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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) 1996-2019, 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/syscache.h"
36 #include "utils/typcache.h"
37 
38 /*
39  * Computes size of a serialized MCV item, depending on the number of
40  * dimensions (columns) the statistic is defined on. The datum values are
41  * stored in a separate array (deduplicated, to minimize the size), and
42  * so the serialized items only store uint16 indexes into that array.
43  *
44  * Each serialized item stores (in this order):
45  *
46  * - indexes to values (ndim * sizeof(uint16))
47  * - null flags (ndim * sizeof(bool))
48  * - frequency (sizeof(double))
49  * - base_frequency (sizeof(double))
50  *
51  * There is no alignment padding within an MCV item.
52  * So in total each MCV item requires this many bytes:
53  *
54  * ndim * (sizeof(uint16) + sizeof(bool)) + 2 * sizeof(double)
55  */
56 #define ITEM_SIZE(ndims) \
57  ((ndims) * (sizeof(uint16) + sizeof(bool)) + 2 * sizeof(double))
58 
59 /*
60  * Used to compute size of serialized MCV list representation.
61  */
62 #define MinSizeOfMCVList \
63  (VARHDRSZ + sizeof(uint32) * 3 + sizeof(AttrNumber))
64 
65 /*
66  * Size of the serialized MCV list, excluding the space needed for
67  * deduplicated per-dimension values. The macro is meant to be used
68  * when it's not yet safe to access the serialized info about amount
69  * of data for each column.
70  */
71 #define SizeOfMCVList(ndims,nitems) \
72  ((MinSizeOfMCVList + sizeof(Oid) * (ndims)) + \
73  ((ndims) * sizeof(DimensionInfo)) + \
74  ((nitems) * ITEM_SIZE(ndims)))
75 
76 static MultiSortSupport build_mss(VacAttrStats **stats, int numattrs);
77 
78 static SortItem *build_distinct_groups(int numrows, SortItem *items,
79  MultiSortSupport mss, int *ndistinct);
80 
81 static SortItem **build_column_frequencies(SortItem *groups, int ngroups,
82  MultiSortSupport mss, int *ncounts);
83 
84 static int count_distinct_groups(int numrows, SortItem *items,
85  MultiSortSupport mss);
86 
87 /*
88  * Compute new value for bitmap item, considering whether it's used for
89  * clauses connected by AND/OR.
90  */
91 #define RESULT_MERGE(value, is_or, match) \
92  ((is_or) ? ((value) || (match)) : ((value) && (match)))
93 
94 /*
95  * When processing a list of clauses, the bitmap item may get set to a value
96  * such that additional clauses can't change it. For example, when processing
97  * a list of clauses connected to AND, as soon as the item gets set to 'false'
98  * then it'll remain like that. Similarly clauses connected by OR and 'true'.
99  *
100  * Returns true when the value in the bitmap can't change no matter how the
101  * remaining clauses are evaluated.
102  */
103 #define RESULT_IS_FINAL(value, is_or) ((is_or) ? (value) : (!(value)))
104 
105 /*
106  * get_mincount_for_mcv_list
107  * Determine the minimum number of times a value needs to appear in
108  * the sample for it to be included in the MCV list.
109  *
110  * We want to keep only values that appear sufficiently often in the
111  * sample that it is reasonable to extrapolate their sample frequencies to
112  * the entire table. We do this by placing an upper bound on the relative
113  * standard error of the sample frequency, so that any estimates the
114  * planner generates from the MCV statistics can be expected to be
115  * reasonably accurate.
116  *
117  * Since we are sampling without replacement, the sample frequency of a
118  * particular value is described by a hypergeometric distribution. A
119  * common rule of thumb when estimating errors in this situation is to
120  * require at least 10 instances of the value in the sample, in which case
121  * the distribution can be approximated by a normal distribution, and
122  * standard error analysis techniques can be applied. Given a sample size
123  * of n, a population size of N, and a sample frequency of p=cnt/n, the
124  * standard error of the proportion p is given by
125  * SE = sqrt(p*(1-p)/n) * sqrt((N-n)/(N-1))
126  * where the second term is the finite population correction. To get
127  * reasonably accurate planner estimates, we impose an upper bound on the
128  * relative standard error of 20% -- i.e., SE/p < 0.2. This 20% relative
129  * error bound is fairly arbitrary, but has been found empirically to work
130  * well. Rearranging this formula gives a lower bound on the number of
131  * instances of the value seen:
132  * cnt > n*(N-n) / (N-n+0.04*n*(N-1))
133  * This bound is at most 25, and approaches 0 as n approaches 0 or N. The
134  * case where n approaches 0 cannot happen in practice, since the sample
135  * size is at least 300. The case where n approaches N corresponds to
136  * sampling the whole the table, in which case it is reasonable to keep
137  * the whole MCV list (have no lower bound), so it makes sense to apply
138  * this formula for all inputs, even though the above derivation is
139  * technically only valid when the right hand side is at least around 10.
140  *
141  * An alternative way to look at this formula is as follows -- assume that
142  * the number of instances of the value seen scales up to the entire
143  * table, so that the population count is K=N*cnt/n. Then the distribution
144  * in the sample is a hypergeometric distribution parameterised by N, n
145  * and K, and the bound above is mathematically equivalent to demanding
146  * that the standard deviation of that distribution is less than 20% of
147  * its mean. Thus the relative errors in any planner estimates produced
148  * from the MCV statistics are likely to be not too large.
149  */
150 static double
151 get_mincount_for_mcv_list(int samplerows, double totalrows)
152 {
153  double n = samplerows;
154  double N = totalrows;
155  double numer,
156  denom;
157 
158  numer = n * (N - n);
159  denom = N - n + 0.04 * n * (N - 1);
160 
161  /* Guard against division by zero (possible if n = N = 1) */
162  if (denom == 0.0)
163  return 0.0;
164 
165  return numer / denom;
166 }
167 
168 /*
169  * Builds MCV list from the set of sampled rows.
170  *
171  * The algorithm is quite simple:
172  *
173  * (1) sort the data (default collation, '<' for the data type)
174  *
175  * (2) count distinct groups, decide how many to keep
176  *
177  * (3) build the MCV list using the threshold determined in (2)
178  *
179  * (4) remove rows represented by the MCV from the sample
180  *
181  */
182 MCVList *
183 statext_mcv_build(int numrows, HeapTuple *rows, Bitmapset *attrs,
184  VacAttrStats **stats, double totalrows, int stattarget)
185 {
186  int i,
187  numattrs,
188  ngroups,
189  nitems;
190  AttrNumber *attnums;
191  double mincount;
192  SortItem *items;
193  SortItem *groups;
194  MCVList *mcvlist = NULL;
195  MultiSortSupport mss;
196 
197  attnums = build_attnums_array(attrs, &numattrs);
198 
199  /* comparator for all the columns */
200  mss = build_mss(stats, numattrs);
201 
202  /* sort the rows */
203  items = build_sorted_items(numrows, &nitems, rows, stats[0]->tupDesc,
204  mss, numattrs, attnums);
205 
206  if (!items)
207  return NULL;
208 
209  /* transform the sorted rows into groups (sorted by frequency) */
210  groups = build_distinct_groups(nitems, items, mss, &ngroups);
211 
212  /*
213  * Maximum number of MCV items to store, based on the statistics target
214  * we computed for the statistics object (from target set for the object
215  * itself, attributes and the system default). In any case, we can't keep
216  * more groups than we have available.
217  */
218  nitems = stattarget;
219  if (nitems > ngroups)
220  nitems = ngroups;
221 
222  /*
223  * Decide how many items to keep in the MCV list. We can't use the same
224  * algorithm as per-column MCV lists, because that only considers the
225  * actual group frequency - but we're primarily interested in how the
226  * actual frequency differs from the base frequency (product of simple
227  * per-column frequencies, as if the columns were independent).
228  *
229  * Using the same algorithm might exclude items that are close to the
230  * "average" frequency of the sample. But that does not say whether the
231  * observed frequency is close to the base frequency or not. We also need
232  * to consider unexpectedly uncommon items (again, compared to the base
233  * frequency), and the single-column algorithm does not have to.
234  *
235  * We simply decide how many items to keep by computing minimum count
236  * using get_mincount_for_mcv_list() and then keep all items that seem to
237  * be more common than that.
238  */
239  mincount = get_mincount_for_mcv_list(numrows, totalrows);
240 
241  /*
242  * Walk the groups until we find the first group with a count below the
243  * mincount threshold (the index of that group is the number of groups we
244  * want to keep).
245  */
246  for (i = 0; i < nitems; i++)
247  {
248  if (groups[i].count < mincount)
249  {
250  nitems = i;
251  break;
252  }
253  }
254 
255  /*
256  * At this point we know the number of items for the MCV list. There might
257  * be none (for uniform distribution with many groups), and in that case
258  * there will be no MCV list. Otherwise construct the MCV list.
259  */
260  if (nitems > 0)
261  {
262  int j;
263  SortItem key;
264  MultiSortSupport tmp;
265 
266  /* frequencies for values in each attribute */
267  SortItem **freqs;
268  int *nfreqs;
269 
270  /* used to search values */
272  + sizeof(SortSupportData));
273 
274  /* compute frequencies for values in each column */
275  nfreqs = (int *) palloc0(sizeof(int) * numattrs);
276  freqs = build_column_frequencies(groups, ngroups, mss, nfreqs);
277 
278  /*
279  * Allocate the MCV list structure, set the global parameters.
