Line data Source code
1 : /*
2 : * brin_bloom.c
3 : * Implementation of Bloom opclass for BRIN
4 : *
5 : * Portions Copyright (c) 1996-2024, 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 <math.h>
118 :
119 : #include "access/brin.h"
120 : #include "access/brin_internal.h"
121 : #include "access/brin_page.h"
122 : #include "access/brin_tuple.h"
123 : #include "access/genam.h"
124 : #include "access/htup_details.h"
125 : #include "access/reloptions.h"
126 : #include "catalog/pg_am.h"
127 : #include "catalog/pg_amop.h"
128 : #include "catalog/pg_type.h"
129 : #include "common/hashfn.h"
130 : #include "utils/fmgrprotos.h"
131 : #include "utils/rel.h"
132 :
133 : #define BloomEqualStrategyNumber 1
134 :
135 : /*
136 : * Additional SQL level support functions. We only have one, which is
137 : * used to calculate hash of the input value.
138 : *
139 : * Procedure numbers must not use values reserved for BRIN itself; see
140 : * brin_internal.h.
141 : */
142 : #define BLOOM_MAX_PROCNUMS 1 /* maximum support procs we need */
143 : #define PROCNUM_HASH 11 /* required */
144 :
145 : /*
146 : * Subtract this from procnum to obtain index in BloomOpaque arrays
147 : * (Must be equal to minimum of private procnums).
148 : */
149 : #define PROCNUM_BASE 11
150 :
151 : /*
152 : * Storage type for BRIN's reloptions.
153 : */
154 : typedef struct BloomOptions
155 : {
156 : int32 vl_len_; /* varlena header (do not touch directly!) */
157 : double nDistinctPerRange; /* number of distinct values per range */
158 : double falsePositiveRate; /* false positive for bloom filter */
159 : } BloomOptions;
160 :
161 : /*
162 : * The current min value (16) is somewhat arbitrary, but it's based
163 : * on the fact that the filter header is ~20B alone, which is about
164 : * the same as the filter bitmap for 16 distinct items with 1% false
165 : * positive rate. So by allowing lower values we'd not gain much. In
166 : * any case, the min should not be larger than MaxHeapTuplesPerPage
167 : * (~290), which is the theoretical maximum for single-page ranges.
168 : */
169 : #define BLOOM_MIN_NDISTINCT_PER_RANGE 16
170 :
171 : /*
172 : * Used to determine number of distinct items, based on the number of rows
173 : * in a page range. The 10% is somewhat similar to what estimate_num_groups
174 : * does, so we use the same factor here.
175 : */
176 : #define BLOOM_DEFAULT_NDISTINCT_PER_RANGE -0.1 /* 10% of values */
177 :
178 : /*
179 : * Allowed range and default value for the false positive range. The exact
180 : * values are somewhat arbitrary, but were chosen considering the various
181 : * parameters (size of filter vs. page size, etc.).
182 : *
183 : * The lower the false-positive rate, the more accurate the filter is, but
184 : * it also gets larger - at some point this eliminates the main advantage
185 : * of BRIN indexes, which is the tiny size. At 0.01% the index is about
186 : * 10% of the table (assuming 290 distinct values per 8kB page).
187 : *
188 : * On the other hand, as the false-positive rate increases, larger part of
189 : * the table has to be scanned due to mismatches - at 25% we're probably
190 : * close to sequential scan being cheaper.
191 : */
192 : #define BLOOM_MIN_FALSE_POSITIVE_RATE 0.0001 /* 0.01% fp rate */
193 : #define BLOOM_MAX_FALSE_POSITIVE_RATE 0.25 /* 25% fp rate */
194 : #define BLOOM_DEFAULT_FALSE_POSITIVE_RATE 0.01 /* 1% fp rate */
195 :
196 : #define BloomGetNDistinctPerRange(opts) \
197 : ((opts) && (((BloomOptions *) (opts))->nDistinctPerRange != 0) ? \
198 : (((BloomOptions *) (opts))->nDistinctPerRange) : \
199 : BLOOM_DEFAULT_NDISTINCT_PER_RANGE)
200 :
201 : #define BloomGetFalsePositiveRate(opts) \
202 : ((opts) && (((BloomOptions *) (opts))->falsePositiveRate != 0.0) ? \
203 : (((BloomOptions *) (opts))->falsePositiveRate) : \
204 : BLOOM_DEFAULT_FALSE_POSITIVE_RATE)
205 :
206 : /*
207 : * And estimate of the largest bloom we can fit onto a page. This is not
208 : * a perfect guarantee, for a couple of reasons. For example, the row may
209 : * be larger because the index has multiple columns.
