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array_typanalyze.c
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1 /*-------------------------------------------------------------------------
2  *
3  * array_typanalyze.c
4  * Functions for gathering statistics from array columns
5  *
6  * Portions Copyright (c) 1996-2022, PostgreSQL Global Development Group
7  * Portions Copyright (c) 1994, Regents of the University of California
8  *
9  *
10  * IDENTIFICATION
11  * src/backend/utils/adt/array_typanalyze.c
12  *
13  *-------------------------------------------------------------------------
14  */
15 #include "postgres.h"
16 
17 #include "access/detoast.h"
18 #include "commands/vacuum.h"
19 #include "utils/array.h"
20 #include "utils/builtins.h"
21 #include "utils/datum.h"
22 #include "utils/lsyscache.h"
23 #include "utils/typcache.h"
24 
25 
26 /*
27  * To avoid consuming too much memory, IO and CPU load during analysis, and/or
28  * too much space in the resulting pg_statistic rows, we ignore arrays that
29  * are wider than ARRAY_WIDTH_THRESHOLD (after detoasting!). Note that this
30  * number is considerably more than the similar WIDTH_THRESHOLD limit used
31  * in analyze.c's standard typanalyze code.
32  */
33 #define ARRAY_WIDTH_THRESHOLD 0x10000
34 
35 /* Extra data for compute_array_stats function */
36 typedef struct
37 {
38  /* Information about array element type */
39  Oid type_id; /* element type's OID */
40  Oid eq_opr; /* default equality operator's OID */
41  Oid coll_id; /* collation to use */
42  bool typbyval; /* physical properties of element type */
44  char typalign;
45 
46  /*
47  * Lookup data for element type's comparison and hash functions (these are
48  * in the type's typcache entry, which we expect to remain valid over the
49  * lifespan of the ANALYZE run)
50  */
53 
54  /* Saved state from std_typanalyze() */
58 
59 /*
60  * While compute_array_stats is running, we keep a pointer to the extra data
61  * here for use by assorted subroutines. compute_array_stats doesn't
62  * currently need to be re-entrant, so avoiding this is not worth the extra
63  * notational cruft that would be needed.
64  */
66 
67 /* A hash table entry for the Lossy Counting algorithm */
68 typedef struct
69 {
70  Datum key; /* This is 'e' from the LC algorithm. */
71  int frequency; /* This is 'f'. */
72  int delta; /* And this is 'delta'. */
73  int last_container; /* For de-duplication of array elements. */
74 } TrackItem;
75 
76 /* A hash table entry for distinct-elements counts */
77 typedef struct
78 {
79  int count; /* Count of distinct elements in an array */
80  int frequency; /* Number of arrays seen with this count */
81 } DECountItem;
82 
83 static void compute_array_stats(VacAttrStats *stats,
84  AnalyzeAttrFetchFunc fetchfunc, int samplerows, double totalrows);
85 static void prune_element_hashtable(HTAB *elements_tab, int b_current);
86 static uint32 element_hash(const void *key, Size keysize);
87 static int element_match(const void *key1, const void *key2, Size keysize);
88 static int element_compare(const void *key1, const void *key2);
89 static int trackitem_compare_frequencies_desc(const void *e1, const void *e2, void *arg);
90 static int trackitem_compare_element(const void *e1, const void *e2, void *arg);
91 static int countitem_compare_count(const void *e1, const void *e2, void *arg);
92 
93 
94 /*
95  * array_typanalyze -- typanalyze function for array columns
96  */
97 Datum
99 {
101  Oid element_typeid;
102  TypeCacheEntry *typentry;
103  ArrayAnalyzeExtraData *extra_data;
104 
105  /*
106  * Call the standard typanalyze function. It may fail to find needed
107  * operators, in which case we also can't do anything, so just fail.
108  */
109  if (!std_typanalyze(stats))
110  PG_RETURN_BOOL(false);
111 
112  /*
113  * Check attribute data type is a varlena array (or a domain over one).
114  */
115  element_typeid = get_base_element_type(stats->attrtypid);
116  if (!OidIsValid(element_typeid))
117  elog(ERROR, "array_typanalyze was invoked for non-array type %u",
118  stats->attrtypid);
119 
120  /*
121  * Gather information about the element type. If we fail to find
122  * something, return leaving the state from std_typanalyze() in place.
