brin_bloom.c

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/*
 * brin_bloom.c
 *      Implementation of Bloom opclass for BRIN
 *
 * Portions Copyright (c) 1996-2023, PostgreSQL Global Development Group
 * Portions Copyright (c) 1994, Regents of the University of California
 *
 *
 * A BRIN opclass summarizing page range into a bloom filter.
 *
 * Bloom filters allow efficient testing whether a given page range contains
 * a particular value. Therefore, if we summarize each page range into a small
 * bloom filter, we can easily (and cheaply) test whether it contains values
 * we get later.
 *
 * The index only supports equality operators, similarly to hash indexes.
 * Bloom indexes are however much smaller, and support only bitmap scans.
 *
 * Note: Don't confuse this with bloom indexes, implemented in a contrib
 * module. That extension implements an entirely new AM, building a bloom
 * filter on multiple columns in a single row. This opclass works with an
 * existing AM (BRIN) and builds bloom filter on a column.
 *
 *
 * values vs. hashes
 * -----------------
 *
 * The original column values are not used directly, but are first hashed
 * using the regular type-specific hash function, producing a uint32 hash.
 * And this hash value is then added to the summary - i.e. it's hashed
 * again and added to the bloom filter.
 *
 * This allows the code to treat all data types (byval/byref/...) the same
 * way, with only minimal space requirements, because we're working with
 * hashes and not the original values. Everything is uint32.
 *
 * Of course, this assumes the built-in hash function is reasonably good,
 * without too many collisions etc. But that does seem to be the case, at
 * least based on past experience. After all, the same hash functions are
 * used for hash indexes, hash partitioning and so on.
 *
 *
 * hashing scheme
 * --------------
 *
 * Bloom filters require a number of independent hash functions. There are
 * different schemes how to construct them - for example we might use
 * hash_uint32_extended with random seeds, but that seems fairly expensive.
 * We use a scheme requiring only two functions described in this paper:
 *
 * Less Hashing, Same Performance:Building a Better Bloom Filter
 * Adam Kirsch, Michael Mitzenmacher, Harvard School of Engineering and
 * Applied Sciences, Cambridge, Massachusetts [DOI 10.1002/rsa.20208]
 *
 * The two hash functions h1 and h2 are calculated using hard-coded seeds,
 * and then combined using (h1 + i * h2) to generate the hash functions.
 *
 *
 * sizing the bloom filter
 * -----------------------
 *
 * Size of a bloom filter depends on the number of distinct values we will
 * store in it, and the desired false positive rate. The higher the number
 * of distinct values and/or the lower the false positive rate, the larger
 * the bloom filter. On the other hand, we want to keep the index as small
 * as possible - that's one of the basic advantages of BRIN indexes.
 *
 * Although the number of distinct elements (in a page range) depends on
 * the data, we can consider it fixed. This simplifies the trade-off to
 * just false positive rate vs. size.
 *
 * At the page range level, false positive rate is a probability the bloom
 * filter matches a random value. For the whole index (with sufficiently
 * many page ranges) it represents the fraction of the index ranges (and
 * thus fraction of the table to be scanned) matching the random value.
 *
 * Furthermore, the size of the bloom filter is subject to implementation
 * limits - it has to fit onto a single index page (8kB by default). As
 * the bitmap is inherently random (when "full" about half the bits is set
 * to 1, randomly), compression can't help very much.
 *
 * To reduce the size of a filter (to fit to a page), we have to either
 * accept higher false positive rate (undesirable), or reduce the number
 * of distinct items to be stored in the filter. We can't alter the input
 * data, of course, but we may make the BRIN page ranges smaller - instead
 * of the default 128 pages (1MB) we may build index with 16-page ranges,
 * or something like that. This should reduce the number of distinct values
 * in the page range, making the filter smaller (with fixed false positive
 * rate). Even for random data sets this should help, as the number of rows
 * per heap page is limited (to ~290 with very narrow tables, likely ~20
 * in practice).
 *
 * Of course, good sizing decisions depend on having the necessary data,
 * i.e. number of distinct values in a page range (of a given size) and
 * table size (to estimate cost change due to change in false positive
 * rate due to having larger index vs. scanning larger indexes). We may
 * not have that data - for example when building an index on empty table
 * it's not really possible. And for some data we only have estimates for
 * the whole table and we can only estimate per-range values (ndistinct).
 *
 * Another challenge is that while the bloom filter is per-column, it's
 * the whole index tuple that has to fit into a page. And for multi-column
 * indexes that may include pieces we have no control over (not necessarily
 * bloom filters, the other columns may use other BRIN opclasses). So it's
 * not entirely clear how to distribute the space between those columns.
 *
 * The current logic, implemented in brin_bloom_get_ndistinct, attempts to
 * make some basic sizing decisions, based on the size of BRIN ranges, and
 * the maximum number of rows per range.
 *
 *
 * IDENTIFICATION
 *    src/backend/access/brin/brin_bloom.c
 */
#include "postgres.h"
 
