Line data Source code
1 : /*-------------------------------------------------------------------------
2 : *
3 : * mvdistinct.c
4 : * POSTGRES multivariate ndistinct coefficients
5 : *
6 : * Estimating number of groups in a combination of columns (e.g. for GROUP BY)
7 : * is tricky, and the estimation error is often significant.
8 :
9 : * The multivariate ndistinct coefficients address this by storing ndistinct
10 : * estimates for combinations of the user-specified columns. So for example
11 : * given a statistics object on three columns (a,b,c), this module estimates
12 : * and stores n-distinct for (a,b), (a,c), (b,c) and (a,b,c). The per-column
13 : * estimates are already available in pg_statistic.
14 : *
15 : *
16 : * Portions Copyright (c) 1996-2025, PostgreSQL Global Development Group
17 : * Portions Copyright (c) 1994, Regents of the University of California
18 : *
19 : * IDENTIFICATION
20 : * src/backend/statistics/mvdistinct.c
21 : *
22 : *-------------------------------------------------------------------------
23 : */
24 : #include "postgres.h"
25 :
26 : #include <math.h>
27 :
28 : #include "catalog/pg_statistic_ext.h"
29 : #include "catalog/pg_statistic_ext_data.h"
30 : #include "lib/stringinfo.h"
31 : #include "statistics/extended_stats_internal.h"
32 : #include "statistics/statistics.h"
33 : #include "utils/fmgrprotos.h"
34 : #include "utils/syscache.h"
35 : #include "utils/typcache.h"
36 : #include "varatt.h"
37 :
38 : static double ndistinct_for_combination(double totalrows, StatsBuildData *data,
39 : int k, int *combination);
40 : static double estimate_ndistinct(double totalrows, int numrows, int d, int f1);
41 : static int n_choose_k(int n, int k);
42 : static int num_combinations(int n);
43 :
44 : /* size of the struct header fields (magic, type, nitems) */
45 : #define SizeOfHeader (3 * sizeof(uint32))
46 :
47 : /* size of a serialized ndistinct item (coefficient, natts, atts) */
48 : #define SizeOfItem(natts) \
49 : (sizeof(double) + sizeof(int) + (natts) * sizeof(AttrNumber))
50 :
51 : /* minimal size of a ndistinct item (with two attributes) */
52 : #define MinSizeOfItem SizeOfItem(2)
53 :
54 : /* minimal size of mvndistinct, when all items are minimal */
55 : #define MinSizeOfItems(nitems) \
56 : (SizeOfHeader + (nitems) * MinSizeOfItem)
57 :
58 : /* Combination generator API */
59 :
60 : /* internal state for generator of k-combinations of n elements */
61 : typedef struct CombinationGenerator
62 : {
63 : int k; /* size of the combination */
64 : int n; /* total number of elements */
65 : int current; /* index of the next combination to return */
66 : int ncombinations; /* number of combinations (size of array) */
67 : int *combinations; /* array of pre-built combinations */
68 : } CombinationGenerator;
69 :
70 : static CombinationGenerator *generator_init(int n, int k);
71 : static void generator_free(CombinationGenerator *state);
72 : static int *generator_next(CombinationGenerator *state);
73 : static void generate_combinations(CombinationGenerator *state);
74 :
75 :
76 : /*
77 : * statext_ndistinct_build
78 : * Compute ndistinct coefficient for the combination of attributes.
79 : *
80 : * This computes the ndistinct estimate using the same estimator used
81 : * in analyze.c and then computes the coefficient.
82 : *
83 : * To handle expressions easily, we treat them as system attributes with
84 : * negative attnums, and offset everything by number of expressions to
85 : * allow using Bitmapsets.
