Line data Source code
1 : /*-------------------------------------------------------------------------
2 : *
3 : * analyze.c
4 : * the Postgres statistics generator
5 : *
6 : * Portions Copyright (c) 1996-2025, 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/progress.h"
33 : #include "commands/tablecmds.h"
34 : #include "commands/vacuum.h"
35 : #include "common/pg_prng.h"
36 : #include "executor/executor.h"
37 : #include "foreign/fdwapi.h"
38 : #include "miscadmin.h"
39 : #include "nodes/nodeFuncs.h"
40 : #include "parser/parse_oper.h"
41 : #include "parser/parse_relation.h"
42 : #include "pgstat.h"
43 : #include "statistics/extended_stats_internal.h"
44 : #include "statistics/statistics.h"
45 : #include "storage/bufmgr.h"
46 : #include "storage/procarray.h"
47 : #include "utils/attoptcache.h"
48 : #include "utils/datum.h"
49 : #include "utils/guc.h"
50 : #include "utils/lsyscache.h"
51 : #include "utils/memutils.h"
52 : #include "utils/pg_rusage.h"
53 : #include "utils/sampling.h"
54 : #include "utils/sortsupport.h"
55 : #include "utils/syscache.h"
56 : #include "utils/timestamp.h"
57 :
58 :
59 : /* Per-index data for ANALYZE */
60 : typedef struct AnlIndexData
61 : {
62 : IndexInfo *indexInfo; /* BuildIndexInfo result */
63 : double tupleFract; /* fraction of rows for partial index */
64 : VacAttrStats **vacattrstats; /* index attrs to analyze */
65 : int attr_cnt;
66 : } AnlIndexData;
67 :
68 :
69 : /* Default statistics target (GUC parameter) */
70 : int default_statistics_target = 100;
71 :
72 : /* A few variables that don't seem worth passing around as parameters */
73 : static MemoryContext anl_context = NULL;
74 : static BufferAccessStrategy vac_strategy;
75 :
76 :
77 : static void do_analyze_rel(Relation onerel,
78 : const VacuumParams params, List *va_cols,
79 : AcquireSampleRowsFunc acquirefunc, BlockNumber relpages,
80 : bool inh, bool in_outer_xact, int elevel);
81 : static void compute_index_stats(Relation onerel, double totalrows,
82 : AnlIndexData *indexdata, int nindexes,
83 : HeapTuple *rows, int numrows,
84 : MemoryContext col_context);
85 : static VacAttrStats *examine_attribute(Relation onerel, int attnum,
86 : Node *index_expr);
87 : static int acquire_sample_rows(Relation onerel, int elevel,
88 : HeapTuple *rows, int targrows,
89 : double *totalrows, double *totaldeadrows);
90 : static int compare_rows(const void *a, const void *b, void *arg);
91 : static int acquire_inherited_sample_rows(Relation onerel, int elevel,
92 : HeapTuple *rows, int targrows,
93 : double *totalrows, double *totaldeadrows);
94 : static void update_attstats(Oid relid, bool inh,
95 : int natts, VacAttrStats **vacattrstats);
96 : static Datum std_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull);
97 : static Datum ind_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull);
98 :
99 :
100 : /*
101 : * analyze_rel() -- analyze one relation
102 : *
103 : * relid identifies the relation to analyze. If relation is supplied, use
104 : * the name therein for reporting any failure to open/lock the rel; do not
105 : * use it once we've successfully opened the rel, since it might be stale.
106 : */
107 : void
108 16242 : analyze_rel(Oid relid, RangeVar *relation,
109 : const VacuumParams params, List *va_cols, bool in_outer_xact,
110 : BufferAccessStrategy bstrategy)
111 : {
112 : Relation onerel;
113 : int elevel;
114 16242 : AcquireSampleRowsFunc acquirefunc = NULL;
115 16242 : BlockNumber relpages = 0;
116 :
117 : /* Select logging level */
118 16242 : if (params.options & VACOPT_VERBOSE)
119 0 : elevel = INFO;
120 : else
121 16242 : elevel = DEBUG2;
122 :
123 : /* Set up static variables */
124 16242 : vac_strategy = bstrategy;
125 :
126 : /*
127 : * Check for user-requested abort.
128 : */
129 16242 : CHECK_FOR_INTERRUPTS();
130 :
131 : /*
132 : * Open the relation, getting ShareUpdateExclusiveLock to ensure that two
133 : * ANALYZEs don't run on it concurrently. (This also locks out a
134 : * concurrent VACUUM, which doesn't matter much at the moment but might
135 : * matter if we ever try to accumulate stats on dead tuples.) If the rel
136 : * has been dropped since we last saw it, we don't need to process it.
137 : *
138 : * Make sure to generate only logs for ANALYZE in this case.
139 : */
140 16242 : onerel = vacuum_open_relation(relid, relation, params.options & ~(VACOPT_VACUUM),
141 16242 : params.log_min_duration >= 0,
142 : ShareUpdateExclusiveLock);
143 :
144 : /* leave if relation could not be opened or locked */
145 16242 : if (!onerel)
146 210 : return;
147 :
148 : /*
149 : * Check if relation needs to be skipped based on privileges. This check
150 : * happens also when building the relation list to analyze for a manual
151 : * operation, and needs to be done additionally here as ANALYZE could
152 : * happen across multiple transactions where privileges could have changed
153 : * in-between. Make sure to generate only logs for ANALYZE in this case.
154 : */
155 16234 : if (!vacuum_is_permitted_for_relation(RelationGetRelid(onerel),
156 : onerel->rd_rel,
157 16234 : params.options & ~VACOPT_VACUUM))
158 : {
159 36 : relation_close(onerel, ShareUpdateExclusiveLock);
160 36 : return;
161 : }
162 :
163 : /*
164 : * Silently ignore tables that are temp tables of other backends ---
165 : * trying to analyze these is rather pointless, since their contents are
166 : * probably not up-to-date on disk. (We don't throw a warning here; it
167 : * would just lead to chatter during a database-wide ANALYZE.)
168 : */
169 16198 : if (RELATION_IS_OTHER_TEMP(onerel))
170 : {
171 0 : relation_close(onerel, ShareUpdateExclusiveLock);
172 0 : return;
173 : }
174 :
175 : /*
176 : * We can ANALYZE any table except pg_statistic. See update_attstats
177 : */
178 16198 : if (RelationGetRelid(onerel) == StatisticRelationId)
179 : {
180 166 : relation_close(onerel, ShareUpdateExclusiveLock);
181 166 : return;
182 : }
183 :
184 : /*
185 : * Check that it's of an analyzable relkind, and set up appropriately.
186 : */
187 16032 : if (onerel->rd_rel->relkind == RELKIND_RELATION ||
188 804 : onerel->rd_rel->relkind == RELKIND_MATVIEW)
189 : {
190 : /* Regular table, so we'll use the regular row acquisition function */
191 15232 : acquirefunc = acquire_sample_rows;
192 : /* Also get regular table's size */
193 15232 : relpages = RelationGetNumberOfBlocks(onerel);
194 : }
195 800 : else if (onerel->rd_rel->relkind == RELKIND_FOREIGN_TABLE)
196 : {
197 : /*
198 : * For a foreign table, call the FDW's hook function to see whether it
199 : * supports analysis.
200 : */
201 : FdwRoutine *fdwroutine;
202 66 : bool ok = false;
203 :
204 66 : fdwroutine = GetFdwRoutineForRelation(onerel, false);
205 :
206 66 : if (fdwroutine->AnalyzeForeignTable != NULL)
207 66 : ok = fdwroutine->AnalyzeForeignTable(onerel,
208 : &acquirefunc,
209 : &relpages);
210 :
211 66 : if (!ok)
212 : {
213 0 : ereport(WARNING,
214 : (errmsg("skipping \"%s\" --- cannot analyze this foreign table",
215 : RelationGetRelationName(onerel))));
216 0 : relation_close(onerel, ShareUpdateExclusiveLock);
217 0 : return;
218 : }
219 : }
220 734 : else if (onerel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE)
221 : {
222 : /*
223 : * For partitioned tables, we want to do the recursive ANALYZE below.
224 : */
225 : }
226 : else
227 : {
228 : /* No need for a WARNING if we already complained during VACUUM */
229 0 : if (!(params.options & VACOPT_VACUUM))
230 0 : ereport(WARNING,
231 : (errmsg("skipping \"%s\" --- cannot analyze non-tables or special system tables",
232 : RelationGetRelationName(onerel))));
233 0 : relation_close(onerel, ShareUpdateExclusiveLock);
234 0 : return;
235 : }
236 :
237 : /*
238 : * OK, let's do it. First, initialize progress reporting.
239 : */
240 16032 : pgstat_progress_start_command(PROGRESS_COMMAND_ANALYZE,
241 : RelationGetRelid(onerel));
242 :
243 : /*
244 : * Do the normal non-recursive ANALYZE. We can skip this for partitioned
245 : * tables, which don't contain any rows.
246 : */
247 16032 : if (onerel->rd_rel->relkind != RELKIND_PARTITIONED_TABLE)
248 15298 : do_analyze_rel(onerel, params, va_cols, acquirefunc,
249 : relpages, false, in_outer_xact, elevel);
250 :
251 : /*
252 : * If there are child tables, do recursive ANALYZE.
253 : */
254 15992 : if (onerel->rd_rel->relhassubclass)
255 862 : do_analyze_rel(onerel, params, va_cols, acquirefunc, relpages,
256 : true, in_outer_xact, elevel);
257 :
258 : /*
259 : * Close source relation now, but keep lock so that no one deletes it
260 : * before we commit. (If someone did, they'd fail to clean up the entries
261 : * we made in pg_statistic. Also, releasing the lock before commit would
262 : * expose us to concurrent-update failures in update_attstats.)
263 : */
264 15974 : relation_close(onerel, NoLock);
265 :
266 15974 : pgstat_progress_end_command();
267 : }
268 :
269 : /*
270 : * do_analyze_rel() -- analyze one relation, recursively or not
271 : *
272 : * Note that "acquirefunc" is only relevant for the non-inherited case.
273 : * For the inherited case, acquire_inherited_sample_rows() determines the
274 : * appropriate acquirefunc for each child table.
275 : */
276 : static void
277 16160 : do_analyze_rel(Relation onerel, const VacuumParams params,
278 : List *va_cols, AcquireSampleRowsFunc acquirefunc,
279 : BlockNumber relpages, bool inh, bool in_outer_xact,
280 : int elevel)
281 : {
282 : int attr_cnt,
283 : tcnt,
284 : i,
285 : ind;
286 : Relation *Irel;
287 : int nindexes;
288 : bool verbose,
289 : instrument,
290 : hasindex;
291 : VacAttrStats **vacattrstats;
292 : AnlIndexData *indexdata;
293 : int targrows,
294 : numrows,
295 : minrows;
296 : double totalrows,
297 : totaldeadrows;
298 : HeapTuple *rows;
299 : PGRUsage ru0;
300 16160 : TimestampTz starttime = 0;
301 : MemoryContext caller_context;
302 : Oid save_userid;
303 : int save_sec_context;
304 : int save_nestlevel;
305 16160 : WalUsage startwalusage = pgWalUsage;
306 16160 : BufferUsage startbufferusage = pgBufferUsage;
307 : BufferUsage bufferusage;
308 16160 : PgStat_Counter startreadtime = 0;
309 16160 : PgStat_Counter startwritetime = 0;
310 :
311 16160 : verbose = (params.options & VACOPT_VERBOSE) != 0;
312 16740 : instrument = (verbose || (AmAutoVacuumWorkerProcess() &&
313 580 : params.log_min_duration >= 0));
314 16160 : if (inh)
315 862 : ereport(elevel,
316 : (errmsg("analyzing \"%s.%s\" inheritance tree",
317 : get_namespace_name(RelationGetNamespace(onerel)),
318 : RelationGetRelationName(onerel))));
319 : else
320 15298 : ereport(elevel,
321 : (errmsg("analyzing \"%s.%s\"",
322 : get_namespace_name(RelationGetNamespace(onerel)),
323 : RelationGetRelationName(onerel))));
324 :
325 : /*
326 : * Set up a working context so that we can easily free whatever junk gets
327 : * created.
328 : */
329 16160 : anl_context = AllocSetContextCreate(CurrentMemoryContext,
330 : "Analyze",
331 : ALLOCSET_DEFAULT_SIZES);
332 16160 : caller_context = MemoryContextSwitchTo(anl_context);
333 :
334 : /*
335 : * Switch to the table owner's userid, so that any index functions are run
336 : * as that user. Also lock down security-restricted operations and
337 : * arrange to make GUC variable changes local to this command.
338 : */
339 16160 : GetUserIdAndSecContext(&save_userid, &save_sec_context);
340 16160 : SetUserIdAndSecContext(onerel->rd_rel->relowner,
341 : save_sec_context | SECURITY_RESTRICTED_OPERATION);
342 16160 : save_nestlevel = NewGUCNestLevel();
343 16160 : RestrictSearchPath();
344 :
345 : /*
346 : * When verbose or autovacuum logging is used, initialize a resource usage
347 : * snapshot and optionally track I/O timing.
348 : */
349 16160 : if (instrument)
350 : {
351 580 : if (track_io_timing)
352 : {
353 0 : startreadtime = pgStatBlockReadTime;
354 0 : startwritetime = pgStatBlockWriteTime;
355 : }
356 :
357 580 : pg_rusage_init(&ru0);
358 : }
359 :
360 : /* Used for instrumentation and stats report */
361 16160 : starttime = GetCurrentTimestamp();
362 :
363 : /*
364 : * Determine which columns to analyze
365 : *
366 : * Note that system attributes are never analyzed, so we just reject them
367 : * at the lookup stage. We also reject duplicate column mentions. (We
368 : * could alternatively ignore duplicates, but analyzing a column twice
369 : * won't work; we'd end up making a conflicting update in pg_statistic.)
370 : */
371 16160 : if (va_cols != NIL)
372 : {
373 100 : Bitmapset *unique_cols = NULL;
374 : ListCell *le;
375 :
376 100 : vacattrstats = (VacAttrStats **) palloc(list_length(va_cols) *
377 : sizeof(VacAttrStats *));
378 100 : tcnt = 0;
379 182 : foreach(le, va_cols)
380 : {
381 132 : char *col = strVal(lfirst(le));
382 :
383 132 : i = attnameAttNum(onerel, col, false);
384 132 : if (i == InvalidAttrNumber)
385 38 : ereport(ERROR,
386 : (errcode(ERRCODE_UNDEFINED_COLUMN),
387 : errmsg("column \"%s\" of relation \"%s\" does not exist",
388 : col, RelationGetRelationName(onerel))));
389 94 : if (bms_is_member(i, unique_cols))
390 12 : ereport(ERROR,
391 : (errcode(ERRCODE_DUPLICATE_COLUMN),
392 : errmsg("column \"%s\" of relation \"%s\" appears more than once",
393 : col, RelationGetRelationName(onerel))));
394 82 : unique_cols = bms_add_member(unique_cols, i);
395 :
396 82 : vacattrstats[tcnt] = examine_attribute(onerel, i, NULL);
397 82 : if (vacattrstats[tcnt] != NULL)
398 82 : tcnt++;
399 : }
400 50 : attr_cnt = tcnt;
401 : }
402 : else
403 : {
404 16060 : attr_cnt = onerel->rd_att->natts;
405 : vacattrstats = (VacAttrStats **)
406 16060 : palloc(attr_cnt * sizeof(VacAttrStats *));
407 16060 : tcnt = 0;
408 132334 : for (i = 1; i <= attr_cnt; i++)
409 : {
410 116274 : vacattrstats[tcnt] = examine_attribute(onerel, i, NULL);
411 116274 : if (vacattrstats[tcnt] != NULL)
412 116214 : tcnt++;
413 : }
414 16060 : attr_cnt = tcnt;
415 : }
416 :
417 : /*
418 : * Open all indexes of the relation, and see if there are any analyzable
419 : * columns in the indexes. We do not analyze index columns if there was
420 : * an explicit column list in the ANALYZE command, however.