#include "access/genam.h"
#include "access/brin.h"
#include "access/brin_internal.h"
#include "access/brin_page.h"
#include "access/brin_tuple.h"
#include "access/hash.h"
#include "access/htup_details.h"
#include "access/reloptions.h"
#include "access/stratnum.h"
#include "catalog/pg_type.h"
#include "catalog/pg_amop.h"
#include "utils/builtins.h"
#include "utils/datum.h"
#include "utils/lsyscache.h"
#include "utils/rel.h"
#include "utils/syscache.h"
 
#include <math.h>
 
#define BloomEqualStrategyNumber    1
 
/*
 * Additional SQL level support functions. We only have one, which is
 * used to calculate hash of the input value.
 *
 * Procedure numbers must not use values reserved for BRIN itself; see
 * brin_internal.h.
 */
#define     BLOOM_MAX_PROCNUMS      1   /* maximum support procs we need */
#define     PROCNUM_HASH            11  /* required */
 
/*
 * Subtract this from procnum to obtain index in BloomOpaque arrays
 * (Must be equal to minimum of private procnums).
 */
#define     PROCNUM_BASE            11
 
/*
 * Storage type for BRIN's reloptions.
 */
typedef struct BloomOptions
{
    int32       vl_len_;        /* varlena header (do not touch directly!) */
    double      nDistinctPerRange;  /* number of distinct values per range */
    double      falsePositiveRate;  /* false positive for bloom filter */
} BloomOptions;
 
/*
 * The current min value (16) is somewhat arbitrary, but it's based
 * on the fact that the filter header is ~20B alone, which is about
 * the same as the filter bitmap for 16 distinct items with 1% false
 * positive rate. So by allowing lower values we'd not gain much. In
 * any case, the min should not be larger than MaxHeapTuplesPerPage
 * (~290), which is the theoretical maximum for single-page ranges.
 */
#define     BLOOM_MIN_NDISTINCT_PER_RANGE       16
 
/*
 * Used to determine number of distinct items, based on the number of rows
 * in a page range. The 10% is somewhat similar to what estimate_num_groups
 * does, so we use the same factor here.
 */
#define     BLOOM_DEFAULT_NDISTINCT_PER_RANGE   -0.1    /* 10% of values */
 
/*
 * Allowed range and default value for the false positive range. The exact
 * values are somewhat arbitrary, but were chosen considering the various
 * parameters (size of filter vs. page size, etc.).
 *
 * The lower the false-positive rate, the more accurate the filter is, but
 * it also gets larger - at some point this eliminates the main advantage
 * of BRIN indexes, which is the tiny size. At 0.01% the index is about
 * 10% of the table (assuming 290 distinct values per 8kB page).
 *
 * On the other hand, as the false-positive rate increases, larger part of
 * the table has to be scanned due to mismatches - at 25% we're probably
 * close to sequential scan being cheaper.
 */
#define     BLOOM_MIN_FALSE_POSITIVE_RATE   0.0001  /* 0.01% fp rate */
#define     BLOOM_MAX_FALSE_POSITIVE_RATE   0.25    /* 25% fp rate */
#define     BLOOM_DEFAULT_FALSE_POSITIVE_RATE   0.01    /* 1% fp rate */
 
#define BloomGetNDistinctPerRange(opts) \
    ((opts) && (((BloomOptions *) (opts))->nDistinctPerRange != 0) ? \
     (((BloomOptions *) (opts))->nDistinctPerRange) : \
     BLOOM_DEFAULT_NDISTINCT_PER_RANGE)
 
#define BloomGetFalsePositiveRate(opts) \
    ((opts) && (((BloomOptions *) (opts))->falsePositiveRate != 0.0) ? \
     (((BloomOptions *) (opts))->falsePositiveRate) : \
     BLOOM_DEFAULT_FALSE_POSITIVE_RATE)
 
