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mcv.c
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1 /*-------------------------------------------------------------------------
2  *
3  * mcv.c
4  * POSTGRES multivariate MCV lists
5  *
6  *
7  * Portions Copyright (c) 1996-2021, PostgreSQL Global Development Group
8  * Portions Copyright (c) 1994, Regents of the University of California
9  *
10  * IDENTIFICATION
11  * src/backend/statistics/mcv.c
12  *
13  *-------------------------------------------------------------------------
14  */
15 #include "postgres.h"
16 
17 #include <math.h>
18 
19 #include "access/htup_details.h"
20 #include "catalog/pg_collation.h"
23 #include "fmgr.h"
24 #include "funcapi.h"
25 #include "nodes/nodeFuncs.h"
26 #include "optimizer/clauses.h"
28 #include "statistics/statistics.h"
29 #include "utils/array.h"
30 #include "utils/builtins.h"
31 #include "utils/bytea.h"
32 #include "utils/fmgroids.h"
33 #include "utils/fmgrprotos.h"
34 #include "utils/lsyscache.h"
35 #include "utils/selfuncs.h"
36 #include "utils/syscache.h"
37 #include "utils/typcache.h"
38 
39 /*
40  * Computes size of a serialized MCV item, depending on the number of
41  * dimensions (columns) the statistic is defined on. The datum values are
42  * stored in a separate array (deduplicated, to minimize the size), and
43  * so the serialized items only store uint16 indexes into that array.
44  *
45  * Each serialized item stores (in this order):
46  *
47  * - indexes to values (ndim * sizeof(uint16))
48  * - null flags (ndim * sizeof(bool))
49  * - frequency (sizeof(double))
50  * - base_frequency (sizeof(double))
51  *
52  * There is no alignment padding within an MCV item.
53  * So in total each MCV item requires this many bytes:
54  *
55  * ndim * (sizeof(uint16) + sizeof(bool)) + 2 * sizeof(double)
56  */
57 #define ITEM_SIZE(ndims) \
58  ((ndims) * (sizeof(uint16) + sizeof(bool)) + 2 * sizeof(double))
59 
60 /*
61  * Used to compute size of serialized MCV list representation.
62  */
63 #define MinSizeOfMCVList \
64  (VARHDRSZ + sizeof(uint32) * 3 + sizeof(AttrNumber))
65 
66 /*
67  * Size of the serialized MCV list, excluding the space needed for
68  * deduplicated per-dimension values. The macro is meant to be used
69  * when it's not yet safe to access the serialized info about amount
70  * of data for each column.
71  */
72 #define SizeOfMCVList(ndims,nitems) \
73  ((MinSizeOfMCVList + sizeof(Oid) * (ndims)) + \
74  ((ndims) * sizeof(DimensionInfo)) + \
75  ((nitems) * ITEM_SIZE(ndims)))
76 
78 
79 static SortItem *build_distinct_groups(int numrows, SortItem *items,
80  MultiSortSupport mss, int *ndistinct);
81 
82 static SortItem **build_column_frequencies(SortItem *groups, int ngroups,
83  MultiSortSupport mss, int *ncounts);
84 
85 static int count_distinct_groups(int numrows, SortItem *items,
86  MultiSortSupport mss);
87 
88 /*
89  * Compute new value for bitmap item, considering whether it's used for
90  * clauses connected by AND/OR.
91  */
92 #define RESULT_MERGE(value, is_or, match) \
93  ((is_or) ? ((value) || (match)) : ((value) && (match)))
94 
95 /*
96  * When processing a list of clauses, the bitmap item may get set to a value
97  * such that additional clauses can't change it. For example, when processing
98  * a list of clauses connected to AND, as soon as the item gets set to 'false'
99  * then it'll remain like that. Similarly clauses connected by OR and 'true'.
100  *
101  * Returns true when the value in the bitmap can't change no matter how the
102  * remaining clauses are evaluated.
103  */
104 #define RESULT_IS_FINAL(value, is_or) ((is_or) ? (value) : (!(value)))
105 
106 /*
107  * get_mincount_for_mcv_list
108  * Determine the minimum number of times a value needs to appear in
109  * the sample for it to be included in the MCV list.
110  *
111  * We want to keep only values that appear sufficiently often in the
112  * sample that it is reasonable to extrapolate their sample frequencies to
113  * the entire table. We do this by placing an upper bound on the relative
114  * standard error of the sample frequency, so that any estimates the
115  * planner generates from the MCV statistics can be expected to be
116  * reasonably accurate.
117  *
118  * Since we are sampling without replacement, the sample frequency of a
119  * particular value is described by a hypergeometric distribution. A
120  * common rule of thumb when estimating errors in this situation is to
121  * require at least 10 instances of the value in the sample, in which case
122  * the distribution can be approximated by a normal distribution, and
123  * standard error analysis techniques can be applied. Given a sample size
124  * of n, a population size of N, and a sample frequency of p=cnt/n, the
125  * standard error of the proportion p is given by
126  * SE = sqrt(p*(1-p)/n) * sqrt((N-n)/(N-1))
127  * where the second term is the finite population correction. To get
128  * reasonably accurate planner estimates, we impose an upper bound on the
129  * relative standard error of 20% -- i.e., SE/p < 0.2. This 20% relative
130  * error bound is fairly arbitrary, but has been found empirically to work
131  * well. Rearranging this formula gives a lower bound on the number of
132  * instances of the value seen:
133  * cnt > n*(N-n) / (N-n+0.04*n*(N-1))
134  * This bound is at most 25, and approaches 0 as n approaches 0 or N. The
135  * case where n approaches 0 cannot happen in practice, since the sample
136  * size is at least 300. The case where n approaches N corresponds to
137  * sampling the whole the table, in which case it is reasonable to keep
138  * the whole MCV list (have no lower bound), so it makes sense to apply
139  * this formula for all inputs, even though the above derivation is
140  * technically only valid when the right hand side is at least around 10.
141  *
142  * An alternative way to look at this formula is as follows -- assume that
143  * the number of instances of the value seen scales up to the entire
144  * table, so that the population count is K=N*cnt/n. Then the distribution
145  * in the sample is a hypergeometric distribution parameterised by N, n
146  * and K, and the bound above is mathematically equivalent to demanding
147  * that the standard deviation of that distribution is less than 20% of
148  * its mean. Thus the relative errors in any planner estimates produced
149  * from the MCV statistics are likely to be not too large.
150  */
151 static double
152 get_mincount_for_mcv_list(int samplerows, double totalrows)
153 {
154  double n = samplerows;
155  double N = totalrows;
156  double numer,
157  denom;
158 
159  numer = n * (N - n);
160  denom = N - n + 0.04 * n * (N - 1);
161 
162  /* Guard against division by zero (possible if n = N = 1) */
163  if (denom == 0.0)
164  return 0.0;
165 
166  return numer / denom;
167 }
168 
169 /*
170  * Builds MCV list from the set of sampled rows.
171  *
172  * The algorithm is quite simple:
173  *
174  * (1) sort the data (default collation, '<' for the data type)
175  *
176  * (2) count distinct groups, decide how many to keep
177  *
178  * (3) build the MCV list using the threshold determined in (2)
179  *
180  * (4) remove rows represented by the MCV from the sample
181  *
182  */
183 MCVList *
184 statext_mcv_build(StatsBuildData *data, double totalrows, int stattarget)
185 {
186  int i,
187  numattrs,
188  numrows,
189  ngroups,
190  nitems;
191  double mincount;
192  SortItem *items;
193  SortItem *groups;
194  MCVList *mcvlist = NULL;
195  MultiSortSupport mss;
196 
197  /* comparator for all the columns */
198  mss = build_mss(data);
199 
200  /* sort the rows */
201  items = build_sorted_items(data, &nitems, mss,
202  data->nattnums, data->attnums);
203 
204  if (!items)
205  return NULL;
206 
207  /* for convenience */
208  numattrs = data->nattnums;
209  numrows = data->numrows;
210 
211  /* transform the sorted rows into groups (sorted by frequency) */
212  groups = build_distinct_groups(nitems, items, mss, &ngroups);
213 
214  /*
215  * The maximum number of MCV items to store, based on the statistics
216  * target we computed for the statistics object (from the target set for
217  * the object itself, attributes and the system default). In any case, we
218  * can't keep more groups than we have available.
219  */
220  nitems = stattarget;
221  if (nitems > ngroups)
222  nitems = ngroups;
223 
224  /*
225  * Decide how many items to keep in the MCV list. We can't use the same
226  * algorithm as per-column MCV lists, because that only considers the
227  * actual group frequency - but we're primarily interested in how the
228  * actual frequency differs from the base frequency (product of simple
229  * per-column frequencies, as if the columns were independent).
230  *
231  * Using the same algorithm might exclude items that are close to the
232  * "average" frequency of the sample. But that does not say whether the
233  * observed frequency is close to the base frequency or not. We also need
234  * to consider unexpectedly uncommon items (again, compared to the base
235  * frequency), and the single-column algorithm does not have to.
236  *
237  * We simply decide how many items to keep by computing the minimum count
238  * using get_mincount_for_mcv_list() and then keep all items that seem to
239  * be more common than that.
240  */
241  mincount = get_mincount_for_mcv_list(numrows, totalrows);
242 
243  /*
244  * Walk the groups until we find the first group with a count below the
245  * mincount threshold (the index of that group is the number of groups we
246  * want to keep).
247  */
248  for (i = 0; i < nitems; i++)
249  {
250  if (groups[i].count < mincount)
251  {
252  nitems = i;
253  break;
254  }
255  }
256 
257  /*
258  * At this point, we know the number of items for the MCV list. There
259  * might be none (for uniform distribution with many groups), and in that
260  * case, there will be no MCV list. Otherwise, construct the MCV list.
