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brin_bloom.c
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1 /*
2  * brin_bloom.c
3  * Implementation of Bloom opclass for BRIN
4  *
5  * Portions Copyright (c) 1996-2021, PostgreSQL Global Development Group
6  * Portions Copyright (c) 1994, Regents of the University of California
7  *
8  *
9  * A BRIN opclass summarizing page range into a bloom filter.
10  *
11  * Bloom filters allow efficient testing whether a given page range contains
12  * a particular value. Therefore, if we summarize each page range into a small
13  * bloom filter, we can easily (and cheaply) test whether it contains values
14  * we get later.
15  *
16  * The index only supports equality operators, similarly to hash indexes.
17  * Bloom indexes are however much smaller, and support only bitmap scans.
18  *
19  * Note: Don't confuse this with bloom indexes, implemented in a contrib
20  * module. That extension implements an entirely new AM, building a bloom
21  * filter on multiple columns in a single row. This opclass works with an
22  * existing AM (BRIN) and builds bloom filter on a column.
23  *
24  *
25  * values vs. hashes
26  * -----------------
27  *
28  * The original column values are not used directly, but are first hashed
29  * using the regular type-specific hash function, producing a uint32 hash.
30  * And this hash value is then added to the summary - i.e. it's hashed
31  * again and added to the bloom filter.
32  *
33  * This allows the code to treat all data types (byval/byref/...) the same
34  * way, with only minimal space requirements, because we're working with
35  * hashes and not the original values. Everything is uint32.
36  *
37  * Of course, this assumes the built-in hash function is reasonably good,
38  * without too many collisions etc. But that does seem to be the case, at
39  * least based on past experience. After all, the same hash functions are
40  * used for hash indexes, hash partitioning and so on.
41  *
42  *
43  * hashing scheme
44  * --------------
45  *
46  * Bloom filters require a number of independent hash functions. There are
47  * different schemes how to construct them - for example we might use
48  * hash_uint32_extended with random seeds, but that seems fairly expensive.
49  * We use a scheme requiring only two functions described in this paper:
50  *
51  * Less Hashing, Same Performance:Building a Better Bloom Filter
52  * Adam Kirsch, Michael Mitzenmacher†, Harvard School of Engineering and
53  * Applied Sciences, Cambridge, Massachusetts [DOI 10.1002/rsa.20208]
54  *
55  * The two hash functions h1 and h2 are calculated using hard-coded seeds,
56  * and then combined using (h1 + i * h2) to generate the hash functions.
57  *
58  *
59  * sizing the bloom filter
60  * -----------------------
61  *
62  * Size of a bloom filter depends on the number of distinct values we will
63  * store in it, and the desired false positive rate. The higher the number
64  * of distinct values and/or the lower the false positive rate, the larger
65  * the bloom filter. On the other hand, we want to keep the index as small
66  * as possible - that's one of the basic advantages of BRIN indexes.
67  *
68  * Although the number of distinct elements (in a page range) depends on
69  * the data, we can consider it fixed. This simplifies the trade-off to
70  * just false positive rate vs. size.
71  *
72  * At the page range level, false positive rate is a probability the bloom
73  * filter matches a random value. For the whole index (with sufficiently
74  * many page ranges) it represents the fraction of the index ranges (and
75  * thus fraction of the table to be scanned) matching the random value.
76  *
77  * Furthermore, the size of the bloom filter is subject to implementation
78  * limits - it has to fit onto a single index page (8kB by default). As
79  * the bitmap is inherently random (when "full" about half the bits is set
80  * to 1, randomly), compression can't help very much.
81  *
82  * To reduce the size of a filter (to fit to a page), we have to either
83  * accept higher false positive rate (undesirable), or reduce the number
84  * of distinct items to be stored in the filter. We can't alter the input
85  * data, of course, but we may make the BRIN page ranges smaller - instead
86  * of the default 128 pages (1MB) we may build index with 16-page ranges,
87  * or something like that. This should reduce the number of distinct values
88  * in the page range, making the filter smaller (with fixed false positive
89  * rate). Even for random data sets this should help, as the number of rows
90  * per heap page is limited (to ~290 with very narrow tables, likely ~20
91  * in practice).
92  *
93  * Of course, good sizing decisions depend on having the necessary data,
94  * i.e. number of distinct values in a page range (of a given size) and
95  * table size (to estimate cost change due to change in false positive
96  * rate due to having larger index vs. scanning larger indexes). We may
97  * not have that data - for example when building an index on empty table
98  * it's not really possible. And for some data we only have estimates for
99  * the whole table and we can only estimate per-range values (ndistinct).
100  *
101  * Another challenge is that while the bloom filter is per-column, it's
102  * the whole index tuple that has to fit into a page. And for multi-column
103  * indexes that may include pieces we have no control over (not necessarily
104  * bloom filters, the other columns may use other BRIN opclasses). So it's
105  * not entirely clear how to distribute the space between those columns.