280  */
281  mcvlist = (MCVList *) palloc0(offsetof(MCVList, items) +
282  sizeof(MCVItem) * nitems);
283 
284  mcvlist->magic = STATS_MCV_MAGIC;
285  mcvlist->type = STATS_MCV_TYPE_BASIC;
286  mcvlist->ndimensions = numattrs;
287  mcvlist->nitems = nitems;
288 
289  /* store info about data type OIDs */
290  for (i = 0; i < numattrs; i++)
291  mcvlist->types[i] = stats[i]->attrtypid;
292 
293  /* Copy the first chunk of groups into the result. */
294  for (i = 0; i < nitems; i++)
295  {
296  /* just pointer to the proper place in the list */
297  MCVItem *item = &mcvlist->items[i];
298 
299  item->values = (Datum *) palloc(sizeof(Datum) * numattrs);
300  item->isnull = (bool *) palloc(sizeof(bool) * numattrs);
301 
302  /* copy values for the group */
303  memcpy(item->values, groups[i].values, sizeof(Datum) * numattrs);
304  memcpy(item->isnull, groups[i].isnull, sizeof(bool) * numattrs);
305 
306  /* groups should be sorted by frequency in descending order */
307  Assert((i == 0) || (groups[i - 1].count >= groups[i].count));
308 
309  /* group frequency */
310  item->frequency = (double) groups[i].count / numrows;
311 
312  /* base frequency, if the attributes were independent */
313  item->base_frequency = 1.0;
314  for (j = 0; j < numattrs; j++)
315  {
316  SortItem *freq;
317 
318  /* single dimension */
319  tmp->ndims = 1;
320  tmp->ssup[0] = mss->ssup[j];
321 
322  /* fill search key */
323  key.values = &groups[i].values[j];
324  key.isnull = &groups[i].isnull[j];
325 
326  freq = (SortItem *) bsearch_arg(&key, freqs[j], nfreqs[j],
327  sizeof(SortItem),
328  multi_sort_compare, tmp);
329 
330  item->base_frequency *= ((double) freq->count) / numrows;
331  }
332  }
333 
334  pfree(nfreqs);
335  pfree(freqs);
336  }
337 
338  pfree(items);
339  pfree(groups);
340 
341  return mcvlist;
342 }
343 
344 /*
345  * build_mss
346  * build MultiSortSupport for the attributes passed in attrs
347  */
348 static MultiSortSupport
349 build_mss(VacAttrStats **stats, int numattrs)
350 {
351  int i;
352 
353  /* Sort by multiple columns (using array of SortSupport) */
354  MultiSortSupport mss = multi_sort_init(numattrs);
355 
356  /* prepare the sort functions for all the attributes */
357  for (i = 0; i < numattrs; i++)
358  {
359  VacAttrStats *colstat = stats[i];
361 
362  type = lookup_type_cache(colstat->attrtypid, TYPECACHE_LT_OPR);
363  if (type->lt_opr == InvalidOid) /* shouldn't happen */
364  elog(ERROR, "cache lookup failed for ordering operator for type %u",
365  colstat->attrtypid);
366 
367  multi_sort_add_dimension(mss, i, type->lt_opr, colstat->attrcollid);
368  }
369 
370  return mss;
371 }
372 
373 /*
374  * count_distinct_groups
375  * count distinct combinations of SortItems in the array
376  *
377  * The array is assumed to be sorted according to the MultiSortSupport.
378  */
379 static int
381 {
382  int i;
383  int ndistinct;
384 
385  ndistinct = 1;
386  for (i = 1; i < numrows; i++)
387  {
388  /* make sure the array really is sorted */
389  Assert(multi_sort_compare(&items[i], &items[i - 1], mss) >= 0);
390 
391  if (multi_sort_compare(&items[i], &items[i - 1], mss) != 0)
392  ndistinct += 1;
393  }
394 
395  return ndistinct;
396 }
397 
398 /*
399  * compare_sort_item_count
400  * comparator for sorting items by count (frequencies) in descending order
401  */
402 static int
403 compare_sort_item_count(const void *a, const void *b)
404 {
405  SortItem *ia = (SortItem *) a;
406  SortItem *ib = (SortItem *) b;
407 
408  if (ia->count == ib->count)
409  return 0;
410  else if (ia->count > ib->count)
411  return -1;
412 
413  return 1;
414 }
415 
416 /*
417  * build_distinct_groups
418  * build an array of SortItems for distinct groups and counts matching items
419  *
420  * The input array is assumed to be sorted
421  */
422 static SortItem *
424  int *ndistinct)
425 {
426  int i,
427  j;
428  int ngroups = count_distinct_groups(numrows, items, mss);
429 
430  SortItem *groups = (SortItem *) palloc(ngroups * sizeof(SortItem));
431 
432  j = 0;
433  groups[0] = items[0];
434  groups[0].count = 1;
435 
436  for (i = 1; i < numrows; i++)
437  {
438  /* Assume sorted in ascending order. */
439  Assert(multi_sort_compare(&items[i], &items[i - 1], mss) >= 0);
440 
441  /* New distinct group detected. */
442  if (multi_sort_compare(&items[i], &items[i - 1], mss) != 0)
443  {
444  groups[++j] = items[i];
445  groups[j].count = 0;
446  }
447 
448  groups[j].count++;
449  }
450 
451  /* ensure we filled the expected number of distinct groups */
452  Assert(j + 1 == ngroups);
453 
454  /* Sort the distinct groups by frequency (in descending order). */
455  pg_qsort((void *) groups, ngroups, sizeof(SortItem),
457 
458  *ndistinct = ngroups;
459  return groups;
460 }
461 
462 /* compare sort items (single dimension) */
463 static int
464 sort_item_compare(const void *a, const void *b, void *arg)
465 {
466  SortSupport ssup = (SortSupport) arg;
467  SortItem *ia = (SortItem *) a;
468  SortItem *ib = (SortItem *) b;
469 
470  return ApplySortComparator(ia->values[0], ia->isnull[0],
471  ib->values[0], ib->isnull[0],
472  ssup);
473 }
474 
475 /*
476  * build_column_frequencies
477  * compute frequencies of values in each column
478  *
479  * This returns an array of SortItems for each attribute the MCV is built
480  * on, with a frequency (number of occurrences) for each value. This is
481  * then used to compute "base" frequency of MCV items.
482  *
483  * All the memory is allocated in a single chunk, so that a single pfree
484  * is enough to release it. We do not allocate space for values/isnull
485  * arrays in the SortItems, because we can simply point into the input
486  * groups directly.
487  */
488 static SortItem **
489 build_column_frequencies(SortItem *groups, int ngroups,
490  MultiSortSupport mss, int *ncounts)
491 {
492  int i,
493  dim;
494  SortItem **result;
495  char *ptr;
496 
497  Assert(groups);
498  Assert(ncounts);
499 
500  /* allocate arrays for all columns as a single chunk */
501  ptr = palloc(MAXALIGN(sizeof(SortItem *) * mss->ndims) +
502  mss->ndims * MAXALIGN(sizeof(SortItem) * ngroups));
503 
504  /* initial array of pointers */
505  result = (SortItem **) ptr;
506  ptr += MAXALIGN(sizeof(SortItem *) * mss->ndims);
507 
508  for (dim = 0; dim < mss->ndims; dim++)
509  {
510  SortSupport ssup = &mss->ssup[dim];
511 
512  /* array of values for a single column */
513  result[dim] = (SortItem *) ptr;
514  ptr += MAXALIGN(sizeof(SortItem) * ngroups);
515 
516  /* extract data for the dimension */
517  for (i = 0; i < ngroups; i++)
518  {
519  /* point into the input groups */
520  result[dim][i].values = &groups[i].values[dim];
521  result[dim][i].isnull = &groups[i].isnull[dim];
522  result[dim][i].count = groups[i].count;
523  }
524 
525  /* sort the values, deduplicate */
526  qsort_arg((void *) result[dim], ngroups, sizeof(SortItem),
527  sort_item_compare, ssup);
528 
529  /*
530  * Identify distinct values, compute frequency (there might be
531  * multiple MCV items containing this value, so we need to sum
532  * counts from all of them.
533  */
534  ncounts[dim] = 1;
535  for (i = 1; i < ngroups; i++)
536  {
537  if (sort_item_compare(&result[dim][i-1], &result[dim][i], ssup) == 0)
538  {
539  result[dim][ncounts[dim]-1].count += result[dim][i].count;
540  continue;
541  }
542 
543  result[dim][ncounts[dim]] = result[dim][i];
544 
545  ncounts[dim]++;
546  }
547  }
548 
549  return result;
550 }
551 
552 /*
553  * statext_mcv_load
554  * Load the MCV list for the indicated pg_statistic_ext tuple
555  */
556 MCVList *
558 {
559  MCVList *result;
560  bool isnull;
561  Datum mcvlist;
563 
564  if (!HeapTupleIsValid(htup))
565  elog(ERROR, "cache lookup failed for statistics object %u", mvoid);
566 
567  mcvlist = SysCacheGetAttr(STATEXTDATASTXOID, htup,
568  Anum_pg_statistic_ext_data_stxdmcv, &isnull);
569 
570  if (isnull)
571  elog(ERROR,
572  "requested statistic kind \"%c\" is not yet built for statistics object %u",
573  STATS_EXT_DEPENDENCIES, mvoid);
574 
575  result = statext_mcv_deserialize(DatumGetByteaP(mcvlist));
576 
577  ReleaseSysCache(htup);
578 
579  return result;
580 }
581 
582 
583 /*
584  * statext_mcv_serialize
585  * Serialize MCV list into a pg_mcv_list value.
586  *
587  * The MCV items may include values of various data types, and it's reasonable
588  * to expect redundancy (values for a given attribute, repeated for multiple
589  * MCV list items). So we deduplicate the values into arrays, and then replace
590  * the values by indexes into those arrays.
591  *
592  * The overall structure of the serialized representation looks like this:
593  *
594  * +---------------+----------------+---------------------+-------+
595  * | header fields | dimension info | deduplicated values | items |
596  * +---------------+----------------+---------------------+-------+
597  *
598  * Where dimension info stores information about type of K-th attribute (e.g.