261  */
262  if (nitems > 0)
263  {
264  int j;
265  SortItem key;
266  MultiSortSupport tmp;
267 
268  /* frequencies for values in each attribute */
269  SortItem **freqs;
270  int *nfreqs;
271 
272  /* used to search values */
274  + sizeof(SortSupportData));
275 
276  /* compute frequencies for values in each column */
277  nfreqs = (int *) palloc0(sizeof(int) * numattrs);
278  freqs = build_column_frequencies(groups, ngroups, mss, nfreqs);
279 
280  /*
281  * Allocate the MCV list structure, set the global parameters.
282  */
283  mcvlist = (MCVList *) palloc0(offsetof(MCVList, items) +
284  sizeof(MCVItem) * nitems);
285 
286  mcvlist->magic = STATS_MCV_MAGIC;
287  mcvlist->type = STATS_MCV_TYPE_BASIC;
288  mcvlist->ndimensions = numattrs;
289  mcvlist->nitems = nitems;
290 
291  /* store info about data type OIDs */
292  for (i = 0; i < numattrs; i++)
293  mcvlist->types[i] = data->stats[i]->attrtypid;
294 
295  /* Copy the first chunk of groups into the result. */
296  for (i = 0; i < nitems; i++)
297  {
298  /* just pointer to the proper place in the list */
299  MCVItem *item = &mcvlist->items[i];
300 
301  item->values = (Datum *) palloc(sizeof(Datum) * numattrs);
302  item->isnull = (bool *) palloc(sizeof(bool) * numattrs);
303 
304  /* copy values for the group */
305  memcpy(item->values, groups[i].values, sizeof(Datum) * numattrs);
306  memcpy(item->isnull, groups[i].isnull, sizeof(bool) * numattrs);
307 
308  /* groups should be sorted by frequency in descending order */
309  Assert((i == 0) || (groups[i - 1].count >= groups[i].count));
310 
311  /* group frequency */
312  item->frequency = (double) groups[i].count / numrows;
313 
314  /* base frequency, if the attributes were independent */
315  item->base_frequency = 1.0;
316  for (j = 0; j < numattrs; j++)
317  {
318  SortItem *freq;
319 
320  /* single dimension */
321  tmp->ndims = 1;
322  tmp->ssup[0] = mss->ssup[j];
323 
324  /* fill search key */
325  key.values = &groups[i].values[j];
326  key.isnull = &groups[i].isnull[j];
327 
328  freq = (SortItem *) bsearch_arg(&key, freqs[j], nfreqs[j],
329  sizeof(SortItem),
330  multi_sort_compare, tmp);
331 
332  item->base_frequency *= ((double) freq->count) / numrows;
333  }
334  }
335 
336  pfree(nfreqs);
337  pfree(freqs);
338  }
339 
340  pfree(items);
341  pfree(groups);
342 
343  return mcvlist;
344 }
345 
346 /*
347  * build_mss
348  * Build a MultiSortSupport for the given StatsBuildData.
349  */
350 static MultiSortSupport
352 {
353  int i;
354  int numattrs = data->nattnums;
355 
356  /* Sort by multiple columns (using array of SortSupport) */
357  MultiSortSupport mss = multi_sort_init(numattrs);
358 
359  /* prepare the sort functions for all the attributes */
360  for (i = 0; i < numattrs; i++)
361  {
362  VacAttrStats *colstat = data->stats[i];
364 
365  type = lookup_type_cache(colstat->attrtypid, TYPECACHE_LT_OPR);
366  if (type->lt_opr == InvalidOid) /* shouldn't happen */
367  elog(ERROR, "cache lookup failed for ordering operator for type %u",
368  colstat->attrtypid);
369 
370  multi_sort_add_dimension(mss, i, type->lt_opr, colstat->attrcollid);
371  }
372 
373  return mss;
374 }
375 
376 /*
377  * count_distinct_groups
378  * Count distinct combinations of SortItems in the array.
379  *
380  * The array is assumed to be sorted according to the MultiSortSupport.
381  */
382 static int
384 {
385  int i;
386  int ndistinct;
387 
388  ndistinct = 1;
389  for (i = 1; i < numrows; i++)
390  {
391  /* make sure the array really is sorted */
392  Assert(multi_sort_compare(&items[i], &items[i - 1], mss) >= 0);
393 
394  if (multi_sort_compare(&items[i], &items[i - 1], mss) != 0)
395  ndistinct += 1;
396  }
397 
398  return ndistinct;
399 }
400 
401 /*
402  * compare_sort_item_count
403  * Comparator for sorting items by count (frequencies) in descending
404  * order.
405  */
406 static int
407 compare_sort_item_count(const void *a, const void *b)
408 {
409  SortItem *ia = (SortItem *) a;
410  SortItem *ib = (SortItem *) b;
411 
412  if (ia->count == ib->count)
413  return 0;
414  else if (ia->count > ib->count)
415  return -1;
416 
417  return 1;
418 }
419 
420 /*
421  * build_distinct_groups
422  * Build an array of SortItems for distinct groups and counts matching
423  * items.
424  *
425  * The 'items' array is assumed to be sorted.
426  */
427 static SortItem *
429  int *ndistinct)
430 {
431  int i,
432  j;
433  int ngroups = count_distinct_groups(numrows, items, mss);
434 
435  SortItem *groups = (SortItem *) palloc(ngroups * sizeof(SortItem));
436 
437  j = 0;
438  groups[0] = items[0];
439  groups[0].count = 1;
440 
441  for (i = 1; i < numrows; i++)
442  {
443  /* Assume sorted in ascending order. */
444  Assert(multi_sort_compare(&items[i], &items[i - 1], mss) >= 0);
445 
446  /* New distinct group detected. */
447  if (multi_sort_compare(&items[i], &items[i - 1], mss) != 0)
448  {
449  groups[++j] = items[i];
450  groups[j].count = 0;
451  }
452 
453  groups[j].count++;
454  }
455 
456  /* ensure we filled the expected number of distinct groups */
457  Assert(j + 1 == ngroups);
458 
459  /* Sort the distinct groups by frequency (in descending order). */
460  pg_qsort((void *) groups, ngroups, sizeof(SortItem),
462 
463  *ndistinct = ngroups;
464  return groups;
465 }
466 
467 /* compare sort items (single dimension) */
468 static int
469 sort_item_compare(const void *a, const void *b, void *arg)
470 {
471  SortSupport ssup = (SortSupport) arg;
472  SortItem *ia = (SortItem *) a;
473  SortItem *ib = (SortItem *) b;
474 
475  return ApplySortComparator(ia->values[0], ia->isnull[0],
476  ib->values[0], ib->isnull[0],
477  ssup);
478 }
479 
480 /*
481  * build_column_frequencies
482  * Compute frequencies of values in each column.
483  *
484  * This returns an array of SortItems for each attribute the MCV is built
485  * on, with a frequency (number of occurrences) for each value. This is
486  * then used to compute "base" frequency of MCV items.
487  *
488  * All the memory is allocated in a single chunk, so that a single pfree
489  * is enough to release it. We do not allocate space for values/isnull
490  * arrays in the SortItems, because we can simply point into the input
491  * groups directly.
492  */
493 static SortItem **
494 build_column_frequencies(SortItem *groups, int ngroups,
495  MultiSortSupport mss, int *ncounts)
496 {
497  int i,
498  dim;
499  SortItem **result;
500  char *ptr;
501 
502  Assert(groups);
503  Assert(ncounts);
504 
505  /* allocate arrays for all columns as a single chunk */
506  ptr = palloc(MAXALIGN(sizeof(SortItem *) * mss->ndims) +
507  mss->ndims * MAXALIGN(sizeof(SortItem) * ngroups));
508 
509  /* initial array of pointers */
510  result = (SortItem **) ptr;
511  ptr += MAXALIGN(sizeof(SortItem *) * mss->ndims);
512 
513  for (dim = 0; dim < mss->ndims; dim++)
514  {
515  SortSupport ssup = &mss->ssup[dim];
516 
517  /* array of values for a single column */
518  result[dim] = (SortItem *) ptr;
519  ptr += MAXALIGN(sizeof(SortItem) * ngroups);
520 
521  /* extract data for the dimension */
522  for (i = 0; i < ngroups; i++)
523  {
524  /* point into the input groups */
525  result[dim][i].values = &groups[i].values[dim];
526  result[dim][i].isnull = &groups[i].isnull[dim];
527  result[dim][i].count = groups[i].count;
528  }
529 
530  /* sort the values, deduplicate */
531  qsort_arg((void *) result[dim], ngroups, sizeof(SortItem),
532  sort_item_compare, ssup);
533 
534  /*
535  * Identify distinct values, compute frequency (there might be
536  * multiple MCV items containing this value, so we need to sum counts
537  * from all of them.
538  */
539  ncounts[dim] = 1;
540  for (i = 1; i < ngroups; i++)
541  {
542  if (sort_item_compare(&result[dim][i - 1], &result[dim][i], ssup) == 0)
543  {
544  result[dim][ncounts[dim] - 1].count += result[dim][i].count;
545  continue;
546  }
547 
548  result[dim][ncounts[dim]] = result[dim][i];
549 
550  ncounts[dim]++;
551  }
552  }
553 
554  return result;
555 }
556 
557 /*
558  * statext_mcv_load
559  * Load the MCV list for the indicated pg_statistic_ext tuple.
560  */
561 MCVList *
563 {
564  MCVList *result;
565  bool isnull;
566  Datum mcvlist;
568 
569  if (!HeapTupleIsValid(htup))
570  elog(ERROR, "cache lookup failed for statistics object %u", mvoid);
571 
572  mcvlist = SysCacheGetAttr(STATEXTDATASTXOID, htup,
573  Anum_pg_statistic_ext_data_stxdmcv, &isnull);
574 
575  if (isnull)
576  elog(ERROR,
577  "requested statistics kind \"%c\" is not yet built for statistics object %u",
578  STATS_EXT_DEPENDENCIES, mvoid);
579 
580  result = statext_mcv_deserialize(DatumGetByteaP(mcvlist));
581 
582  ReleaseSysCache(htup);
583 
584  return result;
585 }
586 
587 
588 /*
589  * statext_mcv_serialize
590  * Serialize MCV list into a pg_mcv_list value.