123  */
124  typentry = lookup_type_cache(element_typeid,
128 
129  if (!OidIsValid(typentry->eq_opr) ||
130  !OidIsValid(typentry->cmp_proc_finfo.fn_oid) ||
131  !OidIsValid(typentry->hash_proc_finfo.fn_oid))
132  PG_RETURN_BOOL(true);
133 
134  /* Store our findings for use by compute_array_stats() */
135  extra_data = (ArrayAnalyzeExtraData *) palloc(sizeof(ArrayAnalyzeExtraData));
136  extra_data->type_id = typentry->type_id;
137  extra_data->eq_opr = typentry->eq_opr;
138  extra_data->coll_id = stats->attrcollid; /* collation we should use */
139  extra_data->typbyval = typentry->typbyval;
140  extra_data->typlen = typentry->typlen;
141  extra_data->typalign = typentry->typalign;
142  extra_data->cmp = &typentry->cmp_proc_finfo;
143  extra_data->hash = &typentry->hash_proc_finfo;
144 
145  /* Save old compute_stats and extra_data for scalar statistics ... */
146  extra_data->std_compute_stats = stats->compute_stats;
147  extra_data->std_extra_data = stats->extra_data;
148 
149  /* ... and replace with our info */
151  stats->extra_data = extra_data;
152 
153  /*
154  * Note we leave stats->minrows set as std_typanalyze set it. Should it
155  * be increased for array analysis purposes?
156  */
157 
158  PG_RETURN_BOOL(true);
159 }
160 
161 /*
162  * compute_array_stats() -- compute statistics for an array column
163  *
164  * This function computes statistics useful for determining selectivity of
165  * the array operators <@, &&, and @>. It is invoked by ANALYZE via the
166  * compute_stats hook after sample rows have been collected.
167  *
168  * We also invoke the standard compute_stats function, which will compute
169  * "scalar" statistics relevant to the btree-style array comparison operators.
170  * However, exact duplicates of an entire array may be rare despite many
171  * arrays sharing individual elements. This especially afflicts long arrays,
172  * which are also liable to lack all scalar statistics due to the low
173  * WIDTH_THRESHOLD used in analyze.c. So, in addition to the standard stats,
174  * we find the most common array elements and compute a histogram of distinct
175  * element counts.
176  *
177  * The algorithm used is Lossy Counting, as proposed in the paper "Approximate
178  * frequency counts over data streams" by G. S. Manku and R. Motwani, in
179  * Proceedings of the 28th International Conference on Very Large Data Bases,
180  * Hong Kong, China, August 2002, section 4.2. The paper is available at
181  * http://www.vldb.org/conf/2002/S10P03.pdf
182  *
183  * The Lossy Counting (aka LC) algorithm goes like this:
184  * Let s be the threshold frequency for an item (the minimum frequency we
185  * are interested in) and epsilon the error margin for the frequency. Let D
186  * be a set of triples (e, f, delta), where e is an element value, f is that
187  * element's frequency (actually, its current occurrence count) and delta is
188  * the maximum error in f. We start with D empty and process the elements in
189  * batches of size w. (The batch size is also known as "bucket size" and is
190  * equal to 1/epsilon.) Let the current batch number be b_current, starting
191  * with 1. For each element e we either increment its f count, if it's
192  * already in D, or insert a new triple into D with values (e, 1, b_current
193  * - 1). After processing each batch we prune D, by removing from it all
194  * elements with f + delta <= b_current. After the algorithm finishes we
195  * suppress all elements from D that do not satisfy f >= (s - epsilon) * N,
196  * where N is the total number of elements in the input. We emit the
197  * remaining elements with estimated frequency f/N. The LC paper proves
198  * that this algorithm finds all elements with true frequency at least s,
199  * and that no frequency is overestimated or is underestimated by more than
200  * epsilon. Furthermore, given reasonable assumptions about the input
201  * distribution, the required table size is no more than about 7 times w.
202  *
203  * In the absence of a principled basis for other particular values, we
204  * follow ts_typanalyze() and use parameters s = 0.07/K, epsilon = s/10.
205  * But we leave out the correction for stopwords, which do not apply to
206  * arrays. These parameters give bucket width w = K/0.007 and maximum
207  * expected hashtable size of about 1000 * K.
208  *
209  * Elements may repeat within an array. Since duplicates do not change the
210  * behavior of <@, && or @>, we want to count each element only once per
211  * array. Therefore, we store in the finished pg_statistic entry each
212  * element's frequency as the fraction of all non-null rows that contain it.
213  * We divide the raw counts by nonnull_cnt to get those figures.
