LCOV - code coverage report
Current view: top level - src/backend/access/brin - brin_bloom.c (source / functions) Hit Total Coverage
Test: PostgreSQL 14devel Lines: 110 139 79.1 %
Date: 2021-05-13 09:07:15 Functions: 9 14 64.3 %
Legend: Lines: hit not hit

          Line data    Source code
       1             : /*
       2             :  * brin_bloom.c
       3             :  *      Implementation of Bloom opclass for BRIN
       4             :  *
       5             :  * Portions Copyright (c) 1996-2017, 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!) */
     248             :     int32       vl_len_;
     249             : 
     250             :     /* space for various flags (unused for now) */
     251             :     uint16      flags;
     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 */
     259             :     char        data[FLEXIBLE_ARRAY_MEMBER];
     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        5268 : 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        5268 :     nbits = ceil(-(ndistinct * log(false_positive_rate)) / pow(log(2.0), 2));
     288             : 
     289             :     /* round m to whole bytes */
     290        5268 :     nbytes = ((nbits + 7) / 8);
     291        5268 :     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        5268 :     if (nbytes > BloomMaxFilterSize)
     311           0 :         elog(ERROR, "the bloom filter is too large (%d > %zu)", nbytes,
     312             :              BloomMaxFilterSize);
     313             : 
     314             :     /*
     315             :      * round(log(2.0) * m / ndistinct), but assume round() may not be
     316             :      * available on Windows
     317             :      */
     318        5268 :     k = log(2.0) * nbits / ndistinct;
     319        5268 :     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        5268 :     len = offsetof(BloomFilter, data) + nbytes;
     326             : 
     327        5268 :     filter = (BloomFilter *) palloc0(len);
     328             : 
     329        5268 :     filter->flags = 0;
     330        5268 :     filter->nhashes = (int) k;
     331        5268 :     filter->nbits = nbits;
     332             : 
     333        5268 :     SET_VARSIZE(filter, len);
     334             : 
     335        5268 :     return filter;
     336             : }
     337             : 
     338             : 
     339             : /*
     340             :  * bloom_add_value
     341             :  *      Add value to the bloom filter.
     342             :  */
     343             : static BloomFilter *
     344       29548 : 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       29548 :     h1 = hash_bytes_uint32_extended(value, BLOOM_SEED_1) % filter->nbits;
     352       29548 :     h2 = hash_bytes_uint32_extended(value, BLOOM_SEED_2) % filter->nbits;
     353             : 
     354             :     /* compute the requested number of hashes */
     355      236384 :     for (i = 0; i < filter->nhashes; i++)
     356             :     {
     357             :         /* h1 + h2 + f(i) */
     358      206836 :         uint32      h = (h1 + i * h2) % filter->nbits;
     359      206836 :         uint32      byte = (h / 8);
     360      206836 :         uint32      bit = (h % 8);
     361             : 
     362             :         /* if the bit is not set, set it and remember we did that */
     363      206836 :         if (!(filter->data[byte] & (0x01 << bit)))
     364             :         {
     365       73772 :             filter->data[byte] |= (0x01 << bit);
     366       73772 :             filter->nbits_set++;
     367       73772 :             if (updated)
     368       73772 :                 *updated = true;
     369             :         }
     370             :     }
     371             : 
     372       29548 :     return filter;
     373             : }
     374             : 
     375             : 
     376             : /*
     377             :  * bloom_contains_value
     378             :  *      Check if the bloom filter contains a particular value.