591  *
592  * The MCV items may include values of various data types, and it's reasonable
593  * to expect redundancy (values for a given attribute, repeated for multiple
594  * MCV list items). So we deduplicate the values into arrays, and then replace
595  * the values by indexes into those arrays.
596  *
597  * The overall structure of the serialized representation looks like this:
598  *
599  * +---------------+----------------+---------------------+-------+
600  * | header fields | dimension info | deduplicated values | items |
601  * +---------------+----------------+---------------------+-------+
602  *
603  * Where dimension info stores information about the type of the K-th
604  * attribute (e.g. typlen, typbyval and length of deduplicated values).
605  * Deduplicated values store deduplicated values for each attribute. And
606  * items store the actual MCV list items, with values replaced by indexes into
607  * the arrays.
608  *
609  * When serializing the items, we use uint16 indexes. The number of MCV items
610  * is limited by the statistics target (which is capped to 10k at the moment).
611  * We might increase this to 65k and still fit into uint16, so there's a bit of
612  * slack. Furthermore, this limit is on the number of distinct values per column,
613  * and we usually have few of those (and various combinations of them for the
614  * those MCV list). So uint16 seems fine for now.
615  *
616  * We don't really expect the serialization to save as much space as for
617  * histograms, as we are not doing any bucket splits (which is the source
618  * of high redundancy in histograms).
619  *
620  * TODO: Consider packing boolean flags (NULL) for each item into a single char
621  * (or a longer type) instead of using an array of bool items.
622  */
623 bytea *
625 {
626  int i;
627  int dim;
628  int ndims = mcvlist->ndimensions;
629 
630  SortSupport ssup;
631  DimensionInfo *info;
632 
633  Size total_length;
634 
635  /* serialized items (indexes into arrays, etc.) */
636  bytea *raw;
637  char *ptr;
638  char *endptr PG_USED_FOR_ASSERTS_ONLY;
639 
640  /* values per dimension (and number of non-NULL values) */
641  Datum **values = (Datum **) palloc0(sizeof(Datum *) * ndims);
642  int *counts = (int *) palloc0(sizeof(int) * ndims);
643 
644  /*
645  * We'll include some rudimentary information about the attribute types
646  * (length, by-val flag), so that we don't have to look them up while
647  * deserializing the MCV list (we already have the type OID in the
648  * header). This is safe because when changing the type of the attribute
649  * the statistics gets dropped automatically. We need to store the info
650  * about the arrays of deduplicated values anyway.
651  */
652  info = (DimensionInfo *) palloc0(sizeof(DimensionInfo) * ndims);
653 
654  /* sort support data for all attributes included in the MCV list */
655  ssup = (SortSupport) palloc0(sizeof(SortSupportData) * ndims);
656 
657  /* collect and deduplicate values for each dimension (attribute) */
658  for (dim = 0; dim < ndims; dim++)
659  {
660  int ndistinct;
661  TypeCacheEntry *typentry;
662 
663  /*
664  * Lookup the LT operator (can't get it from stats extra_data, as we
665  * don't know how to interpret that - scalar vs. array etc.).
666  */
667  typentry = lookup_type_cache(stats[dim]->attrtypid, TYPECACHE_LT_OPR);
668 
669  /* copy important info about the data type (length, by-value) */
670  info[dim].typlen = stats[dim]->attrtype->typlen;
671  info[dim].typbyval = stats[dim]->attrtype->typbyval;
672 
673  /* allocate space for values in the attribute and collect them */
674  values[dim] = (Datum *) palloc0(sizeof(Datum) * mcvlist->nitems);
675 
676  for (i = 0; i < mcvlist->nitems; i++)
677  {
678  /* skip NULL values - we don't need to deduplicate those */
679  if (mcvlist->items[i].isnull[dim])
680  continue;
681 
682  /* append the value at the end */
683  values[dim][counts[dim]] = mcvlist->items[i].values[dim];
684  counts[dim] += 1;
685  }
686 
687  /* if there are just NULL values in this dimension, we're done */
688  if (counts[dim] == 0)
689  continue;
690 
691  /* sort and deduplicate the data */
692  ssup[dim].ssup_cxt = CurrentMemoryContext;
693  ssup[dim].ssup_collation = stats[dim]->attrcollid;
694  ssup[dim].ssup_nulls_first = false;
695 
696  PrepareSortSupportFromOrderingOp(typentry->lt_opr, &ssup[dim]);
697 
698  qsort_arg(values[dim], counts[dim], sizeof(Datum),
699  compare_scalars_simple, &ssup[dim]);
700 
701  /*
702  * Walk through the array and eliminate duplicate values, but keep the
703  * ordering (so that we can do a binary search later). We know there's
704  * at least one item as (counts[dim] != 0), so we can skip the first
705  * element.
706  */
707  ndistinct = 1; /* number of distinct values */
708  for (i = 1; i < counts[dim]; i++)
709  {
710  /* expect sorted array */
711  Assert(compare_datums_simple(values[dim][i - 1], values[dim][i], &ssup[dim]) <= 0);
712 
713  /* if the value is the same as the previous one, we can skip it */
714  if (!compare_datums_simple(values[dim][i - 1], values[dim][i], &ssup[dim]))
715  continue;
716 
717  values[dim][ndistinct] = values[dim][i];
718  ndistinct += 1;
719  }
720 
721  /* we must not exceed PG_UINT16_MAX, as we use uint16 indexes */
722  Assert(ndistinct <= PG_UINT16_MAX);
723 
724  /*
725  * Store additional info about the attribute - number of deduplicated
726  * values, and also size of the serialized data. For fixed-length data
727  * types this is trivial to compute, for varwidth types we need to
728  * actually walk the array and sum the sizes.
729  */
730  info[dim].nvalues = ndistinct;
731 
732  if (info[dim].typbyval) /* by-value data types */
733  {
734  info[dim].nbytes = info[dim].nvalues * info[dim].typlen;
735 
736  /*
737  * We copy the data into the MCV item during deserialization, so
738  * we don't need to allocate any extra space.
739  */
740  info[dim].nbytes_aligned = 0;
741  }
742  else if (info[dim].typlen > 0) /* fixed-length by-ref */
743  {
744  /*
745  * We don't care about alignment in the serialized data, so we
746  * pack the data as much as possible. But we also track how much
747  * data will be needed after deserialization, and in that case we
748  * need to account for alignment of each item.
749  *
750  * Note: As the items are fixed-length, we could easily compute
751  * this during deserialization, but we do it here anyway.
752  */
753  info[dim].nbytes = info[dim].nvalues * info[dim].typlen;
754  info[dim].nbytes_aligned = info[dim].nvalues * MAXALIGN(info[dim].typlen);
755  }
756  else if (info[dim].typlen == -1) /* varlena */
757  {
758  info[dim].nbytes = 0;
759  info[dim].nbytes_aligned = 0;
760  for (i = 0; i < info[dim].nvalues; i++)
761  {
762  Size len;
763 
764  /*
765  * For varlena values, we detoast the values and store the
766  * length and data separately. We don't bother with alignment
767  * here, which means that during deserialization we need to
768  * copy the fields and only access the copies.
769  */
770  values[dim][i] = PointerGetDatum(PG_DETOAST_DATUM(values[dim][i]));
771 
772  /* serialized length (uint32 length + data) */
773  len = VARSIZE_ANY_EXHDR(values[dim][i]);
774  info[dim].nbytes += sizeof(uint32); /* length */
775  info[dim].nbytes += len; /* value (no header) */
776 
777  /*
778  * During deserialization we'll build regular varlena values
779  * with full headers, and we need to align them properly.
780  */
781  info[dim].nbytes_aligned += MAXALIGN(VARHDRSZ + len);
782  }
783  }
784  else if (info[dim].typlen == -2) /* cstring */
785  {
786  info[dim].nbytes = 0;
787  info[dim].nbytes_aligned = 0;
788  for (i = 0; i < info[dim].nvalues; i++)
789  {
790  Size len;
791 
792  /*
793  * cstring is handled similar to varlena - first we store the
794  * length as uint32 and then the data. We don't care about
795  * alignment, which means that during deserialization we need
796  * to copy the fields and only access the copies.
797  */
798 
799  /* c-strings include terminator, so +1 byte */
800  len = strlen(DatumGetCString(values[dim][i])) + 1;
801  info[dim].nbytes += sizeof(uint32); /* length */
802  info[dim].nbytes += len; /* value */
803 
804  /* space needed for properly aligned deserialized copies */
805  info[dim].nbytes_aligned += MAXALIGN(len);
806  }
807  }
808 
809  /* we know (count>0) so there must be some data */
810  Assert(info[dim].nbytes > 0);
811  }
812 
813  /*
814  * Now we can finally compute how much space we'll actually need for the
815  * whole serialized MCV list (varlena header, MCV header, dimension info
816  * for each attribute, deduplicated values and items).
817  */
818  total_length = (3 * sizeof(uint32)) /* magic + type + nitems */
819  + sizeof(AttrNumber) /* ndimensions */
820  + (ndims * sizeof(Oid)); /* attribute types */
821 
822  /* dimension info */
823  total_length += ndims * sizeof(DimensionInfo);
824 
825  /* add space for the arrays of deduplicated values */
826  for (i = 0; i < ndims; i++)
827  total_length += info[i].nbytes;
828 
829  /*
830  * And finally account for the items (those are fixed-length, thanks to
831  * replacing values with uint16 indexes into the deduplicated arrays).
832  */
833  total_length += mcvlist->nitems * ITEM_SIZE(dim);
834 
835  /*
836  * Allocate space for the whole serialized MCV list (we'll skip bytes, so
837  * we set them to zero to make the result more compressible).