421 : *
422 : * If we are doing a recursive scan, we don't want to touch the parent's
423 : * indexes at all. If we're processing a partitioned table, we need to
424 : * know if there are any indexes, but we don't want to process them.
425 : */
426 16110 : if (onerel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE)
427 : {
428 716 : List *idxs = RelationGetIndexList(onerel);
429 :
430 716 : Irel = NULL;
431 716 : nindexes = 0;
432 716 : hasindex = idxs != NIL;
433 716 : list_free(idxs);
434 : }
435 15394 : else if (!inh)
436 : {
437 15266 : vac_open_indexes(onerel, AccessShareLock, &nindexes, &Irel);
438 15266 : hasindex = nindexes > 0;
439 : }
440 : else
441 : {
442 128 : Irel = NULL;
443 128 : nindexes = 0;
444 128 : hasindex = false;
445 : }
446 16110 : indexdata = NULL;
447 16110 : if (nindexes > 0)
448 : {
449 11990 : indexdata = (AnlIndexData *) palloc0(nindexes * sizeof(AnlIndexData));
450 34542 : for (ind = 0; ind < nindexes; ind++)
451 : {
452 22552 : AnlIndexData *thisdata = &indexdata[ind];
453 : IndexInfo *indexInfo;
454 :
455 22552 : thisdata->indexInfo = indexInfo = BuildIndexInfo(Irel[ind]);
456 22552 : thisdata->tupleFract = 1.0; /* fix later if partial */
457 22552 : if (indexInfo->ii_Expressions != NIL && va_cols == NIL)
458 : {
459 110 : ListCell *indexpr_item = list_head(indexInfo->ii_Expressions);
460 :
461 110 : thisdata->vacattrstats = (VacAttrStats **)
462 110 : palloc(indexInfo->ii_NumIndexAttrs * sizeof(VacAttrStats *));
463 110 : tcnt = 0;
464 226 : for (i = 0; i < indexInfo->ii_NumIndexAttrs; i++)
465 : {
466 116 : int keycol = indexInfo->ii_IndexAttrNumbers[i];
467 :
468 116 : if (keycol == 0)
469 : {
470 : /* Found an index expression */
471 : Node *indexkey;
472 :
473 110 : if (indexpr_item == NULL) /* shouldn't happen */
474 0 : elog(ERROR, "too few entries in indexprs list");
475 110 : indexkey = (Node *) lfirst(indexpr_item);
476 110 : indexpr_item = lnext(indexInfo->ii_Expressions,
477 : indexpr_item);
478 220 : thisdata->vacattrstats[tcnt] =
479 110 : examine_attribute(Irel[ind], i + 1, indexkey);
480 110 : if (thisdata->vacattrstats[tcnt] != NULL)
481 110 : tcnt++;
482 : }
483 : }
484 110 : thisdata->attr_cnt = tcnt;
485 : }
486 : }
487 : }
488 :
489 : /*
490 : * Determine how many rows we need to sample, using the worst case from
491 : * all analyzable columns. We use a lower bound of 100 rows to avoid
492 : * possible overflow in Vitter's algorithm. (Note: that will also be the
493 : * target in the corner case where there are no analyzable columns.)
494 : */
495 16110 : targrows = 100;
496 132382 : for (i = 0; i < attr_cnt; i++)
497 : {
498 116272 : if (targrows < vacattrstats[i]->minrows)
499 16038 : targrows = vacattrstats[i]->minrows;
500 : }
501 38662 : for (ind = 0; ind < nindexes; ind++)
502 : {
503 22552 : AnlIndexData *thisdata = &indexdata[ind];
504 :
505 22662 : for (i = 0; i < thisdata->attr_cnt; i++)
506 : {
507 110 : if (targrows < thisdata->vacattrstats[i]->minrows)
508 12 : targrows = thisdata->vacattrstats[i]->minrows;
509 : }
510 : }
511 :
512 : /*
513 : * Look at extended statistics objects too, as those may define custom
514 : * statistics target. So we may need to sample more rows and then build
515 : * the statistics with enough detail.
516 : */
517 16110 : minrows = ComputeExtStatisticsRows(onerel, attr_cnt, vacattrstats);
518 :
519 16110 : if (targrows < minrows)
520 0 : targrows = minrows;
521 :
522 : /*
523 : * Acquire the sample rows
524 : */
525 16110 : rows = (HeapTuple *) palloc(targrows * sizeof(HeapTuple));
526 16110 : pgstat_progress_update_param(PROGRESS_ANALYZE_PHASE,
527 : inh ? PROGRESS_ANALYZE_PHASE_ACQUIRE_SAMPLE_ROWS_INH :
528 : PROGRESS_ANALYZE_PHASE_ACQUIRE_SAMPLE_ROWS);
529 16110 : if (inh)
530 844 : numrows = acquire_inherited_sample_rows(onerel, elevel,
531 : rows, targrows,
532 : &totalrows, &totaldeadrows);
533 : else
534 15266 : numrows = (*acquirefunc) (onerel, elevel,
535 : rows, targrows,
536 : &totalrows, &totaldeadrows);
537 :
538 : /*
539 : * Compute the statistics. Temporary results during the calculations for
540 : * each column are stored in a child context. The calc routines are
541 : * responsible to make sure that whatever they store into the VacAttrStats
542 : * structure is allocated in anl_context.
543 : */
544 16108 : if (numrows > 0)
545 : {
546 : MemoryContext col_context,
547 : old_context;
548 :
549 10910 : pgstat_progress_update_param(PROGRESS_ANALYZE_PHASE,
550 : PROGRESS_ANALYZE_PHASE_COMPUTE_STATS);
551 :
552 10910 : col_context = AllocSetContextCreate(anl_context,
553 : "Analyze Column",
554 : ALLOCSET_DEFAULT_SIZES);
555 10910 : old_context = MemoryContextSwitchTo(col_context);
556 :
557 96224 : for (i = 0; i < attr_cnt; i++)
558 : {
559 85314 : VacAttrStats *stats = vacattrstats[i];
560 : AttributeOpts *aopt;
561 :
562 85314 : stats->rows = rows;
563 85314 : stats->tupDesc = onerel->rd_att;
564 85314 : stats->compute_stats(stats,
565 : std_fetch_func,
566 : numrows,
567 : totalrows);
568 :
569 : /*
570 : * If the appropriate flavor of the n_distinct option is
571 : * specified, override with the corresponding value.
572 : */
573 85314 : aopt = get_attribute_options(onerel->rd_id, stats->tupattnum);
574 85314 : if (aopt != NULL)
575 : {
576 : float8 n_distinct;
577 :
578 6 : n_distinct = inh ? aopt->n_distinct_inherited : aopt->n_distinct;
579 6 : if (n_distinct != 0.0)
580 6 : stats->stadistinct = n_distinct;
581 : }
582 :
583 85314 : MemoryContextReset(col_context);
584 : }
585 :
586 10910 : if (nindexes > 0)
587 7078 : compute_index_stats(onerel, totalrows,
588 : indexdata, nindexes,
589 : rows, numrows,
590 : col_context);
591 :
592 10904 : MemoryContextSwitchTo(old_context);
593 10904 : MemoryContextDelete(col_context);
594 :
595 : /*
596 : * Emit the completed stats rows into pg_statistic, replacing any
597 : * previous statistics for the target columns. (If there are stats in
598 : * pg_statistic for columns we didn't process, we leave them alone.)
599 : */
600 10904 : update_attstats(RelationGetRelid(onerel), inh,
601 : attr_cnt, vacattrstats);
602 :
603 24696 : for (ind = 0; ind < nindexes; ind++)
604 : {
605 13792 : AnlIndexData *thisdata = &indexdata[ind];
606 :
607 13792 : update_attstats(RelationGetRelid(Irel[ind]), false,
608 : thisdata->attr_cnt, thisdata->vacattrstats);
609 : }
610 :
611 : /* Build extended statistics (if there are any). */
612 10904 : BuildRelationExtStatistics(onerel, inh, totalrows, numrows, rows,
613 : attr_cnt, vacattrstats);
614 : }
615 :
616 16102 : pgstat_progress_update_param(PROGRESS_ANALYZE_PHASE,
617 : PROGRESS_ANALYZE_PHASE_FINALIZE_ANALYZE);
618 :
619 : /*
620 : * Update pages/tuples stats in pg_class ... but not if we're doing
621 : * inherited stats.
622 : *
623 : * We assume that VACUUM hasn't set pg_class.reltuples already, even
624 : * during a VACUUM ANALYZE. Although VACUUM often updates pg_class,
625 : * exceptions exist. A "VACUUM (ANALYZE, INDEX_CLEANUP OFF)" command will
626 : * never update pg_class entries for index relations. It's also possible
627 : * that an individual index's pg_class entry won't be updated during
628 : * VACUUM if the index AM returns NULL from its amvacuumcleanup() routine.
629 : */
630 16102 : if (!inh)
631 : {
632 15258 : BlockNumber relallvisible = 0;
633 15258 : BlockNumber relallfrozen = 0;
634 :
635 15258 : if (RELKIND_HAS_STORAGE(onerel->rd_rel->relkind))
636 15194 : visibilitymap_count(onerel, &relallvisible, &relallfrozen);
637 :
638 : /*
639 : * Update pg_class for table relation. CCI first, in case acquirefunc
640 : * updated pg_class.
641 : */
642 15258 : CommandCounterIncrement();
643 15258 : vac_update_relstats(onerel,
644 : relpages,
645 : totalrows,
646 : relallvisible,
647 : relallfrozen,
648 : hasindex,
649 : InvalidTransactionId,
650 : InvalidMultiXactId,
651 : NULL, NULL,
652 : in_outer_xact);
653 :
654 : /* Same for indexes */
655 37798 : for (ind = 0; ind < nindexes; ind++)
656 : {
657 22540 : AnlIndexData *thisdata = &indexdata[ind];
658 : double totalindexrows;
659 :
660 22540 : totalindexrows = ceil(thisdata->tupleFract * totalrows);
661 22540 : vac_update_relstats(Irel[ind],
662 22540 : RelationGetNumberOfBlocks(Irel[ind]),
663 : totalindexrows,
664 : 0, 0,
665 : false,
666 : InvalidTransactionId,
667 : InvalidMultiXactId,
668 : NULL, NULL,
669 : in_outer_xact);
670 : }
671 : }
672 844 : else if (onerel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE)
673 : {
674 : /*
675 : * Partitioned tables don't have storage, so we don't set any fields
676 : * in their pg_class entries except for reltuples and relhasindex.
677 : */
678 716 : CommandCounterIncrement();
679 716 : vac_update_relstats(onerel, -1, totalrows,
680 : 0, 0, hasindex, InvalidTransactionId,
681 : InvalidMultiXactId,
682 : NULL, NULL,
683 : in_outer_xact);
684 : }
685 :
686 : /*
687 : * Now report ANALYZE to the cumulative stats system. For regular tables,
688 : * we do it only if not doing inherited stats. For partitioned tables, we
689 : * only do it for inherited stats. (We're never called for not-inherited
690 : * stats on partitioned tables anyway.)
691 : *
692 : * Reset the mod_since_analyze counter only if we analyzed all columns;
693 : * otherwise, there is still work for auto-analyze to do.
694 : */
695 16102 : if (!inh)
696 15258 : pgstat_report_analyze(onerel, totalrows, totaldeadrows,
697 : (va_cols == NIL), starttime);
698 844 : else if (onerel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE)
699 716 : pgstat_report_analyze(onerel, 0, 0, (va_cols == NIL), starttime);
700 :
701 : /*
702 : * If this isn't part of VACUUM ANALYZE, let index AMs do cleanup.
703 : *
704 : * Note that most index AMs perform a no-op as a matter of policy for
705 : * amvacuumcleanup() when called in ANALYZE-only mode. The only exception
706 : * among core index AMs is GIN/ginvacuumcleanup().
707 : */
708 16102 : if (!(params.options & VACOPT_VACUUM))
709 : {
710 30816 : for (ind = 0; ind < nindexes; ind++)
711 : {
712 : IndexBulkDeleteResult *stats;
713 : IndexVacuumInfo ivinfo;
714 :
715 17698 : ivinfo.index = Irel[ind];
716 17698 : ivinfo.heaprel = onerel;
717 17698 : ivinfo.analyze_only = true;
718 17698 : ivinfo.estimated_count = true;
719 17698 : ivinfo.message_level = elevel;
720 17698 : ivinfo.num_heap_tuples = onerel->rd_rel->reltuples;
721 17698 : ivinfo.strategy = vac_strategy;
722 :
723 17698 : stats = index_vacuum_cleanup(&ivinfo, NULL);
724 :
725 17698 : if (stats)
726 0 : pfree(stats);
727 : }
728 : }
729 :
730 : /* Done with indexes */
731 16102 : vac_close_indexes(nindexes, Irel, NoLock);
732 :
733 : /* Log the action if appropriate */
734 16102 : if (instrument)
735 : {
736 580 : TimestampTz endtime = GetCurrentTimestamp();
737 :
738 1042 : if (verbose || params.log_min_duration == 0 ||
739 462 : TimestampDifferenceExceeds(starttime, endtime,
740 462 : params.log_min_duration))
741 : {
742 : long delay_in_ms;
743 : WalUsage walusage;
744 118 : double read_rate = 0;
745 118 : double write_rate = 0;
746 : char *msgfmt;
747 : StringInfoData buf;
748 : int64 total_blks_hit;
749 : int64 total_blks_read;
750 : int64 total_blks_dirtied;
751 :
752 118 : memset(&bufferusage, 0, sizeof(BufferUsage));
753 118 : BufferUsageAccumDiff(&bufferusage, &pgBufferUsage, &startbufferusage);
754 118 : memset(&walusage, 0, sizeof(WalUsage));
755 118 : WalUsageAccumDiff(&walusage, &pgWalUsage, &startwalusage);
756 :
757 118 : total_blks_hit = bufferusage.shared_blks_hit +
758 118 : bufferusage.local_blks_hit;
759 118 : total_blks_read = bufferusage.shared_blks_read +
760 118 : bufferusage.local_blks_read;
761 118 : total_blks_dirtied = bufferusage.shared_blks_dirtied +
762 118 : bufferusage.local_blks_dirtied;
763 :
764 : /*
765 : * We do not expect an analyze to take > 25 days and it simplifies
766 : * things a bit to use TimestampDifferenceMilliseconds.
767 : */
768 118 : delay_in_ms = TimestampDifferenceMilliseconds(starttime, endtime);
769 :
770 : /*
771 : * Note that we are reporting these read/write rates in the same
772 : * manner as VACUUM does, which means that while the 'average read
773 : * rate' here actually corresponds to page misses and resulting
774 : * reads which are also picked up by track_io_timing, if enabled,
775 : * the 'average write rate' is actually talking about the rate of
776 : * pages being dirtied, not being written out, so it's typical to
777 : * have a non-zero 'avg write rate' while I/O timings only reports
778 : * reads.
779 : *
780 : * It's not clear that an ANALYZE will ever result in
781 : * FlushBuffer() being called, but we track and support reporting
782 : * on I/O write time in case that changes as it's practically free
783 : * to do so anyway.
784 : */
785 :
786 118 : if (delay_in_ms > 0)
787 : {
788 118 : read_rate = (double) BLCKSZ * total_blks_read /
789 118 : (1024 * 1024) / (delay_in_ms / 1000.0);
790 118 : write_rate = (double) BLCKSZ * total_blks_dirtied /
791 118 : (1024 * 1024) / (delay_in_ms / 1000.0);
792 : }
793 :
794 : /*
795 : * We split this up so we don't emit empty I/O timing values when
796 : * track_io_timing isn't enabled.
797 : */
798 :
799 118 : initStringInfo(&buf);
800 :
801 118 : if (AmAutoVacuumWorkerProcess())
802 118 : msgfmt = _("automatic analyze of table \"%s.%s.%s\"\n");
803 : else
804 0 : msgfmt = _("finished analyzing table \"%s.%s.%s\"\n");
805 :
806 118 : appendStringInfo(&buf, msgfmt,
807 : get_database_name(MyDatabaseId),
808 118 : get_namespace_name(RelationGetNamespace(onerel)),
809 118 : RelationGetRelationName(onerel));
810 118 : if (track_cost_delay_timing)
811 : {
812 : /*
813 : * We bypass the changecount mechanism because this value is
814 : * only updated by the calling process.