     379             :  */
     380             : static bool
     381        5472 : bloom_contains_value(BloomFilter *filter, uint32 value)
     382             : {
     383             :     int         i;
     384             :     uint64      h1,
     385             :                 h2;
     386             : 
     387             :     /* calculate the two hashes */
     388        5472 :     h1 = hash_bytes_uint32_extended(value, BLOOM_SEED_1) % filter->nbits;
     389        5472 :     h2 = hash_bytes_uint32_extended(value, BLOOM_SEED_2) % filter->nbits;
     390             : 
     391             :     /* compute the requested number of hashes */
     392        6956 :     for (i = 0; i < filter->nhashes; i++)
     393             :     {
     394             :         /* h1 + h2 + f(i) */
     395        6776 :         uint32      h = (h1 + i * h2) % filter->nbits;
     396        6776 :         uint32      byte = (h / 8);
     397        6776 :         uint32      bit = (h % 8);
     398             : 
     399             :         /* if the bit is not set, the value is not there */
     400        6776 :         if (!(filter->data[byte] & (0x01 << bit)))
     401        5292 :             return false;
     402             :     }
     403             : 
     404             :     /* all hashes found in bloom filter */
     405         180 :     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
     424        3174 : brin_bloom_opcinfo(PG_FUNCTION_ARGS)
     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        3174 :     result = palloc0(MAXALIGN(SizeofBrinOpcInfo(1)) +
     436             :                      sizeof(BloomOpaque));
     437        3174 :     result->oi_nstored = 1;
     438        3174 :     result->oi_regular_nulls = true;
     439        3174 :     result->oi_opaque = (BloomOpaque *)
     440        3174 :         MAXALIGN((char *) result + SizeofBrinOpcInfo(1));
     441        3174 :     result->oi_typcache[0] = lookup_type_cache(PG_BRIN_BLOOM_SUMMARYOID, 0);
     442             : 
     443        3174 :     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
     471        5268 : brin_bloom_get_ndistinct(BrinDesc *bdesc, BloomOptions *opts)
     472             : {
     473             :     double      ndistinct;
     474             :     double      maxtuples;
     475             :     BlockNumber pagesPerRange;
     476             : 
     477        5268 :     pagesPerRange = BrinGetPagesPerRange(bdesc->bd_index);
     478        5268 :     ndistinct = BloomGetNDistinctPerRange(opts);
     479             : 
     480             :     Assert(BlockNumberIsValid(pagesPerRange));
     481             : 
     482        5268 :     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        5268 :     if (ndistinct < 0)
     489        5268 :         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        5268 :     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        5268 :     ndistinct = Min(ndistinct, maxtuples);
     502             : 
     503        5268 :     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
     514       29548 : brin_bloom_add_value(PG_FUNCTION_ARGS)
     515             : {
     516       29548 :     BrinDesc   *bdesc = (BrinDesc *) PG_GETARG_POINTER(0);
     517       29548 :     BrinValues *column = (BrinValues *) PG_GETARG_POINTER(1);
     518       29548 :     Datum       newval = PG_GETARG_DATUM(2);
     519       29548 :     bool        isnull PG_USED_FOR_ASSERTS_ONLY = PG_GETARG_DATUM(3);
     520       29548 :     BloomOptions *opts = (BloomOptions *) PG_GET_OPCLASS_OPTIONS();
     521       29548 :     Oid         colloid = PG_GET_COLLATION();
     522             :     FmgrInfo   *hashFn;
     523             :     uint32      hashValue;
     524       29548 :     bool        updated = false;
     525             :     AttrNumber  attno;
     526             :     BloomFilter *filter;
     527             : 
     528             :     Assert(!isnull);
     529             : 
     530       29548 :     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       29548 :     if (column->bv_allnulls)
     538             :     {
     539       10536 :         filter = bloom_init(brin_bloom_get_ndistinct(bdesc, opts),
     540        5268 :                             BloomGetFalsePositiveRate(opts));
     541        5268 :         column->bv_values[0] = PointerGetDatum(filter);
     542        5268 :         column->bv_allnulls = false;
     543        5268 :         updated = true;
     544             :     }
     545             :     else
     546       24280 :         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       29548 :     hashFn = bloom_get_procinfo(bdesc, attno, PROCNUM_HASH);
     553             : 
     554       29548 :     hashValue = DatumGetUInt32(FunctionCall1Coll(hashFn, colloid, newval));
     555             : 
     556       29548 :     filter = bloom_add_value(filter, hashValue, &updated);
     557             : 
     558       29548 :     column->bv_values[0] = PointerGetDatum(filter);
     559             : 
     560       29548 :     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
     569        5472 : brin_bloom_consistent(PG_FUNCTION_ARGS)
     570             : {
     571        5472 :     BrinDesc   *bdesc = (BrinDesc *) PG_GETARG_POINTER(0);
     572        5472 :     BrinValues *column = (BrinValues *) PG_GETARG_POINTER(1);
     573        5472 :     ScanKey    *keys = (ScanKey *) PG_GETARG_POINTER(2);
     574        5472 :     int         nkeys = PG_GETARG_INT32(3);
     575        5472 :     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        5472 :     filter = (BloomFilter *) PG_DETOAST_DATUM(column->bv_values[0]);
     585             : 
     586             :     Assert(filter);
     587             : 
     588        5472 :     matches = true;
     589             : 
     590        5652 :     for (keyno = 0; keyno < nkeys; keyno++)
     591             :     {
     592        5472 :         ScanKey     key = keys[keyno];
     593             : 
     594             :         /* NULL keys are handled and filtered-out in bringetbitmap */
     595             :         Assert(!(key->sk_flags & SK_ISNULL));
     596             : 
     597        5472 :         attno = key->sk_attno;
     598        5472 :         value = key->sk_argument;
     599             : 
     600        5472 :         switch (key->sk_strategy)
     601             :         {