838  */
839  raw = (bytea *) palloc0(VARHDRSZ + total_length);
840  SET_VARSIZE(raw, VARHDRSZ + total_length);
841 
842  ptr = VARDATA(raw);
843  endptr = ptr + total_length;
844 
845  /* copy the MCV list header fields, one by one */
846  memcpy(ptr, &mcvlist->magic, sizeof(uint32));
847  ptr += sizeof(uint32);
848 
849  memcpy(ptr, &mcvlist->type, sizeof(uint32));
850  ptr += sizeof(uint32);
851 
852  memcpy(ptr, &mcvlist->nitems, sizeof(uint32));
853  ptr += sizeof(uint32);
854 
855  memcpy(ptr, &mcvlist->ndimensions, sizeof(AttrNumber));
856  ptr += sizeof(AttrNumber);
857 
858  memcpy(ptr, mcvlist->types, sizeof(Oid) * ndims);
859  ptr += (sizeof(Oid) * ndims);
860 
861  /* store information about the attributes (data amounts, ...) */
862  memcpy(ptr, info, sizeof(DimensionInfo) * ndims);
863  ptr += sizeof(DimensionInfo) * ndims;
864 
865  /* Copy the deduplicated values for all attributes to the output. */
866  for (dim = 0; dim < ndims; dim++)
867  {
868  /* remember the starting point for Asserts later */
869  char *start PG_USED_FOR_ASSERTS_ONLY = ptr;
870 
871  for (i = 0; i < info[dim].nvalues; i++)
872  {
873  Datum value = values[dim][i];
874 
875  if (info[dim].typbyval) /* passed by value */
876  {
877  Datum tmp;
878 
879  /*
880  * For byval types, we need to copy just the significant bytes
881  * - we can't use memcpy directly, as that assumes
882  * little-endian behavior. store_att_byval does almost what
883  * we need, but it requires a properly aligned buffer - the
884  * output buffer does not guarantee that. So we simply use a
885  * local Datum variable (which guarantees proper alignment),
886  * and then copy the value from it.
887  */
888  store_att_byval(&tmp, value, info[dim].typlen);
889 
890  memcpy(ptr, &tmp, info[dim].typlen);
891  ptr += info[dim].typlen;
892  }
893  else if (info[dim].typlen > 0) /* passed by reference */
894  {
895  /* no special alignment needed, treated as char array */
896  memcpy(ptr, DatumGetPointer(value), info[dim].typlen);
897  ptr += info[dim].typlen;
898  }
899  else if (info[dim].typlen == -1) /* varlena */
900  {
902 
903  /* copy the length */
904  memcpy(ptr, &len, sizeof(uint32));
905  ptr += sizeof(uint32);
906 
907  /* data from the varlena value (without the header) */
908  memcpy(ptr, VARDATA_ANY(DatumGetPointer(value)), len);
909  ptr += len;
910  }
911  else if (info[dim].typlen == -2) /* cstring */
912  {
913  uint32 len = (uint32) strlen(DatumGetCString(value)) + 1;
914 
915  /* copy the length */
916  memcpy(ptr, &len, sizeof(uint32));
917  ptr += sizeof(uint32);
918 
919  /* value */
920  memcpy(ptr, DatumGetCString(value), len);
921  ptr += len;
922  }
923 
924  /* no underflows or overflows */
925  Assert((ptr > start) && ((ptr - start) <= info[dim].nbytes));
926  }
927 
928  /* we should get exactly nbytes of data for this dimension */
929  Assert((ptr - start) == info[dim].nbytes);
930  }
931 
932  /* Serialize the items, with uint16 indexes instead of the values. */
933  for (i = 0; i < mcvlist->nitems; i++)
934  {
935  MCVItem *mcvitem = &mcvlist->items[i];
936 
937  /* don't write beyond the allocated space */
938  Assert(ptr <= (endptr - ITEM_SIZE(dim)));
939 
940  /* copy NULL and frequency flags into the serialized MCV */
941  memcpy(ptr, mcvitem->isnull, sizeof(bool) * ndims);
942  ptr += sizeof(bool) * ndims;
943 
944  memcpy(ptr, &mcvitem->frequency, sizeof(double));
945  ptr += sizeof(double);
946 
947  memcpy(ptr, &mcvitem->base_frequency, sizeof(double));
948  ptr += sizeof(double);
949 
950  /* store the indexes last */
951  for (dim = 0; dim < ndims; dim++)
952  {
953  uint16 index = 0;
954  Datum *value;
955 
956  /* do the lookup only for non-NULL values */
957  if (!mcvitem->isnull[dim])
958  {
959  value = (Datum *) bsearch_arg(&mcvitem->values[dim], values[dim],
960  info[dim].nvalues, sizeof(Datum),
961  compare_scalars_simple, &ssup[dim]);
962 
963  Assert(value != NULL); /* serialization or deduplication
964  * error */
965 
966  /* compute index within the deduplicated array */
967  index = (uint16) (value - values[dim]);
968 
969  /* check the index is within expected bounds */
970  Assert(index < info[dim].nvalues);
971  }
972 
973  /* copy the index into the serialized MCV */
974  memcpy(ptr, &index, sizeof(uint16));
975  ptr += sizeof(uint16);
976  }
977 
978  /* make sure we don't overflow the allocated value */
979  Assert(ptr <= endptr);
980  }
981 
982  /* at this point we expect to match the total_length exactly */
983  Assert(ptr == endptr);
984 
985  pfree(values);
986  pfree(counts);
987 
988  return raw;
989 }
990 
991 /*
992  * statext_mcv_deserialize
993  * Reads serialized MCV list into MCVList structure.
994  *
995  * All the memory needed by the MCV list is allocated as a single chunk, so
996  * it's possible to simply pfree() it at once.
997  */
998 MCVList *
1000 {
1001  int dim,
1002  i;
1003  Size expected_size;
1004  MCVList *mcvlist;
1005  char *raw;
1006  char *ptr;
1007  char *endptr PG_USED_FOR_ASSERTS_ONLY;
1008 
1009  int ndims,
1010  nitems;
1011  DimensionInfo *info = NULL;
1012 
1013  /* local allocation buffer (used only for deserialization) */
1014  Datum **map = NULL;
1015 
1016  /* MCV list */
1017  Size mcvlen;
1018 
1019  /* buffer used for the result */
1020  Size datalen;
1021  char *dataptr;
1022  char *valuesptr;
1023  char *isnullptr;
1024 
1025  if (data == NULL)
1026  return NULL;
1027 
1028  /*
1029  * We can't possibly deserialize a MCV list if there's not even a complete
1030  * header. We need an explicit formula here, because we serialize the
1031  * header fields one by one, so we need to ignore struct alignment.
1032  */
1033  if (VARSIZE_ANY(data) < MinSizeOfMCVList)
1034  elog(ERROR, "invalid MCV size %zd (expected at least %zu)",
1035  VARSIZE_ANY(data), MinSizeOfMCVList);
1036 
1037  /* read the MCV list header */
1038  mcvlist = (MCVList *) palloc0(offsetof(MCVList, items));
1039 
1040  /* pointer to the data part (skip the varlena header) */
1041  raw = (char *) data;
1042  ptr = VARDATA_ANY(raw);
1043  endptr = (char *) raw + VARSIZE_ANY(data);
1044 
1045  /* get the header and perform further sanity checks */
1046  memcpy(&mcvlist->magic, ptr, sizeof(uint32));
1047  ptr += sizeof(uint32);
1048 
1049  memcpy(&mcvlist->type, ptr, sizeof(uint32));
1050  ptr += sizeof(uint32);
1051 
1052  memcpy(&mcvlist->nitems, ptr, sizeof(uint32));
1053  ptr += sizeof(uint32);
1054 
1055  memcpy(&mcvlist->ndimensions, ptr, sizeof(AttrNumber));
1056  ptr += sizeof(AttrNumber);
1057 
1058  if (mcvlist->magic != STATS_MCV_MAGIC)
1059  elog(ERROR, "invalid MCV magic %u (expected %u)",
1060  mcvlist->magic, STATS_MCV_MAGIC);
1061 
1062  if (mcvlist->type != STATS_MCV_TYPE_BASIC)
1063  elog(ERROR, "invalid MCV type %u (expected %u)",
1064  mcvlist->type, STATS_MCV_TYPE_BASIC);
1065 
1066  if (mcvlist->ndimensions == 0)
1067  elog(ERROR, "invalid zero-length dimension array in MCVList");
1068  else if ((mcvlist->ndimensions > STATS_MAX_DIMENSIONS) ||
1069  (mcvlist->ndimensions < 0))
1070  elog(ERROR, "invalid length (%d) dimension array in MCVList",
1071  mcvlist->ndimensions);
1072 
1073  if (mcvlist->nitems == 0)
1074  elog(ERROR, "invalid zero-length item array in MCVList");
1075  else if (mcvlist->nitems > STATS_MCVLIST_MAX_ITEMS)
1076  elog(ERROR, "invalid length (%u) item array in MCVList",
1077  mcvlist->nitems);
1078 
1079  nitems = mcvlist->nitems;
1080  ndims = mcvlist->ndimensions;
1081 
1082  /*
1083  * Check amount of data including DimensionInfo for all dimensions and
1084  * also the serialized items (including uint16 indexes). Also, walk
1085  * through the dimension information and add it to the sum.
1086  */
1087  expected_size = SizeOfMCVList(ndims, nitems);
1088 
1089  /*
1090  * Check that we have at least the dimension and info records, along with
1091  * the items. We don't know the size of the serialized values yet. We need
1092  * to do this check first, before accessing the dimension info.
1093  */
1094  if (VARSIZE_ANY(data) < expected_size)
1095  elog(ERROR, "invalid MCV size %zd (expected %zu)",
1096  VARSIZE_ANY(data), expected_size);
1097 
1098  /* Now copy the array of type Oids. */
1099  memcpy(mcvlist->types, ptr, sizeof(Oid) * ndims);
1100  ptr += (sizeof(Oid) * ndims);
1101 
1102  /* Now it's safe to access the dimension info. */
1103  info = palloc(ndims * sizeof(DimensionInfo));
1104 
1105  memcpy(info, ptr, ndims * sizeof(DimensionInfo));
1106  ptr += (ndims * sizeof(DimensionInfo));
1107 
1108  /* account for the value arrays */
1109  for (dim = 0; dim < ndims; dim++)
1110  {
1111  /*
1112  * XXX I wonder if we can/should rely on asserts here. Maybe those
1113  * checks should be done every time?