815 : */
816 0 : appendStringInfo(&buf, _("delay time: %.3f ms\n"),
817 0 : (double) MyBEEntry->st_progress_param[PROGRESS_ANALYZE_DELAY_TIME] / 1000000.0);
818 : }
819 118 : if (track_io_timing)
820 : {
821 0 : double read_ms = (double) (pgStatBlockReadTime - startreadtime) / 1000;
822 0 : double write_ms = (double) (pgStatBlockWriteTime - startwritetime) / 1000;
823 :
824 0 : appendStringInfo(&buf, _("I/O timings: read: %.3f ms, write: %.3f ms\n"),
825 : read_ms, write_ms);
826 : }
827 118 : appendStringInfo(&buf, _("avg read rate: %.3f MB/s, avg write rate: %.3f MB/s\n"),
828 : read_rate, write_rate);
829 118 : appendStringInfo(&buf, _("buffer usage: %" PRId64 " hits, %" PRId64 " reads, %" PRId64 " dirtied\n"),
830 : total_blks_hit,
831 : total_blks_read,
832 : total_blks_dirtied);
833 118 : appendStringInfo(&buf,
834 118 : _("WAL usage: %" PRId64 " records, %" PRId64 " full page images, %" PRIu64 " bytes, %" PRId64 " buffers full\n"),
835 : walusage.wal_records,
836 : walusage.wal_fpi,
837 : walusage.wal_bytes,
838 : walusage.wal_buffers_full);
839 118 : appendStringInfo(&buf, _("system usage: %s"), pg_rusage_show(&ru0));
840 :
841 118 : ereport(verbose ? INFO : LOG,
842 : (errmsg_internal("%s", buf.data)));
843 :
844 118 : pfree(buf.data);
845 : }
846 : }
847 :
848 : /* Roll back any GUC changes executed by index functions */
849 16102 : AtEOXact_GUC(false, save_nestlevel);
850 :
851 : /* Restore userid and security context */
852 16102 : SetUserIdAndSecContext(save_userid, save_sec_context);
853 :
854 : /* Restore current context and release memory */
855 16102 : MemoryContextSwitchTo(caller_context);
856 16102 : MemoryContextDelete(anl_context);
857 16102 : anl_context = NULL;
858 16102 : }
859 :
860 : /*
861 : * Compute statistics about indexes of a relation
862 : */
863 : static void
864 7078 : compute_index_stats(Relation onerel, double totalrows,
865 : AnlIndexData *indexdata, int nindexes,
866 : HeapTuple *rows, int numrows,
867 : MemoryContext col_context)
868 : {
869 : MemoryContext ind_context,
870 : old_context;
871 : Datum values[INDEX_MAX_KEYS];
872 : bool isnull[INDEX_MAX_KEYS];
873 : int ind,
874 : i;
875 :
876 7078 : ind_context = AllocSetContextCreate(anl_context,
877 : "Analyze Index",
878 : ALLOCSET_DEFAULT_SIZES);
879 7078 : old_context = MemoryContextSwitchTo(ind_context);
880 :
881 20876 : for (ind = 0; ind < nindexes; ind++)
882 : {
883 13804 : AnlIndexData *thisdata = &indexdata[ind];
884 13804 : IndexInfo *indexInfo = thisdata->indexInfo;
885 13804 : int attr_cnt = thisdata->attr_cnt;
886 : TupleTableSlot *slot;
887 : EState *estate;
888 : ExprContext *econtext;
889 : ExprState *predicate;
890 : Datum *exprvals;
891 : bool *exprnulls;
892 : int numindexrows,
893 : tcnt,
894 : rowno;
895 : double totalindexrows;
896 :
897 : /* Ignore index if no columns to analyze and not partial */
898 13804 : if (attr_cnt == 0 && indexInfo->ii_Predicate == NIL)
899 13666 : continue;
900 :
901 : /*
902 : * Need an EState for evaluation of index expressions and
903 : * partial-index predicates. Create it in the per-index context to be
904 : * sure it gets cleaned up at the bottom of the loop.
905 : */
906 138 : estate = CreateExecutorState();
907 138 : econtext = GetPerTupleExprContext(estate);
908 : /* Need a slot to hold the current heap tuple, too */
909 138 : slot = MakeSingleTupleTableSlot(RelationGetDescr(onerel),
910 : &TTSOpsHeapTuple);
911 :
912 : /* Arrange for econtext's scan tuple to be the tuple under test */
913 138 : econtext->ecxt_scantuple = slot;
914 :
915 : /* Set up execution state for predicate. */
916 138 : predicate = ExecPrepareQual(indexInfo->ii_Predicate, estate);
917 :
918 : /* Compute and save index expression values */
919 138 : exprvals = (Datum *) palloc(numrows * attr_cnt * sizeof(Datum));
920 138 : exprnulls = (bool *) palloc(numrows * attr_cnt * sizeof(bool));
921 138 : numindexrows = 0;
922 138 : tcnt = 0;
923 155728 : for (rowno = 0; rowno < numrows; rowno++)
924 : {
925 155596 : HeapTuple heapTuple = rows[rowno];
926 :
927 155596 : vacuum_delay_point(true);
928 :
929 : /*
930 : * Reset the per-tuple context each time, to reclaim any cruft
931 : * left behind by evaluating the predicate or index expressions.
932 : */
933 155596 : ResetExprContext(econtext);
934 :
935 : /* Set up for predicate or expression evaluation */
936 155596 : ExecStoreHeapTuple(heapTuple, slot, false);
937 :
938 : /* If index is partial, check predicate */
939 155596 : if (predicate != NULL)
940 : {
941 40066 : if (!ExecQual(predicate, econtext))
942 21330 : continue;
943 : }
944 134266 : numindexrows++;
945 :
946 134266 : if (attr_cnt > 0)
947 : {
948 : /*
949 : * Evaluate the index row to compute expression values. We
950 : * could do this by hand, but FormIndexDatum is convenient.
951 : */
952 115530 : FormIndexDatum(indexInfo,
953 : slot,
954 : estate,
955 : values,
956 : isnull);
957 :
958 : /*
959 : * Save just the columns we care about. We copy the values
960 : * into ind_context from the estate's per-tuple context.
961 : */
962 231048 : for (i = 0; i < attr_cnt; i++)
963 : {
964 115524 : VacAttrStats *stats = thisdata->vacattrstats[i];
965 115524 : int attnum = stats->tupattnum;
966 :
967 115524 : if (isnull[attnum - 1])
968 : {
969 6 : exprvals[tcnt] = (Datum) 0;
970 6 : exprnulls[tcnt] = true;
971 : }
972 : else
973 : {
974 231036 : exprvals[tcnt] = datumCopy(values[attnum - 1],
975 115518 : stats->attrtype->typbyval,
976 115518 : stats->attrtype->typlen);
977 115518 : exprnulls[tcnt] = false;
978 : }
979 115524 : tcnt++;
980 : }
981 : }
982 : }
983 :
984 : /*
985 : * Having counted the number of rows that pass the predicate in the
986 : * sample, we can estimate the total number of rows in the index.
987 : */
988 132 : thisdata->tupleFract = (double) numindexrows / (double) numrows;
989 132 : totalindexrows = ceil(thisdata->tupleFract * totalrows);
990 :
991 : /*
992 : * Now we can compute the statistics for the expression columns.
993 : */
994 132 : if (numindexrows > 0)
995 : {
996 124 : MemoryContextSwitchTo(col_context);
997 210 : for (i = 0; i < attr_cnt; i++)
998 : {
999 86 : VacAttrStats *stats = thisdata->vacattrstats[i];
1000 :
1001 86 : stats->exprvals = exprvals + i;
1002 86 : stats->exprnulls = exprnulls + i;
1003 86 : stats->rowstride = attr_cnt;
1004 86 : stats->compute_stats(stats,
1005 : ind_fetch_func,
1006 : numindexrows,
1007 : totalindexrows);
1008 :
1009 86 : MemoryContextReset(col_context);
1010 : }
1011 : }
1012 :
1013 : /* And clean up */
1014 132 : MemoryContextSwitchTo(ind_context);
1015 :
1016 132 : ExecDropSingleTupleTableSlot(slot);
1017 132 : FreeExecutorState(estate);
1018 132 : MemoryContextReset(ind_context);
1019 : }
1020 :
1021 7072 : MemoryContextSwitchTo(old_context);
1022 7072 : MemoryContextDelete(ind_context);
1023 7072 : }
1024 :
1025 : /*
1026 : * examine_attribute -- pre-analysis of a single column
1027 : *
1028 : * Determine whether the column is analyzable; if so, create and initialize
1029 : * a VacAttrStats struct for it. If not, return NULL.
1030 : *
1031 : * If index_expr isn't NULL, then we're trying to analyze an expression index,
1032 : * and index_expr is the expression tree representing the column's data.
1033 : */
1034 : static VacAttrStats *
1035 116466 : examine_attribute(Relation onerel, int attnum, Node *index_expr)
1036 : {
1037 116466 : Form_pg_attribute attr = TupleDescAttr(onerel->rd_att, attnum - 1);
1038 : int attstattarget;
1039 : HeapTuple atttuple;
1040 : Datum dat;
1041 : bool isnull;
1042 : HeapTuple typtuple;
1043 : VacAttrStats *stats;
1044 : int i;
1045 : bool ok;
1046 :
1047 : /* Never analyze dropped columns */
1048 116466 : if (attr->attisdropped)
1049 6 : return NULL;
1050 :
1051 : /* Don't analyze virtual generated columns */
1052 116460 : if (attr->attgenerated == ATTRIBUTE_GENERATED_VIRTUAL)
1053 48 : return NULL;
1054 :
1055 : /*
1056 : * Get attstattarget value. Set to -1 if null. (Analyze functions expect
1057 : * -1 to mean use default_statistics_target; see for example
1058 : * std_typanalyze.)
1059 : */
1060 116412 : atttuple = SearchSysCache2(ATTNUM, ObjectIdGetDatum(RelationGetRelid(onerel)), Int16GetDatum(attnum));
1061 116412 : if (!HeapTupleIsValid(atttuple))
1062 0 : elog(ERROR, "cache lookup failed for attribute %d of relation %u",
1063 : attnum, RelationGetRelid(onerel));
1064 116412 : dat = SysCacheGetAttr(ATTNUM, atttuple, Anum_pg_attribute_attstattarget, &isnull);
1065 116412 : attstattarget = isnull ? -1 : DatumGetInt16(dat);
1066 116412 : ReleaseSysCache(atttuple);
1067 :
1068 : /* Don't analyze column if user has specified not to */
1069 116412 : if (attstattarget == 0)
1070 6 : return NULL;
1071 :
1072 : /*
1073 : * Create the VacAttrStats struct.
1074 : */
1075 116406 : stats = (VacAttrStats *) palloc0(sizeof(VacAttrStats));
1076 116406 : stats->attstattarget = attstattarget;
1077 :
1078 : /*
1079 : * When analyzing an expression index, believe the expression tree's type
1080 : * not the column datatype --- the latter might be the opckeytype storage
1081 : * type of the opclass, which is not interesting for our purposes. (Note:
1082 : * if we did anything with non-expression index columns, we'd need to
1083 : * figure out where to get the correct type info from, but for now that's
1084 : * not a problem.) It's not clear whether anyone will care about the
1085 : * typmod, but we store that too just in case.
1086 : */
1087 116406 : if (index_expr)
1088 : {
1089 110 : stats->attrtypid = exprType(index_expr);
1090 110 : stats->attrtypmod = exprTypmod(index_expr);
1091 :
1092 : /*
1093 : * If a collation has been specified for the index column, use that in
1094 : * preference to anything else; but if not, fall back to whatever we
1095 : * can get from the expression.
1096 : */
1097 110 : if (OidIsValid(onerel->rd_indcollation[attnum - 1]))
1098 12 : stats->attrcollid = onerel->rd_indcollation[attnum - 1];
1099 : else
1100 98 : stats->attrcollid = exprCollation(index_expr);
1101 : }
1102 : else
1103 : {
1104 116296 : stats->attrtypid = attr->atttypid;
1105 116296 : stats->attrtypmod = attr->atttypmod;
1106 116296 : stats->attrcollid = attr->attcollation;
1107 : }
1108 :
1109 116406 : typtuple = SearchSysCacheCopy1(TYPEOID,
1110 : ObjectIdGetDatum(stats->attrtypid));
1111 116406 : if (!HeapTupleIsValid(typtuple))
1112 0 : elog(ERROR, "cache lookup failed for type %u", stats->attrtypid);
1113 116406 : stats->attrtype = (Form_pg_type) GETSTRUCT(typtuple);
1114 116406 : stats->anl_context = anl_context;
1115 116406 : stats->tupattnum = attnum;
1116 :
1117 : /*
1118 : * The fields describing the stats->stavalues[n] element types default to
1119 : * the type of the data being analyzed, but the type-specific typanalyze
1120 : * function can change them if it wants to store something else.
1121 : */
1122 698436 : for (i = 0; i < STATISTIC_NUM_SLOTS; i++)
1123 : {
1124 582030 : stats->statypid[i] = stats->attrtypid;
1125 582030 : stats->statyplen[i] = stats->attrtype->typlen;
1126 582030 : stats->statypbyval[i] = stats->attrtype->typbyval;
1127 582030 : stats->statypalign[i] = stats->attrtype->typalign;
1128 : }
1129 :
1130 : /*
1131 : * Call the type-specific typanalyze function. If none is specified, use
1132 : * std_typanalyze().
1133 : */
1134 116406 : if (OidIsValid(stats->attrtype->typanalyze))
1135 7496 : ok = DatumGetBool(OidFunctionCall1(stats->attrtype->typanalyze,
1136 : PointerGetDatum(stats)));
1137 : else
1138 108910 : ok = std_typanalyze(stats);
1139 :
1140 116406 : if (!ok || stats->compute_stats == NULL || stats->minrows <= 0)
1141 : {
1142 0 : heap_freetuple(typtuple);
1143 0 : pfree(stats);
1144 0 : return NULL;
1145 : }
1146 :
1147 116406 : return stats;
1148 : }
1149 :
1150 : /*
1151 : * Read stream callback returning the next BlockNumber as chosen by the
1152 : * BlockSampling algorithm.
1153 : */
1154 : static BlockNumber
1155 160146 : block_sampling_read_stream_next(ReadStream *stream,
1156 : void *callback_private_data,
1157 : void *per_buffer_data)
1158 : {
1159 160146 : BlockSamplerData *bs = callback_private_data;
1160 :
1161 160146 : return BlockSampler_HasMore(bs) ? BlockSampler_Next(bs) : InvalidBlockNumber;
1162 : }
1163 :
1164 : /*
1165 : * acquire_sample_rows -- acquire a random sample of rows from the table
1166 : *
1167 : * Selected rows are returned in the caller-allocated array rows[], which
1168 : * must have at least targrows entries.
1169 : * The actual number of rows selected is returned as the function result.
1170 : * We also estimate the total numbers of live and dead rows in the table,
1171 : * and return them into *totalrows and *totaldeadrows, respectively.
1172 : *
1173 : * The returned list of tuples is in order by physical position in the table.
1174 : * (We will rely on this later to derive correlation estimates.)
1175 : *
1176 : * As of May 2004 we use a new two-stage method: Stage one selects up
1177 : * to targrows random blocks (or all blocks, if there aren't so many).
1178 : * Stage two scans these blocks and uses the Vitter algorithm to create
1179 : * a random sample of targrows rows (or less, if there are less in the
1180 : * sample of blocks). The two stages are executed simultaneously: each
1181 : * block is processed as soon as stage one returns its number and while
1182 : * the rows are read stage two controls which ones are to be inserted
1183 : * into the sample.