210 : */
211 : #define BloomMaxFilterSize \
212 : MAXALIGN_DOWN(BLCKSZ - \
213 : (MAXALIGN(SizeOfPageHeaderData + \
214 : sizeof(ItemIdData)) + \
215 : MAXALIGN(sizeof(BrinSpecialSpace)) + \
216 : SizeOfBrinTuple))
217 :
218 : /*
219 : * Seeds used to calculate two hash functions h1 and h2, which are then used
220 : * to generate k hashes using the (h1 + i * h2) scheme.
221 : */
222 : #define BLOOM_SEED_1 0x71d924af
223 : #define BLOOM_SEED_2 0xba48b314
224 :
225 : /*
226 : * Bloom Filter
227 : *
228 : * Represents a bloom filter, built on hashes of the indexed values. That is,
229 : * we compute a uint32 hash of the value, and then store this hash into the
230 : * bloom filter (and compute additional hashes on it).
231 : *
232 : * XXX We could implement "sparse" bloom filters, keeping only the bytes that
233 : * are not entirely 0. But while indexes don't support TOAST, the varlena can
234 : * still be compressed. So this seems unnecessary, because the compression
235 : * should do the same job.
236 : *
237 : * XXX We can also watch the number of bits set in the bloom filter, and then
238 : * stop using it (and not store the bitmap, to save space) when the false
239 : * positive rate gets too high. But even if the false positive rate exceeds the
240 : * desired value, it still can eliminate some page ranges.
241 : */
242 : typedef struct BloomFilter
243 : {
244 : /* varlena header (do not touch directly!) */
245 : int32 vl_len_;
246 :
247 : /* space for various flags (unused for now) */
248 : uint16 flags;
249 :
250 : /* fields for the HASHED phase */
251 : uint8 nhashes; /* number of hash functions */
252 : uint32 nbits; /* number of bits in the bitmap (size) */
253 : uint32 nbits_set; /* number of bits set to 1 */
254 :
255 : /* data of the bloom filter */
256 : char data[FLEXIBLE_ARRAY_MEMBER];
257 : } BloomFilter;
258 :
259 : /*
260 : * bloom_filter_size
261 : * Calculate Bloom filter parameters (nbits, nbytes, nhashes).
262 : *
263 : * Given expected number of distinct values and desired false positive rate,
264 : * calculates the optimal parameters of the Bloom filter.
265 : *
266 : * The resulting parameters are returned through nbytesp (number of bytes),
267 : * nbitsp (number of bits) and nhashesp (number of hash functions). If a
268 : * pointer is NULL, the parameter is not returned.
269 : */
270 : static void
271 7884 : bloom_filter_size(int ndistinct, double false_positive_rate,
272 : int *nbytesp, int *nbitsp, int *nhashesp)
273 : {
274 : double k;
275 : int nbits,
276 : nbytes;
277 :
278 : /* sizing bloom filter: -(n * ln(p)) / (ln(2))^2 */
279 7884 : nbits = ceil(-(ndistinct * log(false_positive_rate)) / pow(log(2.0), 2));
280 :
281 : /* round m to whole bytes */
282 7884 : nbytes = ((nbits + 7) / 8);
283 7884 : nbits = nbytes * 8;
284 :
285 : /*
286 : * round(log(2.0) * m / ndistinct), but assume round() may not be
287 : * available on Windows
288 : */
289 7884 : k = log(2.0) * nbits / ndistinct;
290 7884 : k = (k - floor(k) >= 0.5) ? ceil(k) : floor(k);
291 :
292 7884 : if (nbytesp)
293 7884 : *nbytesp = nbytes;
294 :
295 7884 : if (nbitsp)
296 7884 : *nbitsp = nbits;
297 :
298 7884 : if (nhashesp)
299 7884 : *nhashesp = (int) k;
300 7884 : }
301 :
302 : /*
303 : * bloom_init
304 : * Initialize the Bloom Filter, allocate all the memory.
305 : *
306 : * The filter is initialized with optimal size for ndistinct expected values
307 : * and the requested false positive rate. The filter is stored as varlena.