1114  */
1115  Assert(info[dim].nvalues >= 0);
1116  Assert(info[dim].nbytes >= 0);
1117 
1118  expected_size += info[dim].nbytes;
1119  }
1120 
1121  /*
1122  * Now we know the total expected MCV size, including all the pieces
1123  * (header, dimension info. items and deduplicated data). So do the final
1124  * check on size.
1125  */
1126  if (VARSIZE_ANY(data) != expected_size)
1127  elog(ERROR, "invalid MCV size %zd (expected %zu)",
1128  VARSIZE_ANY(data), expected_size);
1129 
1130  /*
1131  * We need an array of Datum values for each dimension, so that we can
1132  * easily translate the uint16 indexes later. We also need a top-level
1133  * array of pointers to those per-dimension arrays.
1134  *
1135  * While allocating the arrays for dimensions, compute how much space we
1136  * need for a copy of the by-ref data, as we can't simply point to the
1137  * original values (it might go away).
1138  */
1139  datalen = 0; /* space for by-ref data */
1140  map = (Datum **) palloc(ndims * sizeof(Datum *));
1141 
1142  for (dim = 0; dim < ndims; dim++)
1143  {
1144  map[dim] = (Datum *) palloc(sizeof(Datum) * info[dim].nvalues);
1145 
1146  /* space needed for a copy of data for by-ref types */
1147  datalen += info[dim].nbytes_aligned;
1148  }
1149 
1150  /*
1151  * Now resize the MCV list so that the allocation includes all the data.
1152  *
1153  * Allocate space for a copy of the data, as we can't simply reference the
1154  * serialized data - it's not aligned properly, and it may disappear while
1155  * we're still using the MCV list, e.g. due to catcache release.
1156  *
1157  * We do care about alignment here, because we will allocate all the
1158  * pieces at once, but then use pointers to different parts.
1159  */
1160  mcvlen = MAXALIGN(offsetof(MCVList, items) + (sizeof(MCVItem) * nitems));
1161 
1162  /* arrays of values and isnull flags for all MCV items */
1163  mcvlen += nitems * MAXALIGN(sizeof(Datum) * ndims);
1164  mcvlen += nitems * MAXALIGN(sizeof(bool) * ndims);
1165 
1166  /* we don't quite need to align this, but it makes some asserts easier */
1167  mcvlen += MAXALIGN(datalen);
1168 
1169  /* now resize the deserialized MCV list, and compute pointers to parts */
1170  mcvlist = repalloc(mcvlist, mcvlen);
1171 
1172  /* pointer to the beginning of values/isnull arrays */
1173  valuesptr = (char *) mcvlist
1174  + MAXALIGN(offsetof(MCVList, items) + (sizeof(MCVItem) * nitems));
1175 
1176  isnullptr = valuesptr + (nitems * MAXALIGN(sizeof(Datum) * ndims));
1177 
1178  dataptr = isnullptr + (nitems * MAXALIGN(sizeof(bool) * ndims));
1179 
1180  /*
1181  * Build mapping (index => value) for translating the serialized data into
1182  * the in-memory representation.
1183  */
1184  for (dim = 0; dim < ndims; dim++)
1185  {
1186  /* remember start position in the input array */
1187  char *start PG_USED_FOR_ASSERTS_ONLY = ptr;
1188 
1189  if (info[dim].typbyval)
1190  {
1191  /* for by-val types we simply copy data into the mapping */
1192  for (i = 0; i < info[dim].nvalues; i++)
1193  {
1194  Datum v = 0;
1195 
1196  memcpy(&v, ptr, info[dim].typlen);
1197  ptr += info[dim].typlen;
1198 
1199  map[dim][i] = fetch_att(&v, true, info[dim].typlen);
1200 
1201  /* no under/overflow of input array */
1202  Assert(ptr <= (start + info[dim].nbytes));
1203  }
1204  }
1205  else
1206  {
1207  /* for by-ref types we need to also make a copy of the data */
1208 
1209  /* passed by reference, but fixed length (name, tid, ...) */
1210  if (info[dim].typlen > 0)
1211  {
1212  for (i = 0; i < info[dim].nvalues; i++)
1213  {
1214  memcpy(dataptr, ptr, info[dim].typlen);
1215  ptr += info[dim].typlen;
1216 
1217  /* just point into the array */
1218  map[dim][i] = PointerGetDatum(dataptr);
1219  dataptr += MAXALIGN(info[dim].typlen);
1220  }
1221  }
1222  else if (info[dim].typlen == -1)
1223  {
1224  /* varlena */
1225  for (i = 0; i < info[dim].nvalues; i++)
1226  {
1227  uint32 len;
1228 
1229  /* read the uint32 length */
1230  memcpy(&len, ptr, sizeof(uint32));
1231  ptr += sizeof(uint32);
1232 
1233  /* the length is data-only */
1234  SET_VARSIZE(dataptr, len + VARHDRSZ);
1235  memcpy(VARDATA(dataptr), ptr, len);
1236  ptr += len;
1237 
1238  /* just point into the array */
1239  map[dim][i] = PointerGetDatum(dataptr);
1240 
1241  /* skip to place of the next deserialized value */
1242  dataptr += MAXALIGN(len + VARHDRSZ);
1243  }
1244  }
1245  else if (info[dim].typlen == -2)
1246  {
1247  /* cstring */
1248  for (i = 0; i < info[dim].nvalues; i++)
1249  {
1250  uint32 len;
1251 
1252  memcpy(&len, ptr, sizeof(uint32));
1253  ptr += sizeof(uint32);
1254 
1255  memcpy(dataptr, ptr, len);
1256  ptr += len;
1257 
1258  /* just point into the array */
1259  map[dim][i] = PointerGetDatum(dataptr);
1260  dataptr += MAXALIGN(len);
1261  }
1262  }
1263 
1264  /* no under/overflow of input array */
1265  Assert(ptr <= (start + info[dim].nbytes));
1266 
1267  /* no overflow of the output mcv value */
1268  Assert(dataptr <= ((char *) mcvlist + mcvlen));
1269  }
1270 
1271  /* check we consumed input data for this dimension exactly */
1272  Assert(ptr == (start + info[dim].nbytes));
1273  }
1274 
1275  /* we should have also filled the MCV list exactly */
1276  Assert(dataptr == ((char *) mcvlist + mcvlen));
1277 
1278  /* deserialize the MCV items and translate the indexes to Datums */
1279  for (i = 0; i < nitems; i++)
1280  {
1281  MCVItem *item = &mcvlist->items[i];
1282 
1283  item->values = (Datum *) valuesptr;
1284  valuesptr += MAXALIGN(sizeof(Datum) * ndims);
1285 
1286  item->isnull = (bool *) isnullptr;
1287  isnullptr += MAXALIGN(sizeof(bool) * ndims);
1288 
1289  memcpy(item->isnull, ptr, sizeof(bool) * ndims);
1290  ptr += sizeof(bool) * ndims;
1291 
1292  memcpy(&item->frequency, ptr, sizeof(double));
1293  ptr += sizeof(double);
1294 
1295  memcpy(&item->base_frequency, ptr, sizeof(double));
1296  ptr += sizeof(double);
1297 
1298  /* finally translate the indexes (for non-NULL only) */
1299  for (dim = 0; dim < ndims; dim++)
1300  {
1301  uint16 index;
1302 
1303  memcpy(&index, ptr, sizeof(uint16));
1304  ptr += sizeof(uint16);
1305 
1306  if (item->isnull[dim])
1307  continue;
1308 
1309  item->values[dim] = map[dim][index];
1310  }
1311 
1312  /* check we're not overflowing the input */
1313  Assert(ptr <= endptr);
1314  }
1315 
1316  /* check that we processed all the data */
1317  Assert(ptr == endptr);
1318 
1319  /* release the buffers used for mapping */
1320  for (dim = 0; dim < ndims; dim++)
1321  pfree(map[dim]);
1322 
1323  pfree(map);
1324 
1325  return mcvlist;
1326 }
1327 
1328 /*
1329  * SRF with details about buckets of a histogram:
1330  *
1331  * - item ID (0...nitems)
1332  * - values (string array)
1333  * - nulls only (boolean array)
1334  * - frequency (double precision)
1335  * - base_frequency (double precision)
1336  *
1337  * The input is the OID of the statistics, and there are no rows returned if
1338  * the statistics contains no histogram.