86 : */
87 : MVNDistinct *
88 194 : statext_ndistinct_build(double totalrows, StatsBuildData *data)
89 : {
90 : MVNDistinct *result;
91 : int k;
92 : int itemcnt;
93 194 : int numattrs = data->nattnums;
94 194 : int numcombs = num_combinations(numattrs);
95 :
96 194 : result = palloc(offsetof(MVNDistinct, items) +
97 194 : numcombs * sizeof(MVNDistinctItem));
98 194 : result->magic = STATS_NDISTINCT_MAGIC;
99 194 : result->type = STATS_NDISTINCT_TYPE_BASIC;
100 194 : result->nitems = numcombs;
101 :
102 194 : itemcnt = 0;
103 478 : for (k = 2; k <= numattrs; k++)
104 : {
105 : int *combination;
106 : CombinationGenerator *generator;
107 :
108 : /* generate combinations of K out of N elements */
109 284 : generator = generator_init(numattrs, k);
110 :
111 844 : while ((combination = generator_next(generator)))
112 : {
113 560 : MVNDistinctItem *item = &result->items[itemcnt];
114 : int j;
115 :
116 560 : item->attributes = palloc(sizeof(AttrNumber) * k);
117 560 : item->nattributes = k;
118 :
119 : /* translate the indexes to attnums */
120 1866 : for (j = 0; j < k; j++)
121 : {
122 1306 : item->attributes[j] = data->attnums[combination[j]];
123 :
124 : Assert(AttributeNumberIsValid(item->attributes[j]));
125 : }
126 :
127 560 : item->ndistinct =
128 560 : ndistinct_for_combination(totalrows, data, k, combination);
129 :
130 560 : itemcnt++;
131 : Assert(itemcnt <= result->nitems);
132 : }
133 :
134 284 : generator_free(generator);
135 : }
136 :
137 : /* must consume exactly the whole output array */
138 : Assert(itemcnt == result->nitems);
139 :
140 194 : return result;
141 : }
142 :
143 : /*
144 : * statext_ndistinct_load
145 : * Load the ndistinct value for the indicated pg_statistic_ext tuple
146 : */
147 : MVNDistinct *
148 408 : statext_ndistinct_load(Oid mvoid, bool inh)
149 : {
150 : MVNDistinct *result;
151 : bool isnull;
152 : Datum ndist;
153 : HeapTuple htup;
154 :
155 408 : htup = SearchSysCache2(STATEXTDATASTXOID,
156 : ObjectIdGetDatum(mvoid), BoolGetDatum(inh));
157 408 : if (!HeapTupleIsValid(htup))
158 0 : elog(ERROR, "cache lookup failed for statistics object %u", mvoid);
159 :
160 408 : ndist = SysCacheGetAttr(STATEXTDATASTXOID, htup,
161 : Anum_pg_statistic_ext_data_stxdndistinct, &isnull);
162 408 : if (isnull)
163 0 : elog(ERROR,
164 : "requested statistics kind \"%c\" is not yet built for statistics object %u",
165 : STATS_EXT_NDISTINCT, mvoid);
166 :
167 408 : result = statext_ndistinct_deserialize(DatumGetByteaPP(ndist));
168 :
169 408 : ReleaseSysCache(htup);
170 :
171 408 : return result;
172 : }
173 :
174 : /*
175 : * statext_ndistinct_serialize
176 : * serialize ndistinct to the on-disk bytea format
177 : */
178 : bytea *
179 194 : statext_ndistinct_serialize(MVNDistinct *ndistinct)
180 : {
181 : int i;
182 : bytea *output;
183 : char *tmp;
184 : Size len;
185 :
186 : Assert(ndistinct->magic == STATS_NDISTINCT_MAGIC);
187 : Assert(ndistinct->type == STATS_NDISTINCT_TYPE_BASIC);
188 :
189 : /*
190 : * Base size is size of scalar fields in the struct, plus one base struct
191 : * for each item, including number of items for each.