214  */
215 static void
217  int samplerows, double totalrows)
218 {
219  ArrayAnalyzeExtraData *extra_data;
220  int num_mcelem;
221  int null_elem_cnt = 0;
222  int analyzed_rows = 0;
223 
224  /* This is D from the LC algorithm. */
225  HTAB *elements_tab;
226  HASHCTL elem_hash_ctl;
227  HASH_SEQ_STATUS scan_status;
228 
229  /* This is the current bucket number from the LC algorithm */
230  int b_current;
231 
232  /* This is 'w' from the LC algorithm */
233  int bucket_width;
234  int array_no;
235  int64 element_no;
236  TrackItem *item;
237  int slot_idx;
238  HTAB *count_tab;
239  HASHCTL count_hash_ctl;
240  DECountItem *count_item;
241 
242  extra_data = (ArrayAnalyzeExtraData *) stats->extra_data;
243 
244  /*
245  * Invoke analyze.c's standard analysis function to create scalar-style
246  * stats for the column. It will expect its own extra_data pointer, so
247  * temporarily install that.
248  */
249  stats->extra_data = extra_data->std_extra_data;
250  extra_data->std_compute_stats(stats, fetchfunc, samplerows, totalrows);
251  stats->extra_data = extra_data;
252 
253  /*
254  * Set up static pointer for use by subroutines. We wait till here in
255  * case std_compute_stats somehow recursively invokes us (probably not
256  * possible, but ...)
257  */
258  array_extra_data = extra_data;
259 
260  /*
261  * We want statistics_target * 10 elements in the MCELEM array. This
262  * multiplier is pretty arbitrary, but is meant to reflect the fact that
263  * the number of individual elements tracked in pg_statistic ought to be
264  * more than the number of values for a simple scalar column.
265  */
266  num_mcelem = stats->attr->attstattarget * 10;
267 
268  /*
269  * We set bucket width equal to num_mcelem / 0.007 as per the comment
270  * above.
271  */
272  bucket_width = num_mcelem * 1000 / 7;
273 
274  /*
275  * Create the hashtable. It will be in local memory, so we don't need to
276  * worry about overflowing the initial size. Also we don't need to pay any
277  * attention to locking and memory management.
278  */
279  elem_hash_ctl.keysize = sizeof(Datum);
280  elem_hash_ctl.entrysize = sizeof(TrackItem);
281  elem_hash_ctl.hash = element_hash;
282  elem_hash_ctl.match = element_match;
283  elem_hash_ctl.hcxt = CurrentMemoryContext;
284  elements_tab = hash_create("Analyzed elements table",
285  num_mcelem,
286  &elem_hash_ctl,
288 
289  /* hashtable for array distinct elements counts */
290  count_hash_ctl.keysize = sizeof(int);
291  count_hash_ctl.entrysize = sizeof(DECountItem);
292  count_hash_ctl.hcxt = CurrentMemoryContext;
293  count_tab = hash_create("Array distinct element count table",
294  64,
295  &count_hash_ctl,
297 
298  /* Initialize counters. */
299  b_current = 1;
300  element_no = 0;
301 
302  /* Loop over the arrays. */
303  for (array_no = 0; array_no < samplerows; array_no++)
304  {
305  Datum value;
306  bool isnull;
307  ArrayType *array;
308  int num_elems;
309  Datum *elem_values;
310  bool *elem_nulls;
311  bool null_present;
312  int j;
313  int64 prev_element_no = element_no;
314  int distinct_count;
315  bool count_item_found;
316 
318 
319  value = fetchfunc(stats, array_no, &isnull);
320  if (isnull)
321  {
322  /* ignore arrays that are null overall */
323  continue;
324  }
325 
326  /* Skip too-large values. */
328  continue;
329  else
330  analyzed_rows++;
331 
332  /*
333  * Now detoast the array if needed, and deconstruct into datums.
334  */
335  array = DatumGetArrayTypeP(value);
336 
337  Assert(ARR_ELEMTYPE(array) == extra_data->type_id);
338  deconstruct_array(array,
339  extra_data->type_id,
340  extra_data->typlen,
341  extra_data->typbyval,
342  extra_data->typalign,
343  &elem_values, &elem_nulls, &num_elems);
344 
345  /*
346  * We loop through the elements in the array and add them to our
347  * tracking hashtable.
348  */
349  null_present = false;
350  for (j = 0; j < num_elems; j++)
351  {
352  Datum elem_value;
353  bool found;
354 
355  /* No null element processing other than flag setting here */
356  if (elem_nulls[j])
357  {
358  null_present = true;
359  continue;
360  }
361 
362  /* Lookup current element in hashtable, adding it if new */
363  elem_value = elem_values[j];
364  item = (TrackItem *) hash_search(elements_tab,
365  (const void *) &elem_value,
366  HASH_ENTER, &found);
367 
368  if (found)
369  {
370  /* The element value is already on the tracking list */
371 
372  /*
373  * The operators we assist ignore duplicate array elements, so
374  * count a given distinct element only once per array.