1339  */
1340 Datum
1342 {
1343  FuncCallContext *funcctx;
1344 
1345  /* stuff done only on the first call of the function */
1346  if (SRF_IS_FIRSTCALL())
1347  {
1348  MemoryContext oldcontext;
1349  MCVList *mcvlist;
1350  TupleDesc tupdesc;
1351 
1352  /* create a function context for cross-call persistence */
1353  funcctx = SRF_FIRSTCALL_INIT();
1354 
1355  /* switch to memory context appropriate for multiple function calls */
1356  oldcontext = MemoryContextSwitchTo(funcctx->multi_call_memory_ctx);
1357 
1359 
1360  funcctx->user_fctx = mcvlist;
1361 
1362  /* total number of tuples to be returned */
1363  funcctx->max_calls = 0;
1364  if (funcctx->user_fctx != NULL)
1365  funcctx->max_calls = mcvlist->nitems;
1366 
1367  /* Build a tuple descriptor for our result type */
1368  if (get_call_result_type(fcinfo, NULL, &tupdesc) != TYPEFUNC_COMPOSITE)
1369  ereport(ERROR,
1370  (errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
1371  errmsg("function returning record called in context "
1372  "that cannot accept type record")));
1373  tupdesc = BlessTupleDesc(tupdesc);
1374 
1375  /*
1376  * generate attribute metadata needed later to produce tuples from raw
1377  * C strings
1378  */
1379  funcctx->attinmeta = TupleDescGetAttInMetadata(tupdesc);
1380 
1381  MemoryContextSwitchTo(oldcontext);
1382  }
1383 
1384  /* stuff done on every call of the function */
1385  funcctx = SRF_PERCALL_SETUP();
1386 
1387  if (funcctx->call_cntr < funcctx->max_calls) /* do when there is more
1388  * left to send */
1389  {
1390  Datum values[5];
1391  bool nulls[5];
1392  HeapTuple tuple;
1393  Datum result;
1394  ArrayBuildState *astate_values = NULL;
1395  ArrayBuildState *astate_nulls = NULL;
1396 
1397  int i;
1398  MCVList *mcvlist;
1399  MCVItem *item;
1400 
1401  mcvlist = (MCVList *) funcctx->user_fctx;
1402 
1403  Assert(funcctx->call_cntr < mcvlist->nitems);
1404 
1405  item = &mcvlist->items[funcctx->call_cntr];
1406 
1407  for (i = 0; i < mcvlist->ndimensions; i++)
1408  {
1409 
1410  astate_nulls = accumArrayResult(astate_nulls,
1411  BoolGetDatum(item->isnull[i]),
1412  false,
1413  BOOLOID,
1415 
1416  if (!item->isnull[i])
1417  {
1418  bool isvarlena;
1419  Oid outfunc;
1420  FmgrInfo fmgrinfo;
1421  Datum val;
1422  text *txt;
1423 
1424  /* lookup output func for the type */
1425  getTypeOutputInfo(mcvlist->types[i], &outfunc, &isvarlena);
1426  fmgr_info(outfunc, &fmgrinfo);
1427 
1428  val = FunctionCall1(&fmgrinfo, item->values[i]);
1429  txt = cstring_to_text(DatumGetPointer(val));
1430 
1431  astate_values = accumArrayResult(astate_values,
1432  PointerGetDatum(txt),
1433  false,
1434  TEXTOID,
1436  }
1437  else
1438  astate_values = accumArrayResult(astate_values,
1439  (Datum) 0,
1440  true,
1441  TEXTOID,
1443  }
1444 
1445  values[0] = Int32GetDatum(funcctx->call_cntr);
1446  values[1] = PointerGetDatum(makeArrayResult(astate_values, CurrentMemoryContext));
1447  values[2] = PointerGetDatum(makeArrayResult(astate_nulls, CurrentMemoryContext));
1448  values[3] = Float8GetDatum(item->frequency);
1449  values[4] = Float8GetDatum(item->base_frequency);
1450 
1451  /* no NULLs in the tuple */
1452  memset(nulls, 0, sizeof(nulls));
1453 
1454  /* build a tuple */
1455  tuple = heap_form_tuple(funcctx->attinmeta->tupdesc, values, nulls);
1456 
1457  /* make the tuple into a datum */
1458  result = HeapTupleGetDatum(tuple);
1459 
1460  SRF_RETURN_NEXT(funcctx, result);
1461  }
1462  else /* do when there is no more left */
1463  {
1464  SRF_RETURN_DONE(funcctx);
1465  }
1466 }
1467 
1468 /*
1469  * pg_mcv_list_in - input routine for type pg_mcv_list.
1470  *
1471  * pg_mcv_list is real enough to be a table column, but it has no operations
1472  * of its own, and disallows input too
1473  */
1474 Datum
1476 {
1477  /*
1478  * pg_mcv_list stores the data in binary form and parsing text input is
1479  * not needed, so disallow this.
1480  */
1481  ereport(ERROR,
1482  (errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
1483  errmsg("cannot accept a value of type %s", "pg_mcv_list")));
1484 
1485  PG_RETURN_VOID(); /* keep compiler quiet */
1486 }
1487 
1488 
1489 /*
1490  * pg_mcv_list_out - output routine for type pg_mcv_list.
1491  *
1492  * MCV lists are serialized into a bytea value, so we simply call byteaout()
1493  * to serialize the value into text. But it'd be nice to serialize that into
1494  * a meaningful representation (e.g. for inspection by people).
1495  *
1496  * XXX This should probably return something meaningful, similar to what
1497  * pg_dependencies_out does. Not sure how to deal with the deduplicated
1498  * values, though - do we want to expand that or not?
1499  */
1500 Datum
1502 {
1503  return byteaout(fcinfo);
1504 }
1505 
1506 /*
1507  * pg_mcv_list_recv - binary input routine for type pg_mcv_list.
1508  */
1509 Datum
1511 {
1512  ereport(ERROR,
1513  (errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
1514  errmsg("cannot accept a value of type %s", "pg_mcv_list")));
1515 
1516  PG_RETURN_VOID(); /* keep compiler quiet */
1517 }
1518 
1519 /*
1520  * pg_mcv_list_send - binary output routine for type pg_mcv_list.
1521  *
1522  * MCV lists are serialized in a bytea value (although the type is named
1523  * differently), so let's just send that.
1524  */
1525 Datum
1527 {
1528  return byteasend(fcinfo);
1529 }
1530 
1531 /*
1532  * match the attribute/expression to a dimension of the statistic
1533  *
1534  * Match the attribute/expression to statistics dimension. Optionally
1535  * determine the collation.
1536  */
1537 static int
1538 mcv_match_expression(Node *expr, Bitmapset *keys, List *exprs, Oid *collid)
1539 {
1540  int idx = -1;
1541 
1542  if (IsA(expr, Var))
1543  {
1544  /* simple Var, so just lookup using varattno */
1545  Var *var = (Var *) expr;
1546 
1547  if (collid)
1548  *collid = var->varcollid;
1549 
1550  idx = bms_member_index(keys, var->varattno);
1551 
1552  /* make sure the index is valid */
1553  Assert((idx >= 0) && (idx <= bms_num_members(keys)));
1554  }
1555  else
1556  {
1557  ListCell *lc;
1558 
1559  /* expressions are stored after the simple columns */
1560  idx = bms_num_members(keys);
1561 
1562  if (collid)
1563  *collid = exprCollation(expr);
1564 
1565  /* expression - lookup in stats expressions */
1566  foreach(lc, exprs)
1567  {
1568  Node *stat_expr = (Node *) lfirst(lc);
1569 
1570  if (equal(expr, stat_expr))
1571  break;
1572 
1573  idx++;
1574  }
1575 
1576  /* make sure the index is valid */
1577  Assert((idx >= bms_num_members(keys)) &&
1578  (idx <= bms_num_members(keys) + list_length(exprs)));
1579  }
1580 
1581  Assert((idx >= 0) && (idx < bms_num_members(keys) + list_length(exprs)));
1582 
1583  return idx;
1584 }
1585 
1586 /*
1587  * mcv_get_match_bitmap
1588  * Evaluate clauses using the MCV list, and update the match bitmap.
1589  *
1590  * A match bitmap keeps match/mismatch status for each MCV item, and we
1591  * update it based on additional clauses. We also use it to skip items
1592  * that can't possibly match (e.g. item marked as "mismatch" can't change
1593  * to "match" when evaluating AND clause list).
1594  *
1595  * The function also returns a flag indicating whether there was an
1596  * equality condition for all attributes, the minimum frequency in the MCV
1597  * list, and a total MCV frequency (sum of frequencies for all items).
1598  *
1599  * XXX Currently the match bitmap uses a bool for each MCV item, which is
1600  * somewhat wasteful as we could do with just a single bit, thus reducing
1601  * the size to ~1/8. It would also allow us to combine bitmaps simply using
1602  * & and |, which should be faster than min/max. The bitmaps are fairly
1603  * small, though (thanks to the cap on the MCV list size).
1604  */
1605 static bool *
1607  Bitmapset *keys, List *exprs,
1608  MCVList *mcvlist, bool is_or)
1609 {
1610  int i;
1611  ListCell *l;
1612  bool *matches;
1613 
1614  /* The bitmap may be partially built. */
1615  Assert(clauses != NIL);
1616  Assert(list_length(clauses) >= 1);
1617  Assert(mcvlist != NULL);
1618  Assert(mcvlist->nitems > 0);
1619  Assert(mcvlist->nitems <= STATS_MCVLIST_MAX_ITEMS);
1620 
1621  matches = palloc(sizeof(bool) * mcvlist->nitems);
1622  memset(matches, !is_or, sizeof(bool) * mcvlist->nitems);
1623 
1624  /*
1625  * Loop through the list of clauses, and for each of them evaluate all the
1626  * MCV items not yet eliminated by the preceding clauses.
1627  */
1628  foreach(l, clauses)
1629  {
1630  Node *clause = (Node *) lfirst(l);
1631 
1632  /* if it's a RestrictInfo, then extract the clause */
1633  if (IsA(clause, RestrictInfo))
1634  clause = (Node *) ((RestrictInfo *) clause)->clause;
1635 
1636  /*
1637  * Handle the various types of clauses - OpClause, NullTest and
1638  * AND/OR/NOT
1639  */
1640  if (is_opclause(clause))
1641  {
1642  OpExpr *expr = (OpExpr *) clause;
1643  FmgrInfo opproc;
1644 
1645  /* valid only after examine_opclause_args returns true */
1646  Node *clause_expr;
1647  Const *cst;
1648  bool expronleft;
1649  int idx;
1650  Oid collid;
1651 
1652  fmgr_info(get_opcode(expr->opno), &opproc);
1653 
1654  /* extract the var/expr and const from the expression */
1655  if (!examine_opclause_args(expr->args, &clause_expr, &cst, &expronleft))
1656  elog(ERROR, "incompatible clause");
1657 
1658  /* match the attribute/expression to a dimension of the statistic */
1659  idx = mcv_match_expression(clause_expr, keys, exprs, &collid);
1660 
1661  Assert((idx >= 0) && (idx < bms_num_members(keys) + list_length(exprs)));
1662 
1663  /*
1664  * Walk through the MCV items and evaluate the current clause. We
1665  * can skip items that were already ruled out, and terminate if
1666  * there are no remaining MCV items that might possibly match.