192 : */
193 194 : len = VARHDRSZ + SizeOfHeader;
194 :
195 : /* and also include space for the actual attribute numbers */
196 754 : for (i = 0; i < ndistinct->nitems; i++)
197 : {
198 : int nmembers;
199 :
200 560 : nmembers = ndistinct->items[i].nattributes;
201 : Assert(nmembers >= 2);
202 :
203 560 : len += SizeOfItem(nmembers);
204 : }
205 :
206 194 : output = (bytea *) palloc(len);
207 194 : SET_VARSIZE(output, len);
208 :
209 194 : tmp = VARDATA(output);
210 :
211 : /* Store the base struct values (magic, type, nitems) */
212 194 : memcpy(tmp, &ndistinct->magic, sizeof(uint32));
213 194 : tmp += sizeof(uint32);
214 194 : memcpy(tmp, &ndistinct->type, sizeof(uint32));
215 194 : tmp += sizeof(uint32);
216 194 : memcpy(tmp, &ndistinct->nitems, sizeof(uint32));
217 194 : tmp += sizeof(uint32);
218 :
219 : /*
220 : * store number of attributes and attribute numbers for each entry
221 : */
222 754 : for (i = 0; i < ndistinct->nitems; i++)
223 : {
224 560 : MVNDistinctItem item = ndistinct->items[i];
225 560 : int nmembers = item.nattributes;
226 :
227 560 : memcpy(tmp, &item.ndistinct, sizeof(double));
228 560 : tmp += sizeof(double);
229 560 : memcpy(tmp, &nmembers, sizeof(int));
230 560 : tmp += sizeof(int);
231 :
232 560 : memcpy(tmp, item.attributes, sizeof(AttrNumber) * nmembers);
233 560 : tmp += nmembers * sizeof(AttrNumber);
234 :
235 : /* protect against overflows */
236 : Assert(tmp <= ((char *) output + len));
237 : }
238 :
239 : /* check we used exactly the expected space */
240 : Assert(tmp == ((char *) output + len));
241 :
242 194 : return output;
243 : }
244 :
245 : /*
246 : * statext_ndistinct_deserialize
247 : * Read an on-disk bytea format MVNDistinct to in-memory format
248 : */
249 : MVNDistinct *
250 432 : statext_ndistinct_deserialize(bytea *data)
251 : {
252 : int i;
253 : Size minimum_size;
254 : MVNDistinct ndist;
255 : MVNDistinct *ndistinct;
256 : char *tmp;
257 :
258 432 : if (data == NULL)
259 0 : return NULL;
260 :
261 : /* we expect at least the basic fields of MVNDistinct struct */
262 432 : if (VARSIZE_ANY_EXHDR(data) < SizeOfHeader)
263 0 : elog(ERROR, "invalid MVNDistinct size %zu (expected at least %zu)",
264 : VARSIZE_ANY_EXHDR(data), SizeOfHeader);
265 :
266 : /* initialize pointer to the data part (skip the varlena header) */
267 432 : tmp = VARDATA_ANY(data);
268 :
269 : /* read the header fields and perform basic sanity checks */
270 432 : memcpy(&ndist.magic, tmp, sizeof(uint32));
271 432 : tmp += sizeof(uint32);
272 432 : memcpy(&ndist.type, tmp, sizeof(uint32));
273 432 : tmp += sizeof(uint32);
274 432 : memcpy(&ndist.nitems, tmp, sizeof(uint32));
275 432 : tmp += sizeof(uint32);
276 :
277 432 : if (ndist.magic != STATS_NDISTINCT_MAGIC)
278 0 : elog(ERROR, "invalid ndistinct magic %08x (expected %08x)",
279 : ndist.magic, STATS_NDISTINCT_MAGIC);
280 432 : if (ndist.type != STATS_NDISTINCT_TYPE_BASIC)
281 0 : elog(ERROR, "invalid ndistinct type %d (expected %d)",
282 : ndist.type, STATS_NDISTINCT_TYPE_BASIC);
283 432 : if (ndist.nitems == 0)
284 0 : elog(ERROR, "invalid zero-length item array in MVNDistinct");
285 :
286 : /* what minimum bytea size do we expect for those parameters */
287 432 : minimum_size = MinSizeOfItems(ndist.nitems);
288 432 : if (VARSIZE_ANY_EXHDR(data) < minimum_size)
289 0 : elog(ERROR, "invalid MVNDistinct size %zu (expected at least %zu)",
290 : VARSIZE_ANY_EXHDR(data), minimum_size);
291 :
292 : /*
293 : * Allocate space for the ndistinct items (no space for each item's
294 : * attnos: those live in bitmapsets allocated separately)
295 : */
296 432 : ndistinct = palloc0(MAXALIGN(offsetof(MVNDistinct, items)) +
297 432 : (ndist.nitems * sizeof(MVNDistinctItem)));
298 432 : ndistinct->magic = ndist.magic;
299 432 : ndistinct->type = ndist.type;
300 432 : ndistinct->nitems = ndist.nitems;
301 :
302 2340 : for (i = 0; i < ndistinct->nitems; i++)
303 : {
304 1908 : MVNDistinctItem *item = &ndistinct->items[i];
305 :
306 : /* ndistinct value */
307 1908 : memcpy(&item->ndistinct, tmp, sizeof(double));
308 1908 : tmp += sizeof(double);
309 :
310 : /* number of attributes */
311 1908 : memcpy(&item->nattributes, tmp, sizeof(int));
312 1908 : tmp += sizeof(int);
313 : Assert((item->nattributes >= 2) && (item->nattributes <= STATS_MAX_DIMENSIONS));
314 :
315 : item->attributes
316 1908 : = (AttrNumber *) palloc(item->nattributes * sizeof(AttrNumber));
317 :
318 1908 : memcpy(item->attributes, tmp, sizeof(AttrNumber) * item->nattributes);
319 1908 : tmp += sizeof(AttrNumber) * item->nattributes;
320 :
321 : /* still within the bytea */
322 : Assert(tmp <= ((char *) data + VARSIZE_ANY(data)));
323 : }
324 :
325 : /* we should have consumed the whole bytea exactly */
326 : Assert(tmp == ((char *) data + VARSIZE_ANY(data)));
327 :
328 432 : return ndistinct;
329 : }
330 :
331 : /*
332 : * pg_ndistinct_in
333 : * input routine for type pg_ndistinct
334 : *
335 : * pg_ndistinct is real enough to be a table column, but it has no
336 : * operations of its own, and disallows input (just like pg_node_tree).