308 : */
309 : static BloomFilter *
310 7884 : bloom_init(int ndistinct, double false_positive_rate)
311 : {
312 : Size len;
313 : BloomFilter *filter;
314 :
315 : int nbits; /* size of filter / number of bits */
316 : int nbytes; /* size of filter / number of bytes */
317 : int nhashes; /* number of hash functions */
318 :
319 : Assert(ndistinct > 0);
320 : Assert(false_positive_rate > 0 && false_positive_rate < 1);
321 :
322 : /* calculate bloom filter size / parameters */
323 7884 : bloom_filter_size(ndistinct, false_positive_rate,
324 : &nbytes, &nbits, &nhashes);
325 :
326 : /*
327 : * Reject filters that are obviously too large to store on a page.
328 : *
329 : * Initially the bloom filter is just zeroes and so very compressible, but
330 : * as we add values it gets more and more random, and so less and less
331 : * compressible. So initially everything fits on the page, but we might
332 : * get surprising failures later - we want to prevent that, so we reject
333 : * bloom filter that are obviously too large.
334 : *
335 : * XXX It's not uncommon to oversize the bloom filter a bit, to defend
336 : * against unexpected data anomalies (parts of table with more distinct
337 : * values per range etc.). But we still need to make sure even the
338 : * oversized filter fits on page, if such need arises.
339 : *
340 : * XXX This check is not perfect, because the index may have multiple
341 : * filters that are small individually, but too large when combined.
342 : */
343 7884 : if (nbytes > BloomMaxFilterSize)
344 0 : elog(ERROR, "the bloom filter is too large (%d > %zu)", nbytes,
345 : BloomMaxFilterSize);
346 :
347 : /*
348 : * We allocate the whole filter. Most of it is going to be 0 bits, so the
349 : * varlena is easy to compress.
350 : */
351 7884 : len = offsetof(BloomFilter, data) + nbytes;
352 :
353 7884 : filter = (BloomFilter *) palloc0(len);
354 :
355 7884 : filter->flags = 0;
356 7884 : filter->nhashes = nhashes;
357 7884 : filter->nbits = nbits;
358 :
359 7884 : SET_VARSIZE(filter, len);
360 :
361 7884 : return filter;
362 : }
363 :
364 :
365 : /*
366 : * bloom_add_value
367 : * Add value to the bloom filter.
368 : */
369 : static BloomFilter *
370 67932 : bloom_add_value(BloomFilter *filter, uint32 value, bool *updated)
371 : {
372 : int i;
373 : uint64 h1,
374 : h2;
375 :
376 : /* compute the hashes, used for the bloom filter */
377 67932 : h1 = hash_bytes_uint32_extended(value, BLOOM_SEED_1) % filter->nbits;
378 67932 : h2 = hash_bytes_uint32_extended(value, BLOOM_SEED_2) % filter->nbits;
379 :
380 : /* compute the requested number of hashes */
381 543456 : for (i = 0; i < filter->nhashes; i++)
382 : {
383 : /* h1 + h2 + f(i) */
384 475524 : uint32 h = (h1 + i * h2) % filter->nbits;
385 475524 : uint32 byte = (h / 8);
386 475524 : uint32 bit = (h % 8);
387 :
388 : /* if the bit is not set, set it and remember we did that */
389 475524 : if (!(filter->data[byte] & (0x01 << bit)))
390 : {
391 226254 : filter->data[byte] |= (0x01 << bit);
392 226254 : filter->nbits_set++;
393 226254 : if (updated)
394 226254 : *updated = true;
395 : }
396 : }
397 :
398 67932 : return filter;
399 : }
400 :
401 :
402 : /*
403 : * bloom_contains_value
404 : * Check if the bloom filter contains a particular value.
405 : */
406 : static bool
407 8208 : bloom_contains_value(BloomFilter *filter, uint32 value)
408 : {
409 : int i;
410 : uint64 h1,
411 : h2;
412 :
413 : /* calculate the two hashes */
414 8208 : h1 = hash_bytes_uint32_extended(value, BLOOM_SEED_1) % filter->nbits;
415 8208 : h2 = hash_bytes_uint32_extended(value, BLOOM_SEED_2) % filter->nbits;
416 :
417 : /* compute the requested number of hashes */
418 10434 : for (i = 0; i < filter->nhashes; i++)
419 : {
420 : /* h1 + h2 + f(i) */
421 10164 : uint32 h = (h1 + i * h2) % filter->nbits;
422 10164 : uint32 byte = (h / 8);
423 10164 : uint32 bit = (h % 8);
424 :
425 : /* if the bit is not set, the value is not there */
426 10164 : if (!(filter->data[byte] & (0x01 << bit)))
427 7938 : return false;
428 : }
429 :
430 : /* all hashes found in bloom filter */
431 270 : return true;
432 : }
433 :
434 : typedef struct BloomOpaque
435 : {
436 : /*
437 : * XXX At this point we only need a single proc (to compute the hash), but
438 : * let's keep the array just like inclusion and minmax opclasses, for
439 : * consistency. We may need additional procs in the future.