375  */
376  if (item->last_container == array_no)
377  continue;
378 
379  item->frequency++;
380  item->last_container = array_no;
381  }
382  else
383  {
384  /* Initialize new tracking list element */
385 
386  /*
387  * If element type is pass-by-reference, we must copy it into
388  * palloc'd space, so that we can release the array below. (We
389  * do this so that the space needed for element values is
390  * limited by the size of the hashtable; if we kept all the
391  * array values around, it could be much more.)
392  */
393  item->key = datumCopy(elem_value,
394  extra_data->typbyval,
395  extra_data->typlen);
396 
397  item->frequency = 1;
398  item->delta = b_current - 1;
399  item->last_container = array_no;
400  }
401 
402  /* element_no is the number of elements processed (ie N) */
403  element_no++;
404 
405  /* We prune the D structure after processing each bucket */
406  if (element_no % bucket_width == 0)
407  {
408  prune_element_hashtable(elements_tab, b_current);
409  b_current++;
410  }
411  }
412 
413  /* Count null element presence once per array. */
414  if (null_present)
415  null_elem_cnt++;
416 
417  /* Update frequency of the particular array distinct element count. */
418  distinct_count = (int) (element_no - prev_element_no);
419  count_item = (DECountItem *) hash_search(count_tab, &distinct_count,
420  HASH_ENTER,
421  &count_item_found);
422 
423  if (count_item_found)
424  count_item->frequency++;
425  else
426  count_item->frequency = 1;
427 
428  /* Free memory allocated while detoasting. */
429  if (PointerGetDatum(array) != value)
430  pfree(array);
431  pfree(elem_values);
432  pfree(elem_nulls);
433  }
434 
435  /* Skip pg_statistic slots occupied by standard statistics */
436  slot_idx = 0;
437  while (slot_idx < STATISTIC_NUM_SLOTS && stats->stakind[slot_idx] != 0)
438  slot_idx++;
439  if (slot_idx > STATISTIC_NUM_SLOTS - 2)
440  elog(ERROR, "insufficient pg_statistic slots for array stats");
441 
442  /* We can only compute real stats if we found some non-null values. */
443  if (analyzed_rows > 0)
444  {
445  int nonnull_cnt = analyzed_rows;
446  int count_items_count;
447  int i;
448  TrackItem **sort_table;
449  int track_len;
450  int64 cutoff_freq;
451  int64 minfreq,
452  maxfreq;
453 
454  /*
455  * We assume the standard stats code already took care of setting
456  * stats_valid, stanullfrac, stawidth, stadistinct. We'd have to
457  * re-compute those values if we wanted to not store the standard
458  * stats.
459  */
460 
461  /*
462  * Construct an array of the interesting hashtable items, that is,
463  * those meeting the cutoff frequency (s - epsilon)*N. Also identify
464  * the minimum and maximum frequencies among these items.
465  *
466  * Since epsilon = s/10 and bucket_width = 1/epsilon, the cutoff
467  * frequency is 9*N / bucket_width.
468  */
469  cutoff_freq = 9 * element_no / bucket_width;
470 
471  i = hash_get_num_entries(elements_tab); /* surely enough space */
472  sort_table = (TrackItem **) palloc(sizeof(TrackItem *) * i);
473 
474  hash_seq_init(&scan_status, elements_tab);
475  track_len = 0;
476  minfreq = element_no;
477  maxfreq = 0;
478  while ((item = (TrackItem *) hash_seq_search(&scan_status)) != NULL)
479  {
480  if (item->frequency > cutoff_freq)
481  {
482  sort_table[track_len++] = item;
483  minfreq = Min(minfreq, item->frequency);
484  maxfreq = Max(maxfreq, item->frequency);
485  }
486  }
487  Assert(track_len <= i);
488 
489  /* emit some statistics for debug purposes */
490  elog(DEBUG3, "compute_array_stats: target # mces = %d, "
491  "bucket width = %d, "
492  "# elements = " INT64_FORMAT ", hashtable size = %d, "
493  "usable entries = %d",
494  num_mcelem, bucket_width, element_no, i, track_len);
495 
496  /*
497  * If we obtained more elements than we really want, get rid of those
498  * with least frequencies. The easiest way is to qsort the array into
499  * descending frequency order and truncate the array.