/*
 * And estimate of the largest bloom we can fit onto a page. This is not
 * a perfect guarantee, for a couple of reasons. For example, the row may
 * be larger because the index has multiple columns.
 */
#define BloomMaxFilterSize \
    MAXALIGN_DOWN(BLCKSZ - \
                  (MAXALIGN(SizeOfPageHeaderData + \
                            sizeof(ItemIdData)) + \
                   MAXALIGN(sizeof(BrinSpecialSpace)) + \
                   SizeOfBrinTuple))
 
/*
 * Seeds used to calculate two hash functions h1 and h2, which are then used
 * to generate k hashes using the (h1 + i * h2) scheme.
 */
#define BLOOM_SEED_1    0x71d924af
#define BLOOM_SEED_2    0xba48b314
 
/*
 * Bloom Filter
 *
 * Represents a bloom filter, built on hashes of the indexed values. That is,
 * we compute a uint32 hash of the value, and then store this hash into the
 * bloom filter (and compute additional hashes on it).
 *
 * XXX We could implement "sparse" bloom filters, keeping only the bytes that
 * are not entirely 0. But while indexes don't support TOAST, the varlena can
 * still be compressed. So this seems unnecessary, because the compression
 * should do the same job.
 *
 * XXX We can also watch the number of bits set in the bloom filter, and then
 * stop using it (and not store the bitmap, to save space) when the false
 * positive rate gets too high. But even if the false positive rate exceeds the
 * desired value, it still can eliminate some page ranges.
 */
typedef struct BloomFilter
{
    /* varlena header (do not touch directly!) */
    int32       vl_len_;
 
    /* space for various flags (unused for now) */
    uint16      flags;
 
    /* fields for the HASHED phase */
    uint8       nhashes;        /* number of hash functions */
    uint32      nbits;          /* number of bits in the bitmap (size) */
    uint32      nbits_set;      /* number of bits set to 1 */
 
    /* data of the bloom filter */
    char        data[FLEXIBLE_ARRAY_MEMBER];
} BloomFilter;
 
/*
 * bloom_filter_size
 *      Calculate Bloom filter parameters (nbits, nbytes, nhashes).
 *
 * Given expected number of distinct values and desired false positive rate,
 * calculates the optimal parameters of the Bloom filter.
 *
 * The resulting parameters are returned through nbytesp (number of bytes),
 * nbitsp (number of bits) and nhashesp (number of hash functions). If a
 * pointer is NULL, the parameter is not returned.
 */
static void
bloom_filter_size(int ndistinct, double false_positive_rate,
                  int *nbytesp, int *nbitsp, int *nhashesp)
{
    double      k;
    int         nbits,
                nbytes;
 
    /* sizing bloom filter: -(n * ln(p)) / (ln(2))^2 */
    nbits = ceil(-(ndistinct * log(false_positive_rate)) / pow(log(2.0), 2));
 
    /* round m to whole bytes */
    nbytes = ((nbits + 7) / 8);
    nbits = nbytes * 8;
 
    /*
     * round(log(2.0) * m / ndistinct), but assume round() may not be
     * available on Windows
     */
    k = log(2.0) * nbits / ndistinct;
    k = (k - floor(k) >= 0.5) ? ceil(k) : floor(k);
 
    if (nbytesp)
        *nbytesp = nbytes;
 
    if (nbitsp)
        *nbitsp = nbits;
 
    if (nhashesp)
        *nhashesp = (int) k;
}
 
/*
 * bloom_init
 *      Initialize the Bloom Filter, allocate all the memory.
 *
 * The filter is initialized with optimal size for ndistinct expected values
 * and the requested false positive rate. The filter is stored as varlena.
 */
static BloomFilter *
bloom_init(int ndistinct, double false_positive_rate)
{
    Size        len;
    BloomFilter *filter;
 
    int         nbits;          /* size of filter / number of bits */
    int         nbytes;         /* size of filter / number of bytes */
    int         nhashes;        /* number of hash functions */
 
    Assert(ndistinct > 0);
    Assert((false_positive_rate >= BLOOM_MIN_FALSE_POSITIVE_RATE) &&
           (false_positive_rate < BLOOM_MAX_FALSE_POSITIVE_RATE));
 
    /* calculate bloom filter size / parameters */
    bloom_filter_size(ndistinct, false_positive_rate,
                      &nbytes, &nbits, &nhashes);
 