440 : */
441 : FmgrInfo extra_procinfos[BLOOM_MAX_PROCNUMS];
442 : bool extra_proc_missing[BLOOM_MAX_PROCNUMS];
443 : } BloomOpaque;
444 :
445 : static FmgrInfo *bloom_get_procinfo(BrinDesc *bdesc, uint16 attno,
446 : uint16 procnum);
447 :
448 :
449 : Datum
450 4766 : brin_bloom_opcinfo(PG_FUNCTION_ARGS)
451 : {
452 : BrinOpcInfo *result;
453 :
454 : /*
455 : * opaque->strategy_procinfos is initialized lazily; here it is set to
456 : * all-uninitialized by palloc0 which sets fn_oid to InvalidOid.
457 : *
458 : * bloom indexes only store the filter as a single BYTEA column
459 : */
460 :
461 4766 : result = palloc0(MAXALIGN(SizeofBrinOpcInfo(1)) +
462 : sizeof(BloomOpaque));
463 4766 : result->oi_nstored = 1;
464 4766 : result->oi_regular_nulls = true;
465 4766 : result->oi_opaque = (BloomOpaque *)
466 4766 : MAXALIGN((char *) result + SizeofBrinOpcInfo(1));
467 4766 : result->oi_typcache[0] = lookup_type_cache(PG_BRIN_BLOOM_SUMMARYOID, 0);
468 :
469 4766 : PG_RETURN_POINTER(result);
470 : }
471 :
472 : /*
473 : * brin_bloom_get_ndistinct
474 : * Determine the ndistinct value used to size bloom filter.
475 : *
476 : * Adjust the ndistinct value based on the pagesPerRange value. First,
477 : * if it's negative, it's assumed to be relative to maximum number of
478 : * tuples in the range (assuming each page gets MaxHeapTuplesPerPage
479 : * tuples, which is likely a significant over-estimate). We also clamp
480 : * the value, not to over-size the bloom filter unnecessarily.
481 : *
482 : * XXX We can only do this when the pagesPerRange value was supplied.
483 : * If it wasn't, it has to be a read-only access to the index, in which
484 : * case we don't really care. But perhaps we should fall-back to the
485 : * default pagesPerRange value?
486 : *
487 : * XXX We might also fetch info about ndistinct estimate for the column,
488 : * and compute the expected number of distinct values in a range. But
489 : * that may be tricky due to data being sorted in various ways, so it
490 : * seems better to rely on the upper estimate.
491 : *
492 : * XXX We might also calculate a better estimate of rows per BRIN range,
493 : * instead of using MaxHeapTuplesPerPage (which probably produces values
494 : * much higher than reality).
495 : */
496 : static int
497 7884 : brin_bloom_get_ndistinct(BrinDesc *bdesc, BloomOptions *opts)
498 : {
499 : double ndistinct;
500 : double maxtuples;
501 : BlockNumber pagesPerRange;
502 :
503 7884 : pagesPerRange = BrinGetPagesPerRange(bdesc->bd_index);
504 7884 : ndistinct = BloomGetNDistinctPerRange(opts);
505 :
506 : Assert(BlockNumberIsValid(pagesPerRange));
507 :
508 7884 : maxtuples = MaxHeapTuplesPerPage * pagesPerRange;
509 :
510 : /*
511 : * Similarly to n_distinct, negative values are relative - in this case to
512 : * maximum number of tuples in the page range (maxtuples).
513 : */
514 7884 : if (ndistinct < 0)
515 7884 : ndistinct = (-ndistinct) * maxtuples;
516 :
517 : /*
518 : * Positive values are to be used directly, but we still apply a couple of
519 : * safeties to avoid using unreasonably small bloom filters.