1184 : *
1185 : * Although every row has an equal chance of ending up in the final
1186 : * sample, this sampling method is not perfect: not every possible
1187 : * sample has an equal chance of being selected. For large relations
1188 : * the number of different blocks represented by the sample tends to be
1189 : * too small. We can live with that for now. Improvements are welcome.
1190 : *
1191 : * An important property of this sampling method is that because we do
1192 : * look at a statistically unbiased set of blocks, we should get
1193 : * unbiased estimates of the average numbers of live and dead rows per
1194 : * block. The previous sampling method put too much credence in the row
1195 : * density near the start of the table.
1196 : */
1197 : static int
1198 17158 : acquire_sample_rows(Relation onerel, int elevel,
1199 : HeapTuple *rows, int targrows,
1200 : double *totalrows, double *totaldeadrows)
1201 : {
1202 17158 : int numrows = 0; /* # rows now in reservoir */
1203 17158 : double samplerows = 0; /* total # rows collected */
1204 17158 : double liverows = 0; /* # live rows seen */
1205 17158 : double deadrows = 0; /* # dead rows seen */
1206 17158 : double rowstoskip = -1; /* -1 means not set yet */
1207 : uint32 randseed; /* Seed for block sampler(s) */
1208 : BlockNumber totalblocks;
1209 : TransactionId OldestXmin;
1210 : BlockSamplerData bs;
1211 : ReservoirStateData rstate;
1212 : TupleTableSlot *slot;
1213 : TableScanDesc scan;
1214 : BlockNumber nblocks;
1215 17158 : BlockNumber blksdone = 0;
1216 : ReadStream *stream;
1217 :
1218 : Assert(targrows > 0);
1219 :
1220 17158 : totalblocks = RelationGetNumberOfBlocks(onerel);
1221 :
1222 : /* Need a cutoff xmin for HeapTupleSatisfiesVacuum */
1223 17158 : OldestXmin = GetOldestNonRemovableTransactionId(onerel);
1224 :
1225 : /* Prepare for sampling block numbers */
1226 17158 : randseed = pg_prng_uint32(&pg_global_prng_state);
1227 17158 : nblocks = BlockSampler_Init(&bs, totalblocks, targrows, randseed);
1228 :
1229 : /* Report sampling block numbers */
1230 17158 : pgstat_progress_update_param(PROGRESS_ANALYZE_BLOCKS_TOTAL,
1231 : nblocks);
1232 :
1233 : /* Prepare for sampling rows */
1234 17158 : reservoir_init_selection_state(&rstate, targrows);
1235 :
1236 17158 : scan = table_beginscan_analyze(onerel);
1237 17158 : slot = table_slot_create(onerel, NULL);
1238 :
1239 : /*
1240 : * It is safe to use batching, as block_sampling_read_stream_next never
1241 : * blocks.
1242 : */
1243 17158 : stream = read_stream_begin_relation(READ_STREAM_MAINTENANCE |
1244 : READ_STREAM_USE_BATCHING,
1245 : vac_strategy,
1246 : scan->rs_rd,
1247 : MAIN_FORKNUM,
1248 : block_sampling_read_stream_next,
1249 : &bs,
1250 : 0);
1251 :
1252 : /* Outer loop over blocks to sample */
1253 160146 : while (table_scan_analyze_next_block(scan, stream))
1254 : {
1255 142988 : vacuum_delay_point(true);
1256 :
1257 10968290 : while (table_scan_analyze_next_tuple(scan, OldestXmin, &liverows, &deadrows, slot))
1258 : {
1259 : /*
1260 : * The first targrows sample rows are simply copied into the
1261 : * reservoir. Then we start replacing tuples in the sample until
1262 : * we reach the end of the relation. This algorithm is from Jeff
1263 : * Vitter's paper (see full citation in utils/misc/sampling.c). It
1264 : * works by repeatedly computing the number of tuples to skip
1265 : * before selecting a tuple, which replaces a randomly chosen
1266 : * element of the reservoir (current set of tuples). At all times
1267 : * the reservoir is a true random sample of the tuples we've
1268 : * passed over so far, so when we fall off the end of the relation
1269 : * we're done.
1270 : */
1271 10825302 : if (numrows < targrows)
1272 10572760 : rows[numrows++] = ExecCopySlotHeapTuple(slot);
1273 : else
1274 : {
1275 : /*
1276 : * t in Vitter's paper is the number of records already
1277 : * processed. If we need to compute a new S value, we must
1278 : * use the not-yet-incremented value of samplerows as t.
1279 : */
1280 252542 : if (rowstoskip < 0)
1281 116550 : rowstoskip = reservoir_get_next_S(&rstate, samplerows, targrows);
1282 :
1283 252542 : if (rowstoskip <= 0)
1284 : {
1285 : /*
1286 : * Found a suitable tuple, so save it, replacing one old
1287 : * tuple at random
1288 : */
1289 116492 : int k = (int) (targrows * sampler_random_fract(&rstate.randstate));
1290 :
1291 : Assert(k >= 0 && k < targrows);
1292 116492 : heap_freetuple(rows[k]);
1293 116492 : rows[k] = ExecCopySlotHeapTuple(slot);
1294 : }
1295 :
1296 252542 : rowstoskip -= 1;
1297 : }
1298 :
1299 10825302 : samplerows += 1;
1300 : }
1301 :
1302 142988 : pgstat_progress_update_param(PROGRESS_ANALYZE_BLOCKS_DONE,
1303 : ++blksdone);
1304 : }
1305 :
1306 17158 : read_stream_end(stream);
1307 :
1308 17158 : ExecDropSingleTupleTableSlot(slot);
1309 17158 : table_endscan(scan);
1310 :
1311 : /*
1312 : * If we didn't find as many tuples as we wanted then we're done. No sort
1313 : * is needed, since they're already in order.
1314 : *
1315 : * Otherwise we need to sort the collected tuples by position
1316 : * (itempointer). It's not worth worrying about corner cases where the
1317 : * tuples are already sorted.
1318 : */
1319 17158 : if (numrows == targrows)
1320 162 : qsort_interruptible(rows, numrows, sizeof(HeapTuple),
1321 : compare_rows, NULL);
1322 :
1323 : /*
1324 : * Estimate total numbers of live and dead rows in relation, extrapolating
1325 : * on the assumption that the average tuple density in pages we didn't
1326 : * scan is the same as in the pages we did scan. Since what we scanned is
1327 : * a random sample of the pages in the relation, this should be a good
1328 : * assumption.
1329 : */
1330 17158 : if (bs.m > 0)
1331 : {
1332 12050 : *totalrows = floor((liverows / bs.m) * totalblocks + 0.5);
1333 12050 : *totaldeadrows = floor((deadrows / bs.m) * totalblocks + 0.5);
1334 : }
1335 : else
1336 : {
1337 5108 : *totalrows = 0.0;
1338 5108 : *totaldeadrows = 0.0;
1339 : }
1340 :
1341 : /*
1342 : * Emit some interesting relation info
1343 : */
1344 17158 : ereport(elevel,
1345 : (errmsg("\"%s\": scanned %d of %u pages, "
1346 : "containing %.0f live rows and %.0f dead rows; "
1347 : "%d rows in sample, %.0f estimated total rows",
1348 : RelationGetRelationName(onerel),
1349 : bs.m, totalblocks,
1350 : liverows, deadrows,
1351 : numrows, *totalrows)));
1352 :
1353 17158 : return numrows;
1354 : }
1355 :
1356 : /*
1357 : * Comparator for sorting rows[] array
1358 : */
1359 : static int
1360 3988216 : compare_rows(const void *a, const void *b, void *arg)
1361 : {
1362 3988216 : HeapTuple ha = *(const HeapTuple *) a;
1363 3988216 : HeapTuple hb = *(const HeapTuple *) b;
1364 3988216 : BlockNumber ba = ItemPointerGetBlockNumber(&ha->t_self);
1365 3988216 : OffsetNumber oa = ItemPointerGetOffsetNumber(&ha->t_self);
1366 3988216 : BlockNumber bb = ItemPointerGetBlockNumber(&hb->t_self);
1367 3988216 : OffsetNumber ob = ItemPointerGetOffsetNumber(&hb->t_self);
1368 :
1369 3988216 : if (ba < bb)
1370 868692 : return -1;
1371 3119524 : if (ba > bb)
1372 870826 : return 1;
1373 2248698 : if (oa < ob)
1374 1513742 : return -1;
1375 734956 : if (oa > ob)
1376 734956 : return 1;
1377 0 : return 0;
1378 : }
1379 :
1380 :
1381 : /*
1382 : * acquire_inherited_sample_rows -- acquire sample rows from inheritance tree
1383 : *
1384 : * This has the same API as acquire_sample_rows, except that rows are
1385 : * collected from all inheritance children as well as the specified table.
1386 : * We fail and return zero if there are no inheritance children, or if all
1387 : * children are foreign tables that don't support ANALYZE.
1388 : */
1389 : static int
1390 844 : acquire_inherited_sample_rows(Relation onerel, int elevel,
1391 : HeapTuple *rows, int targrows,
1392 : double *totalrows, double *totaldeadrows)
1393 : {
1394 : List *tableOIDs;
1395 : Relation *rels;
1396 : AcquireSampleRowsFunc *acquirefuncs;
1397 : double *relblocks;
1398 : double totalblocks;
1399 : int numrows,
1400 : nrels,
1401 : i;
1402 : ListCell *lc;
1403 : bool has_child;
1404 :
1405 : /* Initialize output parameters to zero now, in case we exit early */
1406 844 : *totalrows = 0;
1407 844 : *totaldeadrows = 0;
1408 :
1409 : /*
1410 : * Find all members of inheritance set. We only need AccessShareLock on
1411 : * the children.
1412 : */
1413 : tableOIDs =
1414 844 : find_all_inheritors(RelationGetRelid(onerel), AccessShareLock, NULL);
1415 :
1416 : /*
1417 : * Check that there's at least one descendant, else fail. This could
1418 : * happen despite analyze_rel's relhassubclass check, if table once had a
1419 : * child but no longer does. In that case, we can clear the
1420 : * relhassubclass field so as not to make the same mistake again later.
1421 : * (This is safe because we hold ShareUpdateExclusiveLock.)
1422 : */
1423 844 : if (list_length(tableOIDs) < 2)
1424 : {
1425 : /* CCI because we already updated the pg_class row in this command */
1426 20 : CommandCounterIncrement();
1427 20 : SetRelationHasSubclass(RelationGetRelid(onerel), false);
1428 20 : ereport(elevel,
1429 : (errmsg("skipping analyze of \"%s.%s\" inheritance tree --- this inheritance tree contains no child tables",
1430 : get_namespace_name(RelationGetNamespace(onerel)),
1431 : RelationGetRelationName(onerel))));
1432 20 : return 0;
1433 : }
1434 :
1435 : /*
1436 : * Identify acquirefuncs to use, and count blocks in all the relations.
1437 : * The result could overflow BlockNumber, so we use double arithmetic.
1438 : */
1439 824 : rels = (Relation *) palloc(list_length(tableOIDs) * sizeof(Relation));
1440 : acquirefuncs = (AcquireSampleRowsFunc *)
1441 824 : palloc(list_length(tableOIDs) * sizeof(AcquireSampleRowsFunc));
1442 824 : relblocks = (double *) palloc(list_length(tableOIDs) * sizeof(double));
1443 824 : totalblocks = 0;
1444 824 : nrels = 0;
1445 824 : has_child = false;
1446 3754 : foreach(lc, tableOIDs)
1447 : {
1448 2930 : Oid childOID = lfirst_oid(lc);
1449 : Relation childrel;
1450 2930 : AcquireSampleRowsFunc acquirefunc = NULL;
1451 2930 : BlockNumber relpages = 0;
1452 :
1453 : /* We already got the needed lock */
1454 2930 : childrel = table_open(childOID, NoLock);
1455 :
1456 : /* Ignore if temp table of another backend */
1457 2930 : if (RELATION_IS_OTHER_TEMP(childrel))
1458 : {
1459 : /* ... but release the lock on it */
1460 : Assert(childrel != onerel);
1461 0 : table_close(childrel, AccessShareLock);
1462 776 : continue;
1463 : }
1464 :
1465 : /* Check table type (MATVIEW can't happen, but might as well allow) */
1466 2930 : if (childrel->rd_rel->relkind == RELKIND_RELATION ||
1467 806 : childrel->rd_rel->relkind == RELKIND_MATVIEW)
1468 : {
1469 : /* Regular table, so use the regular row acquisition function */
1470 2124 : acquirefunc = acquire_sample_rows;
1471 2124 : relpages = RelationGetNumberOfBlocks(childrel);
1472 : }
1473 806 : else if (childrel->rd_rel->relkind == RELKIND_FOREIGN_TABLE)
1474 : {
1475 : /*
1476 : * For a foreign table, call the FDW's hook function to see
1477 : * whether it supports analysis.
1478 : */
1479 : FdwRoutine *fdwroutine;
1480 30 : bool ok = false;
1481 :
1482 30 : fdwroutine = GetFdwRoutineForRelation(childrel, false);
1483 :
1484 30 : if (fdwroutine->AnalyzeForeignTable != NULL)
1485 30 : ok = fdwroutine->AnalyzeForeignTable(childrel,
1486 : &acquirefunc,
1487 : &relpages);
1488 :
1489 30 : if (!ok)
1490 : {
1491 : /* ignore, but release the lock on it */
1492 : Assert(childrel != onerel);
1493 0 : table_close(childrel, AccessShareLock);
1494 0 : continue;
1495 : }
1496 : }
1497 : else
1498 : {
1499 : /*
1500 : * ignore, but release the lock on it. don't try to unlock the
1501 : * passed-in relation
1502 : */
1503 : Assert(childrel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE);
1504 776 : if (childrel != onerel)
1505 66 : table_close(childrel, AccessShareLock);
1506 : else
1507 710 : table_close(childrel, NoLock);
1508 776 : continue;
1509 : }
1510 :
1511 : /* OK, we'll process this child */
1512 2154 : has_child = true;
1513 2154 : rels[nrels] = childrel;
1514 2154 : acquirefuncs[nrels] = acquirefunc;
1515 2154 : relblocks[nrels] = (double) relpages;
1516 2154 : totalblocks += (double) relpages;
1517 2154 : nrels++;
1518 : }
1519 :
1520 : /*
1521 : * If we don't have at least one child table to consider, fail. If the
1522 : * relation is a partitioned table, it's not counted as a child table.
1523 : */
1524 824 : if (!has_child)
1525 : {
1526 0 : ereport(elevel,
1527 : (errmsg("skipping analyze of \"%s.%s\" inheritance tree --- this inheritance tree contains no analyzable child tables",
1528 : get_namespace_name(RelationGetNamespace(onerel)),
1529 : RelationGetRelationName(onerel))));
1530 0 : return 0;
1531 : }
1532 :
1533 : /*
1534 : * Now sample rows from each relation, proportionally to its fraction of
1535 : * the total block count. (This might be less than desirable if the child
1536 : * rels have radically different free-space percentages, but it's not
1537 : * clear that it's worth working harder.)
1538 : */
1539 824 : pgstat_progress_update_param(PROGRESS_ANALYZE_CHILD_TABLES_TOTAL,
1540 : nrels);
1541 824 : numrows = 0;
1542 2978 : for (i = 0; i < nrels; i++)
1543 : {
1544 2154 : Relation childrel = rels[i];
1545 2154 : AcquireSampleRowsFunc acquirefunc = acquirefuncs[i];
1546 2154 : double childblocks = relblocks[i];
1547 :
1548 : /*
1549 : * Report progress. The sampling function will normally report blocks
1550 : * done/total, but we need to reset them to 0 here, so that they don't
1551 : * show an old value until that.