106  *
107  * The current logic, implemented in brin_bloom_get_ndistinct, attempts to
108  * make some basic sizing decisions, based on the size of BRIN ranges, and
109  * the maximum number of rows per range.
110  *
111  *
112  * IDENTIFICATION
113  * src/backend/access/brin/brin_bloom.c
114  */
115 #include "postgres.h"
116 
117 #include "access/genam.h"
118 #include "access/brin.h"
119 #include "access/brin_internal.h"
120 #include "access/brin_page.h"
121 #include "access/brin_tuple.h"
122 #include "access/hash.h"
123 #include "access/htup_details.h"
124 #include "access/reloptions.h"
125 #include "access/stratnum.h"
126 #include "catalog/pg_type.h"
127 #include "catalog/pg_amop.h"
128 #include "utils/builtins.h"
129 #include "utils/datum.h"
130 #include "utils/lsyscache.h"
131 #include "utils/rel.h"
132 #include "utils/syscache.h"
133 
134 #include <math.h>
135 
136 #define BloomEqualStrategyNumber 1
137 
138 /*
139  * Additional SQL level support functions. We only have one, which is
140  * used to calculate hash of the input value.
141  *
142  * Procedure numbers must not use values reserved for BRIN itself; see
143  * brin_internal.h.
144  */
145 #define BLOOM_MAX_PROCNUMS 1 /* maximum support procs we need */
146 #define PROCNUM_HASH 11 /* required */
147 
148 /*
149  * Subtract this from procnum to obtain index in BloomOpaque arrays
150  * (Must be equal to minimum of private procnums).
151  */
152 #define PROCNUM_BASE 11
153 
154 /*
155  * Storage type for BRIN's reloptions.
156  */
157 typedef struct BloomOptions
158 {
159  int32 vl_len_; /* varlena header (do not touch directly!) */
160  double nDistinctPerRange; /* number of distinct values per range */
161  double falsePositiveRate; /* false positive for bloom filter */
162 } BloomOptions;
163 
164 /*
165  * The current min value (16) is somewhat arbitrary, but it's based
166  * on the fact that the filter header is ~20B alone, which is about
167  * the same as the filter bitmap for 16 distinct items with 1% false
168  * positive rate. So by allowing lower values we'd not gain much. In
169  * any case, the min should not be larger than MaxHeapTuplesPerPage
170  * (~290), which is the theoretical maximum for single-page ranges.
171  */
172 #define BLOOM_MIN_NDISTINCT_PER_RANGE 16
173 
174 /*
175  * Used to determine number of distinct items, based on the number of rows
176  * in a page range. The 10% is somewhat similar to what estimate_num_groups
177  * does, so we use the same factor here.
178  */
179 #define BLOOM_DEFAULT_NDISTINCT_PER_RANGE -0.1 /* 10% of values */
180 
181 /*
182  * Allowed range and default value for the false positive range. The exact
183  * values are somewhat arbitrary, but were chosen considering the various
184  * parameters (size of filter vs. page size, etc.).
185  *
186  * The lower the false-positive rate, the more accurate the filter is, but
187  * it also gets larger - at some point this eliminates the main advantage
188  * of BRIN indexes, which is the tiny size. At 0.01% the index is about
189  * 10% of the table (assuming 290 distinct values per 8kB page).
190  *
191  * On the other hand, as the false-positive rate increases, larger part of
192  * the table has to be scanned due to mismatches - at 25% we're probably
193  * close to sequential scan being cheaper.
194  */
195 #define BLOOM_MIN_FALSE_POSITIVE_RATE 0.0001 /* 0.01% fp rate */
196 #define BLOOM_MAX_FALSE_POSITIVE_RATE 0.25 /* 25% fp rate */
197 #define BLOOM_DEFAULT_FALSE_POSITIVE_RATE 0.01 /* 1% fp rate */
198 
199 #define BloomGetNDistinctPerRange(opts) \
200  ((opts) && (((BloomOptions *) (opts))->nDistinctPerRange != 0) ? \
201  (((BloomOptions *) (opts))->nDistinctPerRange) : \
202  BLOOM_DEFAULT_NDISTINCT_PER_RANGE)
203 
204 #define BloomGetFalsePositiveRate(opts) \
205  ((opts) && (((BloomOptions *) (opts))->falsePositiveRate != 0.0) ? \
206  (((BloomOptions *) (opts))->falsePositiveRate) : \
207  BLOOM_DEFAULT_FALSE_POSITIVE_RATE)
208 
209 /*
210  * And estimate of the largest bloom we can fit onto a page. This is not
211  * a perfect guarantee, for a couple of reasons. For example, the row may
212  * be larger because the index has multiple columns.
213  */
214 #define BloomMaxFilterSize \
215  MAXALIGN_DOWN(BLCKSZ - \
216  (MAXALIGN(SizeOfPageHeaderData + \
217  sizeof(ItemIdData)) + \
218  MAXALIGN(sizeof(BrinSpecialSpace)) + \
219  SizeOfBrinTuple))
220 
221 /*
222  * Seeds used to calculate two hash functions h1 and h2, which are then used
223  * to generate k hashes using the (h1 + i * h2) scheme.