    /*
     * Reject filters that are obviously too large to store on a page.
     *
     * Initially the bloom filter is just zeroes and so very compressible, but
     * as we add values it gets more and more random, and so less and less
     * compressible. So initially everything fits on the page, but we might
     * get surprising failures later - we want to prevent that, so we reject
     * bloom filter that are obviously too large.
     *
     * XXX It's not uncommon to oversize the bloom filter a bit, to defend
     * against unexpected data anomalies (parts of table with more distinct
     * values per range etc.). But we still need to make sure even the
     * oversized filter fits on page, if such need arises.
     *
     * XXX This check is not perfect, because the index may have multiple
     * filters that are small individually, but too large when combined.
     */
    if (nbytes > BloomMaxFilterSize)
        elog(ERROR, "the bloom filter is too large (%d > %zu)", nbytes,
             BloomMaxFilterSize);
 
    /*
     * We allocate the whole filter. Most of it is going to be 0 bits, so the
     * varlena is easy to compress.
     */
    len = offsetof(BloomFilter, data) + nbytes;
 
    filter = (BloomFilter *) palloc0(len);
 
    filter->flags = 0;
    filter->nhashes = nhashes;
    filter->nbits = nbits;
 
    SET_VARSIZE(filter, len);
 
    return filter;
}
 
 
/*
 * bloom_add_value
 *      Add value to the bloom filter.
 */
static BloomFilter *
bloom_add_value(BloomFilter *filter, uint32 value, bool *updated)
{
    int         i;
    uint64      h1,
                h2;
 
    /* compute the hashes, used for the bloom filter */
    h1 = hash_bytes_uint32_extended(value, BLOOM_SEED_1) % filter->nbits;
    h2 = hash_bytes_uint32_extended(value, BLOOM_SEED_2) % filter->nbits;
 
    /* compute the requested number of hashes */
    for (i = 0; i < filter->nhashes; i++)
    {
        /* h1 + h2 + f(i) */
        uint32      h = (h1 + i * h2) % filter->nbits;
        uint32      byte = (h / 8);
        uint32      bit = (h % 8);
 
        /* if the bit is not set, set it and remember we did that */
        if (!(filter->data[byte] & (0x01 << bit)))
        {
            filter->data[byte] |= (0x01 << bit);
            filter->nbits_set++;
            if (updated)
                *updated = true;
        }
    }
 
    return filter;
}
 
 
/*
 * bloom_contains_value
 *      Check if the bloom filter contains a particular value.
 */
static bool
bloom_contains_value(BloomFilter *filter, uint32 value)
{
    int         i;
    uint64      h1,
                h2;
 
    /* calculate the two hashes */
    h1 = hash_bytes_uint32_extended(value, BLOOM_SEED_1) % filter->nbits;
    h2 = hash_bytes_uint32_extended(value, BLOOM_SEED_2) % filter->nbits;
 
    /* compute the requested number of hashes */
    for (i = 0; i < filter->nhashes; i++)
    {
        /* h1 + h2 + f(i) */
        uint32      h = (h1 + i * h2) % filter->nbits;
        uint32      byte = (h / 8);
        uint32      bit = (h % 8);
 
        /* if the bit is not set, the value is not there */
        if (!(filter->data[byte] & (0x01 << bit)))
            return false;
    }
 
    /* all hashes found in bloom filter */
    return true;
}
 
typedef struct BloomOpaque
{
    /*
     * XXX At this point we only need a single proc (to compute the hash), but
     * let's keep the array just like inclusion and minmax opclasses, for
     * consistency. We may need additional procs in the future.
     */
    FmgrInfo    extra_procinfos[BLOOM_MAX_PROCNUMS];
    bool        extra_proc_missing[BLOOM_MAX_PROCNUMS];
} BloomOpaque;
 
static FmgrInfo *bloom_get_procinfo(BrinDesc *bdesc, uint16 attno,
                                    uint16 procnum);
 
 
Datum
brin_bloom_opcinfo(PG_FUNCTION_ARGS)
{
    BrinOpcInfo *result;
 
    /*
     * opaque->strategy_procinfos is initialized lazily; here it is set to
     * all-uninitialized by palloc0 which sets fn_oid to InvalidOid.
     *
     * bloom indexes only store the filter as a single BYTEA column
     */
 
    result = palloc0(MAXALIGN(SizeofBrinOpcInfo(1)) +
                     sizeof(BloomOpaque));
    result->oi_nstored = 1;
    result->oi_regular_nulls = true;
    result->oi_opaque = (BloomOpaque *)
        MAXALIGN((char *) result + SizeofBrinOpcInfo(1));
    result->oi_typcache[0] = lookup_type_cache(PG_BRIN_BLOOM_SUMMARYOID, 0);
 
