX: Empirically, this approach seems to work quite well, but it
3016 : * may be worth considering more advanced techniques for estimating
3017 : * the confidence interval of the hypergeometric distribution.
3018 : */
3019 972 : N = totalrows;
3020 972 : n = samplerows;
3021 972 : K = N * mcv_counts[num_mcv - 1] / n;
3022 972 : variance = n * K * (N - K) * (N - n) / (N * N * (N - 1));
3023 972 : stddev = sqrt(variance);
3024 :
3025 972 : if (mcv_counts[num_mcv - 1] > selec * samplerows + 2 * stddev + 0.5)
3026 : {
3027 : /*
3028 : * The value is significantly more common than the non-MCV
3029 : * selectivity would suggest. Keep it, and all the other more
3030 : * common values in the list.
3031 : */
3032 776 : break;
3033 : }
3034 : else
3035 : {
3036 : /* Discard this value and consider the next least common value */
3037 196 : num_mcv--;
3038 196 : if (num_mcv == 0)
3039 58 : break;
3040 138 : sumcount -= mcv_counts[num_mcv - 1];
3041 : }
3042 : }
3043 834 : return num_mcv;
3044 : }
|