224  */
225 #define BLOOM_SEED_1 0x71d924af
226 #define BLOOM_SEED_2 0xba48b314
227 
228 /*
229  * Bloom Filter
230  *
231  * Represents a bloom filter, built on hashes of the indexed values. That is,
232  * we compute a uint32 hash of the value, and then store this hash into the
233  * bloom filter (and compute additional hashes on it).
234  *
235  * XXX We could implement "sparse" bloom filters, keeping only the bytes that
236  * are not entirely 0. But while indexes don't support TOAST, the varlena can
237  * still be compressed. So this seems unnecessary, because the compression
238  * should do the same job.
239  *
240  * XXX We can also watch the number of bits set in the bloom filter, and then
241  * stop using it (and not store the bitmap, to save space) when the false
242  * positive rate gets too high. But even if the false positive rate exceeds the
243  * desired value, it still can eliminate some page ranges.
244  */
245 typedef struct BloomFilter
246 {
247  /* varlena header (do not touch directly!) */
249 
250  /* space for various flags (unused for now) */
252 
253  /* fields for the HASHED phase */
254  uint8 nhashes; /* number of hash functions */
255  uint32 nbits; /* number of bits in the bitmap (size) */
256  uint32 nbits_set; /* number of bits set to 1 */
257 
258  /* data of the bloom filter */
260 
261 } BloomFilter;
262 
263 
264 /*
265  * bloom_init
266  * Initialize the Bloom Filter, allocate all the memory.
267  *
268  * The filter is initialized with optimal size for ndistinct expected values
269  * and the requested false positive rate. The filter is stored as varlena.
270  */
271 static BloomFilter *
272 bloom_init(int ndistinct, double false_positive_rate)
273 {
274  Size len;
275  BloomFilter *filter;
276 
277  int nbits; /* size of filter / number of bits */
278  int nbytes; /* size of filter / number of bytes */
279 
280  double k; /* number of hash functions */
281 
282  Assert(ndistinct > 0);
283  Assert((false_positive_rate >= BLOOM_MIN_FALSE_POSITIVE_RATE) &&
284  (false_positive_rate < BLOOM_MAX_FALSE_POSITIVE_RATE));
285 
286  /* sizing bloom filter: -(n * ln(p)) / (ln(2))^2 */
287  nbits = ceil(-(ndistinct * log(false_positive_rate)) / pow(log(2.0), 2));
288 
289  /* round m to whole bytes */
290  nbytes = ((nbits + 7) / 8);
291  nbits = nbytes * 8;
292 
293  /*
294  * Reject filters that are obviously too large to store on a page.
295  *
296  * Initially the bloom filter is just zeroes and so very compressible, but
297  * as we add values it gets more and more random, and so less and less
298  * compressible. So initially everything fits on the page, but we might
299  * get surprising failures later - we want to prevent that, so we reject
300  * bloom filter that are obviously too large.
301  *
302  * XXX It's not uncommon to oversize the bloom filter a bit, to defend
303  * against unexpected data anomalies (parts of table with more distinct
304  * values per range etc.). But we still need to make sure even the
305  * oversized filter fits on page, if such need arises.
306  *
307  * XXX This check is not perfect, because the index may have multiple
308  * filters that are small individually, but too large when combined.
309  */
310  if (nbytes > BloomMaxFilterSize)
311  elog(ERROR, "the bloom filter is too large (%d > %zu)", nbytes,
313 
314  /*
315  * round(log(2.0) * m / ndistinct), but assume round() may not be
316  * available on Windows
317  */
318  k = log(2.0) * nbits / ndistinct;
319  k = (k - floor(k) >= 0.5) ? ceil(k) : floor(k);
320 
321  /*
322  * We allocate the whole filter. Most of it is going to be 0 bits, so the
323  * varlena is easy to compress.
324  */
325  len = offsetof(BloomFilter, data) + nbytes;
326 
327  filter = (BloomFilter *) palloc0(len);
328 
329  filter->flags = 0;
330  filter->nhashes = (int) k;
331  filter->nbits = nbits;
332 
333  SET_VARSIZE(filter, len);
334 
335  return filter;
336 }
337 
338 
339 /*
340  * bloom_add_value
341  * Add value to the bloom filter.