1667  */
1668  for (i = 0; i < mcvlist->nitems; i++)
1669  {
1670  bool match = true;
1671  MCVItem *item = &mcvlist->items[i];
1672 
1673  Assert(idx >= 0);
1674 
1675  /*
1676  * When the MCV item or the Const value is NULL we can treat
1677  * this as a mismatch. We must not call the operator because
1678  * of strictness.
1679  */
1680  if (item->isnull[idx] || cst->constisnull)
1681  {
1682  matches[i] = RESULT_MERGE(matches[i], is_or, false);
1683  continue;
1684  }
1685 
1686  /*
1687  * Skip MCV items that can't change result in the bitmap. Once
1688  * the value gets false for AND-lists, or true for OR-lists,
1689  * we don't need to look at more clauses.
1690  */
1691  if (RESULT_IS_FINAL(matches[i], is_or))
1692  continue;
1693 
1694  /*
1695  * First check whether the constant is below the lower
1696  * boundary (in that case we can skip the bucket, because
1697  * there's no overlap).
1698  *
1699  * We don't store collations used to build the statistics, but
1700  * we can use the collation for the attribute itself, as
1701  * stored in varcollid. We do reset the statistics after a
1702  * type change (including collation change), so this is OK.
1703  * For expressions, we use the collation extracted from the
1704  * expression itself.
1705  */
1706  if (expronleft)
1707  match = DatumGetBool(FunctionCall2Coll(&opproc,
1708  collid,
1709  item->values[idx],
1710  cst->constvalue));
1711  else
1712  match = DatumGetBool(FunctionCall2Coll(&opproc,
1713  collid,
1714  cst->constvalue,
1715  item->values[idx]));
1716 
1717  /* update the match bitmap with the result */
1718  matches[i] = RESULT_MERGE(matches[i], is_or, match);
1719  }
1720  }
1721  else if (IsA(clause, ScalarArrayOpExpr))
1722  {
1723  ScalarArrayOpExpr *expr = (ScalarArrayOpExpr *) clause;
1724  FmgrInfo opproc;
1725 
1726  /* valid only after examine_opclause_args returns true */
1727  Node *clause_expr;
1728  Const *cst;
1729  bool expronleft;
1730  Oid collid;
1731  int idx;
1732 
1733  /* array evaluation */
1734  ArrayType *arrayval;
1735  int16 elmlen;
1736  bool elmbyval;
1737  char elmalign;
1738  int num_elems;
1739  Datum *elem_values;
1740  bool *elem_nulls;
1741 
1742  fmgr_info(get_opcode(expr->opno), &opproc);
1743 
1744  /* extract the var/expr and const from the expression */
1745  if (!examine_opclause_args(expr->args, &clause_expr, &cst, &expronleft))
1746  elog(ERROR, "incompatible clause");
1747 
1748  /* ScalarArrayOpExpr has the Var always on the left */
1749  Assert(expronleft);
1750 
1751  /* XXX what if (cst->constisnull == NULL)? */
1752  if (!cst->constisnull)
1753  {
1754  arrayval = DatumGetArrayTypeP(cst->constvalue);
1756  &elmlen, &elmbyval, &elmalign);
1757  deconstruct_array(arrayval,
1758  ARR_ELEMTYPE(arrayval),
1759  elmlen, elmbyval, elmalign,
1760  &elem_values, &elem_nulls, &num_elems);
1761  }
1762 
1763  /* match the attribute/expression to a dimension of the statistic */
1764  idx = mcv_match_expression(clause_expr, keys, exprs, &collid);
1765 
1766  /*
1767  * Walk through the MCV items and evaluate the current clause. We
1768  * can skip items that were already ruled out, and terminate if
1769  * there are no remaining MCV items that might possibly match.
1770  */
1771  for (i = 0; i < mcvlist->nitems; i++)
1772  {
1773  int j;
1774  bool match = !expr->useOr;
1775  MCVItem *item = &mcvlist->items[i];
1776 
1777  /*
1778  * When the MCV item or the Const value is NULL we can treat
1779  * this as a mismatch. We must not call the operator because
1780  * of strictness.
1781  */
1782  if (item->isnull[idx] || cst->constisnull)
1783  {
1784  matches[i] = RESULT_MERGE(matches[i], is_or, false);
1785  continue;
1786  }
1787 
1788  /*
1789  * Skip MCV items that can't change result in the bitmap. Once
1790  * the value gets false for AND-lists, or true for OR-lists,
1791  * we don't need to look at more clauses.
1792  */
1793  if (RESULT_IS_FINAL(matches[i], is_or))
1794  continue;
1795 
1796  for (j = 0; j < num_elems; j++)
1797  {
1798  Datum elem_value = elem_values[j];
1799  bool elem_isnull = elem_nulls[j];
1800  bool elem_match;
1801 
1802  /* NULL values always evaluate as not matching. */
1803  if (elem_isnull)
1804  {
1805  match = RESULT_MERGE(match, expr->useOr, false);
1806  continue;
1807  }
1808 
1809  /*
1810  * Stop evaluating the array elements once we reach a
1811  * matching value that can't change - ALL() is the same as
1812  * AND-list, ANY() is the same as OR-list.
1813  */
1814  if (RESULT_IS_FINAL(match, expr->useOr))
1815  break;
1816 
1817  elem_match = DatumGetBool(FunctionCall2Coll(&opproc,
1818  collid,
1819  item->values[idx],
1820  elem_value));
1821 
1822  match = RESULT_MERGE(match, expr->useOr, elem_match);
1823  }
1824 
1825  /* update the match bitmap with the result */
1826  matches[i] = RESULT_MERGE(matches[i], is_or, match);
1827  }
1828  }
1829  else if (IsA(clause, NullTest))
1830  {
1831  NullTest *expr = (NullTest *) clause;
1832  Node *clause_expr = (Node *) (expr->arg);
1833 
1834  /* match the attribute/expression to a dimension of the statistic */
1835  int idx = mcv_match_expression(clause_expr, keys, exprs, NULL);
1836 
1837  /*
1838  * Walk through the MCV items and evaluate the current clause. We
1839  * can skip items that were already ruled out, and terminate if
1840  * there are no remaining MCV items that might possibly match.
1841  */
1842  for (i = 0; i < mcvlist->nitems; i++)
1843  {
1844  bool match = false; /* assume mismatch */
1845  MCVItem *item = &mcvlist->items[i];
1846 
1847  /* if the clause mismatches the MCV item, update the bitmap */
1848  switch (expr->nulltesttype)
1849  {
1850  case IS_NULL:
1851  match = (item->isnull[idx]) ? true : match;
1852  break;
1853 
1854  case IS_NOT_NULL:
1855  match = (!item->isnull[idx]) ? true : match;
1856  break;
1857  }
1858 
1859  /* now, update the match bitmap, depending on OR/AND type */
1860  matches[i] = RESULT_MERGE(matches[i], is_or, match);
1861  }
1862  }
1863  else if (is_orclause(clause) || is_andclause(clause))
1864  {
1865  /* AND/OR clause, with all subclauses being compatible */
1866 
1867  int i;
1868  BoolExpr *bool_clause = ((BoolExpr *) clause);
1869  List *bool_clauses = bool_clause->args;
1870 
1871  /* match/mismatch bitmap for each MCV item */
1872  bool *bool_matches = NULL;
1873 
1874  Assert(bool_clauses != NIL);
1875  Assert(list_length(bool_clauses) >= 2);
1876 
1877  /* build the match bitmap for the OR-clauses */
1878  bool_matches = mcv_get_match_bitmap(root, bool_clauses, keys, exprs,
1879  mcvlist, is_orclause(clause));
1880 
1881  /*
1882  * Merge the bitmap produced by mcv_get_match_bitmap into the
1883  * current one. We need to consider if we're evaluating AND or OR
1884  * condition when merging the results.
1885  */
1886  for (i = 0; i < mcvlist->nitems; i++)
1887  matches[i] = RESULT_MERGE(matches[i], is_or, bool_matches[i]);
1888 
1889  pfree(bool_matches);
1890  }
1891  else if (is_notclause(clause))
1892  {
1893  /* NOT clause, with all subclauses compatible */
1894 
1895  int i;
1896  BoolExpr *not_clause = ((BoolExpr *) clause);
1897  List *not_args = not_clause->args;
1898 
1899  /* match/mismatch bitmap for each MCV item */
1900  bool *not_matches = NULL;
1901 
1902  Assert(not_args != NIL);
1903  Assert(list_length(not_args) == 1);
1904 
1905  /* build the match bitmap for the NOT-clause */
1906  not_matches = mcv_get_match_bitmap(root, not_args, keys, exprs,
1907  mcvlist, false);
1908 
1909  /*
1910  * Merge the bitmap produced by mcv_get_match_bitmap into the
1911  * current one. We're handling a NOT clause, so invert the result
1912  * before merging it into the global bitmap.
1913  */
1914  for (i = 0; i < mcvlist->nitems; i++)
1915  matches[i] = RESULT_MERGE(matches[i], is_or, !not_matches[i]);
1916 
1917  pfree(not_matches);
1918  }
1919  else if (IsA(clause, Var))
1920  {
1921  /* Var (has to be a boolean Var, possibly from below NOT) */
1922 
1923  Var *var = (Var *) (clause);
1924 
1925  /* match the attribute to a dimension of the statistic */
1926  int idx = bms_member_index(keys, var->varattno);
1927 
1928  Assert(var->vartype == BOOLOID);
1929 
1930  /*
1931  * Walk through the MCV items and evaluate the current clause. We
1932  * can skip items that were already ruled out, and terminate if
1933  * there are no remaining MCV items that might possibly match.