599  * typlen, typbyval and length of deduplicated values). Deduplicated values
600  * store deduplicated values for each attribute. And items store the actual
601  * MCV list items, with values replaced by indexes into the arrays.
602  *
603  * When serializing the items, we use uint16 indexes. The number of MCV items
604  * is limited by the statistics target (which is capped to 10k at the moment).
605  * We might increase this to 65k and still fit into uint16, so there's a bit of
606  * slack. Furthermore, this limit is on the number of distinct values per column,
607  * and we usually have few of those (and various combinations of them for the
608  * those MCV list). So uint16 seems fine for now.
609  *
610  * We don't really expect the serialization to save as much space as for
611  * histograms, as we are not doing any bucket splits (which is the source
612  * of high redundancy in histograms).
613  *
614  * TODO: Consider packing boolean flags (NULL) for each item into a single char
615  * (or a longer type) instead of using an array of bool items.
616  */
617 bytea *
619 {
620  int i;
621  int dim;
622  int ndims = mcvlist->ndimensions;
623 
624  SortSupport ssup;
625  DimensionInfo *info;
626 
627  Size total_length;
628 
629  /* serialized items (indexes into arrays, etc.) */
630  bytea *raw;
631  char *ptr;
632  char *endptr PG_USED_FOR_ASSERTS_ONLY;
633 
634  /* values per dimension (and number of non-NULL values) */
635  Datum **values = (Datum **) palloc0(sizeof(Datum *) * ndims);
636  int *counts = (int *) palloc0(sizeof(int) * ndims);
637 
638  /*
639  * We'll include some rudimentary information about the attribute types
640  * (length, by-val flag), so that we don't have to look them up while
641  * deserializating the MCV list (we already have the type OID in the
642  * header). This is safe, because when changing type of the attribute the
643  * statistics gets dropped automatically. We need to store the info about
644  * the arrays of deduplicated values anyway.
645  */
646  info = (DimensionInfo *) palloc0(sizeof(DimensionInfo) * ndims);
647 
648  /* sort support data for all attributes included in the MCV list */
649  ssup = (SortSupport) palloc0(sizeof(SortSupportData) * ndims);
650 
651  /* collect and deduplicate values for each dimension (attribute) */
652  for (dim = 0; dim < ndims; dim++)
653  {
654  int ndistinct;
655  TypeCacheEntry *typentry;
656 
657  /*
658  * Lookup the LT operator (can't get it from stats extra_data, as we
659  * don't know how to interpret that - scalar vs. array etc.).
660  */
661  typentry = lookup_type_cache(stats[dim]->attrtypid, TYPECACHE_LT_OPR);
662 
663  /* copy important info about the data type (length, by-value) */
664  info[dim].typlen = stats[dim]->attrtype->typlen;
665  info[dim].typbyval = stats[dim]->attrtype->typbyval;
666 
667  /* allocate space for values in the attribute and collect them */
668  values[dim] = (Datum *) palloc0(sizeof(Datum) * mcvlist->nitems);
669 
670  for (i = 0; i < mcvlist->nitems; i++)
671  {
672  /* skip NULL values - we don't need to deduplicate those */
673  if (mcvlist->items[i].isnull[dim])
674  continue;
675 
676  /* append the value at the end */
677  values[dim][counts[dim]] = mcvlist->items[i].values[dim];
678  counts[dim] += 1;
679  }
680 
681  /* if there are just NULL values in this dimension, we're done */
682  if (counts[dim] == 0)
683  continue;
684 
685  /* sort and deduplicate the data */
686  ssup[dim].ssup_cxt = CurrentMemoryContext;
687  ssup[dim].ssup_collation = stats[dim]->attrcollid;
688  ssup[dim].ssup_nulls_first = false;
689 
690  PrepareSortSupportFromOrderingOp(typentry->lt_opr, &ssup[dim]);
691 
692  qsort_arg(values[dim], counts[dim], sizeof(Datum),
693  compare_scalars_simple, &ssup[dim]);
694 
695  /*
696  * Walk through the array and eliminate duplicate values, but keep the
697  * ordering (so that we can do bsearch later). We know there's at
698  * least one item as (counts[dim] != 0), so we can skip the first
699  * element.
700  */
701  ndistinct = 1; /* number of distinct values */
702  for (i = 1; i < counts[dim]; i++)
703  {
704  /* expect sorted array */
705  Assert(compare_datums_simple(values[dim][i - 1], values[dim][i], &ssup[dim]) <= 0);
706 
707  /* if the value is the same as the previous one, we can skip it */
708  if (!compare_datums_simple(values[dim][i - 1], values[dim][i], &ssup[dim]))
709  continue;
710 
711  values[dim][ndistinct] = values[dim][i];
712  ndistinct += 1;
713  }
714 
715  /* we must not exceed PG_UINT16_MAX, as we use uint16 indexes */
716  Assert(ndistinct <= PG_UINT16_MAX);
717 
718  /*
719  * Store additional info about the attribute - number of deduplicated
720  * values, and also size of the serialized data. For fixed-length data
721  * types this is trivial to compute, for varwidth types we need to
722  * actually walk the array and sum the sizes.
723  */
724  info[dim].nvalues = ndistinct;
725 
726  if (info[dim].typbyval) /* by-value data types */
727  {
728  info[dim].nbytes = info[dim].nvalues * info[dim].typlen;
729 
730  /*
731  * We copy the data into the MCV item during deserialization, so
732  * we don't need to allocate any extra space.
733  */
734  info[dim].nbytes_aligned = 0;
735  }
736  else if (info[dim].typlen > 0) /* fixed-length by-ref */
737  {
738  /*
739  * We don't care about alignment in the serialized data, so we
740  * pack the data as much as possible. But we also track how much
741  * data will be needed after deserialization, and in that case
742  * we need to account for alignment of each item.
743  *
744  * Note: As the items are fixed-length, we could easily compute
745  * this during deserialization, but we do it here anyway.
746  */
747  info[dim].nbytes = info[dim].nvalues * info[dim].typlen;
748  info[dim].nbytes_aligned = info[dim].nvalues * MAXALIGN(info[dim].typlen);
749  }
750  else if (info[dim].typlen == -1) /* varlena */
751  {
752  info[dim].nbytes = 0;
753  info[dim].nbytes_aligned = 0;
754  for (i = 0; i < info[dim].nvalues; i++)
755  {
756  Size len;
757 
758  /*
759  * For varlena values, we detoast the values and store the
760  * length and data separately. We don't bother with alignment
761  * here, which means that during deserialization we need to
762  * copy the fields and only access the copies.
763  */
764  values[dim][i] = PointerGetDatum(PG_DETOAST_DATUM(values[dim][i]));
765 
766  /* serialized length (uint32 length + data) */
767  len = VARSIZE_ANY_EXHDR(values[dim][i]);
768  info[dim].nbytes += sizeof(uint32); /* length */
769  info[dim].nbytes += len; /* value (no header) */
770 
771  /*
772  * During deserialization we'll build regular varlena values
773  * with full headers, and we need to align them properly.
774  */
775  info[dim].nbytes_aligned += MAXALIGN(VARHDRSZ + len);
776  }
777  }
778  else if (info[dim].typlen == -2) /* cstring */
779  {
780  info[dim].nbytes = 0;
781  info[dim].nbytes_aligned = 0;
782  for (i = 0; i < info[dim].nvalues; i++)
783  {
784  Size len;
785 
786  /*
787  * For cstring, we do similar thing as for varlena - first we
788  * store the length as uint32 and then the data. We don't care
789  * about alignment, which means that during deserialization we
790  * need to copy the fields and only access the copies.
791  */
792 
793  /* c-strings include terminator, so +1 byte */
794  len = strlen(DatumGetCString(values[dim][i])) + 1;
795  info[dim].nbytes += sizeof(uint32); /* length */
796  info[dim].nbytes += len; /* value */
797 
798  /* space needed for properly aligned deserialized copies */
799  info[dim].nbytes_aligned += MAXALIGN(len);
800  }
801  }
802 
803  /* we know (count>0) so there must be some data */
804  Assert(info[dim].nbytes > 0);
805  }
806 
807  /*
808  * Now we can finally compute how much space we'll actually need for the
809  * whole serialized MCV list (varlena header, MCV header, dimension info
810  * for each attribute, deduplicated values and items).
811  */
812  total_length = (3 * sizeof(uint32)) /* magic + type + nitems */
813  + sizeof(AttrNumber) /* ndimensions */
814  + (ndims * sizeof(Oid)); /* attribute types */
815 
816  /* dimension info */
817  total_length += ndims * sizeof(DimensionInfo);
818 
819  /* add space for the arrays of deduplicated values */
820  for (i = 0; i < ndims; i++)
821  total_length += info[i].nbytes;
822 
823  /*
824  * And finally account for the items (those are fixed-length, thanks to
825  * replacing values with uint16 indexes into the deduplicated arrays).
826  */
827  total_length += mcvlist->nitems * ITEM_SIZE(dim);
828 
829  /*
830  * Allocate space for the whole serialized MCV list (we'll skip bytes, so
831  * we set them to zero to make the result more compressible).