520 : */
521 7884 : ndistinct = Max(ndistinct, BLOOM_MIN_NDISTINCT_PER_RANGE);
522 :
523 : /*
524 : * And don't use more than the maximum possible number of tuples, in the
525 : * range, which would be entirely wasteful.
526 : */
527 7884 : ndistinct = Min(ndistinct, maxtuples);
528 :
529 7884 : return (int) ndistinct;
530 : }
531 :
532 : /*
533 : * Examine the given index tuple (which contains partial status of a certain
534 : * page range) by comparing it to the given value that comes from another heap
535 : * tuple. If the new value is outside the bloom filter specified by the
536 : * existing tuple values, update the index tuple and return true. Otherwise,
537 : * return false and do not modify in this case.
538 : */
539 : Datum
540 67932 : brin_bloom_add_value(PG_FUNCTION_ARGS)
541 : {
542 67932 : BrinDesc *bdesc = (BrinDesc *) PG_GETARG_POINTER(0);
543 67932 : BrinValues *column = (BrinValues *) PG_GETARG_POINTER(1);
544 67932 : Datum newval = PG_GETARG_DATUM(2);
545 67932 : bool isnull PG_USED_FOR_ASSERTS_ONLY = PG_GETARG_DATUM(3);
546 67932 : BloomOptions *opts = (BloomOptions *) PG_GET_OPCLASS_OPTIONS();
547 67932 : Oid colloid = PG_GET_COLLATION();
548 : FmgrInfo *hashFn;
549 : uint32 hashValue;
550 67932 : bool updated = false;
551 : AttrNumber attno;
552 : BloomFilter *filter;
553 :
554 : Assert(!isnull);
555 :
556 67932 : attno = column->bv_attno;
557 :
558 : /*
559 : * If this is the first non-null value, we need to initialize the bloom
560 : * filter. Otherwise just extract the existing bloom filter from
561 : * BrinValues.
562 : */
563 67932 : if (column->bv_allnulls)
564 : {
565 15768 : filter = bloom_init(brin_bloom_get_ndistinct(bdesc, opts),
566 7884 : BloomGetFalsePositiveRate(opts));
567 7884 : column->bv_values[0] = PointerGetDatum(filter);
568 7884 : column->bv_allnulls = false;
569 7884 : updated = true;
570 : }
571 : else
572 60048 : filter = (BloomFilter *) PG_DETOAST_DATUM(column->bv_values[0]);
573 :
574 : /*
575 : * Compute the hash of the new value, using the supplied hash function,
576 : * and then add the hash value to the bloom filter.
577 : */
578 67932 : hashFn = bloom_get_procinfo(bdesc, attno, PROCNUM_HASH);
579 :
580 67932 : hashValue = DatumGetUInt32(FunctionCall1Coll(hashFn, colloid, newval));
581 :
582 67932 : filter = bloom_add_value(filter, hashValue, &updated);
583 :
584 67932 : column->bv_values[0] = PointerGetDatum(filter);
585 :
586 67932 : PG_RETURN_BOOL(updated);
587 : }
588 :
589 : /*
590 : * Given an index tuple corresponding to a certain page range and a scan key,
591 : * return whether the scan key is consistent with the index tuple's bloom
592 : * filter. Return true if so, false otherwise.
593 : */
594 : Datum
595 8208 : brin_bloom_consistent(PG_FUNCTION_ARGS)
596 : {
597 8208 : BrinDesc *bdesc = (BrinDesc *) PG_GETARG_POINTER(0);
598 8208 : BrinValues *column = (BrinValues *) PG_GETARG_POINTER(1);
599 8208 : ScanKey *keys = (ScanKey *) PG_GETARG_POINTER(2);
600 8208 : int nkeys = PG_GETARG_INT32(3);
601 8208 : Oid colloid = PG_GET_COLLATION();
602 : AttrNumber attno;
603 : Datum value;
604 : bool matches;
605 : FmgrInfo *finfo;
606 : uint32 hashValue;
607 : BloomFilter *filter;
608 : int keyno;
609 :
610 8208 : filter = (BloomFilter *) PG_DETOAST_DATUM(column->bv_values[0]);
611 :
612 : Assert(filter);
613 :
614 : /*
615 : * Assume all scan keys match. We'll be searching for a scan key
616 : * eliminating the page range (we can stop on the first such key).