1552 : */
1553 : {
1554 2154 : const int progress_index[] = {
1555 : PROGRESS_ANALYZE_CURRENT_CHILD_TABLE_RELID,
1556 : PROGRESS_ANALYZE_BLOCKS_DONE,
1557 : PROGRESS_ANALYZE_BLOCKS_TOTAL
1558 : };
1559 2154 : const int64 progress_vals[] = {
1560 2154 : RelationGetRelid(childrel),
1561 : 0,
1562 : 0,
1563 : };
1564 :
1565 2154 : pgstat_progress_update_multi_param(3, progress_index, progress_vals);
1566 : }
1567 :
1568 2154 : if (childblocks > 0)
1569 : {
1570 : int childtargrows;
1571 :
1572 1988 : childtargrows = (int) rint(targrows * childblocks / totalblocks);
1573 : /* Make sure we don't overrun due to roundoff error */
1574 1988 : childtargrows = Min(childtargrows, targrows - numrows);
1575 1988 : if (childtargrows > 0)
1576 : {
1577 : int childrows;
1578 : double trows,
1579 : tdrows;
1580 :
1581 : /* Fetch a random sample of the child's rows */
1582 1988 : childrows = (*acquirefunc) (childrel, elevel,
1583 1988 : rows + numrows, childtargrows,
1584 : &trows, &tdrows);
1585 :
1586 : /* We may need to convert from child's rowtype to parent's */
1587 1988 : if (childrows > 0 &&
1588 1988 : !equalRowTypes(RelationGetDescr(childrel),
1589 : RelationGetDescr(onerel)))
1590 : {
1591 : TupleConversionMap *map;
1592 :
1593 1898 : map = convert_tuples_by_name(RelationGetDescr(childrel),
1594 : RelationGetDescr(onerel));
1595 1898 : if (map != NULL)
1596 : {
1597 : int j;
1598 :
1599 106760 : for (j = 0; j < childrows; j++)
1600 : {
1601 : HeapTuple newtup;
1602 :
1603 106626 : newtup = execute_attr_map_tuple(rows[numrows + j], map);
1604 106626 : heap_freetuple(rows[numrows + j]);
1605 106626 : rows[numrows + j] = newtup;
1606 : }
1607 134 : free_conversion_map(map);
1608 : }
1609 : }
1610 :
1611 : /* And add to counts */
1612 1988 : numrows += childrows;
1613 1988 : *totalrows += trows;
1614 1988 : *totaldeadrows += tdrows;
1615 : }
1616 : }
1617 :
1618 : /*
1619 : * Note: we cannot release the child-table locks, since we may have
1620 : * pointers to their TOAST tables in the sampled rows.
1621 : */
1622 2154 : table_close(childrel, NoLock);
1623 2154 : pgstat_progress_update_param(PROGRESS_ANALYZE_CHILD_TABLES_DONE,
1624 2154 : i + 1);
1625 : }
1626 :
1627 824 : return numrows;
1628 : }
1629 :
1630 :
1631 : /*
1632 : * update_attstats() -- update attribute statistics for one relation
1633 : *
1634 : * Statistics are stored in several places: the pg_class row for the
1635 : * relation has stats about the whole relation, and there is a
1636 : * pg_statistic row for each (non-system) attribute that has ever
1637 : * been analyzed. The pg_class values are updated by VACUUM, not here.
1638 : *
1639 : * pg_statistic rows are just added or updated normally. This means
1640 : * that pg_statistic will probably contain some deleted rows at the
1641 : * completion of a vacuum cycle, unless it happens to get vacuumed last.
1642 : *
1643 : * To keep things simple, we punt for pg_statistic, and don't try
1644 : * to compute or store rows for pg_statistic itself in pg_statistic.
1645 : * This could possibly be made to work, but it's not worth the trouble.
1646 : * Note analyze_rel() has seen to it that we won't come here when
1647 : * vacuuming pg_statistic itself.
1648 : *
1649 : * Note: there would be a race condition here if two backends could
1650 : * ANALYZE the same table concurrently. Presently, we lock that out
1651 : * by taking a self-exclusive lock on the relation in analyze_rel().
1652 : */
1653 : static void
1654 24696 : update_attstats(Oid relid, bool inh, int natts, VacAttrStats **vacattrstats)
1655 : {
1656 : Relation sd;
1657 : int attno;
1658 24696 : CatalogIndexState indstate = NULL;
1659 :
1660 24696 : if (natts <= 0)
1661 13718 : return; /* nothing to do */
1662 :
1663 10978 : sd = table_open(StatisticRelationId, RowExclusiveLock);
1664 :
1665 96378 : for (attno = 0; attno < natts; attno++)
1666 : {
1667 85400 : VacAttrStats *stats = vacattrstats[attno];
1668 : HeapTuple stup,
1669 : oldtup;
1670 : int i,
1671 : k,
1672 : n;
1673 : Datum values[Natts_pg_statistic];
1674 : bool nulls[Natts_pg_statistic];
1675 : bool replaces[Natts_pg_statistic];
1676 :
1677 : /* Ignore attr if we weren't able to collect stats */
1678 85400 : if (!stats->stats_valid)
1679 6 : continue;
1680 :
1681 : /*
1682 : * Construct a new pg_statistic tuple
1683 : */
1684 2732608 : for (i = 0; i < Natts_pg_statistic; ++i)
1685 : {
1686 2647214 : nulls[i] = false;
1687 2647214 : replaces[i] = true;
1688 : }
1689 :
1690 85394 : values[Anum_pg_statistic_starelid - 1] = ObjectIdGetDatum(relid);
1691 85394 : values[Anum_pg_statistic_staattnum - 1] = Int16GetDatum(stats->tupattnum);
1692 85394 : values[Anum_pg_statistic_stainherit - 1] = BoolGetDatum(inh);
1693 85394 : values[Anum_pg_statistic_stanullfrac - 1] = Float4GetDatum(stats->stanullfrac);
1694 85394 : values[Anum_pg_statistic_stawidth - 1] = Int32GetDatum(stats->stawidth);
1695 85394 : values[Anum_pg_statistic_stadistinct - 1] = Float4GetDatum(stats->stadistinct);
1696 85394 : i = Anum_pg_statistic_stakind1 - 1;
1697 512364 : for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
1698 : {
1699 426970 : values[i++] = Int16GetDatum(stats->stakind[k]); /* stakindN */
1700 : }
1701 85394 : i = Anum_pg_statistic_staop1 - 1;
1702 512364 : for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
1703 : {
1704 426970 : values[i++] = ObjectIdGetDatum(stats->staop[k]); /* staopN */
1705 : }
1706 85394 : i = Anum_pg_statistic_stacoll1 - 1;
1707 512364 : for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
1708 : {
1709 426970 : values[i++] = ObjectIdGetDatum(stats->stacoll[k]); /* stacollN */
1710 : }
1711 85394 : i = Anum_pg_statistic_stanumbers1 - 1;
1712 512364 : for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
1713 : {
1714 426970 : int nnum = stats->numnumbers[k];
1715 :
1716 426970 : if (nnum > 0)
1717 : {
1718 133488 : Datum *numdatums = (Datum *) palloc(nnum * sizeof(Datum));
1719 : ArrayType *arry;
1720 :
1721 1112344 : for (n = 0; n < nnum; n++)
1722 978856 : numdatums[n] = Float4GetDatum(stats->stanumbers[k][n]);
1723 133488 : arry = construct_array_builtin(numdatums, nnum, FLOAT4OID);
1724 133488 : values[i++] = PointerGetDatum(arry); /* stanumbersN */
1725 : }
1726 : else
1727 : {
1728 293482 : nulls[i] = true;
1729 293482 : values[i++] = (Datum) 0;
1730 : }
1731 : }
1732 85394 : i = Anum_pg_statistic_stavalues1 - 1;
1733 512364 : for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
1734 : {
1735 426970 : if (stats->numvalues[k] > 0)
1736 : {
1737 : ArrayType *arry;
1738 :
1739 94160 : arry = construct_array(stats->stavalues[k],
1740 : stats->numvalues[k],
1741 : stats->statypid[k],
1742 94160 : stats->statyplen[k],
1743 94160 : stats->statypbyval[k],
1744 94160 : stats->statypalign[k]);
1745 94160 : values[i++] = PointerGetDatum(arry); /* stavaluesN */
1746 : }
1747 : else
1748 : {
1749 332810 : nulls[i] = true;
1750 332810 : values[i++] = (Datum) 0;
1751 : }
1752 : }
1753 :
1754 : /* Is there already a pg_statistic tuple for this attribute? */
1755 170788 : oldtup = SearchSysCache3(STATRELATTINH,
1756 : ObjectIdGetDatum(relid),
1757 85394 : Int16GetDatum(stats->tupattnum),
1758 : BoolGetDatum(inh));
1759 :
1760 : /* Open index information when we know we need it */
1761 85394 : if (indstate == NULL)
1762 10972 : indstate = CatalogOpenIndexes(sd);
1763 :
1764 85394 : if (HeapTupleIsValid(oldtup))
1765 : {
1766 : /* Yes, replace it */
1767 37828 : stup = heap_modify_tuple(oldtup,
1768 : RelationGetDescr(sd),
1769 : values,
1770 : nulls,
1771 : replaces);
1772 37828 : ReleaseSysCache(oldtup);
1773 37828 : CatalogTupleUpdateWithInfo(sd, &stup->t_self, stup, indstate);
1774 : }
1775 : else
1776 : {
1777 : /* No, insert new tuple */
1778 47566 : stup = heap_form_tuple(RelationGetDescr(sd), values, nulls);
1779 47566 : CatalogTupleInsertWithInfo(sd, stup, indstate);
1780 : }
1781 :
1782 85394 : heap_freetuple(stup);
1783 : }
1784 :
1785 10978 : if (indstate != NULL)
1786 10972 : CatalogCloseIndexes(indstate);
1787 10978 : table_close(sd, RowExclusiveLock);
1788 : }
1789 :
1790 : /*
1791 : * Standard fetch function for use by compute_stats subroutines.
1792 : *
1793 : * This exists to provide some insulation between compute_stats routines
1794 : * and the actual storage of the sample data.
1795 : */
1796 : static Datum
1797 82861344 : std_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull)
1798 : {
1799 82861344 : int attnum = stats->tupattnum;
1800 82861344 : HeapTuple tuple = stats->rows[rownum];
1801 82861344 : TupleDesc tupDesc = stats->tupDesc;
1802 :
1803 82861344 : return heap_getattr(tuple, attnum, tupDesc, isNull);
1804 : }
1805 :
1806 : /*
1807 : * Fetch function for analyzing index expressions.
1808 : *
1809 : * We have not bothered to construct index tuples, instead the data is
1810 : * just in Datum arrays.
1811 : */
1812 : static Datum
1813 115524 : ind_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull)
1814 : {
1815 : int i;
1816 :
1817 : /* exprvals and exprnulls are already offset for proper column */
1818 115524 : i = rownum * stats->rowstride;
1819 115524 : *isNull = stats->exprnulls[i];
1820 115524 : return stats->exprvals[i];
1821 : }
1822 :
1823 :
1824 : /*==========================================================================
1825 : *
1826 : * Code below this point represents the "standard" type-specific statistics
1827 : * analysis algorithms. This code can be replaced on a per-data-type basis
1828 : * by setting a nonzero value in pg_type.typanalyze.
1829 : *
1830 : *==========================================================================
1831 : */
1832 :
1833 :
1834 : /*
1835 : * To avoid consuming too much memory during analysis and/or too much space
1836 : * in the resulting pg_statistic rows, we ignore varlena datums that are wider
1837 : * than WIDTH_THRESHOLD (after detoasting!). This is legitimate for MCV
1838 : * and distinct-value calculations since a wide value is unlikely to be
1839 : * duplicated at all, much less be a most-common value. For the same reason,
1840 : * ignoring wide values will not affect our estimates of histogram bin
1841 : * boundaries very much.
1842 : */
1843 : #define WIDTH_THRESHOLD 1024
1844 :
1845 : #define swapInt(a,b) do {int _tmp; _tmp=a; a=b; b=_tmp;} while(0)
1846 : #define swapDatum(a,b) do {Datum _tmp; _tmp=a; a=b; b=_tmp;} while(0)
1847 :
1848 : /*
1849 : * Extra information used by the default analysis routines
1850 : */
1851 : typedef struct
1852 : {
1853 : int count; /* # of duplicates */
1854 : int first; /* values[] index of first occurrence */
1855 : } ScalarMCVItem;
1856 :
1857 : typedef struct
1858 : {
1859 : SortSupport ssup;
1860 : int *tupnoLink;
1861 : } CompareScalarsContext;
1862 :
1863 :
1864 : static void compute_trivial_stats(VacAttrStatsP stats,
1865 : AnalyzeAttrFetchFunc fetchfunc,
1866 : int samplerows,
1867 : double totalrows);
1868 : static void compute_distinct_stats(VacAttrStatsP stats,
1869 : AnalyzeAttrFetchFunc fetchfunc,
1870 : int samplerows,
1871 : double totalrows);
1872 : static void compute_scalar_stats(VacAttrStatsP stats,
1873 : AnalyzeAttrFetchFunc fetchfunc,
1874 : int samplerows,
1875 : double totalrows);
1876 : static int compare_scalars(const void *a, const void *b, void *arg);
1877 : static int compare_mcvs(const void *a, const void *b, void *arg);
1878 : static int analyze_mcv_list(int *mcv_counts,
1879 : int num_mcv,
1880 : double stadistinct,
1881 : double stanullfrac,
1882 : int samplerows,
1883 : double totalrows);
1884 :
1885 :
1886 : /*
1887 : * std_typanalyze -- the default type-specific typanalyze function
1888 : */
1889 : bool
1890 117562 : std_typanalyze(VacAttrStats *stats)
1891 : {
1892 : Oid ltopr;
1893 : Oid eqopr;
1894 : StdAnalyzeData *mystats;
1895 :
1896 : /* If the attstattarget column is negative, use the default value */
1897 117562 : if (stats->attstattarget < 0)
1898 116956 : stats->attstattarget = default_statistics_target;
1899 :
1900 : /* Look for default "<" and "=" operators for column's type */
1901 117562 : get_sort_group_operators(stats->attrtypid,
1902 : false, false, false,
1903 : <opr, &eqopr, NULL,
1904 : NULL);
1905 :
1906 : /* Save the operator info for compute_stats routines */
1907 117562 : mystats = (StdAnalyzeData *) palloc(sizeof(StdAnalyzeData));
1908 117562 : mystats->eqopr = eqopr;
1909 117562 : mystats->eqfunc = OidIsValid(eqopr) ? get_opcode(eqopr) : InvalidOid;
1910 117562 : mystats->ltopr = ltopr;
1911 117562 : stats->extra_data = mystats;
1912 :
1913 : /*
1914 : * Determine which standard statistics algorithm to use
1915 : */
1916 117562 : if (OidIsValid(eqopr) && OidIsValid(ltopr))
1917 : {
1918 : /* Seems to be a scalar datatype */
1919 113926 : stats->compute_stats = compute_scalar_stats;
1920 : /*--------------------
1921 : * The following choice of minrows is based on the paper
1922 : * "Random sampling for histogram construction: how much is enough?"
1923 : * by Surajit Chaudhuri, Rajeev Motwani and Vivek Narasayya, in
1924 : * Proceedings of ACM SIGMOD International Conference on Management
1925 : * of Data, 1998, Pages 436-447. Their Corollary 1 to Theorem 5
1926 : * says that for table size n, histogram size k, maximum relative
1927 : * error in bin size f, and error probability gamma, the minimum
1928 : * random sample size is
1929 : * r = 4 * k * ln(2*n/gamma) / f^2
1930 : * Taking f = 0.5, gamma = 0.01, n = 10^6 rows, we obtain
1931 : * r = 305.82 * k
1932 : * Note that because of the log function, the dependence on n is
1933 : * quite weak; even at n = 10^12, a 300*k sample gives <= 0.66
1934 : * bin size error with probability 0.99. So there's no real need to
1935 : * scale for n, which is a good thing because we don't necessarily
1936 : * know it at this point.