342  */
343 static BloomFilter *
344 bloom_add_value(BloomFilter *filter, uint32 value, bool *updated)
345 {
346  int i;
347  uint64 h1,
348  h2;
349 
350  /* compute the hashes, used for the bloom filter */
351  h1 = hash_bytes_uint32_extended(value, BLOOM_SEED_1) % filter->nbits;
352  h2 = hash_bytes_uint32_extended(value, BLOOM_SEED_2) % filter->nbits;
353 
354  /* compute the requested number of hashes */
355  for (i = 0; i < filter->nhashes; i++)
356  {
357  /* h1 + h2 + f(i) */
358  uint32 h = (h1 + i * h2) % filter->nbits;
359  uint32 byte = (h / 8);
360  uint32 bit = (h % 8);
361 
362  /* if the bit is not set, set it and remember we did that */
363  if (!(filter->data[byte] & (0x01 << bit)))
364  {
365  filter->data[byte] |= (0x01 << bit);
366  filter->nbits_set++;
367  if (updated)
368  *updated = true;
369  }
370  }
371 
372  return filter;
373 }
374 
375 
376 /*
377  * bloom_contains_value
378  * Check if the bloom filter contains a particular value.
379  */
380 static bool
382 {
383  int i;
384  uint64 h1,
385  h2;
386 
387  /* calculate the two hashes */
388  h1 = hash_bytes_uint32_extended(value, BLOOM_SEED_1) % filter->nbits;
389  h2 = hash_bytes_uint32_extended(value, BLOOM_SEED_2) % filter->nbits;
390 
391  /* compute the requested number of hashes */
392  for (i = 0; i < filter->nhashes; i++)
393  {
394  /* h1 + h2 + f(i) */
395  uint32 h = (h1 + i * h2) % filter->nbits;
396  uint32 byte = (h / 8);
397  uint32 bit = (h % 8);
398 
399  /* if the bit is not set, the value is not there */
400  if (!(filter->data[byte] & (0x01 << bit)))
401  return false;
402  }
403 
404  /* all hashes found in bloom filter */
405  return true;
406 }
407 
408 typedef struct BloomOpaque
409 {
410  /*
411  * XXX At this point we only need a single proc (to compute the hash), but
412  * let's keep the array just like inclusion and minmax opclasses, for
413  * consistency. We may need additional procs in the future.
414  */
415  FmgrInfo extra_procinfos[BLOOM_MAX_PROCNUMS];
416  bool extra_proc_missing[BLOOM_MAX_PROCNUMS];
417 } BloomOpaque;
418 
419 static FmgrInfo *bloom_get_procinfo(BrinDesc *bdesc, uint16 attno,
420  uint16 procnum);
421 
422 
423 Datum
425 {
426  BrinOpcInfo *result;
427 
428  /*
429  * opaque->strategy_procinfos is initialized lazily; here it is set to
430  * all-uninitialized by palloc0 which sets fn_oid to InvalidOid.
431  *
432  * bloom indexes only store the filter as a single BYTEA column
433  */
434 
435  result = palloc0(MAXALIGN(SizeofBrinOpcInfo(1)) +
436  sizeof(BloomOpaque));
437  result->oi_nstored = 1;
438  result->oi_regular_nulls = true;
439  result->oi_opaque = (BloomOpaque *)
440  MAXALIGN((char *) result + SizeofBrinOpcInfo(1));
441  result->oi_typcache[0] = lookup_type_cache(PG_BRIN_BLOOM_SUMMARYOID, 0);
442 
443  PG_RETURN_POINTER(result);
444 }
445 
446 /*
447  * brin_bloom_get_ndistinct
448  * Determine the ndistinct value used to size bloom filter.
449  *
450  * Adjust the ndistinct value based on the pagesPerRange value. First,
451  * if it's negative, it's assumed to be relative to maximum number of
452  * tuples in the range (assuming each page gets MaxHeapTuplesPerPage
453  * tuples, which is likely a significant over-estimate). We also clamp
454  * the value, not to over-size the bloom filter unnecessarily.
455  *
456  * XXX We can only do this when the pagesPerRange value was supplied.
457  * If it wasn't, it has to be a read-only access to the index, in which
458  * case we don't really care. But perhaps we should fall-back to the
459  * default pagesPerRange value?
460  *
461  * XXX We might also fetch info about ndistinct estimate for the column,
462  * and compute the expected number of distinct values in a range. But
463  * that may be tricky due to data being sorted in various ways, so it
464  * seems better to rely on the upper estimate.
465  *
466  * XXX We might also calculate a better estimate of rows per BRIN range,
467  * instead of using MaxHeapTuplesPerPage (which probably produces values
468  * much higher than reality).
469  */
470 static int
472 {
473  double ndistinct;
474  double maxtuples;
475  BlockNumber pagesPerRange;
476 
477  pagesPerRange = BrinGetPagesPerRange(bdesc->bd_index);
478  ndistinct = BloomGetNDistinctPerRange(opts);
479 
480  Assert(BlockNumberIsValid(pagesPerRange));
481 
482  maxtuples = MaxHeapTuplesPerPage * pagesPerRange;
483 
484  /*
485  * Similarly to n_distinct, negative values are relative - in this case to
486  * maximum number of tuples in the page range (maxtuples).
487  */
488  if (ndistinct < 0)
489  ndistinct = (-ndistinct) * maxtuples;
490 
491  /*
492  * Positive values are to be used directly, but we still apply a couple of
493  * safeties to avoid using unreasonably small bloom filters.