832  */
833  raw = (bytea *) palloc0(VARHDRSZ + total_length);
834  SET_VARSIZE(raw, VARHDRSZ + total_length);
835 
836  ptr = VARDATA(raw);
837  endptr = ptr + total_length;
838 
839  /* copy the MCV list header fields, one by one */
840  memcpy(ptr, &mcvlist->magic, sizeof(uint32));
841  ptr += sizeof(uint32);
842 
843  memcpy(ptr, &mcvlist->type, sizeof(uint32));
844  ptr += sizeof(uint32);
845 
846  memcpy(ptr, &mcvlist->nitems, sizeof(uint32));
847  ptr += sizeof(uint32);
848 
849  memcpy(ptr, &mcvlist->ndimensions, sizeof(AttrNumber));
850  ptr += sizeof(AttrNumber);
851 
852  memcpy(ptr, mcvlist->types, sizeof(Oid) * ndims);
853  ptr += (sizeof(Oid) * ndims);
854 
855  /* store information about the attributes (data amounts, ...) */
856  memcpy(ptr, info, sizeof(DimensionInfo) * ndims);
857  ptr += sizeof(DimensionInfo) * ndims;
858 
859  /* Copy the deduplicated values for all attributes to the output. */
860  for (dim = 0; dim < ndims; dim++)
861  {
862  /* remember the starting point for Asserts later */
863  char *start PG_USED_FOR_ASSERTS_ONLY = ptr;
864 
865  for (i = 0; i < info[dim].nvalues; i++)
866  {
867  Datum value = values[dim][i];
868 
869  if (info[dim].typbyval) /* passed by value */
870  {
871  Datum tmp;
872 
873  /*
874  * For values passed by value, we need to copy just the
875  * significant bytes - we can't use memcpy directly, as that
876  * assumes little endian behavior. store_att_byval does
877  * almost what we need, but it requires properly aligned
878  * buffer - the output buffer does not guarantee that. So we
879  * simply use a local Datum variable (which guarantees proper
880  * alignment), and then copy the value from it.
881  */
882  store_att_byval(&tmp, value, info[dim].typlen);
883 
884  memcpy(ptr, &tmp, info[dim].typlen);
885  ptr += info[dim].typlen;
886  }
887  else if (info[dim].typlen > 0) /* passed by reference */
888  {
889  /* no special alignment needed, treated as char array */
890  memcpy(ptr, DatumGetPointer(value), info[dim].typlen);
891  ptr += info[dim].typlen;
892  }
893  else if (info[dim].typlen == -1) /* varlena */
894  {
896 
897  /* copy the length */
898  memcpy(ptr, &len, sizeof(uint32));
899  ptr += sizeof(uint32);
900 
901  /* data from the varlena value (without the header) */
902  memcpy(ptr, VARDATA_ANY(DatumGetPointer(value)), len);
903  ptr += len;
904  }
905  else if (info[dim].typlen == -2) /* cstring */
906  {
907  uint32 len = (uint32) strlen(DatumGetCString(value)) + 1;
908 
909  /* copy the length */
910  memcpy(ptr, &len, sizeof(uint32));
911  ptr += sizeof(uint32);
912 
913  /* value */
914  memcpy(ptr, DatumGetCString(value), len);
915  ptr += len;
916  }
917 
918  /* no underflows or overflows */
919  Assert((ptr > start) && ((ptr - start) <= info[dim].nbytes));
920  }
921 
922  /* we should get exactly nbytes of data for this dimension */
923  Assert((ptr - start) == info[dim].nbytes);
924  }
925 
926  /* Serialize the items, with uint16 indexes instead of the values. */
927  for (i = 0; i < mcvlist->nitems; i++)
928  {
929  MCVItem *mcvitem = &mcvlist->items[i];
930 
931  /* don't write beyond the allocated space */
932  Assert(ptr <= (endptr - ITEM_SIZE(dim)));
933 
934  /* copy NULL and frequency flags into the serialized MCV */
935  memcpy(ptr, mcvitem->isnull, sizeof(bool) * ndims);
936  ptr += sizeof(bool) * ndims;
937 
938  memcpy(ptr, &mcvitem->frequency, sizeof(double));
939  ptr += sizeof(double);
940 
941  memcpy(ptr, &mcvitem->base_frequency, sizeof(double));
942  ptr += sizeof(double);
943 
944  /* store the indexes last */
945  for (dim = 0; dim < ndims; dim++)
946  {
947  uint16 index = 0;
948  Datum *value;
949 
950  /* do the lookup only for non-NULL values */
951  if (!mcvitem->isnull[dim])
952  {
953  value = (Datum *) bsearch_arg(&mcvitem->values[dim], values[dim],
954  info[dim].nvalues, sizeof(Datum),
955  compare_scalars_simple, &ssup[dim]);
956 
957  Assert(value != NULL); /* serialization or deduplication error */
958 
959  /* compute index within the deduplicated array */
960  index = (uint16) (value - values[dim]);
961 
962  /* check the index is within expected bounds */
963  Assert(index < info[dim].nvalues);
964  }
965 
966  /* copy the index into the serialized MCV */
967  memcpy(ptr, &index, sizeof(uint16));
968  ptr += sizeof(uint16);
969  }
970 
971  /* make sure we don't overflow the allocated value */
972  Assert(ptr <= endptr);
973  }
974 
975  /* at this point we expect to match the total_length exactly */
976  Assert(ptr == endptr);
977 
978  pfree(values);
979  pfree(counts);
980 
981  return raw;
982 }
983 
984 /*
985  * statext_mcv_deserialize
986  * Reads serialized MCV list into MCVList structure.
987  *
988  * All the memory needed by the MCV list is allocated as a single chunk, so
989  * it's possible to simply pfree() it at once.
990  */
991 MCVList *
993 {
994  int dim,
995  i;
996  Size expected_size;
997  MCVList *mcvlist;
998  char *raw;
999  char *ptr;
1000  char *endptr PG_USED_FOR_ASSERTS_ONLY;
1001 
1002  int ndims,
1003  nitems;
1004  DimensionInfo *info = NULL;
1005 
1006  /* local allocation buffer (used only for deserialization) */
1007  Datum **map = NULL;
1008 
1009  /* MCV list */
1010  Size mcvlen;
1011 
1012  /* buffer used for the result */
1013  Size datalen;
1014  char *dataptr;
1015  char *valuesptr;
1016  char *isnullptr;
1017 
1018  if (data == NULL)
1019  return NULL;
1020 
1021  /*
1022  * We can't possibly deserialize a MCV list if there's not even a complete
1023  * header. We need an explicit formula here, because we serialize the
1024  * header fields one by one, so we need to ignore struct alignment.
1025  */
1026  if (VARSIZE_ANY(data) < MinSizeOfMCVList)
1027  elog(ERROR, "invalid MCV size %zd (expected at least %zu)",
1028  VARSIZE_ANY(data), MinSizeOfMCVList);
1029 
1030  /* read the MCV list header */
1031  mcvlist = (MCVList *) palloc0(offsetof(MCVList, items));
1032 
1033  /* pointer to the data part (skip the varlena header) */
1034  raw = (char *) data;
1035  ptr = VARDATA_ANY(raw);
1036  endptr = (char *) raw + VARSIZE_ANY(data);
1037 
1038  /* get the header and perform further sanity checks */
1039  memcpy(&mcvlist->magic, ptr, sizeof(uint32));
1040  ptr += sizeof(uint32);
1041 
1042  memcpy(&mcvlist->type, ptr, sizeof(uint32));
1043  ptr += sizeof(uint32);
1044 
1045  memcpy(&mcvlist->nitems, ptr, sizeof(uint32));
1046  ptr += sizeof(uint32);
1047 
1048  memcpy(&mcvlist->ndimensions, ptr, sizeof(AttrNumber));
1049  ptr += sizeof(AttrNumber);
1050 
1051  if (mcvlist->magic != STATS_MCV_MAGIC)
1052  elog(ERROR, "invalid MCV magic %u (expected %u)",
1053  mcvlist->magic, STATS_MCV_MAGIC);
1054 
1055  if (mcvlist->type != STATS_MCV_TYPE_BASIC)
1056  elog(ERROR, "invalid MCV type %u (expected %u)",
1057  mcvlist->type, STATS_MCV_TYPE_BASIC);
1058 
1059  if (mcvlist->ndimensions == 0)
1060  elog(ERROR, "invalid zero-length dimension array in MCVList");
1061  else if ((mcvlist->ndimensions > STATS_MAX_DIMENSIONS) ||
1062  (mcvlist->ndimensions < 0))
1063  elog(ERROR, "invalid length (%d) dimension array in MCVList",
1064  mcvlist->ndimensions);
1065 
1066  if (mcvlist->nitems == 0)
1067  elog(ERROR, "invalid zero-length item array in MCVList");
1068  else if (mcvlist->nitems > STATS_MCVLIST_MAX_ITEMS)
1069  elog(ERROR, "invalid length (%u) item array in MCVList",
1070  mcvlist->nitems);
1071 
1072  nitems = mcvlist->nitems;
1073  ndims = mcvlist->ndimensions;
1074 
1075  /*
1076  * Check amount of data including DimensionInfo for all dimensions and
1077  * also the serialized items (including uint16 indexes). Also, walk
1078  * through the dimension information and add it to the sum.
1079  */
1080  expected_size = SizeOfMCVList(ndims, nitems);
1081 
1082  /*
1083  * Check that we have at least the dimension and info records, along with
1084  * the items. We don't know the size of the serialized values yet. We need
1085  * to do this check first, before accessing the dimension info.
1086  */
1087  if (VARSIZE_ANY(data) < expected_size)
1088  elog(ERROR, "invalid MCV size %zd (expected %zu)",
1089  VARSIZE_ANY(data), expected_size);
1090 
1091  /* Now copy the array of type Oids. */
1092  memcpy(mcvlist->types, ptr, sizeof(Oid) * ndims);
1093  ptr += (sizeof(Oid) * ndims);
1094 
1095  /* Now it's safe to access the dimension info. */
1096  info = palloc(ndims * sizeof(DimensionInfo));
1097 
1098  memcpy(info, ptr, ndims * sizeof(DimensionInfo));
1099  ptr += (ndims * sizeof(DimensionInfo));
1100 
1101  /* account for the value arrays */
1102  for (dim = 0; dim < ndims; dim++)
1103  {
1104  /*
1105  * XXX I wonder if we can/should rely on asserts here. Maybe those
1106  * checks should be done every time?