617 : */
618 8208 : matches = true;
619 :
620 8478 : for (keyno = 0; keyno < nkeys; keyno++)
621 : {
622 8208 : ScanKey key = keys[keyno];
623 :
624 : /* NULL keys are handled and filtered-out in bringetbitmap */
625 : Assert(!(key->sk_flags & SK_ISNULL));
626 :
627 8208 : attno = key->sk_attno;
628 8208 : value = key->sk_argument;
629 :
630 8208 : switch (key->sk_strategy)
631 : {
632 8208 : case BloomEqualStrategyNumber:
633 :
634 : /*
635 : * We want to return the current page range if the bloom
636 : * filter seems to contain the value.
637 : */
638 8208 : finfo = bloom_get_procinfo(bdesc, attno, PROCNUM_HASH);
639 :
640 8208 : hashValue = DatumGetUInt32(FunctionCall1Coll(finfo, colloid, value));
641 8208 : matches &= bloom_contains_value(filter, hashValue);
642 :
643 8208 : break;
644 0 : default:
645 : /* shouldn't happen */
646 0 : elog(ERROR, "invalid strategy number %d", key->sk_strategy);
647 : matches = false;
648 : break;
649 : }
650 :
651 8208 : if (!matches)
652 7938 : break;
653 : }
654 :
655 8208 : PG_RETURN_BOOL(matches);
656 : }
657 :
658 : /*
659 : * Given two BrinValues, update the first of them as a union of the summary
660 : * values contained in both. The second one is untouched.
661 : *
662 : * XXX We assume the bloom filters have the same parameters for now. In the
663 : * future we should have 'can union' function, to decide if we can combine
664 : * two particular bloom filters.
665 : */
666 : Datum
667 0 : brin_bloom_union(PG_FUNCTION_ARGS)
668 : {
669 : int i;
670 : int nbytes;
671 0 : BrinValues *col_a = (BrinValues *) PG_GETARG_POINTER(1);
672 0 : BrinValues *col_b = (BrinValues *) PG_GETARG_POINTER(2);
673 : BloomFilter *filter_a;
674 : BloomFilter *filter_b;
675 :
676 : Assert(col_a->bv_attno == col_b->bv_attno);
677 : Assert(!col_a->bv_allnulls && !col_b->bv_allnulls);
678 :
679 0 : filter_a = (BloomFilter *) PG_DETOAST_DATUM(col_a->bv_values[0]);
680 0 : filter_b = (BloomFilter *) PG_DETOAST_DATUM(col_b->bv_values[0]);
681 :
682 : /* make sure the filters use the same parameters */
683 : Assert(filter_a && filter_b);
684 : Assert(filter_a->nbits == filter_b->nbits);
685 : Assert(filter_a->nhashes == filter_b->nhashes);
686 : Assert((filter_a->nbits > 0) && (filter_a->nbits % 8 == 0));
687 :
688 0 : nbytes = (filter_a->nbits) / 8;
689 :
690 : /* simply OR the bitmaps */
691 0 : for (i = 0; i < nbytes; i++)
692 0 : filter_a->data[i] |= filter_b->data[i];
693 :
694 : /* update the number of bits set in the filter */
695 0 : filter_a->nbits_set = pg_popcount((const char *) filter_a->data, nbytes);
696 :
697 0 : PG_RETURN_VOID();
698 : }
699 :
700 : /*
701 : * Cache and return inclusion opclass support procedure
702 : *
703 : * Return the procedure corresponding to the given function support number
704 : * or null if it does not exist.
705 : */
706 : static FmgrInfo *
707 76140 : bloom_get_procinfo(BrinDesc *bdesc, uint16 attno, uint16 procnum)
708 : {
709 : BloomOpaque *opaque;
710 76140 : uint16 basenum = procnum - PROCNUM_BASE;
711 :
712 : /*
713 : * We cache these in the opaque struct, to avoid repetitive syscache
714 : * lookups.
715 : */
716 76140 : opaque = (BloomOpaque *) bdesc->bd_info[attno - 1]->oi_opaque;
717 :
718 : /*
719 : * If we already searched for this proc and didn't find it, don't bother
720 : * searching again.