1937 : *--------------------
1938 : */
1939 113926 : stats->minrows = 300 * stats->attstattarget;
1940 : }
1941 3636 : else if (OidIsValid(eqopr))
1942 : {
1943 : /* We can still recognize distinct values */
1944 3106 : stats->compute_stats = compute_distinct_stats;
1945 : /* Might as well use the same minrows as above */
1946 3106 : stats->minrows = 300 * stats->attstattarget;
1947 : }
1948 : else
1949 : {
1950 : /* Can't do much but the trivial stuff */
1951 530 : stats->compute_stats = compute_trivial_stats;
1952 : /* Might as well use the same minrows as above */
1953 530 : stats->minrows = 300 * stats->attstattarget;
1954 : }
1955 :
1956 117562 : return true;
1957 : }
1958 :
1959 :
1960 : /*
1961 : * compute_trivial_stats() -- compute very basic column statistics
1962 : *
1963 : * We use this when we cannot find a hash "=" operator for the datatype.
1964 : *
1965 : * We determine the fraction of non-null rows and the average datum width.
1966 : */
1967 : static void
1968 364 : compute_trivial_stats(VacAttrStatsP stats,
1969 : AnalyzeAttrFetchFunc fetchfunc,
1970 : int samplerows,
1971 : double totalrows)
1972 : {
1973 : int i;
1974 364 : int null_cnt = 0;
1975 364 : int nonnull_cnt = 0;
1976 364 : double total_width = 0;
1977 728 : bool is_varlena = (!stats->attrtype->typbyval &&
1978 364 : stats->attrtype->typlen == -1);
1979 728 : bool is_varwidth = (!stats->attrtype->typbyval &&
1980 364 : stats->attrtype->typlen < 0);
1981 :
1982 1199256 : for (i = 0; i < samplerows; i++)
1983 : {
1984 : Datum value;
1985 : bool isnull;
1986 :
1987 1198892 : vacuum_delay_point(true);
1988 :
1989 1198892 : value = fetchfunc(stats, i, &isnull);
1990 :
1991 : /* Check for null/nonnull */
1992 1198892 : if (isnull)
1993 : {
1994 638986 : null_cnt++;
1995 638986 : continue;
1996 : }
1997 559906 : nonnull_cnt++;
1998 :
1999 : /*
2000 : * If it's a variable-width field, add up widths for average width
2001 : * calculation. Note that if the value is toasted, we use the toasted
2002 : * width. We don't bother with this calculation if it's a fixed-width
2003 : * type.
2004 : */
2005 559906 : if (is_varlena)
2006 : {
2007 124506 : total_width += VARSIZE_ANY(DatumGetPointer(value));
2008 : }
2009 435400 : else if (is_varwidth)
2010 : {
2011 : /* must be cstring */
2012 0 : total_width += strlen(DatumGetCString(value)) + 1;
2013 : }
2014 : }
2015 :
2016 : /* We can only compute average width if we found some non-null values. */
2017 364 : if (nonnull_cnt > 0)
2018 : {
2019 180 : stats->stats_valid = true;
2020 : /* Do the simple null-frac and width stats */
2021 180 : stats->stanullfrac = (double) null_cnt / (double) samplerows;
2022 180 : if (is_varwidth)
2023 82 : stats->stawidth = total_width / (double) nonnull_cnt;
2024 : else
2025 98 : stats->stawidth = stats->attrtype->typlen;
2026 180 : stats->stadistinct = 0.0; /* "unknown" */
2027 : }
2028 184 : else if (null_cnt > 0)
2029 : {
2030 : /* We found only nulls; assume the column is entirely null */
2031 184 : stats->stats_valid = true;
2032 184 : stats->stanullfrac = 1.0;
2033 184 : if (is_varwidth)
2034 184 : stats->stawidth = 0; /* "unknown" */
2035 : else
2036 0 : stats->stawidth = stats->attrtype->typlen;
2037 184 : stats->stadistinct = 0.0; /* "unknown" */
2038 : }
2039 364 : }
2040 :
2041 :
2042 : /*
2043 : * compute_distinct_stats() -- compute column statistics including ndistinct
2044 : *
2045 : * We use this when we can find only an "=" operator for the datatype.
2046 : *
2047 : * We determine the fraction of non-null rows, the average width, the
2048 : * most common values, and the (estimated) number of distinct values.
2049 : *
2050 : * The most common values are determined by brute force: we keep a list
2051 : * of previously seen values, ordered by number of times seen, as we scan
2052 : * the samples. A newly seen value is inserted just after the last
2053 : * multiply-seen value, causing the bottommost (oldest) singly-seen value
2054 : * to drop off the list. The accuracy of this method, and also its cost,
2055 : * depend mainly on the length of the list we are willing to keep.
2056 : */
2057 : static void
2058 2276 : compute_distinct_stats(VacAttrStatsP stats,
2059 : AnalyzeAttrFetchFunc fetchfunc,
2060 : int samplerows,
2061 : double totalrows)
2062 : {
2063 : int i;
2064 2276 : int null_cnt = 0;
2065 2276 : int nonnull_cnt = 0;
2066 2276 : int toowide_cnt = 0;
2067 2276 : double total_width = 0;
2068 3848 : bool is_varlena = (!stats->attrtype->typbyval &&
2069 1572 : stats->attrtype->typlen == -1);
2070 3848 : bool is_varwidth = (!stats->attrtype->typbyval &&
2071 1572 : stats->attrtype->typlen < 0);
2072 : FmgrInfo f_cmpeq;
2073 : typedef struct
2074 : {
2075 : Datum value;
2076 : int count;
2077 : } TrackItem;
2078 : TrackItem *track;
2079 : int track_cnt,
2080 : track_max;
2081 2276 : int num_mcv = stats->attstattarget;
2082 2276 : StdAnalyzeData *mystats = (StdAnalyzeData *) stats->extra_data;
2083 :
2084 : /*
2085 : * We track up to 2*n values for an n-element MCV list; but at least 10
2086 : */
2087 2276 : track_max = 2 * num_mcv;
2088 2276 : if (track_max < 10)
2089 78 : track_max = 10;
2090 2276 : track = (TrackItem *) palloc(track_max * sizeof(TrackItem));
2091 2276 : track_cnt = 0;
2092 :
2093 2276 : fmgr_info(mystats->eqfunc, &f_cmpeq);
2094 :
2095 1619284 : for (i = 0; i < samplerows; i++)
2096 : {
2097 : Datum value;
2098 : bool isnull;
2099 : bool match;
2100 : int firstcount1,
2101 : j;
2102 :
2103 1617008 : vacuum_delay_point(true);
2104 :
2105 1617008 : value = fetchfunc(stats, i, &isnull);
2106 :
2107 : /* Check for null/nonnull */
2108 1617008 : if (isnull)
2109 : {
2110 1351178 : null_cnt++;
2111 1351178 : continue;
2112 : }
2113 265830 : nonnull_cnt++;
2114 :
2115 : /*
2116 : * If it's a variable-width field, add up widths for average width
2117 : * calculation. Note that if the value is toasted, we use the toasted
2118 : * width. We don't bother with this calculation if it's a fixed-width
2119 : * type.
2120 : */
2121 265830 : if (is_varlena)
2122 : {
2123 94738 : total_width += VARSIZE_ANY(DatumGetPointer(value));
2124 :
2125 : /*
2126 : * If the value is toasted, we want to detoast it just once to
2127 : * avoid repeated detoastings and resultant excess memory usage
2128 : * during the comparisons. Also, check to see if the value is
2129 : * excessively wide, and if so don't detoast at all --- just
2130 : * ignore the value.
2131 : */
2132 94738 : if (toast_raw_datum_size(value) > WIDTH_THRESHOLD)
2133 : {
2134 0 : toowide_cnt++;
2135 0 : continue;
2136 : }
2137 94738 : value = PointerGetDatum(PG_DETOAST_DATUM(value));
2138 : }
2139 171092 : else if (is_varwidth)
2140 : {
2141 : /* must be cstring */
2142 0 : total_width += strlen(DatumGetCString(value)) + 1;
2143 : }
2144 :
2145 : /*
2146 : * See if the value matches anything we're already tracking.
2147 : */
2148 265830 : match = false;
2149 265830 : firstcount1 = track_cnt;
2150 640886 : for (j = 0; j < track_cnt; j++)
2151 : {
2152 633318 : if (DatumGetBool(FunctionCall2Coll(&f_cmpeq,
2153 : stats->attrcollid,
2154 633318 : value, track[j].value)))
2155 : {
2156 258262 : match = true;
2157 258262 : break;
2158 : }
2159 375056 : if (j < firstcount1 && track[j].count == 1)
2160 5496 : firstcount1 = j;
2161 : }
2162 :
2163 265830 : if (match)
2164 : {
2165 : /* Found a match */
2166 258262 : track[j].count++;
2167 : /* This value may now need to "bubble up" in the track list */
2168 269600 : while (j > 0 && track[j].count > track[j - 1].count)
2169 : {
2170 11338 : swapDatum(track[j].value, track[j - 1].value);
2171 11338 : swapInt(track[j].count, track[j - 1].count);
2172 11338 : j--;
2173 : }
2174 : }
2175 : else
2176 : {
2177 : /* No match. Insert at head of count-1 list */
2178 7568 : if (track_cnt < track_max)
2179 7150 : track_cnt++;
2180 172628 : for (j = track_cnt - 1; j > firstcount1; j--)
2181 : {
2182 165060 : track[j].value = track[j - 1].value;
2183 165060 : track[j].count = track[j - 1].count;
2184 : }
2185 7568 : if (firstcount1 < track_cnt)
2186 : {
2187 7568 : track[firstcount1].value = value;
2188 7568 : track[firstcount1].count = 1;
2189 : }
2190 : }
2191 : }
2192 :
2193 : /* We can only compute real stats if we found some non-null values. */
2194 2276 : if (nonnull_cnt > 0)
2195 : {
2196 : int nmultiple,
2197 : summultiple;
2198 :
2199 1664 : stats->stats_valid = true;
2200 : /* Do the simple null-frac and width stats */
2201 1664 : stats->stanullfrac = (double) null_cnt / (double) samplerows;
2202 1664 : if (is_varwidth)
2203 960 : stats->stawidth = total_width / (double) nonnull_cnt;
2204 : else
2205 704 : stats->stawidth = stats->attrtype->typlen;
2206 :
2207 : /* Count the number of values we found multiple times */
2208 1664 : summultiple = 0;
2209 6406 : for (nmultiple = 0; nmultiple < track_cnt; nmultiple++)
2210 : {
2211 5586 : if (track[nmultiple].count == 1)
2212 844 : break;
2213 4742 : summultiple += track[nmultiple].count;
2214 : }
2215 :
2216 1664 : if (nmultiple == 0)
2217 : {
2218 : /*
2219 : * If we found no repeated non-null values, assume it's a unique
2220 : * column; but be sure to discount for any nulls we found.
2221 : */
2222 194 : stats->stadistinct = -1.0 * (1.0 - stats->stanullfrac);
2223 : }
2224 1470 : else if (track_cnt < track_max && toowide_cnt == 0 &&
2225 : nmultiple == track_cnt)
2226 : {
2227 : /*
2228 : * Our track list includes every value in the sample, and every
2229 : * value appeared more than once. Assume the column has just
2230 : * these values. (This case is meant to address columns with
2231 : * small, fixed sets of possible values, such as boolean or enum
2232 : * columns. If there are any values that appear just once in the
2233 : * sample, including too-wide values, we should assume that that's
2234 : * not what we're dealing with.)
2235 : */
2236 820 : stats->stadistinct = track_cnt;
2237 : }
2238 : else
2239 : {
2240 : /*----------
2241 : * Estimate the number of distinct values using the estimator
2242 : * proposed by Haas and Stokes in IBM Research Report RJ 10025:
2243 : * n*d / (n - f1 + f1*n/N)
2244 : * where f1 is the number of distinct values that occurred
2245 : * exactly once in our sample of n rows (from a total of N),
2246 : * and d is the total number of distinct values in the sample.
2247 : * This is their Duj1 estimator; the other estimators they
2248 : * recommend are considerably more complex, and are numerically
2249 : * very unstable when n is much smaller than N.
2250 : *
2251 : * In this calculation, we consider only non-nulls. We used to
2252 : * include rows with null values in the n and N counts, but that
2253 : * leads to inaccurate answers in columns with many nulls, and
2254 : * it's intuitively bogus anyway considering the desired result is
2255 : * the number of distinct non-null values.
2256 : *
2257 : * We assume (not very reliably!) that all the multiply-occurring
2258 : * values are reflected in the final track[] list, and the other
2259 : * nonnull values all appeared but once. (XXX this usually
2260 : * results in a drastic overestimate of ndistinct. Can we do
2261 : * any better?)
2262 : *----------
2263 : */
2264 650 : int f1 = nonnull_cnt - summultiple;
2265 650 : int d = f1 + nmultiple;
2266 650 : double n = samplerows - null_cnt;
2267 650 : double N = totalrows * (1.0 - stats->stanullfrac);
2268 : double stadistinct;
2269 :
2270 : /* N == 0 shouldn't happen, but just in case ... */
2271 650 : if (N > 0)
2272 650 : stadistinct = (n * d) / ((n - f1) + f1 * n / N);
2273 : else
2274 0 : stadistinct = 0;
2275 :
2276 : /* Clamp to sane range in case of roundoff error */
2277 650 : if (stadistinct < d)
2278 180 : stadistinct = d;
2279 650 : if (stadistinct > N)
2280 0 : stadistinct = N;
2281 : /* And round to integer */
2282 650 : stats->stadistinct = floor(stadistinct + 0.5);
2283 : }
2284 :
2285 : /*
2286 : * If we estimated the number of distinct values at more than 10% of
2287 : * the total row count (a very arbitrary limit), then assume that
2288 : * stadistinct should scale with the row count rather than be a fixed
2289 : * value.
2290 : */
2291 1664 : if (stats->stadistinct > 0.1 * totalrows)
2292 386 : stats->stadistinct = -(stats->stadistinct / totalrows);
2293 :
2294 : /*
2295 : * Decide how many values are worth storing as most-common values. If
2296 : * we are able to generate a complete MCV list (all the values in the
2297 : * sample will fit, and we think these are all the ones in the table),
2298 : * then do so. Otherwise, store only those values that are
2299 : * significantly more common than the values not in the list.
2300 : *
2301 : * Note: the first of these cases is meant to address columns with
2302 : * small, fixed sets of possible values, such as boolean or enum
2303 : * columns. If we can *completely* represent the column population by
2304 : * an MCV list that will fit into the stats target, then we should do
2305 : * so and thus provide the planner with complete information. But if
2306 : * the MCV list is not complete, it's generally worth being more
2307 : * selective, and not just filling it all the way up to the stats
2308 : * target.