494  */
495  ndistinct = Max(ndistinct, BLOOM_MIN_NDISTINCT_PER_RANGE);
496 
497  /*
498  * And don't use more than the maximum possible number of tuples, in the
499  * range, which would be entirely wasteful.
500  */
501  ndistinct = Min(ndistinct, maxtuples);
502 
503  return (int) ndistinct;
504 }
505 
506 /*
507  * Examine the given index tuple (which contains partial status of a certain
508  * page range) by comparing it to the given value that comes from another heap
509  * tuple. If the new value is outside the bloom filter specified by the
510  * existing tuple values, update the index tuple and return true. Otherwise,
511  * return false and do not modify in this case.
512  */
513 Datum
515 {
516  BrinDesc *bdesc = (BrinDesc *) PG_GETARG_POINTER(0);
517  BrinValues *column = (BrinValues *) PG_GETARG_POINTER(1);
519  bool isnull PG_USED_FOR_ASSERTS_ONLY = PG_GETARG_DATUM(3);
521  Oid colloid = PG_GET_COLLATION();
522  FmgrInfo *hashFn;
523  uint32 hashValue;
524  bool updated = false;
525  AttrNumber attno;
526  BloomFilter *filter;
527 
528  Assert(!isnull);
529 
530  attno = column->bv_attno;
531 
532  /*
533  * If this is the first non-null value, we need to initialize the bloom
534  * filter. Otherwise just extract the existing bloom filter from
535  * BrinValues.
536  */
537  if (column->bv_allnulls)
538  {
539  filter = bloom_init(brin_bloom_get_ndistinct(bdesc, opts),
541  column->bv_values[0] = PointerGetDatum(filter);
542  column->bv_allnulls = false;
543  updated = true;
544  }
545  else
546  filter = (BloomFilter *) PG_DETOAST_DATUM(column->bv_values[0]);
547 
548  /*
549  * Compute the hash of the new value, using the supplied hash function,
550  * and then add the hash value to the bloom filter.
551  */
552  hashFn = bloom_get_procinfo(bdesc, attno, PROCNUM_HASH);
553 
554  hashValue = DatumGetUInt32(FunctionCall1Coll(hashFn, colloid, newval));
555 
556  filter = bloom_add_value(filter, hashValue, &updated);
557 
558  column->bv_values[0] = PointerGetDatum(filter);
559 
560  PG_RETURN_BOOL(updated);
561 }
562 
563 /*
564  * Given an index tuple corresponding to a certain page range and a scan key,
565  * return whether the scan key is consistent with the index tuple's bloom
566  * filter. Return true if so, false otherwise.
567  */
568 Datum
570 {
571  BrinDesc *bdesc = (BrinDesc *) PG_GETARG_POINTER(0);
572  BrinValues *column = (BrinValues *) PG_GETARG_POINTER(1);
573  ScanKey *keys = (ScanKey *) PG_GETARG_POINTER(2);
574  int nkeys = PG_GETARG_INT32(3);
575  Oid colloid = PG_GET_COLLATION();
576  AttrNumber attno;
577  Datum value;
578  Datum matches;
579  FmgrInfo *finfo;
580  uint32 hashValue;
581  BloomFilter *filter;
582  int keyno;
583 
584  filter = (BloomFilter *) PG_DETOAST_DATUM(column->bv_values[0]);
585 
586  Assert(filter);
587 
588  matches = true;
589 
590  for (keyno = 0; keyno < nkeys; keyno++)
591  {
592  ScanKey key = keys[keyno];
593 
594  /* NULL keys are handled and filtered-out in bringetbitmap */
595  Assert(!(key->sk_flags & SK_ISNULL));
596 
597  attno = key->sk_attno;
598  value = key->sk_argument;
599 
600  switch (key->sk_strategy)
601  {
603 
604  /*
605  * In the equality case (WHERE col = someval), we want to
606  * return the current page range if the minimum value in the
607  * range <= scan key, and the maximum value >= scan key.
608  */
609  finfo = bloom_get_procinfo(bdesc, attno, PROCNUM_HASH);
610 
611  hashValue = DatumGetUInt32(FunctionCall1Coll(finfo, colloid, value));
612  matches &= bloom_contains_value(filter, hashValue);
613 
614  break;
615  default:
616  /* shouldn't happen */
617  elog(ERROR, "invalid strategy number %d", key->sk_strategy);
618  matches = 0;
619  break;
620  }
621 
622  if (!matches)
623  break;
624  }
625 
626  PG_RETURN_DATUM(matches);
627 }
628 
629 /*
630  * Given two BrinValues, update the first of them as a union of the summary
631  * values contained in both. The second one is untouched.
632  *
633  * XXX We assume the bloom filters have the same parameters for now. In the
634  * future we should have 'can union' function, to decide if we can combine
635  * two particular bloom filters.