1107  */
1108  Assert(info[dim].nvalues >= 0);
1109  Assert(info[dim].nbytes >= 0);
1110 
1111  expected_size += info[dim].nbytes;
1112  }
1113 
1114  /*
1115  * Now we know the total expected MCV size, including all the pieces
1116  * (header, dimension info. items and deduplicated data). So do the final
1117  * check on size.
1118  */
1119  if (VARSIZE_ANY(data) != expected_size)
1120  elog(ERROR, "invalid MCV size %zd (expected %zu)",
1121  VARSIZE_ANY(data), expected_size);
1122 
1123  /*
1124  * We need an array of Datum values for each dimension, so that we can
1125  * easily translate the uint16 indexes later. We also need a top-level
1126  * array of pointers to those per-dimension arrays.
1127  *
1128  * While allocating the arrays for dimensions, compute how much space we
1129  * need for a copy of the by-ref data, as we can't simply point to the
1130  * original values (it might go away).
1131  */
1132  datalen = 0; /* space for by-ref data */
1133  map = (Datum **) palloc(ndims * sizeof(Datum *));
1134 
1135  for (dim = 0; dim < ndims; dim++)
1136  {
1137  map[dim] = (Datum *) palloc(sizeof(Datum) * info[dim].nvalues);
1138 
1139  /* space needed for a copy of data for by-ref types */
1140  datalen += info[dim].nbytes_aligned;
1141  }
1142 
1143  /*
1144  * Now resize the MCV list so that the allocation includes all the data.
1145  *
1146  * Allocate space for a copy of the data, as we can't simply reference the
1147  * serialized data - it's not aligned properly, and it may disappear while
1148  * we're still using the MCV list, e.g. due to catcache release.
1149  *
1150  * We do care about alignment here, because we will allocate all the pieces
1151  * at once, but then use pointers to different parts.
1152  */
1153  mcvlen = MAXALIGN(offsetof(MCVList, items) + (sizeof(MCVItem) * nitems));
1154 
1155  /* arrays of values and isnull flags for all MCV items */
1156  mcvlen += nitems * MAXALIGN(sizeof(Datum) * ndims);
1157  mcvlen += nitems * MAXALIGN(sizeof(bool) * ndims);
1158 
1159  /* we don't quite need to align this, but it makes some asserts easier */
1160  mcvlen += MAXALIGN(datalen);
1161 
1162  /* now resize the deserialized MCV list, and compute pointers to parts */
1163  mcvlist = repalloc(mcvlist, mcvlen);
1164 
1165  /* pointer to the beginning of values/isnull arrays */
1166  valuesptr = (char *) mcvlist
1167  + MAXALIGN(offsetof(MCVList, items) + (sizeof(MCVItem) * nitems));
1168 
1169  isnullptr = valuesptr + (nitems * MAXALIGN(sizeof(Datum) * ndims));
1170 
1171  dataptr = isnullptr + (nitems * MAXALIGN(sizeof(bool) * ndims));
1172 
1173  /*
1174  * Build mapping (index => value) for translating the serialized data into
1175  * the in-memory representation.
1176  */
1177  for (dim = 0; dim < ndims; dim++)
1178  {
1179  /* remember start position in the input array */
1180  char *start PG_USED_FOR_ASSERTS_ONLY = ptr;
1181 
1182  if (info[dim].typbyval)
1183  {
1184  /* for by-val types we simply copy data into the mapping */
1185  for (i = 0; i < info[dim].nvalues; i++)
1186  {
1187  Datum v = 0;
1188 
1189  memcpy(&v, ptr, info[dim].typlen);
1190  ptr += info[dim].typlen;
1191 
1192  map[dim][i] = fetch_att(&v, true, info[dim].typlen);
1193 
1194  /* no under/overflow of input array */
1195  Assert(ptr <= (start + info[dim].nbytes));
1196  }
1197  }
1198  else
1199  {
1200  /* for by-ref types we need to also make a copy of the data */
1201 
1202  /* passed by reference, but fixed length (name, tid, ...) */
1203  if (info[dim].typlen > 0)
1204  {
1205  for (i = 0; i < info[dim].nvalues; i++)
1206  {
1207  memcpy(dataptr, ptr, info[dim].typlen);
1208  ptr += info[dim].typlen;
1209 
1210  /* just point into the array */
1211  map[dim][i] = PointerGetDatum(dataptr);
1212  dataptr += MAXALIGN(info[dim].typlen);
1213  }
1214  }
1215  else if (info[dim].typlen == -1)
1216  {
1217  /* varlena */
1218  for (i = 0; i < info[dim].nvalues; i++)
1219  {
1220  uint32 len;
1221 
1222  /* read the uint32 length */
1223  memcpy(&len, ptr, sizeof(uint32));
1224  ptr += sizeof(uint32);
1225 
1226  /* the length is data-only */
1227  SET_VARSIZE(dataptr, len + VARHDRSZ);
1228  memcpy(VARDATA(dataptr), ptr, len);
1229  ptr += len;
1230 
1231  /* just point into the array */
1232  map[dim][i] = PointerGetDatum(dataptr);
1233 
1234  /* skip to place of the next deserialized value */
1235  dataptr += MAXALIGN(len + VARHDRSZ);
1236  }
1237  }
1238  else if (info[dim].typlen == -2)
1239  {
1240  /* cstring */
1241  for (i = 0; i < info[dim].nvalues; i++)
1242  {
1243  uint32 len;
1244 
1245  memcpy(&len, ptr, sizeof(uint32));
1246  ptr += sizeof(uint32);
1247 
1248  memcpy(dataptr, ptr, len);
1249  ptr += len;
1250 
1251  /* just point into the array */
1252  map[dim][i] = PointerGetDatum(dataptr);
1253  dataptr += MAXALIGN(len);
1254  }
1255  }
1256 
1257  /* no under/overflow of input array */
1258  Assert(ptr <= (start + info[dim].nbytes));
1259 
1260  /* no overflow of the output mcv value */
1261  Assert(dataptr <= ((char *) mcvlist + mcvlen));
1262  }
1263 
1264  /* check we consumed input data for this dimension exactly */
1265  Assert(ptr == (start + info[dim].nbytes));
1266  }
1267 
1268  /* we should have also filled the MCV list exactly */
1269  Assert(dataptr == ((char *) mcvlist + mcvlen));
1270 
1271  /* deserialize the MCV items and translate the indexes to Datums */
1272  for (i = 0; i < nitems; i++)
1273  {
1274  MCVItem *item = &mcvlist->items[i];
1275 
1276  item->values = (Datum *) valuesptr;
1277  valuesptr += MAXALIGN(sizeof(Datum) * ndims);
1278 
1279  item->isnull = (bool *) isnullptr;
1280  isnullptr += MAXALIGN(sizeof(bool) * ndims);
1281 
1282  memcpy(item->isnull, ptr, sizeof(bool) * ndims);
1283  ptr += sizeof(bool) * ndims;
1284 
1285  memcpy(&item->frequency, ptr, sizeof(double));
1286  ptr += sizeof(double);
1287 
1288  memcpy(&item->base_frequency, ptr, sizeof(double));
1289  ptr += sizeof(double);
1290 
1291  /* finally translate the indexes (for non-NULL only) */
1292  for (dim = 0; dim < ndims; dim++)
1293  {
1294  uint16 index;
1295 
1296  memcpy(&index, ptr, sizeof(uint16));
1297  ptr += sizeof(uint16);
1298 
1299  if (item->isnull[dim])
1300  continue;
1301 
1302  item->values[dim] = map[dim][index];
1303  }
1304 
1305  /* check we're not overflowing the input */
1306  Assert(ptr <= endptr);
1307  }
1308 
1309  /* check that we processed all the data */
1310  Assert(ptr == endptr);
1311 
1312  /* release the buffers used for mapping */
1313  for (dim = 0; dim < ndims; dim++)
1314  pfree(map[dim]);
1315 
1316  pfree(map);
1317 
1318  return mcvlist;
1319 }
1320 
1321 /*
1322  * SRF with details about buckets of a histogram:
1323  *
1324  * - item ID (0...nitems)
1325  * - values (string array)
1326  * - nulls only (boolean array)
1327  * - frequency (double precision)
1328  * - base_frequency (double precision)
1329  *
1330  * The input is the OID of the statistics, and there are no rows returned if
1331  * the statistics contains no histogram.