337 : */
338 : Datum
339 0 : pg_ndistinct_in(PG_FUNCTION_ARGS)
340 : {
341 0 : ereport(ERROR,
342 : (errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
343 : errmsg("cannot accept a value of type %s", "pg_ndistinct")));
344 :
345 : PG_RETURN_VOID(); /* keep compiler quiet */
346 : }
347 :
348 : /*
349 : * pg_ndistinct
350 : * output routine for type pg_ndistinct
351 : *
352 : * Produces a human-readable representation of the value.
353 : */
354 : Datum
355 24 : pg_ndistinct_out(PG_FUNCTION_ARGS)
356 : {
357 24 : bytea *data = PG_GETARG_BYTEA_PP(0);
358 24 : MVNDistinct *ndist = statext_ndistinct_deserialize(data);
359 : int i;
360 : StringInfoData str;
361 :
362 24 : initStringInfo(&str);
363 24 : appendStringInfoChar(&str, '{');
364 :
365 120 : for (i = 0; i < ndist->nitems; i++)
366 : {
367 : int j;
368 96 : MVNDistinctItem item = ndist->items[i];
369 :
370 96 : if (i > 0)
371 72 : appendStringInfoString(&str, ", ");
372 :
373 312 : for (j = 0; j < item.nattributes; j++)
374 : {
375 216 : AttrNumber attnum = item.attributes[j];
376 :
377 216 : appendStringInfo(&str, "%s%d", (j == 0) ? "\"" : ", ", attnum);
378 : }
379 96 : appendStringInfo(&str, "\": %d", (int) item.ndistinct);
380 : }
381 :
382 24 : appendStringInfoChar(&str, '}');
383 :
384 24 : PG_RETURN_CSTRING(str.data);
385 : }
386 :
387 : /*
388 : * pg_ndistinct_recv
389 : * binary input routine for type pg_ndistinct
390 : */
391 : Datum
392 0 : pg_ndistinct_recv(PG_FUNCTION_ARGS)
393 : {
394 0 : ereport(ERROR,
395 : (errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
396 : errmsg("cannot accept a value of type %s", "pg_ndistinct")));
397 :
398 : PG_RETURN_VOID(); /* keep compiler quiet */
399 : }
400 :
401 : /*
402 : * pg_ndistinct_send
403 : * binary output routine for type pg_ndistinct
404 : *
405 : * n-distinct is serialized into a bytea value, so let's send that.
406 : */
407 : Datum
408 0 : pg_ndistinct_send(PG_FUNCTION_ARGS)
409 : {
410 0 : return byteasend(fcinfo);
411 : }
412 :
413 : /*
414 : * ndistinct_for_combination
415 : * Estimates number of distinct values in a combination of columns.
416 : *
417 : * This uses the same ndistinct estimator as compute_scalar_stats() in
418 : * ANALYZE, i.e.,
419 : * n*d / (n - f1 + f1*n/N)
420 : *
421 : * except that instead of values in a single column we are dealing with
422 : * combination of multiple columns.