2309 : */
2310 1664 : if (track_cnt < track_max && toowide_cnt == 0 &&
2311 1652 : stats->stadistinct > 0 &&
2312 : track_cnt <= num_mcv)
2313 : {
2314 : /* Track list includes all values seen, and all will fit */
2315 1054 : num_mcv = track_cnt;
2316 : }
2317 : else
2318 : {
2319 : int *mcv_counts;
2320 :
2321 : /* Incomplete list; decide how many values are worth keeping */
2322 610 : if (num_mcv > track_cnt)
2323 546 : num_mcv = track_cnt;
2324 :
2325 610 : if (num_mcv > 0)
2326 : {
2327 610 : mcv_counts = (int *) palloc(num_mcv * sizeof(int));
2328 2296 : for (i = 0; i < num_mcv; i++)
2329 1686 : mcv_counts[i] = track[i].count;
2330 :
2331 610 : num_mcv = analyze_mcv_list(mcv_counts, num_mcv,
2332 610 : stats->stadistinct,
2333 610 : stats->stanullfrac,
2334 : samplerows, totalrows);
2335 : }
2336 : }
2337 :
2338 : /* Generate MCV slot entry */
2339 1664 : if (num_mcv > 0)
2340 : {
2341 : MemoryContext old_context;
2342 : Datum *mcv_values;
2343 : float4 *mcv_freqs;
2344 :
2345 : /* Must copy the target values into anl_context */
2346 1656 : old_context = MemoryContextSwitchTo(stats->anl_context);
2347 1656 : mcv_values = (Datum *) palloc(num_mcv * sizeof(Datum));
2348 1656 : mcv_freqs = (float4 *) palloc(num_mcv * sizeof(float4));
2349 8032 : for (i = 0; i < num_mcv; i++)
2350 : {
2351 12752 : mcv_values[i] = datumCopy(track[i].value,
2352 6376 : stats->attrtype->typbyval,
2353 6376 : stats->attrtype->typlen);
2354 6376 : mcv_freqs[i] = (double) track[i].count / (double) samplerows;
2355 : }
2356 1656 : MemoryContextSwitchTo(old_context);
2357 :
2358 1656 : stats->stakind[0] = STATISTIC_KIND_MCV;
2359 1656 : stats->staop[0] = mystats->eqopr;
2360 1656 : stats->stacoll[0] = stats->attrcollid;
2361 1656 : stats->stanumbers[0] = mcv_freqs;
2362 1656 : stats->numnumbers[0] = num_mcv;
2363 1656 : stats->stavalues[0] = mcv_values;
2364 1656 : stats->numvalues[0] = num_mcv;
2365 :
2366 : /*
2367 : * Accept the defaults for stats->statypid and others. They have
2368 : * been set before we were called (see vacuum.h)
2369 : */
2370 : }
2371 : }
2372 612 : else if (null_cnt > 0)
2373 : {
2374 : /* We found only nulls; assume the column is entirely null */
2375 612 : stats->stats_valid = true;
2376 612 : stats->stanullfrac = 1.0;
2377 612 : if (is_varwidth)
2378 612 : stats->stawidth = 0; /* "unknown" */
2379 : else
2380 0 : stats->stawidth = stats->attrtype->typlen;
2381 612 : stats->stadistinct = 0.0; /* "unknown" */
2382 : }
2383 :
2384 : /* We don't need to bother cleaning up any of our temporary palloc's */
2385 2276 : }
2386 :
2387 :
2388 : /*
2389 : * compute_scalar_stats() -- compute column statistics
2390 : *
2391 : * We use this when we can find "=" and "<" operators for the datatype.
2392 : *
2393 : * We determine the fraction of non-null rows, the average width, the
2394 : * most common values, the (estimated) number of distinct values, the
2395 : * distribution histogram, and the correlation of physical to logical order.
2396 : *
2397 : * The desired stats can be determined fairly easily after sorting the
2398 : * data values into order.
2399 : */
2400 : static void
2401 83016 : compute_scalar_stats(VacAttrStatsP stats,
2402 : AnalyzeAttrFetchFunc fetchfunc,
2403 : int samplerows,
2404 : double totalrows)
2405 : {
2406 : int i;
2407 83016 : int null_cnt = 0;
2408 83016 : int nonnull_cnt = 0;
2409 83016 : int toowide_cnt = 0;
2410 83016 : double total_width = 0;
2411 103300 : bool is_varlena = (!stats->attrtype->typbyval &&
2412 20284 : stats->attrtype->typlen == -1);
2413 103300 : bool is_varwidth = (!stats->attrtype->typbyval &&
2414 20284 : stats->attrtype->typlen < 0);
2415 : double corr_xysum;
2416 : SortSupportData ssup;
2417 : ScalarItem *values;
2418 83016 : int values_cnt = 0;
2419 : int *tupnoLink;
2420 : ScalarMCVItem *track;
2421 83016 : int track_cnt = 0;
2422 83016 : int num_mcv = stats->attstattarget;
2423 83016 : int num_bins = stats->attstattarget;
2424 83016 : StdAnalyzeData *mystats = (StdAnalyzeData *) stats->extra_data;
2425 :
2426 83016 : values = (ScalarItem *) palloc(samplerows * sizeof(ScalarItem));
2427 83016 : tupnoLink = (int *) palloc(samplerows * sizeof(int));
2428 83016 : track = (ScalarMCVItem *) palloc(num_mcv * sizeof(ScalarMCVItem));
2429 :
2430 83016 : memset(&ssup, 0, sizeof(ssup));
2431 83016 : ssup.ssup_cxt = CurrentMemoryContext;
2432 83016 : ssup.ssup_collation = stats->attrcollid;
2433 83016 : ssup.ssup_nulls_first = false;
2434 :
2435 : /*
2436 : * For now, don't perform abbreviated key conversion, because full values
2437 : * are required for MCV slot generation. Supporting that optimization
2438 : * would necessitate teaching compare_scalars() to call a tie-breaker.
2439 : */
2440 83016 : ssup.abbreviate = false;
2441 :
2442 83016 : PrepareSortSupportFromOrderingOp(mystats->ltopr, &ssup);
2443 :
2444 : /* Initial scan to find sortable values */
2445 75449748 : for (i = 0; i < samplerows; i++)
2446 : {
2447 : Datum value;
2448 : bool isnull;
2449 :
2450 75366732 : vacuum_delay_point(true);
2451 :
2452 75366732 : value = fetchfunc(stats, i, &isnull);
2453 :
2454 : /* Check for null/nonnull */
2455 75366732 : if (isnull)
2456 : {
2457 9812920 : null_cnt++;
2458 9849660 : continue;
2459 : }
2460 65553812 : nonnull_cnt++;
2461 :
2462 : /*
2463 : * If it's a variable-width field, add up widths for average width
2464 : * calculation. Note that if the value is toasted, we use the toasted
2465 : * width. We don't bother with this calculation if it's a fixed-width
2466 : * type.
2467 : */
2468 65553812 : if (is_varlena)
2469 : {
2470 7788154 : total_width += VARSIZE_ANY(DatumGetPointer(value));
2471 :
2472 : /*
2473 : * If the value is toasted, we want to detoast it just once to
2474 : * avoid repeated detoastings and resultant excess memory usage
2475 : * during the comparisons. Also, check to see if the value is
2476 : * excessively wide, and if so don't detoast at all --- just
2477 : * ignore the value.
2478 : */
2479 7788154 : if (toast_raw_datum_size(value) > WIDTH_THRESHOLD)
2480 : {
2481 36740 : toowide_cnt++;
2482 36740 : continue;
2483 : }
2484 7751414 : value = PointerGetDatum(PG_DETOAST_DATUM(value));
2485 : }
2486 57765658 : else if (is_varwidth)
2487 : {
2488 : /* must be cstring */
2489 0 : total_width += strlen(DatumGetCString(value)) + 1;
2490 : }
2491 :
2492 : /* Add it to the list to be sorted */
2493 65517072 : values[values_cnt].value = value;
2494 65517072 : values[values_cnt].tupno = values_cnt;
2495 65517072 : tupnoLink[values_cnt] = values_cnt;
2496 65517072 : values_cnt++;
2497 : }
2498 :
2499 : /* We can only compute real stats if we found some sortable values. */
2500 83016 : if (values_cnt > 0)
2501 : {
2502 : int ndistinct, /* # distinct values in sample */
2503 : nmultiple, /* # that appear multiple times */
2504 : num_hist,
2505 : dups_cnt;
2506 77558 : int slot_idx = 0;
2507 : CompareScalarsContext cxt;
2508 :
2509 : /* Sort the collected values */
2510 77558 : cxt.ssup = &ssup;
2511 77558 : cxt.tupnoLink = tupnoLink;
2512 77558 : qsort_interruptible(values, values_cnt, sizeof(ScalarItem),
2513 : compare_scalars, &cxt);
2514 :
2515 : /*
2516 : * Now scan the values in order, find the most common ones, and also
2517 : * accumulate ordering-correlation statistics.
2518 : *
2519 : * To determine which are most common, we first have to count the
2520 : * number of duplicates of each value. The duplicates are adjacent in
2521 : * the sorted list, so a brute-force approach is to compare successive
2522 : * datum values until we find two that are not equal. However, that
2523 : * requires N-1 invocations of the datum comparison routine, which are
2524 : * completely redundant with work that was done during the sort. (The
2525 : * sort algorithm must at some point have compared each pair of items
2526 : * that are adjacent in the sorted order; otherwise it could not know
2527 : * that it's ordered the pair correctly.) We exploit this by having
2528 : * compare_scalars remember the highest tupno index that each
2529 : * ScalarItem has been found equal to. At the end of the sort, a
2530 : * ScalarItem's tupnoLink will still point to itself if and only if it
2531 : * is the last item of its group of duplicates (since the group will
2532 : * be ordered by tupno).
2533 : */
2534 77558 : corr_xysum = 0;
2535 77558 : ndistinct = 0;
2536 77558 : nmultiple = 0;
2537 77558 : dups_cnt = 0;
2538 65594630 : for (i = 0; i < values_cnt; i++)
2539 : {
2540 65517072 : int tupno = values[i].tupno;
2541 :
2542 65517072 : corr_xysum += ((double) i) * ((double) tupno);
2543 65517072 : dups_cnt++;
2544 65517072 : if (tupnoLink[tupno] == tupno)
2545 : {
2546 : /* Reached end of duplicates of this value */
2547 13812036 : ndistinct++;
2548 13812036 : if (dups_cnt > 1)
2549 : {
2550 1195644 : nmultiple++;
2551 1195644 : if (track_cnt < num_mcv ||
2552 510500 : dups_cnt > track[track_cnt - 1].count)
2553 : {
2554 : /*
2555 : * Found a new item for the mcv list; find its
2556 : * position, bubbling down old items if needed. Loop
2557 : * invariant is that j points at an empty/ replaceable
2558 : * slot.
2559 : */
2560 : int j;
2561 :
2562 788442 : if (track_cnt < num_mcv)
2563 685144 : track_cnt++;
2564 10489196 : for (j = track_cnt - 1; j > 0; j--)
2565 : {
2566 10402732 : if (dups_cnt <= track[j - 1].count)
2567 701978 : break;
2568 9700754 : track[j].count = track[j - 1].count;
2569 9700754 : track[j].first = track[j - 1].first;
2570 : }
2571 788442 : track[j].count = dups_cnt;
2572 788442 : track[j].first = i + 1 - dups_cnt;
2573 : }
2574 : }
2575 13812036 : dups_cnt = 0;
2576 : }
2577 : }
2578 :
2579 77558 : stats->stats_valid = true;
2580 : /* Do the simple null-frac and width stats */
2581 77558 : stats->stanullfrac = (double) null_cnt / (double) samplerows;
2582 77558 : if (is_varwidth)
2583 11260 : stats->stawidth = total_width / (double) nonnull_cnt;
2584 : else
2585 66298 : stats->stawidth = stats->attrtype->typlen;
2586 :
2587 77558 : if (nmultiple == 0)
2588 : {
2589 : /*
2590 : * If we found no repeated non-null values, assume it's a unique
2591 : * column; but be sure to discount for any nulls we found.
2592 : */
2593 20182 : stats->stadistinct = -1.0 * (1.0 - stats->stanullfrac);
2594 : }
2595 57376 : else if (toowide_cnt == 0 && nmultiple == ndistinct)
2596 : {
2597 : /*
2598 : * Every value in the sample appeared more than once. Assume the
2599 : * column has just these values. (This case is meant to address
2600 : * columns with small, fixed sets of possible values, such as
2601 : * boolean or enum columns. If there are any values that appear
2602 : * just once in the sample, including too-wide values, we should
2603 : * assume that that's not what we're dealing with.)
2604 : */
2605 35030 : stats->stadistinct = ndistinct;
2606 : }
2607 : else
2608 : {
2609 : /*----------
2610 : * Estimate the number of distinct values using the estimator
2611 : * proposed by Haas and Stokes in IBM Research Report RJ 10025:
2612 : * n*d / (n - f1 + f1*n/N)
2613 : * where f1 is the number of distinct values that occurred
2614 : * exactly once in our sample of n rows (from a total of N),
2615 : * and d is the total number of distinct values in the sample.
2616 : * This is their Duj1 estimator; the other estimators they
2617 : * recommend are considerably more complex, and are numerically
2618 : * very unstable when n is much smaller than N.
2619 : *
2620 : * In this calculation, we consider only non-nulls. We used to
2621 : * include rows with null values in the n and N counts, but that
2622 : * leads to inaccurate answers in columns with many nulls, and
2623 : * it's intuitively bogus anyway considering the desired result is
2624 : * the number of distinct non-null values.
2625 : *
2626 : * Overwidth values are assumed to have been distinct.
2627 : *----------
2628 : */
2629 22346 : int f1 = ndistinct - nmultiple + toowide_cnt;
2630 22346 : int d = f1 + nmultiple;
2631 22346 : double n = samplerows - null_cnt;
2632 22346 : double N = totalrows * (1.0 - stats->stanullfrac);
2633 : double stadistinct;
2634 :
2635 : /* N == 0 shouldn't happen, but just in case ... */
2636 22346 : if (N > 0)
2637 22346 : stadistinct = (n * d) / ((n - f1) + f1 * n / N);
2638 : else
2639 0 : stadistinct = 0;
2640 :
2641 : /* Clamp to sane range in case of roundoff error */
2642 22346 : if (stadistinct < d)
2643 806 : stadistinct = d;
2644 22346 : if (stadistinct > N)
2645 0 : stadistinct = N;
2646 : /* And round to integer */
2647 22346 : stats->stadistinct = floor(stadistinct + 0.5);
2648 : }
2649 :
2650 : /*
2651 : * If we estimated the number of distinct values at more than 10% of
2652 : * the total row count (a very arbitrary limit), then assume that
2653 : * stadistinct should scale with the row count rather than be a fixed
2654 : * value.
2655 : */
2656 77558 : if (stats->stadistinct > 0.1 * totalrows)
2657 16532 : stats->stadistinct = -(stats->stadistinct / totalrows);
2658 :
2659 : /*
2660 : * Decide how many values are worth storing as most-common values. If
2661 : * we are able to generate a complete MCV list (all the values in the
2662 : * sample will fit, and we think these are all the ones in the table),
2663 : * then do so. Otherwise, store only those values that are
2664 : * significantly more common than the values not in the list.
2665 : *
2666 : * Note: the first of these cases is meant to address columns with
2667 : * small, fixed sets of possible values, such as boolean or enum
2668 : * columns. If we can *completely* represent the column population by
2669 : * an MCV list that will fit into the stats target, then we should do
2670 : * so and thus provide the planner with complete information. But if
2671 : * the MCV list is not complete, it's generally worth being more
2672 : * selective, and not just filling it all the way up to the stats
2673 : * target.
2674 : */
2675 77558 : if (track_cnt == ndistinct && toowide_cnt == 0 &&
2676 34338 : stats->stadistinct > 0 &&
2677 : track_cnt <= num_mcv)
2678 : {
2679 : /* Track list includes all values seen, and all will fit */
2680 30770 : num_mcv = track_cnt;
2681 : }
2682 : else
2683 : {
2684 : int *mcv_counts;
2685 :
2686 : /* Incomplete list; decide how many values are worth keeping */
2687 46788 : if (num_mcv > track_cnt)
2688 42802 : num_mcv = track_cnt;
2689 :
2690 46788 : if (num_mcv > 0)
2691 : {
2692 26606 : mcv_counts = (int *) palloc(num_mcv * sizeof(int));
2693 547914 : for (i = 0; i < num_mcv; i++)
2694 521308 : mcv_counts[i] = track[i].count;
2695 :
2696 26606 : num_mcv = analyze_mcv_list(mcv_counts, num_mcv,
2697 26606 : stats->stadistinct,
2698 26606 : stats->stanullfrac,
2699 : samplerows, totalrows);
2700 : }
2701 : }
2702 :
2703 : /* Generate MCV slot entry */
2704 77558 : if (num_mcv > 0)
2705 : {
2706 : MemoryContext old_context;
2707 : Datum *mcv_values;
2708 : float4 *mcv_freqs;
2709 :
2710 : /* Must copy the target values into anl_context */
2711 57330 : old_context = MemoryContextSwitchTo(stats->anl_context);
2712 57330 : mcv_values = (Datum *) palloc(num_mcv * sizeof(Datum));
2713 57330 : mcv_freqs = (float4 *) palloc(num_mcv * sizeof(float4));
2714 742266 : for (i = 0; i < num_mcv; i++)
2715 : {
2716 1369872 : mcv_values[i] = datumCopy(values[track[i].first].value,
2717 684936 : stats->attrtype->typbyval,
2718 684936 : stats->attrtype->typlen);
2719 684936 : mcv_freqs[i] = (double) track[i].count / (double) samplerows;
2720 : }
2721 57330 : MemoryContextSwitchTo(old_context);
2722 :
2723 57330 : stats->stakind[slot_idx] = STATISTIC_KIND_MCV;
2724 57330 : stats->staop[slot_idx] = mystats->eqopr;
2725 57330 : stats->stacoll[slot_idx] = stats->attrcollid;
2726 57330 : stats->stanumbers[slot_idx] = mcv_freqs;
2727 57330 : stats->numnumbers[slot_idx] = num_mcv;
2728 57330 : stats->stavalues[slot_idx] = mcv_values;
2729 57330 : stats->numvalues[slot_idx] = num_mcv;
2730 :
2731 : /*
2732 : * Accept the defaults for stats->statypid and others. They have
2733 : * been set before we were called (see vacuum.h)
2734 : */
2735 57330 : slot_idx++;
2736 : }
2737 :
2738 : /*
2739 : * Generate a histogram slot entry if there are at least two distinct
2740 : * values not accounted for in the MCV list. (This ensures the
2741 : * histogram won't collapse to empty or a singleton.)