500  */
501  if (num_mcelem < track_len)
502  {
503  qsort_interruptible(sort_table, track_len, sizeof(TrackItem *),
505  /* reset minfreq to the smallest frequency we're keeping */
506  minfreq = sort_table[num_mcelem - 1]->frequency;
507  }
508  else
509  num_mcelem = track_len;
510 
511  /* Generate MCELEM slot entry */
512  if (num_mcelem > 0)
513  {
514  MemoryContext old_context;
515  Datum *mcelem_values;
516  float4 *mcelem_freqs;
517 
518  /*
519  * We want to store statistics sorted on the element value using
520  * the element type's default comparison function. This permits
521  * fast binary searches in selectivity estimation functions.
522  */
523  qsort_interruptible(sort_table, num_mcelem, sizeof(TrackItem *),
525 
526  /* Must copy the target values into anl_context */
527  old_context = MemoryContextSwitchTo(stats->anl_context);
528 
529  /*
530  * We sorted statistics on the element value, but we want to be
531  * able to find the minimal and maximal frequencies without going
532  * through all the values. We also want the frequency of null
533  * elements. Store these three values at the end of mcelem_freqs.
534  */
535  mcelem_values = (Datum *) palloc(num_mcelem * sizeof(Datum));
536  mcelem_freqs = (float4 *) palloc((num_mcelem + 3) * sizeof(float4));
537 
538  /*
539  * See comments above about use of nonnull_cnt as the divisor for
540  * the final frequency estimates.
541  */
542  for (i = 0; i < num_mcelem; i++)
543  {
544  TrackItem *titem = sort_table[i];
545 
546  mcelem_values[i] = datumCopy(titem->key,
547  extra_data->typbyval,
548  extra_data->typlen);
549  mcelem_freqs[i] = (double) titem->frequency /
550  (double) nonnull_cnt;
551  }
552  mcelem_freqs[i++] = (double) minfreq / (double) nonnull_cnt;
553  mcelem_freqs[i++] = (double) maxfreq / (double) nonnull_cnt;
554  mcelem_freqs[i++] = (double) null_elem_cnt / (double) nonnull_cnt;
555 
556  MemoryContextSwitchTo(old_context);
557 
558  stats->stakind[slot_idx] = STATISTIC_KIND_MCELEM;
559  stats->staop[slot_idx] = extra_data->eq_opr;
560  stats->stacoll[slot_idx] = extra_data->coll_id;
561  stats->stanumbers[slot_idx] = mcelem_freqs;
562  /* See above comment about extra stanumber entries */
563  stats->numnumbers[slot_idx] = num_mcelem + 3;
564  stats->stavalues[slot_idx] = mcelem_values;
565  stats->numvalues[slot_idx] = num_mcelem;
566  /* We are storing values of element type */
567  stats->statypid[slot_idx] = extra_data->type_id;
568  stats->statyplen[slot_idx] = extra_data->typlen;
569  stats->statypbyval[slot_idx] = extra_data->typbyval;
570  stats->statypalign[slot_idx] = extra_data->typalign;
571  slot_idx++;
572  }
573 
574  /* Generate DECHIST slot entry */
575  count_items_count = hash_get_num_entries(count_tab);
576  if (count_items_count > 0)
577  {
578  int num_hist = stats->attr->attstattarget;
579  DECountItem **sorted_count_items;
580  int j;
581  int delta;
582  int64 frac;
583  float4 *hist;
584 
585  /* num_hist must be at least 2 for the loop below to work */
586  num_hist = Max(num_hist, 2);
587 
588  /*
589  * Create an array of DECountItem pointers, and sort them into
590  * increasing count order.
591  */
592  sorted_count_items = (DECountItem **)
593  palloc(sizeof(DECountItem *) * count_items_count);
594  hash_seq_init(&scan_status, count_tab);
595  j = 0;
596  while ((count_item = (DECountItem *) hash_seq_search(&scan_status)) != NULL)
597  {
598  sorted_count_items[j++] = count_item;
599  }
600  qsort_interruptible(sorted_count_items, count_items_count,
601  sizeof(DECountItem *),
603 
604  /*
605  * Prepare to fill stanumbers with the histogram, followed by the
606  * average count. This array must be stored in anl_context.
607  */
608  hist = (float4 *)
610  sizeof(float4) * (num_hist + 1));
611  hist[num_hist] = (double) element_no / (double) nonnull_cnt;
612 
613  /*----------
614  * Construct the histogram of distinct-element counts (DECs).