423 : */
424 : static double
425 560 : ndistinct_for_combination(double totalrows, StatsBuildData *data,
426 : int k, int *combination)
427 : {
428 : int i,
429 : j;
430 : int f1,
431 : cnt,
432 : d;
433 : bool *isnull;
434 : Datum *values;
435 : SortItem *items;
436 : MultiSortSupport mss;
437 560 : int numrows = data->numrows;
438 :
439 560 : mss = multi_sort_init(k);
440 :
441 : /*
442 : * In order to determine the number of distinct elements, create separate
443 : * values[]/isnull[] arrays with all the data we have, then sort them
444 : * using the specified column combination as dimensions. We could try to
445 : * sort in place, but it'd probably be more complex and bug-prone.
446 : */
447 560 : items = (SortItem *) palloc(numrows * sizeof(SortItem));
448 560 : values = (Datum *) palloc0(sizeof(Datum) * numrows * k);
449 560 : isnull = (bool *) palloc0(sizeof(bool) * numrows * k);
450 :
451 723748 : for (i = 0; i < numrows; i++)
452 : {
453 723188 : items[i].values = &values[i * k];
454 723188 : items[i].isnull = &isnull[i * k];
455 : }
456 :
457 : /*
458 : * For each dimension, set up sort-support and fill in the values from the
459 : * sample data.
460 : *
461 : * We use the column data types' default sort operators and collations;
462 : * perhaps at some point it'd be worth using column-specific collations?
463 : */
464 1866 : for (i = 0; i < k; i++)
465 : {
466 : Oid typid;
467 : TypeCacheEntry *type;
468 1306 : Oid collid = InvalidOid;
469 1306 : VacAttrStats *colstat = data->stats[combination[i]];
470 :
471 1306 : typid = colstat->attrtypid;
472 1306 : collid = colstat->attrcollid;
473 :
474 1306 : type = lookup_type_cache(typid, TYPECACHE_LT_OPR);
475 1306 : if (type->lt_opr == InvalidOid) /* shouldn't happen */
476 0 : elog(ERROR, "cache lookup failed for ordering operator for type %u",
477 : typid);
478 :
479 : /* prepare the sort function for this dimension */
480 1306 : multi_sort_add_dimension(mss, i, type->lt_opr, collid);
481 :
482 : /* accumulate all the data for this dimension into the arrays */
483 1687676 : for (j = 0; j < numrows; j++)
484 : {
485 1686370 : items[j].values[i] = data->values[combination[i]][j];
486 1686370 : items[j].isnull[i] = data->nulls[combination[i]][j];
487 : }
488 : }
489 :
490 : /* We can sort the array now ... */
491 560 : qsort_interruptible(items, numrows, sizeof(SortItem),
492 : multi_sort_compare, mss);
493 :
494 : /* ... and count the number of distinct combinations */
495 :
496 560 : f1 = 0;
497 560 : cnt = 1;
498 560 : d = 1;
499 723188 : for (i = 1; i < numrows; i++)
500 : {
501 722628 : if (multi_sort_compare(&items[i], &items[i - 1], mss) != 0)
502 : {
503 165606 : if (cnt == 1)
504 85956 : f1 += 1;
505 :
506 165606 : d++;
507 165606 : cnt = 0;
508 : }
509 :
510 722628 : cnt += 1;
511 : }
512 :
513 560 : if (cnt == 1)
514 128 : f1 += 1;
515 :
516 560 : return estimate_ndistinct(totalrows, numrows, d, f1);
517 : }
518 :
519 : /* The Duj1 estimator (already used in analyze.c). */
520 : static double
521 560 : estimate_ndistinct(double totalrows, int numrows, int d, int f1)
522 : {
523 : double numer,
524 : denom,
525 : ndistinct;
526 :
527 560 : numer = (double) numrows * (double) d;
528 :
529 560 : denom = (double) (numrows - f1) +
530 560 : (double) f1 * (double) numrows / totalrows;
531 :
532 560 : ndistinct = numer / denom;
533 :
534 : /* Clamp to sane range in case of roundoff error */
535 560 : if (ndistinct < (double) d)
536 0 : ndistinct = (double) d;
537 :
538 560 : if (ndistinct > totalrows)
539 0 : ndistinct = totalrows;
540 :
541 560 : return floor(ndistinct + 0.5);
542 : }
543 :
544 : /*
545 : * n_choose_k
546 : * computes binomial coefficients using an algorithm that is both
547 : * efficient and prevents overflows
548 : */
549 : static int
550 284 : n_choose_k(int n, int k)
551 : {
552 : int d,
553 : r;
554 :
555 : Assert((k > 0) && (n >= k));
556 :
557 : /* use symmetry of the binomial coefficients */
558 284 : k = Min(k, n - k);
559 :
560 284 : r = 1;
561 398 : for (d = 1; d <= k; ++d)
562 : {
563 114 : r *= n--;
564 114 : r /= d;
565 : }
566 :
567 284 : return r;
568 : }
569 :
570 : /*
571 : * num_combinations
572 : * number of combinations, excluding single-value combinations
573 : */
574 : static int
575 194 : num_combinations(int n)
576 : {
577 194 : return (1 << n) - (n + 1);
578 : }
579 :
580 : /*
581 : * generator_init
582 : * initialize the generator of combinations
583 : *
584 : * The generator produces combinations of K elements in the interval (0..N).