636  */
637 Datum
639 {
640  int i;
641  int nbytes;
642  BrinValues *col_a = (BrinValues *) PG_GETARG_POINTER(1);
643  BrinValues *col_b = (BrinValues *) PG_GETARG_POINTER(2);
644  BloomFilter *filter_a;
645  BloomFilter *filter_b;
646 
647  Assert(col_a->bv_attno == col_b->bv_attno);
648  Assert(!col_a->bv_allnulls && !col_b->bv_allnulls);
649 
650  filter_a = (BloomFilter *) PG_DETOAST_DATUM(col_a->bv_values[0]);
651  filter_b = (BloomFilter *) PG_DETOAST_DATUM(col_b->bv_values[0]);
652 
653  /* make sure the filters use the same parameters */
654  Assert(filter_a && filter_b);
655  Assert(filter_a->nbits == filter_b->nbits);
656  Assert(filter_a->nhashes == filter_b->nhashes);
657  Assert((filter_a->nbits > 0) && (filter_a->nbits % 8 == 0));
658 
659  nbytes = (filter_a->nbits) / 8;
660 
661  /* simply OR the bitmaps */
662  for (i = 0; i < nbytes; i++)
663  filter_a->data[i] |= filter_b->data[i];
664 
665  PG_RETURN_VOID();
666 }
667 
668 /*
669  * Cache and return inclusion opclass support procedure
670  *
671  * Return the procedure corresponding to the given function support number
672  * or null if it does not exist.
673  */
674 static FmgrInfo *
675 bloom_get_procinfo(BrinDesc *bdesc, uint16 attno, uint16 procnum)
676 {
677  BloomOpaque *opaque;
678  uint16 basenum = procnum - PROCNUM_BASE;
679 
680  /*
681  * We cache these in the opaque struct, to avoid repetitive syscache
682  * lookups.
683  */
684  opaque = (BloomOpaque *) bdesc->bd_info[attno - 1]->oi_opaque;
685 
686  /*
687  * If we already searched for this proc and didn't find it, don't bother
688  * searching again.
689  */
690  if (opaque->extra_proc_missing[basenum])
691  return NULL;
692 
693  if (opaque->extra_procinfos[basenum].fn_oid == InvalidOid)
694  {
695  if (RegProcedureIsValid(index_getprocid(bdesc->bd_index, attno,
696  procnum)))
697  {
698  fmgr_info_copy(&opaque->extra_procinfos[basenum],
699  index_getprocinfo(bdesc->bd_index, attno, procnum),
700  bdesc->bd_context);
701  }
702  else
703  {
704  opaque->extra_proc_missing[basenum] = true;
705  return NULL;
706  }
707  }
708 
709  return &opaque->extra_procinfos[basenum];
710 }
711 
712 Datum
714 {
716 
717  init_local_reloptions(relopts, sizeof(BloomOptions));
718 
719  add_local_real_reloption(relopts, "n_distinct_per_range",
720  "number of distinct items expected in a BRIN page range",
722  -1.0, INT_MAX, offsetof(BloomOptions, nDistinctPerRange));
723 
724  add_local_real_reloption(relopts, "false_positive_rate",
725  "desired false-positive rate for the bloom filters",
730 
731  PG_RETURN_VOID();
732 }
733 
734 /*
735  * brin_bloom_summary_in
736  * - input routine for type brin_bloom_summary.
737  *
738  * brin_bloom_summary is only used internally to represent summaries
739  * in BRIN bloom indexes, so it has no operations of its own, and we
740  * disallow input too.
741  */
742 Datum
744 {
745  /*
746  * brin_bloom_summary stores the data in binary form and parsing text
747  * input is not needed, so disallow this.
748  */
749  ereport(ERROR,
750  (errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
751  errmsg("cannot accept a value of type %s", "pg_brin_bloom_summary")));
752 
753  PG_RETURN_VOID(); /* keep compiler quiet */
754 }
755 
756 
757 /*
758  * brin_bloom_summary_out
759  * - output routine for type brin_bloom_summary.
760  *
761  * BRIN bloom summaries are serialized into a bytea value, but we want
762  * to output something nicer humans can understand.
763  */
764 Datum
766 {
767  BloomFilter *filter;
769 
770  /* detoast the data to get value with a full 4B header */
772 
773  initStringInfo(&str);
774  appendStringInfoChar(&str, '{');
775 
776  appendStringInfo(&str, "mode: hashed nhashes: %u nbits: %u nbits_set: %u",
777  filter->nhashes, filter->nbits, filter->nbits_set);
778 
779  appendStringInfoChar(&str, '}');
780 
781  PG_RETURN_CSTRING(str.data);
782 }
783 
784 /*
785  * brin_bloom_summary_recv
786  * - binary input routine for type brin_bloom_summary.
787  */
788 Datum
790 {
791  ereport(ERROR,
792  (errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
793  errmsg("cannot accept a value of type %s", "pg_brin_bloom_summary")));
794 
795  PG_RETURN_VOID(); /* keep compiler quiet */
796 }
797 
798 /*
799  * brin_bloom_summary_send
800  * - binary output routine for type brin_bloom_summary.