615  *
616  * The object of this loop is to copy the min and max DECs to
617  * hist[0] and hist[num_hist - 1], along with evenly-spaced DECs
618  * in between (where "evenly-spaced" is with reference to the
619  * whole input population of arrays). If we had a complete sorted
620  * array of DECs, one per analyzed row, the i'th hist value would
621  * come from DECs[i * (analyzed_rows - 1) / (num_hist - 1)]
622  * (compare the histogram-making loop in compute_scalar_stats()).
623  * But instead of that we have the sorted_count_items[] array,
624  * which holds unique DEC values with their frequencies (that is,
625  * a run-length-compressed version of the full array). So we
626  * control advancing through sorted_count_items[] with the
627  * variable "frac", which is defined as (x - y) * (num_hist - 1),
628  * where x is the index in the notional DECs array corresponding
629  * to the start of the next sorted_count_items[] element's run,
630  * and y is the index in DECs from which we should take the next
631  * histogram value. We have to advance whenever x <= y, that is
632  * frac <= 0. The x component is the sum of the frequencies seen
633  * so far (up through the current sorted_count_items[] element),
634  * and of course y * (num_hist - 1) = i * (analyzed_rows - 1),
635  * per the subscript calculation above. (The subscript calculation
636  * implies dropping any fractional part of y; in this formulation
637  * that's handled by not advancing until frac reaches 1.)
638  *
639  * Even though frac has a bounded range, it could overflow int32
640  * when working with very large statistics targets, so we do that
641  * math in int64.
642  *----------
643  */
644  delta = analyzed_rows - 1;
645  j = 0; /* current index in sorted_count_items */
646  /* Initialize frac for sorted_count_items[0]; y is initially 0 */
647  frac = (int64) sorted_count_items[0]->frequency * (num_hist - 1);
648  for (i = 0; i < num_hist; i++)
649  {
650  while (frac <= 0)
651  {
652  /* Advance, and update x component of frac */
653  j++;
654  frac += (int64) sorted_count_items[j]->frequency * (num_hist - 1);
655  }
656  hist[i] = sorted_count_items[j]->count;
657  frac -= delta; /* update y for upcoming i increment */
658  }
659  Assert(j == count_items_count - 1);
660 
661  stats->stakind[slot_idx] = STATISTIC_KIND_DECHIST;
662  stats->staop[slot_idx] = extra_data->eq_opr;
663  stats->stacoll[slot_idx] = extra_data->coll_id;
664  stats->stanumbers[slot_idx] = hist;
665  stats->numnumbers[slot_idx] = num_hist + 1;
666  slot_idx++;
667  }
668  }
669 
670  /*
671  * We don't need to bother cleaning up any of our temporary palloc's. The
672  * hashtable should also go away, as it used a child memory context.
673  */
674 }
675 
676 /*
677  * A function to prune the D structure from the Lossy Counting algorithm.
678  * Consult compute_tsvector_stats() for wider explanation.
679  */
680 static void
681 prune_element_hashtable(HTAB *elements_tab, int b_current)
682 {
683  HASH_SEQ_STATUS scan_status;
684  TrackItem *item;
685 
686  hash_seq_init(&scan_status, elements_tab);
687  while ((item = (TrackItem *) hash_seq_search(&scan_status)) != NULL)
688  {
689  if (item->frequency + item->delta <= b_current)
690  {
691  Datum value = item->key;
692 
693  if (hash_search(elements_tab, (const void *) &item->key,
694  HASH_REMOVE, NULL) == NULL)
695  elog(ERROR, "hash table corrupted");
696  /* We should free memory if element is not passed by value */
699  }
700  }
701 }
702 
703 /*
704  * Hash function for elements.
705  *
706  * We use the element type's default hash opclass, and the column collation
707  * if the type is collation-sensitive.
708  */
709 static uint32
710 element_hash(const void *key, Size keysize)
711 {
712  Datum d = *((const Datum *) key);
713  Datum h;
714 
717  d);
718  return DatumGetUInt32(h);
719 }
720 
721 /*
722  * Matching function for elements, to be used in hashtable lookups.
723  */
724 static int
725 element_match(const void *key1, const void *key2, Size keysize)
726 {
727  /* The keysize parameter is superfluous here */
728  return element_compare(key1, key2);
729 }
730 
731 /*
732  * Comparison function for elements.
733  *
734  * We use the element type's default btree opclass, and the column collation
735  * if the type is collation-sensitive.