721 : */
722 76140 : if (opaque->extra_proc_missing[basenum])
723 0 : return NULL;
724 :
725 76140 : if (opaque->extra_procinfos[basenum].fn_oid == InvalidOid)
726 : {
727 720 : if (RegProcedureIsValid(index_getprocid(bdesc->bd_index, attno,
728 : procnum)))
729 : {
730 720 : fmgr_info_copy(&opaque->extra_procinfos[basenum],
731 : index_getprocinfo(bdesc->bd_index, attno, procnum),
732 : bdesc->bd_context);
733 : }
734 : else
735 : {
736 0 : opaque->extra_proc_missing[basenum] = true;
737 0 : return NULL;
738 : }
739 : }
740 :
741 76140 : return &opaque->extra_procinfos[basenum];
742 : }
743 :
744 : Datum
745 704 : brin_bloom_options(PG_FUNCTION_ARGS)
746 : {
747 704 : local_relopts *relopts = (local_relopts *) PG_GETARG_POINTER(0);
748 :
749 704 : init_local_reloptions(relopts, sizeof(BloomOptions));
750 :
751 704 : add_local_real_reloption(relopts, "n_distinct_per_range",
752 : "number of distinct items expected in a BRIN page range",
753 : BLOOM_DEFAULT_NDISTINCT_PER_RANGE,
754 : -1.0, INT_MAX, offsetof(BloomOptions, nDistinctPerRange));
755 :
756 704 : add_local_real_reloption(relopts, "false_positive_rate",
757 : "desired false-positive rate for the bloom filters",
758 : BLOOM_DEFAULT_FALSE_POSITIVE_RATE,
759 : BLOOM_MIN_FALSE_POSITIVE_RATE,
760 : BLOOM_MAX_FALSE_POSITIVE_RATE,
761 : offsetof(BloomOptions, falsePositiveRate));
762 :
763 704 : PG_RETURN_VOID();
764 : }
765 :
766 : /*
767 : * brin_bloom_summary_in
768 : * - input routine for type brin_bloom_summary.
769 : *
770 : * brin_bloom_summary is only used internally to represent summaries
771 : * in BRIN bloom indexes, so it has no operations of its own, and we
772 : * disallow input too.
773 : */
774 : Datum
775 0 : brin_bloom_summary_in(PG_FUNCTION_ARGS)
776 : {
777 : /*
778 : * brin_bloom_summary stores the data in binary form and parsing text
779 : * input is not needed, so disallow this.
780 : */
781 0 : ereport(ERROR,
782 : (errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
783 : errmsg("cannot accept a value of type %s", "pg_brin_bloom_summary")));
784 :
785 : PG_RETURN_VOID(); /* keep compiler quiet */
786 : }
787 :
788 :
789 : /*
790 : * brin_bloom_summary_out
791 : * - output routine for type brin_bloom_summary.
792 : *
793 : * BRIN bloom summaries are serialized into a bytea value, but we want
794 : * to output something nicer humans can understand.
795 : */
796 : Datum
797 256 : brin_bloom_summary_out(PG_FUNCTION_ARGS)
798 : {
799 : BloomFilter *filter;
800 : StringInfoData str;
801 :
802 : /* detoast the data to get value with a full 4B header */
803 256 : filter = (BloomFilter *) PG_DETOAST_DATUM(PG_GETARG_DATUM(0));
804 :
805 256 : initStringInfo(&str);
806 256 : appendStringInfoChar(&str, '{');
807 :
808 256 : appendStringInfo(&str, "mode: hashed nhashes: %u nbits: %u nbits_set: %u",
809 256 : filter->nhashes, filter->nbits, filter->nbits_set);
810 :
811 256 : appendStringInfoChar(&str, '}');
812 :
813 256 : PG_RETURN_CSTRING(str.data);
814 : }
815 :
816 : /*
817 : * brin_bloom_summary_recv
818 : * - binary input routine for type brin_bloom_summary.
819 : */
820 : Datum
821 0 : brin_bloom_summary_recv(PG_FUNCTION_ARGS)
822 : {
823 0 : ereport(ERROR,
824 : (errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
825 : errmsg("cannot accept a value of type %s", "pg_brin_bloom_summary")));
826 :
827 : PG_RETURN_VOID(); /* keep compiler quiet */
828 : }
829 :
830 : /*
831 : * brin_bloom_summary_send
832 : * - binary output routine for type brin_bloom_summary.
833 : *
834 : * BRIN bloom summaries are serialized in a bytea value (although the
835 : * type is named differently), so let's just send that.
836 : */
837 : Datum
838 0 : brin_bloom_summary_send(PG_FUNCTION_ARGS)
839 : {
840 0 : return byteasend(fcinfo);
841 : }
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