    PG_RETURN_POINTER(result);
}
 
/*
 * brin_bloom_get_ndistinct
 *      Determine the ndistinct value used to size bloom filter.
 *
 * Adjust the ndistinct value based on the pagesPerRange value. First,
 * if it's negative, it's assumed to be relative to maximum number of
 * tuples in the range (assuming each page gets MaxHeapTuplesPerPage
 * tuples, which is likely a significant over-estimate). We also clamp
 * the value, not to over-size the bloom filter unnecessarily.
 *
 * XXX We can only do this when the pagesPerRange value was supplied.
 * If it wasn't, it has to be a read-only access to the index, in which
 * case we don't really care. But perhaps we should fall-back to the
 * default pagesPerRange value?
 *
 * XXX We might also fetch info about ndistinct estimate for the column,
 * and compute the expected number of distinct values in a range. But
 * that may be tricky due to data being sorted in various ways, so it
 * seems better to rely on the upper estimate.
 *
 * XXX We might also calculate a better estimate of rows per BRIN range,
 * instead of using MaxHeapTuplesPerPage (which probably produces values
 * much higher than reality).
 */
static int
brin_bloom_get_ndistinct(BrinDesc *bdesc, BloomOptions *opts)
{
    double      ndistinct;
    double      maxtuples;
    BlockNumber pagesPerRange;
 
    pagesPerRange = BrinGetPagesPerRange(bdesc->bd_index);
    ndistinct = BloomGetNDistinctPerRange(opts);
 
    Assert(BlockNumberIsValid(pagesPerRange));
 
    maxtuples = MaxHeapTuplesPerPage * pagesPerRange;
 
    /*
     * Similarly to n_distinct, negative values are relative - in this case to
     * maximum number of tuples in the page range (maxtuples).
     */
    if (ndistinct < 0)
        ndistinct = (-ndistinct) * maxtuples;
 
    /*
     * Positive values are to be used directly, but we still apply a couple of
     * safeties to avoid using unreasonably small bloom filters.
     */
    ndistinct = Max(ndistinct, BLOOM_MIN_NDISTINCT_PER_RANGE);
 
    /*
     * And don't use more than the maximum possible number of tuples, in the
     * range, which would be entirely wasteful.
     */
    ndistinct = Min(ndistinct, maxtuples);
 
    return (int) ndistinct;
}
 
/*
 * Examine the given index tuple (which contains partial status of a certain
 * page range) by comparing it to the given value that comes from another heap
 * tuple.  If the new value is outside the bloom filter specified by the
 * existing tuple values, update the index tuple and return true.  Otherwise,
 * return false and do not modify in this case.
 */
Datum
brin_bloom_add_value(PG_FUNCTION_ARGS)
{
    BrinDesc   *bdesc = (BrinDesc *) PG_GETARG_POINTER(0);
    BrinValues *column = (BrinValues *) PG_GETARG_POINTER(1);
    Datum       newval = PG_GETARG_DATUM(2);
    bool        isnull PG_USED_FOR_ASSERTS_ONLY = PG_GETARG_DATUM(3);
    BloomOptions *opts = (BloomOptions *) PG_GET_OPCLASS_OPTIONS();
    Oid         colloid = PG_GET_COLLATION();
    FmgrInfo   *hashFn;
    uint32      hashValue;
    bool        updated = false;
    AttrNumber  attno;
    BloomFilter *filter;
 
    Assert(!isnull);
 
    attno = column->bv_attno;
 
    /*
     * If this is the first non-null value, we need to initialize the bloom
     * filter. Otherwise just extract the existing bloom filter from
     * BrinValues.
     */
    if (column->bv_allnulls)
    {
        filter = bloom_init(brin_bloom_get_ndistinct(bdesc, opts),
                            BloomGetFalsePositiveRate(opts));
        column->bv_values[0] = PointerGetDatum(filter);
        column->bv_allnulls = false;
        updated = true;
    }
    else
        filter = (BloomFilter *) PG_DETOAST_DATUM(column->bv_values[0]);
 