     602        5472 :             case BloomEqualStrategyNumber:
     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        5472 :                 finfo = bloom_get_procinfo(bdesc, attno, PROCNUM_HASH);
     610             : 
     611        5472 :                 hashValue = DatumGetUInt32(FunctionCall1Coll(finfo, colloid, value));
     612        5472 :                 matches &= bloom_contains_value(filter, hashValue);
     613             : 
     614        5472 :                 break;
     615           0 :             default:
     616             :                 /* shouldn't happen */
     617           0 :                 elog(ERROR, "invalid strategy number %d", key->sk_strategy);
     618             :                 matches = 0;
     619             :                 break;
     620             :         }
     621             : 
     622        5472 :         if (!matches)
     623        5292 :             break;
     624             :     }
     625             : 
     626        5472 :     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
     638           0 : brin_bloom_union(PG_FUNCTION_ARGS)
     639             : {
     640             :     int         i;
     641             :     int         nbytes;
     642           0 :     BrinValues *col_a = (BrinValues *) PG_GETARG_POINTER(1);
     643           0 :     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           0 :     filter_a = (BloomFilter *) PG_DETOAST_DATUM(col_a->bv_values[0]);
     651           0 :     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           0 :     nbytes = (filter_a->nbits) / 8;
     660             : 
     661             :     /* simply OR the bitmaps */
     662           0 :     for (i = 0; i < nbytes; i++)
     663           0 :         filter_a->data[i] |= filter_b->data[i];
     664             : 
     665           0 :     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       35020 : bloom_get_procinfo(BrinDesc *bdesc, uint16 attno, uint16 procnum)
     676             : {
     677             :     BloomOpaque *opaque;
     678       35020 :     uint16      basenum = procnum - PROCNUM_BASE;
     679             : 
     680             :     /*
     681             :      * We cache these in the opaque struct, to avoid repetitive syscache
     682             :      * lookups.
     683             :      */
     684       35020 :     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       35020 :     if (opaque->extra_proc_missing[basenum])
     691           0 :         return NULL;
     692             : 
     693       35020 :     if (opaque->extra_procinfos[basenum].fn_oid == InvalidOid)
     694             :     {
     695         568 :         if (RegProcedureIsValid(index_getprocid(bdesc->bd_index, attno,
     696             :                                                 procnum)))
     697             :         {
     698         568 :             fmgr_info_copy(&opaque->extra_procinfos[basenum],
     699             :                            index_getprocinfo(bdesc->bd_index, attno, procnum),
     700             :                            bdesc->bd_context);
     701             :         }
     702             :         else
     703             :         {
     704           0 :             opaque->extra_proc_missing[basenum] = true;
     705           0 :             return NULL;
     706             :         }
     707             :     }
     708             : 
     709       35020 :     return &opaque->extra_procinfos[basenum];
     710             : }
     711             : 
     712             : Datum
     713         438 : brin_bloom_options(PG_FUNCTION_ARGS)
     714             : {
     715         438 :     local_relopts *relopts = (local_relopts *) PG_GETARG_POINTER(0);
     716             : 
     717         438 :     init_local_reloptions(relopts, sizeof(BloomOptions));
     718             : 
     719         438 :     add_local_real_reloption(relopts, "n_distinct_per_range",
     720             :                              "number of distinct items expected in a BRIN page range",
     721             :                              BLOOM_DEFAULT_NDISTINCT_PER_RANGE,
     722             :                              -1.0, INT_MAX, offsetof(BloomOptions, nDistinctPerRange));
     723             : 
     724         438 :     add_local_real_reloption(relopts, "false_positive_rate",
     725             :                              "desired false-positive rate for the bloom filters",
     726             :                              BLOOM_DEFAULT_FALSE_POSITIVE_RATE,
     727             :                              BLOOM_MIN_FALSE_POSITIVE_RATE,
     728             :                              BLOOM_MAX_FALSE_POSITIVE_RATE,
     729             :                              offsetof(BloomOptions, falsePositiveRate));
     730             : 
     731         438 :     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
     743           0 : brin_bloom_summary_in(PG_FUNCTION_ARGS)
     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           0 :     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
     765           0 : brin_bloom_summary_out(PG_FUNCTION_ARGS)
     766             : {
     767             :     BloomFilter *filter;
     768             :     StringInfoData str;
     769             : 
     770             :     /* detoast the data to get value with a full 4B header */
     771           0 :     filter = (BloomFilter *) PG_DETOAST_DATUM(PG_GETARG_BYTEA_PP(0));
     772             : 
     773           0 :     initStringInfo(&str);
     774           0 :     appendStringInfoChar(&str, '{');
     775             : 
     776           0 :     appendStringInfo(&str, "mode: hashed  nhashes: %u  nbits: %u  nbits_set: %u",
     777           0 :                      filter->nhashes, filter->nbits, filter->nbits_set);
     778             : 
     779           0 :     appendStringInfoChar(&str, '}');
     780             : 
     781           0 :     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
     789           0 : brin_bloom_summary_recv(PG_FUNCTION_ARGS)
     790             : {
     791           0 :     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
     806           0 : brin_bloom_summary_send(PG_FUNCTION_ARGS)
     807             : {
     808           0 :     return byteasend(fcinfo);
     809             : }

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