801  *
802  * BRIN bloom summaries are serialized in a bytea value (although the
803  * type is named differently), so let's just send that.
804  */
805 Datum
807 {
808  return byteasend(fcinfo);
809 }
#define BLOOM_MIN_FALSE_POSITIVE_RATE
Definition: brin_bloom.c:195
#define PG_RETURN_POINTER(x)
Definition: fmgr.h:361
#define DatumGetUInt32(X)
Definition: postgres.h:530
Datum brin_bloom_summary_send(PG_FUNCTION_ARGS)
Definition: brin_bloom.c:806
static BloomFilter * bloom_init(int ndistinct, double false_positive_rate)
Definition: brin_bloom.c:272
#define PG_GETARG_INT32(n)
Definition: fmgr.h:269
static bool bloom_contains_value(BloomFilter *filter, uint32 value)
Definition: brin_bloom.c:381
#define BLOOM_MIN_NDISTINCT_PER_RANGE
Definition: brin_bloom.c:172
Definition: fmgr.h:56
#define PROCNUM_BASE
Definition: brin_bloom.c:152
void init_local_reloptions(local_relopts *opts, Size relopt_struct_size)
Definition: reloptions.c:727
FmgrInfo * index_getprocinfo(Relation irel, AttrNumber attnum, uint16 procnum)
Definition: indexam.c:803
struct BloomOpaque BloomOpaque
struct BloomFilter BloomFilter
#define BLOOM_DEFAULT_NDISTINCT_PER_RANGE
Definition: brin_bloom.c:179
void add_local_real_reloption(local_relopts *relopts, const char *name, const char *desc, double default_val, double min_val, double max_val, int offset)
Definition: reloptions.c:965
Datum brin_bloom_union(PG_FUNCTION_ARGS)
Definition: brin_bloom.c:638
#define PointerGetDatum(X)
Definition: postgres.h:600
#define PG_GETARG_DATUM(n)
Definition: fmgr.h:268
#define SizeofBrinOpcInfo(ncols)
Definition: brin_internal.h:41
uint64 hash_bytes_uint32_extended(uint32 k, uint64 seed)
Definition: hashfn.c:631
#define Min(x, y)
Definition: c.h:986
#define MaxHeapTuplesPerPage
Definition: htup_details.h:573
bool bv_allnulls
Definition: brin_tuple.h:33
unsigned char uint8
Definition: c.h:439
#define FLEXIBLE_ARRAY_MEMBER
Definition: c.h:350
Datum brin_bloom_summary_out(PG_FUNCTION_ARGS)
Definition: brin_bloom.c:765
int errcode(int sqlerrcode)
Definition: elog.c:698
#define PG_GETARG_POINTER(n)
Definition: fmgr.h:276
uint32 BlockNumber
Definition: block.h:31
Datum brin_bloom_summary_in(PG_FUNCTION_ARGS)
Definition: brin_bloom.c:743
static BloomFilter * bloom_add_value(BloomFilter *filter, uint32 value, bool *updated)
Definition: brin_bloom.c:344
unsigned int Oid
Definition: postgres_ext.h:31
Datum brin_bloom_add_value(PG_FUNCTION_ARGS)
Definition: brin_bloom.c:514
#define BrinGetPagesPerRange(relation)
Definition: brin.h:39
#define PG_GET_COLLATION()
Definition: fmgr.h:198
Datum brin_bloom_consistent(PG_FUNCTION_ARGS)
Definition: brin_bloom.c:569
signed int int32
Definition: c.h:429
#define BLOOM_DEFAULT_FALSE_POSITIVE_RATE
Definition: brin_bloom.c:197
char data[FLEXIBLE_ARRAY_MEMBER]
Definition: brin_bloom.c:259
double nDistinctPerRange
Definition: brin_bloom.c:160
#define PG_GET_OPCLASS_OPTIONS()
Definition: fmgr.h:342
#define BloomGetFalsePositiveRate(opts)
Definition: brin_bloom.c:204
uint32 nbits_set
Definition: brin_bloom.c:256
unsigned short uint16
Definition: c.h:440
void appendStringInfo(StringInfo str, const char *fmt,...)