    /*
     * Compute the hash of the new value, using the supplied hash function,
     * and then add the hash value to the bloom filter.
     */
    hashFn = bloom_get_procinfo(bdesc, attno, PROCNUM_HASH);
 
    hashValue = DatumGetUInt32(FunctionCall1Coll(hashFn, colloid, newval));
 
    filter = bloom_add_value(filter, hashValue, &updated);
 
    column->bv_values[0] = PointerGetDatum(filter);
 
    PG_RETURN_BOOL(updated);
}
 
/*
 * Given an index tuple corresponding to a certain page range and a scan key,
 * return whether the scan key is consistent with the index tuple's bloom
 * filter.  Return true if so, false otherwise.
 */
Datum
brin_bloom_consistent(PG_FUNCTION_ARGS)
{
    BrinDesc   *bdesc = (BrinDesc *) PG_GETARG_POINTER(0);
    BrinValues *column = (BrinValues *) PG_GETARG_POINTER(1);
    ScanKey    *keys = (ScanKey *) PG_GETARG_POINTER(2);
    int         nkeys = PG_GETARG_INT32(3);
    Oid         colloid = PG_GET_COLLATION();
    AttrNumber  attno;
    Datum       value;
    bool        matches;
    FmgrInfo   *finfo;
    uint32      hashValue;
    BloomFilter *filter;
    int         keyno;
 
    filter = (BloomFilter *) PG_DETOAST_DATUM(column->bv_values[0]);
 
    Assert(filter);
 
    /*
     * Assume all scan keys match. We'll be searching for a scan key
     * eliminating the page range (we can stop on the first such key).
     */
    matches = true;
 
    for (keyno = 0; keyno < nkeys; keyno++)
    {
        ScanKey     key = keys[keyno];
 
        /* NULL keys are handled and filtered-out in bringetbitmap */
        Assert(!(key->sk_flags & SK_ISNULL));
 
        attno = key->sk_attno;
        value = key->sk_argument;
 
        switch (key->sk_strategy)
        {
            case BloomEqualStrategyNumber:
 
                /*
                 * We want to return the current page range if the bloom
                 * filter seems to contain the value.
                 */
                finfo = bloom_get_procinfo(bdesc, attno, PROCNUM_HASH);
 
                hashValue = DatumGetUInt32(FunctionCall1Coll(finfo, colloid, value));
                matches &= bloom_contains_value(filter, hashValue);
 
                break;
            default:
                /* shouldn't happen */
                elog(ERROR, "invalid strategy number %d", key->sk_strategy);
                matches = false;
                break;
        }
 
        if (!matches)
            break;
    }
 
    PG_RETURN_BOOL(matches);
}
 
/*
 * Given two BrinValues, update the first of them as a union of the summary
 * values contained in both.  The second one is untouched.
 *
 * XXX We assume the bloom filters have the same parameters for now. In the
 * future we should have 'can union' function, to decide if we can combine
 * two particular bloom filters.
 */
Datum
brin_bloom_union(PG_FUNCTION_ARGS)
{
    int         i;
    int         nbytes;
    BrinValues *col_a = (BrinValues *) PG_GETARG_POINTER(1);
    BrinValues *col_b = (BrinValues *) PG_GETARG_POINTER(2);
    BloomFilter *filter_a;
    BloomFilter *filter_b;
 
    Assert(col_a->bv_attno == col_b->bv_attno);
    Assert(!col_a->bv_allnulls && !col_b->bv_allnulls);
 
    filter_a = (BloomFilter *) PG_DETOAST_DATUM(col_a->bv_values[0]);
    filter_b = (BloomFilter *) PG_DETOAST_DATUM(col_b->bv_values[0]);
 
    /* make sure the filters use the same parameters */
    Assert(filter_a && filter_b);
    Assert(filter_a->nbits == filter_b->nbits);
    Assert(filter_a->nhashes == filter_b->nhashes);
    Assert((filter_a->nbits > 0) && (filter_a->nbits % 8 == 0));
 
    nbytes = (filter_a->nbits) / 8;
 
    /* simply OR the bitmaps */
    for (i = 0; i < nbytes; i++)
        filter_a->data[i] |= filter_b->data[i];
 
    PG_RETURN_VOID();
}
 
/*
 * Cache and return inclusion opclass support procedure
 *
 * Return the procedure corresponding to the given function support number
 * or null if it does not exist.
 */
static FmgrInfo *
bloom_get_procinfo(BrinDesc *bdesc, uint16 attno, uint16 procnum)
{
    BloomOpaque *opaque;
    uint16      basenum = procnum - PROCNUM_BASE;
 