1934  */
1935  for (i = 0; i < mcvlist->nitems; i++)
1936  {
1937  MCVItem *item = &mcvlist->items[i];
1938  bool match = false;
1939 
1940  /* if the item is NULL, it's a mismatch */
1941  if (!item->isnull[idx] && DatumGetBool(item->values[idx]))
1942  match = true;
1943 
1944  /* update the result bitmap */
1945  matches[i] = RESULT_MERGE(matches[i], is_or, match);
1946  }
1947  }
1948  else
1949  elog(ERROR, "unknown clause type: %d", clause->type);
1950  }
1951 
1952  return matches;
1953 }
1954 
1955 
1956 /*
1957  * mcv_combine_selectivities
1958  * Combine per-column and multi-column MCV selectivity estimates.
1959  *
1960  * simple_sel is a "simple" selectivity estimate (produced without using any
1961  * extended statistics, essentially assuming independence of columns/clauses).
1962  *
1963  * mcv_sel and mcv_basesel are sums of the frequencies and base frequencies of
1964  * all matching MCV items. The difference (mcv_sel - mcv_basesel) is then
1965  * essentially interpreted as a correction to be added to simple_sel, as
1966  * described below.
1967  *
1968  * mcv_totalsel is the sum of the frequencies of all MCV items (not just the
1969  * matching ones). This is used as an upper bound on the portion of the
1970  * selectivity estimates not covered by the MCV statistics.
1971  *
1972  * Note: While simple and base selectivities are defined in a quite similar
1973  * way, the values are computed differently and are not therefore equal. The
1974  * simple selectivity is computed as a product of per-clause estimates, while
1975  * the base selectivity is computed by adding up base frequencies of matching
1976  * items of the multi-column MCV list. So the values may differ for two main
1977  * reasons - (a) the MCV list may not cover 100% of the data and (b) some of
1978  * the MCV items did not match the estimated clauses.
1979  *
1980  * As both (a) and (b) reduce the base selectivity value, it generally holds
1981  * that (simple_sel >= mcv_basesel). If the MCV list covers all the data, the
1982  * values may be equal.
1983  *
1984  * So, other_sel = (simple_sel - mcv_basesel) is an estimate for the part not
1985  * covered by the MCV list, and (mcv_sel - mcv_basesel) may be seen as a
1986  * correction for the part covered by the MCV list. Those two statements are
1987  * actually equivalent.
1988  */
1991  Selectivity mcv_sel,
1992  Selectivity mcv_basesel,
1993  Selectivity mcv_totalsel)
1994 {
1995  Selectivity other_sel;
1996  Selectivity sel;
1997 
1998  /* estimated selectivity of values not covered by MCV matches */
1999  other_sel = simple_sel - mcv_basesel;
2000  CLAMP_PROBABILITY(other_sel);
2001 
2002  /* this non-MCV selectivity cannot exceed 1 - mcv_totalsel */
2003  if (other_sel > 1.0 - mcv_totalsel)
2004  other_sel = 1.0 - mcv_totalsel;
2005 
2006  /* overall selectivity is the sum of the MCV and non-MCV parts */
2007  sel = mcv_sel + other_sel;
2008  CLAMP_PROBABILITY(sel);
2009 
2010  return sel;
2011 }
2012 
2013 
2014 /*
2015  * mcv_clauselist_selectivity
2016  * Use MCV statistics to estimate the selectivity of an implicitly-ANDed
2017  * list of clauses.
2018  *
2019  * This determines which MCV items match every clause in the list and returns
2020  * the sum of the frequencies of those items.
2021  *
2022  * In addition, it returns the sum of the base frequencies of each of those
2023  * items (that is the sum of the selectivities that each item would have if
2024  * the columns were independent of one another), and the total selectivity of
2025  * all the MCV items (not just the matching ones). These are expected to be
2026  * used together with a "simple" selectivity estimate (one based only on
2027  * per-column statistics) to produce an overall selectivity estimate that
2028  * makes use of both per-column and multi-column statistics --- see
2029  * mcv_combine_selectivities().
2030  */
2033  List *clauses, int varRelid,
2034  JoinType jointype, SpecialJoinInfo *sjinfo,
2035  RelOptInfo *rel,
2036  Selectivity *basesel, Selectivity *totalsel)
2037 {
2038  int i;
2039  MCVList *mcv;
2040  Selectivity s = 0.0;
2041 
2042  /* match/mismatch bitmap for each MCV item */
2043  bool *matches = NULL;
2044 
2045  /* load the MCV list stored in the statistics object */
2046  mcv = statext_mcv_load(stat->statOid);
2047 
2048  /* build a match bitmap for the clauses */
2049  matches = mcv_get_match_bitmap(root, clauses, stat->keys, stat->exprs,
2050  mcv, false);
2051 
2052  /* sum frequencies for all the matching MCV items */
2053  *basesel = 0.0;
2054  *totalsel = 0.0;
2055  for (i = 0; i < mcv->nitems; i++)
2056  {
2057  *totalsel += mcv->items[i].frequency;
2058 
2059  if (matches[i] != false)
2060  {
2061  *basesel += mcv->items[i].base_frequency;
2062  s += mcv->items[i].frequency;
2063  }
2064  }
2065 
2066  return s;
2067 }
2068 
2069 
2070 /*
2071  * mcv_clause_selectivity_or
2072  * Use MCV statistics to estimate the selectivity of a clause that
2073  * appears in an ORed list of clauses.
2074  *
2075  * As with mcv_clauselist_selectivity() this determines which MCV items match
2076  * the clause and returns both the sum of the frequencies and the sum of the
2077  * base frequencies of those items, as well as the sum of the frequencies of
2078  * all MCV items (not just the matching ones) so that this information can be
2079  * used by mcv_combine_selectivities() to produce a selectivity estimate that
2080  * makes use of both per-column and multi-column statistics.
2081  *
2082  * Additionally, we return information to help compute the overall selectivity
2083  * of the ORed list of clauses assumed to contain this clause. This function
2084  * is intended to be called for each clause in the ORed list of clauses,
2085  * allowing the overall selectivity to be computed using the following
2086  * algorithm:
2087  *
2088  * Suppose P[n] = P(C[1] OR C[2] OR ... OR C[n]) is the combined selectivity
2089  * of the first n clauses in the list. Then the combined selectivity taking
2090  * into account the next clause C[n+1] can be written as
2091  *
2092  * P[n+1] = P[n] + P(C[n+1]) - P((C[1] OR ... OR C[n]) AND C[n+1])
2093  *
2094  * The final term above represents the overlap between the clauses examined so
2095  * far and the (n+1)'th clause. To estimate its selectivity, we track the
2096  * match bitmap for the ORed list of clauses examined so far and examine its
2097  * intersection with the match bitmap for the (n+1)'th clause.
2098  *
2099  * We then also return the sums of the MCV item frequencies and base
2100  * frequencies for the match bitmap intersection corresponding to the overlap
2101  * term above, so that they can be combined with a simple selectivity estimate
2102  * for that term.
2103  *
2104  * The parameter "or_matches" is an in/out parameter tracking the match bitmap
2105  * for the clauses examined so far. The caller is expected to set it to NULL
2106  * the first time it calls this function.
2107  */
2110  MCVList *mcv, Node *clause, bool **or_matches,
2111  Selectivity *basesel, Selectivity *overlap_mcvsel,
2112  Selectivity *overlap_basesel, Selectivity *totalsel)
2113 {
2114  Selectivity s = 0.0;
2115  bool *new_matches;
2116  int i;
2117 
2118  /* build the OR-matches bitmap, if not built already */
2119  if (*or_matches == NULL)
2120  *or_matches = palloc0(sizeof(bool) * mcv->nitems);
2121 
2122  /* build the match bitmap for the new clause */
2123  new_matches = mcv_get_match_bitmap(root, list_make1(clause), stat->keys,
2124  stat->exprs, mcv, false);
2125 
2126  /*
2127  * Sum the frequencies for all the MCV items matching this clause and also
2128  * those matching the overlap between this clause and any of the preceding
2129  * clauses as described above.
2130  */
2131  *basesel = 0.0;
2132  *overlap_mcvsel = 0.0;
2133  *overlap_basesel = 0.0;
2134  *totalsel = 0.0;
2135  for (i = 0; i < mcv->nitems; i++)
2136  {
2137  *totalsel += mcv->items[i].frequency;
2138 
2139  if (new_matches[i])
2140  {
2141  s += mcv->items[i].frequency;
2142  *basesel += mcv->items[i].base_frequency;
2143 
2144  if ((*or_matches)[i])
2145  {
2146  *overlap_mcvsel += mcv->items[i].frequency;
2147  *overlap_basesel += mcv->items[i].base_frequency;
2148  }
2149  }
2150 
2151  /* update the OR-matches bitmap for the next clause */
2152  (*or_matches)[i] = (*or_matches)[i] || new_matches[i];
2153  }
2154 
2155  pfree(new_matches);
2156 
2157  return s;
2158 }
Datum constvalue
Definition: primnodes.h:219
static SortItem * build_distinct_groups(int numrows, SortItem *items, MultiSortSupport mss, int *ndistinct)
Definition: mcv.c:428
struct SortSupportData * SortSupport
Definition: sortsupport.h:58
uint32 nitems
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uint64 call_cntr
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signed short int16
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static SortItem ** build_column_frequencies(SortItem *groups, int ngroups, MultiSortSupport mss, int *ncounts)
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JoinType
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Selectivity mcv_clauselist_selectivity(PlannerInfo *root, StatisticExtInfo *stat, List *clauses, int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo, RelOptInfo *rel, Selectivity *basesel, Selectivity *totalsel)
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Definition: c.h:621
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Definition: fmgr.h:193
MultiSortSupportData * MultiSortSupport
Datum pg_mcv_list_out(PG_FUNCTION_ARGS)
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Definition: mcv.c:383
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Definition: pg_list.h:50
#define ARR_ELEMTYPE(a)
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Definition: attnum.h:21
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unsigned char bool
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Selectivity mcv_clause_selectivity_or(PlannerInfo *root, StatisticExtInfo *stat, MCVList *mcv, Node *clause, bool **or_matches, Selectivity *basesel, Selectivity *overlap_mcvsel, Selectivity *overlap_basesel, Selectivity *totalsel)
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#define SRF_FIRSTCALL_INIT()
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