736  *
737  * XXX consider using SortSupport infrastructure
738  */
739 static int
740 element_compare(const void *key1, const void *key2)
741 {
742  Datum d1 = *((const Datum *) key1);
743  Datum d2 = *((const Datum *) key2);
744  Datum c;
745 
748  d1, d2);
749  return DatumGetInt32(c);
750 }
751 
752 /*
753  * Comparator for sorting TrackItems by frequencies (descending sort)
754  */
755 static int
756 trackitem_compare_frequencies_desc(const void *e1, const void *e2, void *arg)
757 {
758  const TrackItem *const *t1 = (const TrackItem *const *) e1;
759  const TrackItem *const *t2 = (const TrackItem *const *) e2;
760 
761  return (*t2)->frequency - (*t1)->frequency;
762 }
763 
764 /*
765  * Comparator for sorting TrackItems by element values
766  */
767 static int
768 trackitem_compare_element(const void *e1, const void *e2, void *arg)
769 {
770  const TrackItem *const *t1 = (const TrackItem *const *) e1;
771  const TrackItem *const *t2 = (const TrackItem *const *) e2;
772 
773  return element_compare(&(*t1)->key, &(*t2)->key);
774 }
775 
776 /*
777  * Comparator for sorting DECountItems by count
778  */
779 static int
780 countitem_compare_count(const void *e1, const void *e2, void *arg)
781 {
782  const DECountItem *const *t1 = (const DECountItem *const *) e1;
783  const DECountItem *const *t2 = (const DECountItem *const *) e2;
784 
785  if ((*t1)->count < (*t2)->count)
786  return -1;
787  else if ((*t1)->count == (*t2)->count)
788  return 0;
789  else
790  return 1;
791 }
#define DatumGetArrayTypeP(X)
Definition: array.h:254
#define ARR_ELEMTYPE(a)
Definition: array.h:285
Datum array_typanalyze(PG_FUNCTION_ARGS)
static int trackitem_compare_frequencies_desc(const void *e1, const void *e2, void *arg)
static void compute_array_stats(VacAttrStats *stats, AnalyzeAttrFetchFunc fetchfunc, int samplerows, double totalrows)
static int trackitem_compare_element(const void *e1, const void *e2, void *arg)
static int element_match(const void *key1, const void *key2, Size keysize)
static uint32 element_hash(const void *key, Size keysize)
#define ARRAY_WIDTH_THRESHOLD
static int countitem_compare_count(const void *e1, const void *e2, void *arg)
static int element_compare(const void *key1, const void *key2)
static ArrayAnalyzeExtraData * array_extra_data
static void prune_element_hashtable(HTAB *elements_tab, int b_current)
void deconstruct_array(ArrayType *array, Oid elmtype, int elmlen, bool elmbyval, char elmalign, Datum **elemsp, bool **nullsp, int *nelemsp)
Definition: arrayfuncs.c:3576
unsigned int uint32
Definition: c.h:442
#define Min(x, y)
Definition: c.h:937
signed short int16
Definition: c.h:429
#define Max(x, y)
Definition: c.h:931
#define INT64_FORMAT
Definition: c.h:484
float float4
Definition: c.h:565
#define OidIsValid(objectId)
Definition: c.h:711
size_t Size
Definition: c.h:541
bool std_typanalyze(VacAttrStats *stats)
Definition: analyze.c:1858
Datum datumCopy(Datum value, bool typByVal, int typLen)
Definition: datum.c:132
Size toast_raw_datum_size(Datum value)
Definition: detoast.c:545
void * hash_search(HTAB *hashp, const void *keyPtr, HASHACTION action, bool *foundPtr)
Definition: dynahash.c:953
HTAB * hash_create(const char *tabname, long nelem, const HASHCTL *info, int flags)
Definition: dynahash.c:350
long hash_get_num_entries(HTAB *hashp)
Definition: dynahash.c:1377
void * hash_seq_search(HASH_SEQ_STATUS *status)
Definition: dynahash.c:1431
void hash_seq_init(HASH_SEQ_STATUS *status, HTAB *hashp)
Definition: dynahash.c:1421
#define DEBUG3
Definition: elog.h:24
#define ERROR
Definition: elog.h:35
Datum FunctionCall2Coll(FmgrInfo *flinfo, Oid collation, Datum arg1, Datum arg2)
Definition: fmgr.c:1134