    /*
     * We cache these in the opaque struct, to avoid repetitive syscache
     * lookups.
     */
    opaque = (BloomOpaque *) bdesc->bd_info[attno - 1]->oi_opaque;
 
    /*
     * If we already searched for this proc and didn't find it, don't bother
     * searching again.
     */
    if (opaque->extra_proc_missing[basenum])
        return NULL;
 
    if (opaque->extra_procinfos[basenum].fn_oid == InvalidOid)
    {
        if (RegProcedureIsValid(index_getprocid(bdesc->bd_index, attno,
                                                procnum)))
        {
            fmgr_info_copy(&opaque->extra_procinfos[basenum],
                           index_getprocinfo(bdesc->bd_index, attno, procnum),
                           bdesc->bd_context);
        }
        else
        {
            opaque->extra_proc_missing[basenum] = true;
            return NULL;
        }
    }
 
    return &opaque->extra_procinfos[basenum];
}
 
Datum
brin_bloom_options(PG_FUNCTION_ARGS)
{
    local_relopts *relopts = (local_relopts *) PG_GETARG_POINTER(0);
 
    init_local_reloptions(relopts, sizeof(BloomOptions));
 
    add_local_real_reloption(relopts, "n_distinct_per_range",
                             "number of distinct items expected in a BRIN page range",
                             BLOOM_DEFAULT_NDISTINCT_PER_RANGE,
                             -1.0, INT_MAX, offsetof(BloomOptions, nDistinctPerRange));
 
    add_local_real_reloption(relopts, "false_positive_rate",
                             "desired false-positive rate for the bloom filters",
                             BLOOM_DEFAULT_FALSE_POSITIVE_RATE,
                             BLOOM_MIN_FALSE_POSITIVE_RATE,
                             BLOOM_MAX_FALSE_POSITIVE_RATE,
                             offsetof(BloomOptions, falsePositiveRate));
 
    PG_RETURN_VOID();
}
 
/*
 * brin_bloom_summary_in
 *      - input routine for type brin_bloom_summary.
 *
 * brin_bloom_summary is only used internally to represent summaries
 * in BRIN bloom indexes, so it has no operations of its own, and we
 * disallow input too.
 */
Datum
brin_bloom_summary_in(PG_FUNCTION_ARGS)
{
    /*
     * brin_bloom_summary stores the data in binary form and parsing text
     * input is not needed, so disallow this.
     */
    ereport(ERROR,
            (errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
             errmsg("cannot accept a value of type %s", "pg_brin_bloom_summary")));
 
    PG_RETURN_VOID();           /* keep compiler quiet */
}
 
 
/*
 * brin_bloom_summary_out
 *      - output routine for type brin_bloom_summary.
 *
 * BRIN bloom summaries are serialized into a bytea value, but we want
 * to output something nicer humans can understand.
 */
Datum
brin_bloom_summary_out(PG_FUNCTION_ARGS)
{
    BloomFilter *filter;
    StringInfoData str;
 
    /* detoast the data to get value with a full 4B header */
    filter = (BloomFilter *) PG_DETOAST_DATUM_PACKED(PG_GETARG_DATUM(0));
 
    initStringInfo(&str);
    appendStringInfoChar(&str, '{');
 
    appendStringInfo(&str, "mode: hashed  nhashes: %u  nbits: %u  nbits_set: %u",
                     filter->nhashes, filter->nbits, filter->nbits_set);
 
    appendStringInfoChar(&str, '}');
 
    PG_RETURN_CSTRING(str.data);
}
 
/*
 * brin_bloom_summary_recv
 *      - binary input routine for type brin_bloom_summary.
 */
Datum
brin_bloom_summary_recv(PG_FUNCTION_ARGS)
{
    ereport(ERROR,
            (errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
             errmsg("cannot accept a value of type %s", "pg_brin_bloom_summary")));
 
    PG_RETURN_VOID();           /* keep compiler quiet */
}
 
/*
 * brin_bloom_summary_send
 *      - binary output routine for type brin_bloom_summary.
 *
 * BRIN bloom summaries are serialized in a bytea value (although the
 * type is named differently), so let's just send that.
 */
Datum
brin_bloom_summary_send(PG_FUNCTION_ARGS)
{
    return byteasend(fcinfo);
}