1332  */
1333 Datum
1335 {
1336  FuncCallContext *funcctx;
1337 
1338  /* stuff done only on the first call of the function */
1339  if (SRF_IS_FIRSTCALL())
1340  {
1341  MemoryContext oldcontext;
1342  MCVList *mcvlist;
1343  TupleDesc tupdesc;
1344 
1345  /* create a function context for cross-call persistence */
1346  funcctx = SRF_FIRSTCALL_INIT();
1347 
1348  /* switch to memory context appropriate for multiple function calls */
1349  oldcontext = MemoryContextSwitchTo(funcctx->multi_call_memory_ctx);
1350 
1352 
1353  funcctx->user_fctx = mcvlist;
1354 
1355  /* total number of tuples to be returned */
1356  funcctx->max_calls = 0;
1357  if (funcctx->user_fctx != NULL)
1358  funcctx->max_calls = mcvlist->nitems;
1359 
1360  /* Build a tuple descriptor for our result type */
1361  if (get_call_result_type(fcinfo, NULL, &tupdesc) != TYPEFUNC_COMPOSITE)
1362  ereport(ERROR,
1363  (errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
1364  errmsg("function returning record called in context "
1365  "that cannot accept type record")));
1366  tupdesc = BlessTupleDesc(tupdesc);
1367 
1368  /*
1369  * generate attribute metadata needed later to produce tuples from raw
1370  * C strings
1371  */
1372  funcctx->attinmeta = TupleDescGetAttInMetadata(tupdesc);
1373 
1374  MemoryContextSwitchTo(oldcontext);
1375  }
1376 
1377  /* stuff done on every call of the function */
1378  funcctx = SRF_PERCALL_SETUP();
1379 
1380  if (funcctx->call_cntr < funcctx->max_calls) /* do when there is more left to send */
1381  {
1382  Datum values[5];
1383  bool nulls[5];
1384  HeapTuple tuple;
1385  Datum result;
1386  ArrayBuildState *astate_values = NULL;
1387  ArrayBuildState *astate_nulls = NULL;
1388 
1389  int i;
1390  MCVList *mcvlist;
1391  MCVItem *item;
1392 
1393  mcvlist = (MCVList *) funcctx->user_fctx;
1394 
1395  Assert(funcctx->call_cntr < mcvlist->nitems);
1396 
1397  item = &mcvlist->items[funcctx->call_cntr];
1398 
1399  for (i = 0; i < mcvlist->ndimensions; i++)
1400  {
1401 
1402  astate_nulls = accumArrayResult(astate_nulls,
1403  BoolGetDatum(item->isnull[i]),
1404  false,
1405  BOOLOID,
1407 
1408  if (!item->isnull[i])
1409  {
1410  bool isvarlena;
1411  Oid outfunc;
1412  FmgrInfo fmgrinfo;
1413  Datum val;
1414  text *txt;
1415 
1416  /* lookup output func for the type */
1417  getTypeOutputInfo(mcvlist->types[i], &outfunc, &isvarlena);
1418  fmgr_info(outfunc, &fmgrinfo);
1419 
1420  val = FunctionCall1(&fmgrinfo, item->values[i]);
1421  txt = cstring_to_text(DatumGetPointer(val));
1422 
1423  astate_values = accumArrayResult(astate_values,
1424  PointerGetDatum(txt),
1425  false,
1426  TEXTOID,
1428  }
1429  else
1430  astate_values = accumArrayResult(astate_values,
1431  (Datum) 0,
1432  true,
1433  TEXTOID,
1435  }
1436 
1437  values[0] = Int32GetDatum(funcctx->call_cntr);
1438  values[1] = PointerGetDatum(makeArrayResult(astate_values, CurrentMemoryContext));
1439  values[2] = PointerGetDatum(makeArrayResult(astate_nulls, CurrentMemoryContext));
1440  values[3] = Float8GetDatum(item->frequency);
1441  values[4] = Float8GetDatum(item->base_frequency);
1442 
1443  /* no NULLs in the tuple */
1444  memset(nulls, 0, sizeof(nulls));
1445 
1446  /* build a tuple */
1447  tuple = heap_form_tuple(funcctx->attinmeta->tupdesc, values, nulls);
1448 
1449  /* make the tuple into a datum */
1450  result = HeapTupleGetDatum(tuple);
1451 
1452  SRF_RETURN_NEXT(funcctx, result);
1453  }
1454  else /* do when there is no more left */
1455  {
1456  SRF_RETURN_DONE(funcctx);
1457  }
1458 }
1459 
1460 /*
1461  * pg_mcv_list_in - input routine for type pg_mcv_list.
1462  *
1463  * pg_mcv_list is real enough to be a table column, but it has no operations
1464  * of its own, and disallows input too
1465  */
1466 Datum
1468 {
1469  /*
1470  * pg_mcv_list stores the data in binary form and parsing text input is
1471  * not needed, so disallow this.
1472  */
1473  ereport(ERROR,
1474  (errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
1475  errmsg("cannot accept a value of type %s", "pg_mcv_list")));
1476 
1477  PG_RETURN_VOID(); /* keep compiler quiet */
1478 }
1479 
1480 
1481 /*
1482  * pg_mcv_list_out - output routine for type pg_mcv_list.
1483  *
1484  * MCV lists are serialized into a bytea value, so we simply call byteaout()
1485  * to serialize the value into text. But it'd be nice to serialize that into
1486  * a meaningful representation (e.g. for inspection by people).
1487  *
1488  * XXX This should probably return something meaningful, similar to what
1489  * pg_dependencies_out does. Not sure how to deal with the deduplicated
1490  * values, though - do we want to expand that or not?
1491  */
1492 Datum
1494 {
1495  return byteaout(fcinfo);
1496 }
1497 
1498 /*
1499  * pg_mcv_list_recv - binary input routine for type pg_mcv_list.
1500  */
1501 Datum
1503 {
1504  ereport(ERROR,
1505  (errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
1506  errmsg("cannot accept a value of type %s", "pg_mcv_list")));
1507 
1508  PG_RETURN_VOID(); /* keep compiler quiet */
1509 }
1510 
1511 /*
1512  * pg_mcv_list_send - binary output routine for type pg_mcv_list.
1513  *
1514  * MCV lists are serialized in a bytea value (although the type is named
1515  * differently), so let's just send that.
1516  */
1517 Datum
1519 {
1520  return byteasend(fcinfo);
1521 }
1522 
1523 /*
1524  * mcv_get_match_bitmap
1525  * Evaluate clauses using the MCV list, and update the match bitmap.
1526  *
1527  * A match bitmap keeps match/mismatch status for each MCV item, and we
1528  * update it based on additional clauses. We also use it to skip items
1529  * that can't possibly match (e.g. item marked as "mismatch" can't change
1530  * to "match" when evaluating AND clause list).
1531  *
1532  * The function also returns a flag indicating whether there was an
1533  * equality condition for all attributes, the minimum frequency in the MCV
1534  * list, and a total MCV frequency (sum of frequencies for all items).
1535  *
1536  * XXX Currently the match bitmap uses a bool for each MCV item, which is
1537  * somewhat wasteful as we could do with just a single bit, thus reducing
1538  * the size to ~1/8. It would also allow us to combine bitmaps simply using
1539  * & and |, which should be faster than min/max. The bitmaps are fairly
1540  * small, though (thanks to the cap on the MCV list size).
1541  */
1542 static bool *
1544  Bitmapset *keys, MCVList *mcvlist, bool is_or)
1545 {
1546  int i;
1547  ListCell *l;
1548  bool *matches;
1549 
1550  /* The bitmap may be partially built. */
1551  Assert(clauses != NIL);
1552  Assert(list_length(clauses) >= 1);
1553  Assert(mcvlist != NULL);
1554  Assert(mcvlist->nitems > 0);
1555  Assert(mcvlist->nitems <= STATS_MCVLIST_MAX_ITEMS);
1556 
1557  matches = palloc(sizeof(bool) * mcvlist->nitems);
1558  memset(matches, (is_or) ? false : true,
1559  sizeof(bool) * mcvlist->nitems);
1560 
1561  /*
1562  * Loop through the list of clauses, and for each of them evaluate all the
1563  * MCV items not yet eliminated by the preceding clauses.
1564  */
1565  foreach(l, clauses)
1566  {
1567  Node *clause = (Node *) lfirst(l);
1568 
1569  /* if it's a RestrictInfo, then extract the clause */
1570  if (IsA(clause, RestrictInfo))
1571  clause = (Node *) ((RestrictInfo *) clause)->clause;
1572 
1573  /*
1574  * Handle the various types of clauses - OpClause, NullTest and
1575  * AND/OR/NOT
1576  */
1577  if (is_opclause(clause))
1578  {
1579  OpExpr *expr = (OpExpr *) clause;
1580  FmgrInfo opproc;
1581 
1582  /* valid only after examine_opclause_expression returns true */
1583  Var *var;
1584  Const *cst;
1585  bool varonleft;
1586 
1587  fmgr_info(get_opcode(expr->opno), &opproc);
1588 
1589  /* extract the var and const from the expression */
1590  if (examine_opclause_expression(expr, &var, &cst, &varonleft))
1591  {
1592  int idx;
1593 
1594  /* match the attribute to a dimension of the statistic */
1595  idx = bms_member_index(keys, var->varattno);
1596 
1597  /*
1598  * Walk through the MCV items and evaluate the current clause.
1599  * We can skip items that were already ruled out, and
1600  * terminate if there are no remaining MCV items that might
1601  * possibly match.
1602  */
1603  for (i = 0; i < mcvlist->nitems; i++)
1604  {
1605  bool match = true;
1606  MCVItem *item = &mcvlist->items[i];
1607 
1608  /*
1609  * When the MCV item or the Const value is NULL we can treat
1610  * this as a mismatch. We must not call the operator because
1611  * of strictness.
1612  */
1613  if (item->isnull[idx] || cst->constisnull)
1614  {
1615  matches[i] = RESULT_MERGE(matches[i], is_or, false);
1616  continue;
1617  }
1618 
1619  /*
1620  * Skip MCV items that can't change result in the bitmap.
1621  * Once the value gets false for AND-lists, or true for
1622  * OR-lists, we don't need to look at more clauses.
1623  */
1624  if (RESULT_IS_FINAL(matches[i], is_or))
1625  continue;
1626 
1627  /*
1628  * First check whether the constant is below the lower
1629  * boundary (in that case we can skip the bucket, because
1630  * there's no overlap).
1631  *
1632  * We don't store collations used to build the statistics,
1633  * but we can use the collation for the attribute itself,
1634  * as stored in varcollid. We do reset the statistics after
1635  * a type change (including collation change), so this is
1636  * OK. We may need to relax this after allowing extended
1637  * statistics on expressions.