585 : * We prebuild all the combinations in this method, which is simpler than
586 : * generating them on the fly.
587 : */
588 : static CombinationGenerator *
589 284 : generator_init(int n, int k)
590 : {
591 : CombinationGenerator *state;
592 :
593 : Assert((n >= k) && (k > 0));
594 :
595 : /* allocate the generator state as a single chunk of memory */
596 284 : state = (CombinationGenerator *) palloc(sizeof(CombinationGenerator));
597 :
598 284 : state->ncombinations = n_choose_k(n, k);
599 :
600 : /* pre-allocate space for all combinations */
601 284 : state->combinations = (int *) palloc(sizeof(int) * k * state->ncombinations);
602 :
603 284 : state->current = 0;
604 284 : state->k = k;
605 284 : state->n = n;
606 :
607 : /* now actually pre-generate all the combinations of K elements */
608 284 : generate_combinations(state);
609 :
610 : /* make sure we got the expected number of combinations */
611 : Assert(state->current == state->ncombinations);
612 :
613 : /* reset the number, so we start with the first one */
614 284 : state->current = 0;
615 :
616 284 : return state;
617 : }
618 :
619 : /*
620 : * generator_next
621 : * returns the next combination from the prebuilt list
622 : *
623 : * Returns a combination of K array indexes (0 .. N), as specified to
624 : * generator_init), or NULL when there are no more combination.
625 : */
626 : static int *
627 844 : generator_next(CombinationGenerator *state)
628 : {
629 844 : if (state->current == state->ncombinations)
630 284 : return NULL;
631 :
632 560 : return &state->combinations[state->k * state->current++];
633 : }
634 :
635 : /*
636 : * generator_free
637 : * free the internal state of the generator
638 : *
639 : * Releases the generator internal state (pre-built combinations).
640 : */
641 : static void
642 284 : generator_free(CombinationGenerator *state)
643 : {
644 284 : pfree(state->combinations);
645 284 : pfree(state);
646 284 : }
647 :
648 : /*
649 : * generate_combinations_recurse
650 : * given a prefix, generate all possible combinations
651 : *
652 : * Given a prefix (first few elements of the combination), generate following
653 : * elements recursively. We generate the combinations in lexicographic order,
654 : * which eliminates permutations of the same combination.
655 : */
656 : static void
657 2150 : generate_combinations_recurse(CombinationGenerator *state,
658 : int index, int start, int *current)
659 : {
660 : /* If we haven't filled all the elements, simply recurse. */
661 2150 : if (index < state->k)
662 : {
663 : int i;
664 :
665 : /*
666 : * The values have to be in ascending order, so make sure we start
667 : * with the value passed by parameter.
668 : */
669 :
670 3456 : for (i = start; i < state->n; i++)
671 : {
672 1866 : current[index] = i;
673 1866 : generate_combinations_recurse(state, (index + 1), (i + 1), current);
674 : }
675 :
676 1590 : return;
677 : }
678 : else
679 : {
680 : /* we got a valid combination, add it to the array */
681 560 : memcpy(&state->combinations[(state->k * state->current)],
682 560 : current, state->k * sizeof(int));
683 560 : state->current++;
684 : }
685 : }
686 :
687 : /*
688 : * generate_combinations
689 : * generate all k-combinations of N elements
690 : */
691 : static void
692 284 : generate_combinations(CombinationGenerator *state)
693 : {
694 284 : int *current = (int *) palloc0(sizeof(int) * state->k);
695 :
696 284 : generate_combinations_recurse(state, 0, 0, current);
697 :
698 284 : pfree(current);
699 284 : }
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