2742 : */
2743 77558 : num_hist = ndistinct - num_mcv;
2744 77558 : if (num_hist > num_bins)
2745 12670 : num_hist = num_bins + 1;
2746 77558 : if (num_hist >= 2)
2747 : {
2748 : MemoryContext old_context;
2749 : Datum *hist_values;
2750 : int nvals;
2751 : int pos,
2752 : posfrac,
2753 : delta,
2754 : deltafrac;
2755 :
2756 : /* Sort the MCV items into position order to speed next loop */
2757 34374 : qsort_interruptible(track, num_mcv, sizeof(ScalarMCVItem),
2758 : compare_mcvs, NULL);
2759 :
2760 : /*
2761 : * Collapse out the MCV items from the values[] array.
2762 : *
2763 : * Note we destroy the values[] array here... but we don't need it
2764 : * for anything more. We do, however, still need values_cnt.
2765 : * nvals will be the number of remaining entries in values[].
2766 : */
2767 34374 : if (num_mcv > 0)
2768 : {
2769 : int src,
2770 : dest;
2771 : int j;
2772 :
2773 18736 : src = dest = 0;
2774 18736 : j = 0; /* index of next interesting MCV item */
2775 677660 : while (src < values_cnt)
2776 : {
2777 : int ncopy;
2778 :
2779 658924 : if (j < num_mcv)
2780 : {
2781 644668 : int first = track[j].first;
2782 :
2783 644668 : if (src >= first)
2784 : {
2785 : /* advance past this MCV item */
2786 470388 : src = first + track[j].count;
2787 470388 : j++;
2788 470388 : continue;
2789 : }
2790 174280 : ncopy = first - src;
2791 : }
2792 : else
2793 14256 : ncopy = values_cnt - src;
2794 188536 : memmove(&values[dest], &values[src],
2795 : ncopy * sizeof(ScalarItem));
2796 188536 : src += ncopy;
2797 188536 : dest += ncopy;
2798 : }
2799 18736 : nvals = dest;
2800 : }
2801 : else
2802 15638 : nvals = values_cnt;
2803 : Assert(nvals >= num_hist);
2804 :
2805 : /* Must copy the target values into anl_context */
2806 34374 : old_context = MemoryContextSwitchTo(stats->anl_context);
2807 34374 : hist_values = (Datum *) palloc(num_hist * sizeof(Datum));
2808 :
2809 : /*
2810 : * The object of this loop is to copy the first and last values[]
2811 : * entries along with evenly-spaced values in between. So the
2812 : * i'th value is values[(i * (nvals - 1)) / (num_hist - 1)]. But
2813 : * computing that subscript directly risks integer overflow when
2814 : * the stats target is more than a couple thousand. Instead we
2815 : * add (nvals - 1) / (num_hist - 1) to pos at each step, tracking
2816 : * the integral and fractional parts of the sum separately.
2817 : */
2818 34374 : delta = (nvals - 1) / (num_hist - 1);
2819 34374 : deltafrac = (nvals - 1) % (num_hist - 1);
2820 34374 : pos = posfrac = 0;
2821 :
2822 1801938 : for (i = 0; i < num_hist; i++)
2823 : {
2824 3535128 : hist_values[i] = datumCopy(values[pos].value,
2825 1767564 : stats->attrtype->typbyval,
2826 1767564 : stats->attrtype->typlen);
2827 1767564 : pos += delta;
2828 1767564 : posfrac += deltafrac;
2829 1767564 : if (posfrac >= (num_hist - 1))
2830 : {
2831 : /* fractional part exceeds 1, carry to integer part */
2832 577418 : pos++;
2833 577418 : posfrac -= (num_hist - 1);
2834 : }
2835 : }
2836 :
2837 34374 : MemoryContextSwitchTo(old_context);
2838 :
2839 34374 : stats->stakind[slot_idx] = STATISTIC_KIND_HISTOGRAM;
2840 34374 : stats->staop[slot_idx] = mystats->ltopr;
2841 34374 : stats->stacoll[slot_idx] = stats->attrcollid;
2842 34374 : stats->stavalues[slot_idx] = hist_values;
2843 34374 : stats->numvalues[slot_idx] = num_hist;
2844 :
2845 : /*
2846 : * Accept the defaults for stats->statypid and others. They have
2847 : * been set before we were called (see vacuum.h)
2848 : */
2849 34374 : slot_idx++;
2850 : }
2851 :
2852 : /* Generate a correlation entry if there are multiple values */
2853 77558 : if (values_cnt > 1)
2854 : {
2855 : MemoryContext old_context;
2856 : float4 *corrs;
2857 : double corr_xsum,
2858 : corr_x2sum;
2859 :
2860 : /* Must copy the target values into anl_context */
2861 72968 : old_context = MemoryContextSwitchTo(stats->anl_context);
2862 72968 : corrs = (float4 *) palloc(sizeof(float4));
2863 72968 : MemoryContextSwitchTo(old_context);
2864 :
2865 : /*----------
2866 : * Since we know the x and y value sets are both
2867 : * 0, 1, ..., values_cnt-1
2868 : * we have sum(x) = sum(y) =
2869 : * (values_cnt-1)*values_cnt / 2
2870 : * and sum(x^2) = sum(y^2) =
2871 : * (values_cnt-1)*values_cnt*(2*values_cnt-1) / 6.
2872 : *----------
2873 : */
2874 72968 : corr_xsum = ((double) (values_cnt - 1)) *
2875 72968 : ((double) values_cnt) / 2.0;
2876 72968 : corr_x2sum = ((double) (values_cnt - 1)) *
2877 72968 : ((double) values_cnt) * (double) (2 * values_cnt - 1) / 6.0;
2878 :
2879 : /* And the correlation coefficient reduces to */
2880 72968 : corrs[0] = (values_cnt * corr_xysum - corr_xsum * corr_xsum) /
2881 72968 : (values_cnt * corr_x2sum - corr_xsum * corr_xsum);
2882 :
2883 72968 : stats->stakind[slot_idx] = STATISTIC_KIND_CORRELATION;
2884 72968 : stats->staop[slot_idx] = mystats->ltopr;
2885 72968 : stats->stacoll[slot_idx] = stats->attrcollid;
2886 72968 : stats->stanumbers[slot_idx] = corrs;
2887 72968 : stats->numnumbers[slot_idx] = 1;
2888 72968 : slot_idx++;
2889 : }
2890 : }
2891 5458 : else if (nonnull_cnt > 0)
2892 : {
2893 : /* We found some non-null values, but they were all too wide */
2894 : Assert(nonnull_cnt == toowide_cnt);
2895 348 : stats->stats_valid = true;
2896 : /* Do the simple null-frac and width stats */
2897 348 : stats->stanullfrac = (double) null_cnt / (double) samplerows;
2898 348 : if (is_varwidth)
2899 348 : stats->stawidth = total_width / (double) nonnull_cnt;
2900 : else
2901 0 : stats->stawidth = stats->attrtype->typlen;
2902 : /* Assume all too-wide values are distinct, so it's a unique column */
2903 348 : stats->stadistinct = -1.0 * (1.0 - stats->stanullfrac);
2904 : }
2905 5110 : else if (null_cnt > 0)
2906 : {
2907 : /* We found only nulls; assume the column is entirely null */
2908 5110 : stats->stats_valid = true;
2909 5110 : stats->stanullfrac = 1.0;
2910 5110 : if (is_varwidth)
2911 4438 : stats->stawidth = 0; /* "unknown" */
2912 : else
2913 672 : stats->stawidth = stats->attrtype->typlen;
2914 5110 : stats->stadistinct = 0.0; /* "unknown" */
2915 : }
2916 :
2917 : /* We don't need to bother cleaning up any of our temporary palloc's */
2918 83016 : }
2919 :
2920 : /*
2921 : * Comparator for sorting ScalarItems
2922 : *
2923 : * Aside from sorting the items, we update the tupnoLink[] array
2924 : * whenever two ScalarItems are found to contain equal datums. The array
2925 : * is indexed by tupno; for each ScalarItem, it contains the highest
2926 : * tupno that that item's datum has been found to be equal to. This allows
2927 : * us to avoid additional comparisons in compute_scalar_stats().
2928 : */
2929 : static int
2930 611903048 : compare_scalars(const void *a, const void *b, void *arg)
2931 : {
2932 611903048 : Datum da = ((const ScalarItem *) a)->value;
2933 611903048 : int ta = ((const ScalarItem *) a)->tupno;
2934 611903048 : Datum db = ((const ScalarItem *) b)->value;
2935 611903048 : int tb = ((const ScalarItem *) b)->tupno;
2936 611903048 : CompareScalarsContext *cxt = (CompareScalarsContext *) arg;
2937 : int compare;
2938 :
2939 611903048 : compare = ApplySortComparator(da, false, db, false, cxt->ssup);
2940 611903048 : if (compare != 0)
2941 234385898 : return compare;
2942 :
2943 : /*
2944 : * The two datums are equal, so update cxt->tupnoLink[].
2945 : */
2946 377517150 : if (cxt->tupnoLink[ta] < tb)
2947 54190806 : cxt->tupnoLink[ta] = tb;
2948 377517150 : if (cxt->tupnoLink[tb] < ta)
2949 3768940 : cxt->tupnoLink[tb] = ta;
2950 :
2951 : /*
2952 : * For equal datums, sort by tupno
2953 : */
2954 377517150 : return ta - tb;
2955 : }
2956 :
2957 : /*
2958 : * Comparator for sorting ScalarMCVItems by position
2959 : */
2960 : static int
2961 2452744 : compare_mcvs(const void *a, const void *b, void *arg)
2962 : {
2963 2452744 : int da = ((const ScalarMCVItem *) a)->first;
2964 2452744 : int db = ((const ScalarMCVItem *) b)->first;
2965 :
2966 2452744 : return da - db;
2967 : }
2968 :
2969 : /*
2970 : * Analyze the list of common values in the sample and decide how many are
2971 : * worth storing in the table's MCV list.
2972 : *
2973 : * mcv_counts is assumed to be a list of the counts of the most common values
2974 : * seen in the sample, starting with the most common. The return value is the
2975 : * number that are significantly more common than the values not in the list,
2976 : * and which are therefore deemed worth storing in the table's MCV list.
2977 : */
2978 : static int
2979 27216 : analyze_mcv_list(int *mcv_counts,
2980 : int num_mcv,
2981 : double stadistinct,
2982 : double stanullfrac,
2983 : int samplerows,
2984 : double totalrows)
2985 : {
2986 : double ndistinct_table;
2987 : double sumcount;
2988 : int i;
2989 :
2990 : /*
2991 : * If the entire table was sampled, keep the whole list. This also
2992 : * protects us against division by zero in the code below.
2993 : */
2994 27216 : if (samplerows == totalrows || totalrows <= 1.0)
2995 26388 : return num_mcv;
2996 :
2997 : /* Re-extract the estimated number of distinct nonnull values in table */
2998 828 : ndistinct_table = stadistinct;
2999 828 : if (ndistinct_table < 0)
3000 152 : ndistinct_table = -ndistinct_table * totalrows;
3001 :
3002 : /*
3003 : * Exclude the least common values from the MCV list, if they are not
3004 : * significantly more common than the estimated selectivity they would
3005 : * have if they weren't in the list. All non-MCV values are assumed to be
3006 : * equally common, after taking into account the frequencies of all the
3007 : * values in the MCV list and the number of nulls (c.f. eqsel()).
3008 : *
3009 : * Here sumcount tracks the total count of all but the last (least common)
3010 : * value in the MCV list, allowing us to determine the effect of excluding
3011 : * that value from the list.
3012 : *
3013 : * Note that we deliberately do this by removing values from the full
3014 : * list, rather than starting with an empty list and adding values,
3015 : * because the latter approach can fail to add any values if all the most
3016 : * common values have around the same frequency and make up the majority
3017 : * of the table, so that the overall average frequency of all values is
3018 : * roughly the same as that of the common values. This would lead to any
3019 : * uncommon values being significantly overestimated.
3020 : */
3021 828 : sumcount = 0.0;
3022 1732 : for (i = 0; i < num_mcv - 1; i++)
3023 904 : sumcount += mcv_counts[i];
3024 :
3025 990 : while (num_mcv > 0)
3026 : {
3027 : double selec,
3028 : otherdistinct,
3029 : N,
3030 : n,
3031 : K,
3032 : variance,
3033 : stddev;
3034 :
3035 : /*
3036 : * Estimated selectivity the least common value would have if it
3037 : * wasn't in the MCV list (c.f. eqsel()).
3038 : */
3039 990 : selec = 1.0 - sumcount / samplerows - stanullfrac;
3040 990 : if (selec < 0.0)
3041 0 : selec = 0.0;
3042 990 : if (selec > 1.0)
3043 0 : selec = 1.0;
3044 990 : otherdistinct = ndistinct_table - (num_mcv - 1);
3045 990 : if (otherdistinct > 1)
3046 990 : selec /= otherdistinct;
3047 :
3048 : /*
3049 : * If the value is kept in the MCV list, its population frequency is
3050 : * assumed to equal its sample frequency. We use the lower end of a
3051 : * textbook continuity-corrected Wald-type confidence interval to
3052 : * determine if that is significantly more common than the non-MCV
3053 : * frequency --- specifically we assume the population frequency is
3054 : * highly likely to be within around 2 standard errors of the sample
3055 : * frequency, which equates to an interval of 2 standard deviations
3056 : * either side of the sample count, plus an additional 0.5 for the
3057 : * continuity correction. Since we are sampling without replacement,
3058 : * this is a hypergeometric distribution.
3059 : *
3060 : * XXX: Empirically, this approach seems to work quite well, but it
3061 : * may be worth considering more advanced techniques for estimating
3062 : * the confidence interval of the hypergeometric distribution.
3063 : */
3064 990 : N = totalrows;
3065 990 : n = samplerows;
3066 990 : K = N * mcv_counts[num_mcv - 1] / n;
3067 990 : variance = n * K * (N - K) * (N - n) / (N * N * (N - 1));
3068 990 : stddev = sqrt(variance);
3069 :
3070 990 : if (mcv_counts[num_mcv - 1] > selec * samplerows + 2 * stddev + 0.5)
3071 : {
3072 : /*
3073 : * The value is significantly more common than the non-MCV
3074 : * selectivity would suggest. Keep it, and all the other more
3075 : * common values in the list.
3076 : */
3077 774 : break;
3078 : }
3079 : else
3080 : {
3081 : /* Discard this value and consider the next least common value */
3082 216 : num_mcv--;
3083 216 : if (num_mcv == 0)
3084 54 : break;
3085 162 : sumcount -= mcv_counts[num_mcv - 1];
3086 : }
3087 : }
3088 828 : return num_mcv;
3089 : }
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