1638  */
1639  if (varonleft)
1640  match = DatumGetBool(FunctionCall2Coll(&opproc,
1641  var->varcollid,
1642  item->values[idx],
1643  cst->constvalue));
1644  else
1645  match = DatumGetBool(FunctionCall2Coll(&opproc,
1646  var->varcollid,
1647  cst->constvalue,
1648  item->values[idx]));
1649 
1650  /* update the match bitmap with the result */
1651  matches[i] = RESULT_MERGE(matches[i], is_or, match);
1652  }
1653  }
1654  }
1655  else if (IsA(clause, NullTest))
1656  {
1657  NullTest *expr = (NullTest *) clause;
1658  Var *var = (Var *) (expr->arg);
1659 
1660  /* match the attribute to a dimension of the statistic */
1661  int idx = bms_member_index(keys, var->varattno);
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 = false; /* assume mismatch */
1671  MCVItem *item = &mcvlist->items[i];
1672 
1673  /* if the clause mismatches the MCV item, update the bitmap */
1674  switch (expr->nulltesttype)
1675  {
1676  case IS_NULL:
1677  match = (item->isnull[idx]) ? true : match;
1678  break;
1679 
1680  case IS_NOT_NULL:
1681  match = (!item->isnull[idx]) ? true : match;
1682  break;
1683  }
1684 
1685  /* now, update the match bitmap, depending on OR/AND type */
1686  matches[i] = RESULT_MERGE(matches[i], is_or, match);
1687  }
1688  }
1689  else if (is_orclause(clause) || is_andclause(clause))
1690  {
1691  /* AND/OR clause, with all subclauses being compatible */
1692 
1693  int i;
1694  BoolExpr *bool_clause = ((BoolExpr *) clause);
1695  List *bool_clauses = bool_clause->args;
1696 
1697  /* match/mismatch bitmap for each MCV item */
1698  bool *bool_matches = NULL;
1699 
1700  Assert(bool_clauses != NIL);
1701  Assert(list_length(bool_clauses) >= 2);
1702 
1703  /* build the match bitmap for the OR-clauses */
1704  bool_matches = mcv_get_match_bitmap(root, bool_clauses, keys,
1705  mcvlist, is_orclause(clause));
1706 
1707  /*
1708  * Merge the bitmap produced by mcv_get_match_bitmap into the
1709  * current one. We need to consider if we're evaluating AND or OR
1710  * condition when merging the results.
1711  */
1712  for (i = 0; i < mcvlist->nitems; i++)
1713  matches[i] = RESULT_MERGE(matches[i], is_or, bool_matches[i]);
1714 
1715  pfree(bool_matches);
1716  }
1717  else if (is_notclause(clause))
1718  {
1719  /* NOT clause, with all subclauses compatible */
1720 
1721  int i;
1722  BoolExpr *not_clause = ((BoolExpr *) clause);
1723  List *not_args = not_clause->args;
1724 
1725  /* match/mismatch bitmap for each MCV item */
1726  bool *not_matches = NULL;
1727 
1728  Assert(not_args != NIL);
1729  Assert(list_length(not_args) == 1);
1730 
1731  /* build the match bitmap for the NOT-clause */
1732  not_matches = mcv_get_match_bitmap(root, not_args, keys,
1733  mcvlist, false);
1734 
1735  /*
1736  * Merge the bitmap produced by mcv_get_match_bitmap into the
1737  * current one. We're handling a NOT clause, so invert the result
1738  * before merging it into the global bitmap.
1739  */
1740  for (i = 0; i < mcvlist->nitems; i++)
1741  matches[i] = RESULT_MERGE(matches[i], is_or, !not_matches[i]);
1742 
1743  pfree(not_matches);
1744  }
1745  else if (IsA(clause, Var))
1746  {
1747  /* Var (has to be a boolean Var, possibly from below NOT) */
1748 
1749  Var *var = (Var *) (clause);
1750 
1751  /* match the attribute to a dimension of the statistic */
1752  int idx = bms_member_index(keys, var->varattno);
1753 
1754  Assert(var->vartype == BOOLOID);
1755 
1756  /*
1757  * Walk through the MCV items and evaluate the current clause. We
1758  * can skip items that were already ruled out, and terminate if
1759  * there are no remaining MCV items that might possibly match.
1760  */
1761  for (i = 0; i < mcvlist->nitems; i++)
1762  {
1763  MCVItem *item = &mcvlist->items[i];
1764  bool match = false;
1765 
1766  /* if the item is NULL, it's a mismatch */
1767  if (!item->isnull[idx] && DatumGetBool(item->values[idx]))
1768  match = true;
1769 
1770  /* update the result bitmap */
1771  matches[i] = RESULT_MERGE(matches[i], is_or, match);
1772  }
1773  }
1774  else
1775  elog(ERROR, "unknown clause type: %d", clause->type);
1776  }
1777 
1778  return matches;
1779 }
1780 
1781 
1782 /*
1783  * mcv_clauselist_selectivity
1784  * Return the selectivity estimate computed using an MCV list.
1785  *
1786  * First builds a bitmap of MCV items matching the clauses, and then sums
1787  * the frequencies of matching items.
1788  *
1789  * It also produces two additional interesting selectivities - total
1790  * selectivity of all the MCV items (not just the matching ones), and the
1791  * base frequency computed on the assumption of independence.
1792  */
1795  List *clauses, int varRelid,
1796  JoinType jointype, SpecialJoinInfo *sjinfo,
1797  RelOptInfo *rel,
1798  Selectivity *basesel, Selectivity *totalsel)
1799 {
1800  int i;
1801  MCVList *mcv;
1802  Selectivity s = 0.0;
1803 
1804  /* match/mismatch bitmap for each MCV item */
1805  bool *matches = NULL;
1806 
1807  /* load the MCV list stored in the statistics object */
1808  mcv = statext_mcv_load(stat->statOid);
1809 
1810  /* build a match bitmap for the clauses */
1811  matches = mcv_get_match_bitmap(root, clauses, stat->keys, mcv, false);
1812 
1813  /* sum frequencies for all the matching MCV items */
1814  *basesel = 0.0;
1815  *totalsel = 0.0;
1816  for (i = 0; i < mcv->nitems; i++)
1817  {
1818  *totalsel += mcv->items[i].frequency;
1819 
1820  if (matches[i] != false)
1821  {
1822  /* XXX Shouldn't the basesel be outside the if condition? */
1823  *basesel += mcv->items[i].base_frequency;
1824  s += mcv->items[i].frequency;
1825  }
1826  }
1827 
1828  return s;
1829 }
Datum constvalue
Definition: primnodes.h:200
static SortItem * build_distinct_groups(int numrows, SortItem *items, MultiSortSupport mss, int *ndistinct)
Definition: mcv.c:423
struct SortSupportData * SortSupport
Definition: sortsupport.h:58
uint32 nitems
Definition: statistics.h:90
uint64 call_cntr
Definition: funcapi.h:66
static SortItem ** build_column_frequencies(SortItem *groups, int ngroups, MultiSortSupport mss, int *ncounts)
Definition: mcv.c:489
Datum pg_stats_ext_mcvlist_items(PG_FUNCTION_ARGS)
Definition: mcv.c:1334
Datum byteaout(PG_FUNCTION_ARGS)
Definition: varlena.c:373
bool ssup_nulls_first
Definition: sortsupport.h:75
#define NIL
Definition: pg_list.h:65
Definition: fmgr.h:56
MCVList * statext_mcv_build(int numrows, HeapTuple *rows, Bitmapset *attrs, VacAttrStats **stats, double totalrows, int stattarget)
Definition: mcv.c:183
#define IsA(nodeptr, _type_)
Definition: nodes.h:576
TypeFuncClass get_call_result_type(FunctionCallInfo fcinfo, Oid *resultTypeId, TupleDesc *resultTupleDesc)
Definition: funcapi.c:196
void getTypeOutputInfo(Oid type, Oid *typOutput, bool *typIsVarlena)
Definition: lsyscache.c:2674
#define VARDATA_ANY(PTR)
Definition: postgres.h:348
#define VARDATA(PTR)
Definition: postgres.h:302
void * bsearch_arg(const void *key, const void *base, size_t nmemb, size_t size, int(*compar)(const void *, const void *, void *), void *arg)
Oid types[STATS_MAX_DIMENSIONS]
Definition: statistics.h:92
static bool is_orclause(const void *clause)
Definition: nodeFuncs.h:103
MCVList * statext_mcv_deserialize(bytea *data)
Definition: mcv.c:992
#define SRF_IS_FIRSTCALL()
Definition: funcapi.h:283
#define PointerGetDatum(X)
Definition: postgres.h:556
Datum pg_mcv_list_in(PG_FUNCTION_ARGS)
Definition: mcv.c:1467
#define VARHDRSZ
Definition: c.h:556
static MultiSortSupport build_mss(VacAttrStats **stats, int numattrs)
Definition: mcv.c:349
void PrepareSortSupportFromOrderingOp(Oid orderingOp, SortSupport ssup)
Definition: sortsupport.c:134
static bool is_andclause(const void *clause)
Definition: nodeFuncs.h:94
SortItem * build_sorted_items(int numrows, int *nitems, HeapTuple *rows, TupleDesc tdesc, MultiSortSupport mss, int numattrs, AttrNumber *attnums)
#define MinSizeOfMCVList
Definition: mcv.c:62
static MemoryContext MemoryContextSwitchTo(MemoryContext context)
Definition: palloc.h:109
static struct @145 value
Definition: nodes.h:525
struct DimensionInfo DimensionInfo
uint32 magic
Definition: statistics.h:88
int errcode(int sqlerrcode)