Definition: stringinfo.c:91
#define BLOOM_MAX_PROCNUMS
Definition: brin_bloom.c:145
#define ERROR
Definition: elog.h:46
Relation bd_index
Definition: brin_internal.h:50
TypeCacheEntry * oi_typcache[FLEXIBLE_ARRAY_MEMBER]
Definition: brin_internal.h:37
StrategyNumber sk_strategy
Definition: skey.h:68
void fmgr_info_copy(FmgrInfo *dstinfo, FmgrInfo *srcinfo, MemoryContext destcxt)
Definition: fmgr.c:608
AttrNumber bv_attno
Definition: brin_tuple.h:31
Datum brin_bloom_opcinfo(PG_FUNCTION_ARGS)
Definition: brin_bloom.c:424
uint16 oi_nstored
Definition: brin_internal.h:28
uint8 nhashes
Definition: brin_bloom.c:254
#define BLOOM_SEED_2
Definition: brin_bloom.c:226
#define RegProcedureIsValid(p)
Definition: c.h:712
uint16 flags
Definition: brin_bloom.c:251
#define BLOOM_MAX_FALSE_POSITIVE_RATE
Definition: brin_bloom.c:196
unsigned int uint32
Definition: c.h:441
bool oi_regular_nulls
Definition: brin_internal.h:31
#define SK_ISNULL
Definition: skey.h:115
Datum brin_bloom_summary_recv(PG_FUNCTION_ARGS)
Definition: brin_bloom.c:789
static int brin_bloom_get_ndistinct(BrinDesc *bdesc, BloomOptions *opts)
Definition: brin_bloom.c:471
#define BloomEqualStrategyNumber
Definition: brin_bloom.c:136
struct BloomOptions BloomOptions
static AmcheckOptions opts
Definition: pg_amcheck.c:110
#define byte(x, n)
Definition: rijndael.c:68
void appendStringInfoChar(StringInfo str, char ch)
Definition: stringinfo.c:188
void initStringInfo(StringInfo str)
Definition: stringinfo.c:59
Datum byteasend(PG_FUNCTION_ARGS)
Definition: varlena.c:493
int32 vl_len_
Definition: bloom.h:103
static FmgrInfo * bloom_get_procinfo(BrinDesc *bdesc, uint16 attno, uint16 procnum)
Definition: brin_bloom.c:675
void * palloc0(Size size)
Definition: mcxt.c:1093
#define PG_RETURN_BOOL(x)
Definition: fmgr.h:359
uintptr_t Datum
Definition: postgres.h:411
FmgrInfo extra_procinfos[BLOOM_MAX_PROCNUMS]
Definition: brin_bloom.c:415
#define PG_RETURN_DATUM(x)
Definition: fmgr.h:353
void * oi_opaque
Definition: brin_internal.h:34
Datum FunctionCall1Coll(FmgrInfo *flinfo, Oid collation, Datum arg1)
Definition: fmgr.c:1128
uint32 nbits
Definition: brin_bloom.c:255
BrinOpcInfo * bd_info[FLEXIBLE_ARRAY_MEMBER]
Definition: brin_internal.h:62
TypeCacheEntry * lookup_type_cache(Oid type_id, int flags)
Definition: typcache.c:339
#define InvalidOid
Definition: postgres_ext.h:36
Oid fn_oid
Definition: fmgr.h:59
static struct @143 value
#define ereport(elevel,...)
Definition: elog.h:157
#define BlockNumberIsValid(blockNumber)
Definition: block.h:70
#define PG_RETURN_VOID()
Definition: fmgr.h:349
#define Max(x, y)
Definition: c.h:980
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Definition: skey.h:66
Datum bit(PG_FUNCTION_ARGS)
Definition: varbit.c:391
#define Assert(condition)
Definition: c.h:804
#define PG_RETURN_CSTRING(x)
Definition: fmgr.h:362
size_t Size
Definition: c.h:540
#define newval
#define PG_GETARG_BYTEA_PP(n)
Definition: fmgr.h:308
#define MAXALIGN(LEN)
Definition: c.h:757
#define PROCNUM_HASH
Definition: brin_bloom.c:146
MemoryContext bd_context
Definition: brin_internal.h:47
Datum brin_bloom_options(PG_FUNCTION_ARGS)
Definition: brin_bloom.c:713
int errmsg(const char *fmt,...)
Definition: elog.c:909
double falsePositiveRate
Definition: brin_bloom.c:161
bool extra_proc_missing[BLOOM_MAX_PROCNUMS]
Definition: brin_bloom.c:416
#define elog(elevel,...)
Definition: elog.h:232
int i
#define BLOOM_SEED_1
Definition: brin_bloom.c:225
int32 vl_len_
Definition: brin_bloom.c:248
#define PG_DETOAST_DATUM(datum)
Definition: fmgr.h:240
#define PG_FUNCTION_ARGS
Definition: fmgr.h:193
#define SET_VARSIZE(PTR, len)
Definition: postgres.h:342
#define BloomMaxFilterSize
Definition: brin_bloom.c:214
Datum * bv_values
Definition: brin_tuple.h:34
Datum sk_argument
Definition: skey.h:72
#define BloomGetNDistinctPerRange(opts)
Definition: brin_bloom.c:199
int16 AttrNumber
Definition: attnum.h:21
#define offsetof(type, field)
Definition: c.h:727
AttrNumber sk_attno
Definition: skey.h:67
#define PG_USED_FOR_ASSERTS_ONLY
Definition: c.h:155
RegProcedure index_getprocid(Relation irel, AttrNumber attnum, uint16 procnum)
Definition: indexam.c:769