Datum FunctionCall1Coll(FmgrInfo *flinfo, Oid collation, Datum arg1)
Definition: fmgr.c:1114
#define PG_GETARG_POINTER(n)
Definition: fmgr.h:276
#define PG_FUNCTION_ARGS
Definition: fmgr.h:193
#define PG_RETURN_BOOL(x)
Definition: fmgr.h:359
@ HASH_REMOVE
Definition: hsearch.h:115
@ HASH_ENTER
Definition: hsearch.h:114
#define HASH_CONTEXT
Definition: hsearch.h:102
#define HASH_ELEM
Definition: hsearch.h:95
#define HASH_COMPARE
Definition: hsearch.h:99
#define HASH_FUNCTION
Definition: hsearch.h:98
#define HASH_BLOBS
Definition: hsearch.h:97
static struct @143 value
int j
Definition: isn.c:74
int i
Definition: isn.c:73
Assert(fmt[strlen(fmt) - 1] !='\n')
Oid get_base_element_type(Oid typid)
Definition: lsyscache.c:2790
void pfree(void *pointer)
Definition: mcxt.c:1306
MemoryContext CurrentMemoryContext
Definition: mcxt.c:124
void * MemoryContextAlloc(MemoryContext context, Size size)
Definition: mcxt.c:994
void * palloc(Size size)
Definition: mcxt.c:1199
static MemoryContext MemoryContextSwitchTo(MemoryContext context)
Definition: palloc.h:135
void * arg
#define STATISTIC_NUM_SLOTS
Definition: pg_statistic.h:127
void qsort_interruptible(void *base, size_t nel, size_t elsize, qsort_arg_comparator cmp, void *arg)
static uint32 DatumGetUInt32(Datum X)
Definition: postgres.h:570
static Datum PointerGetDatum(const void *X)
Definition: postgres.h:670
uintptr_t Datum
Definition: postgres.h:412
static Pointer DatumGetPointer(Datum X)
Definition: postgres.h:660
static int32 DatumGetInt32(Datum X)
Definition: postgres.h:550
unsigned int Oid
Definition: postgres_ext.h:31
char * c
AnalyzeAttrComputeStatsFunc std_compute_stats
Definition: fmgr.h:57
Oid fn_oid
Definition: fmgr.h:59
Size keysize
Definition: hsearch.h:75
HashValueFunc hash
Definition: hsearch.h:78
Size entrysize
Definition: hsearch.h:76
HashCompareFunc match
Definition: hsearch.h:80
MemoryContext hcxt
Definition: hsearch.h:86
Definition: dynahash.c:220
LexemeHashKey key
Definition: ts_typanalyze.c:34
FmgrInfo hash_proc_finfo
Definition: typcache.h:77
FmgrInfo cmp_proc_finfo
Definition: typcache.h:76
char typalign
Definition: typcache.h:41
bool typbyval
Definition: typcache.h:40
int16 typlen
Definition: typcache.h:39
int16 stakind[STATISTIC_NUM_SLOTS]
Definition: vacuum.h:151
MemoryContext anl_context
Definition: vacuum.h:133
Oid statypid[STATISTIC_NUM_SLOTS]
Definition: vacuum.h:165
Oid staop[STATISTIC_NUM_SLOTS]
Definition: vacuum.h:152
Oid stacoll[STATISTIC_NUM_SLOTS]
Definition: vacuum.h:153
char statypalign[STATISTIC_NUM_SLOTS]
Definition: vacuum.h:168
float4 * stanumbers[STATISTIC_NUM_SLOTS]
Definition: vacuum.h:155
Oid attrtypid
Definition: vacuum.h:129
Form_pg_attribute attr
Definition: vacuum.h:128
void * extra_data
Definition: vacuum.h:141
bool statypbyval[STATISTIC_NUM_SLOTS]
Definition: vacuum.h:167
int16 statyplen[STATISTIC_NUM_SLOTS]
Definition: vacuum.h:166
int numvalues[STATISTIC_NUM_SLOTS]
Definition: vacuum.h:156
Datum * stavalues[STATISTIC_NUM_SLOTS]
Definition: vacuum.h:157
int numnumbers[STATISTIC_NUM_SLOTS]
Definition: vacuum.h:154
AnalyzeAttrComputeStatsFunc compute_stats
Definition: vacuum.h:139
Oid attrcollid
Definition: vacuum.h:132
TypeCacheEntry * lookup_type_cache(Oid type_id, int flags)
Definition: typcache.c:339
#define TYPECACHE_HASH_PROC_FINFO
Definition: typcache.h:143
#define TYPECACHE_EQ_OPR
Definition: typcache.h:136
#define TYPECACHE_CMP_PROC_FINFO
Definition: typcache.h:142
void vacuum_delay_point(void)
Definition: vacuum.c:2166
Datum(* AnalyzeAttrFetchFunc)(VacAttrStatsP stats, int rownum, bool *isNull)
Definition: vacuum.h:107
void(* AnalyzeAttrComputeStatsFunc)(VacAttrStatsP stats, AnalyzeAttrFetchFunc fetchfunc, int samplerows, double totalrows)
Definition: vacuum.h:110