Definition: elog.c:608
AttrNumber varattno
Definition: primnodes.h:172
static int compare_sort_item_count(const void *a, const void *b)
Definition: mcv.c:403
AttrNumber ndimensions
Definition: statistics.h:91
Datum idx(PG_FUNCTION_ARGS)
Definition: _int_op.c:263
struct MCVItem MCVItem
HeapTuple heap_form_tuple(TupleDesc tupleDescriptor, Datum *values, bool *isnull)
Definition: heaptuple.c:1020
Datum * values
Definition: statistics.h:82
double Selectivity
Definition: nodes.h:658
MCVItem items[FLEXIBLE_ARRAY_MEMBER]
Definition: statistics.h:93
Datum FunctionCall2Coll(FmgrInfo *flinfo, Oid collation, Datum arg1, Datum arg2)
Definition: fmgr.c:1150
unsigned int Oid
Definition: postgres_ext.h:31
Definition: primnodes.h:167
static double get_mincount_for_mcv_list(int samplerows, double totalrows)
Definition: mcv.c:151
#define SRF_PERCALL_SETUP()
Definition: funcapi.h:287
static bool * mcv_get_match_bitmap(PlannerInfo *root, List *clauses, Bitmapset *keys, MCVList *mcvlist, bool is_or)
Definition: mcv.c:1543
Datum Float8GetDatum(float8 X)
Definition: fmgr.c:1723
JoinType
Definition: nodes.h:692
Definition: type.h:89
#define SRF_RETURN_NEXT(_funcctx, _result)
Definition: funcapi.h:289
#define PG_GETARG_BYTEA_P(n)
Definition: fmgr.h:329
unsigned short uint16
Definition: c.h:358
void pfree(void *pointer)
Definition: mcxt.c:1056
Oid attrtypid
Definition: vacuum.h:86
SortSupportData ssup[FLEXIBLE_ARRAY_MEMBER]
#define ObjectIdGetDatum(X)
Definition: postgres.h:507
#define ERROR
Definition: elog.h:43
#define PG_UINT16_MAX
Definition: c.h:440
#define DatumGetCString(X)
Definition: postgres.h:566
void multi_sort_add_dimension(MultiSortSupport mss, int sortdim, Oid oper, Oid collation)
Oid vartype
Definition: primnodes.h:174
void fmgr_info(Oid functionId, FmgrInfo *finfo)
Definition: fmgr.c:124
MemoryContext ssup_cxt
Definition: sortsupport.h:66
Expr * arg
Definition: primnodes.h:1205
#define DatumGetByteaP(X)
Definition: fmgr.h:325
bytea * statext_mcv_serialize(MCVList *mcvlist, VacAttrStats **stats)
Definition: mcv.c:618
#define RESULT_MERGE(value, is_or, match)
Definition: mcv.c:91
TupleDesc BlessTupleDesc(TupleDesc tupdesc)
Definition: execTuples.c:2052
NodeTag type
Definition: nodes.h:527
AttrNumber * build_attnums_array(Bitmapset *attrs, int *numattrs)
AttInMetadata * attinmeta
Definition: funcapi.h:92
TupleDesc tupdesc
Definition: funcapi.h:39
#define DatumGetBool(X)
Definition: postgres.h:393
int bms_member_index(Bitmapset *a, int x)
Definition: bitmapset.c:453
int compare_scalars_simple(const void *a, const void *b, void *arg)
unsigned int uint32
Definition: c.h:359
#define RESULT_IS_FINAL(value, is_or)
Definition: mcv.c:103
#define SizeOfMCVList(ndims, nitems)
Definition: mcv.c:71
MemoryContext CurrentMemoryContext
Definition: mcxt.c:38
MCVList * statext_mcv_load(Oid mvoid)
Definition: mcv.c:557
#define ereport(elevel, rest)
Definition: elog.h:141
Datum makeArrayResult(ArrayBuildState *astate, MemoryContext rcontext)
Definition: arrayfuncs.c:5117
uint32 type
Definition: statistics.h:89
MultiSortSupport multi_sort_init(int ndims)
static int sort_item_compare(const void *a, const void *b, void *arg)
Definition: mcv.c:464
void qsort_arg(void *base, size_t nel, size_t elsize, qsort_arg_comparator cmp, void *arg)
Definition: qsort_arg.c:113
Datum byteasend(PG_FUNCTION_ARGS)
Definition: varlena.c:464
#define STATS_MCV_TYPE_BASIC
Definition: statistics.h:66
#define stat(a, b)
Definition: win32_port.h:255
struct SortSupportData SortSupportData
HeapTuple SearchSysCache1(int cacheId, Datum key1)
Definition: syscache.c:1116
static bool is_notclause(const void *clause)
Definition: nodeFuncs.h:112
int compare_datums_simple(Datum a, Datum b, SortSupport ssup)
#define store_att_byval(T, newdatum, attlen)
Definition: tupmacs.h:224
void * palloc0(Size size)
Definition: mcxt.c:980
uintptr_t Datum
Definition: postgres.h:367
void ReleaseSysCache(HeapTuple tuple)
Definition: syscache.c:1164
Datum SysCacheGetAttr(int cacheId, HeapTuple tup, AttrNumber attributeNumber, bool *isNull)
Definition: syscache.c:1377
#define STATS_MCVLIST_MAX_ITEMS
Definition: statistics.h:69
AttInMetadata * TupleDescGetAttInMetadata(TupleDesc tupdesc)
Definition: execTuples.c:2067
NullTestType nulltesttype
Definition: primnodes.h:1206
#define VARSIZE_ANY(PTR)
Definition: postgres.h:335
#define BoolGetDatum(X)
Definition: postgres.h:402
TypeCacheEntry * lookup_type_cache(Oid type_id, int flags)
Definition: typcache.c:322
#define InvalidOid
Definition: postgres_ext.h:36
RegProcedure get_opcode(Oid opno)
Definition: lsyscache.c:1092
#define ITEM_SIZE(ndims)
Definition: mcv.c:56
#define PG_RETURN_VOID()
Definition: fmgr.h:339
text * cstring_to_text(const char *s)
Definition: varlena.c:171
#define HeapTupleIsValid(tuple)
Definition: htup.h:78
#define Assert(condition)
Definition: c.h:733
#define lfirst(lc)
Definition: pg_list.h:190
double base_frequency
Definition: statistics.h:80
#define STATS_MCV_MAGIC
Definition: statistics.h:65
int multi_sort_compare(const void *a, const void *b, void *arg)
MemoryContext multi_call_memory_ctx
Definition: funcapi.h:102
size_t Size
Definition: c.h:467
void pg_qsort(void *base, size_t nel, size_t elsize, int(*cmp)(const void *, const void *))
Definition: qsort.c:113
static int list_length(const List *l)
Definition: pg_list.h:169
#define MAXALIGN(LEN)
Definition: c.h:686
#define HeapTupleGetDatum(tuple)
Definition: funcapi.h:221
Datum pg_mcv_list_send(PG_FUNCTION_ARGS)
Definition: mcv.c:1518
Oid attrcollid
Definition: vacuum.h:89
List * args
Definition: primnodes.h:569
void * repalloc(void *pointer, Size size)
Definition: mcxt.c:1069
#define DatumGetPointer(X)
Definition: postgres.h:549
bool * isnull
Definition: statistics.h:81
static Datum values[MAXATTR]
Definition: bootstrap.c:167
ArrayBuildState * accumArrayResult(ArrayBuildState *astate, Datum dvalue, bool disnull, Oid element_type, MemoryContext rcontext)
Definition: arrayfuncs.c:5053
#define Int32GetDatum(X)
Definition: postgres.h:479
Bitmapset * keys
Definition: pathnodes.h:885
void * user_fctx
Definition: funcapi.h:83
Form_pg_type attrtype
Definition: vacuum.h:88
#define STATS_MAX_DIMENSIONS
Definition: statistics.h:19
#define VARSIZE_ANY_EXHDR(PTR)
Definition: postgres.h:341
void * palloc(Size size)
Definition: mcxt.c:949
int errmsg(const char *fmt,...)
Definition: elog.c:822
#define fetch_att(T, attbyval, attlen)
Definition: tupmacs.h:73
#define elog(elevel,...)
Definition: elog.h:228
int i
#define FunctionCall1(flinfo, arg1)
Definition: fmgr.h:635
#define TYPECACHE_LT_OPR
Definition: typcache.h:129
Oid varcollid
Definition: primnodes.h:176
Selectivity mcv_clauselist_selectivity(PlannerInfo *root, StatisticExtInfo *stat, List *clauses, int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo, RelOptInfo *rel, Selectivity *basesel, Selectivity *totalsel)
Definition: mcv.c:1794
void * arg
#define PG_DETOAST_DATUM(datum)
Definition: fmgr.h:235
Definition: c.h:550
#define PG_FUNCTION_ARGS
Definition: fmgr.h:188
MultiSortSupportData * MultiSortSupport
Datum pg_mcv_list_out(PG_FUNCTION_ARGS)
Definition: mcv.c:1493
Oid opno
Definition: primnodes.h:502
#define SET_VARSIZE(PTR, len)
Definition: postgres.h:329
static bool is_opclause(const void *clause)
Definition: nodeFuncs.h:63
bool examine_opclause_expression(OpExpr *expr, Var **varp, Const **cstp, bool *varonleftp)
#define PG_USED_FOR_ASSERTS_ONLY
Definition: c.h:123
static int ApplySortComparator(Datum datum1, bool isNull1, Datum datum2, bool isNull2, SortSupport ssup)
Definition: sortsupport.h:200
static int count_distinct_groups(int numrows, SortItem *items, MultiSortSupport mss)
Definition: mcv.c:380
Datum pg_mcv_list_recv(PG_FUNCTION_ARGS)
Definition: mcv.c:1502
Definition: pg_list.h:50
int16 AttrNumber
Definition: attnum.h:21
long val
Definition: informix.c:684
bool constisnull
Definition: primnodes.h:201
#define offsetof(type, field)
Definition: c.h:656
uint64 max_calls
Definition: funcapi.h:75
#define SRF_RETURN_DONE(_funcctx)
Definition: funcapi.h:307
double frequency
Definition: statistics.h:79
unsigned char bool
Definition: c.h:309
#define SRF_FIRSTCALL_INIT()
Definition: funcapi.h:285