rangetypes_selfuncs.c

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/*-------------------------------------------------------------------------
 *
 * rangetypes_selfuncs.c
 *    Functions for selectivity estimation of range operators
 *
 * Estimates are based on histograms of lower and upper bounds, and the
 * fraction of empty ranges.
 *
 * Portions Copyright (c) 1996-2026, PostgreSQL Global Development Group
 * Portions Copyright (c) 1994, Regents of the University of California
 *
 *
 * IDENTIFICATION
 *    src/backend/utils/adt/rangetypes_selfuncs.c
 *
 *-------------------------------------------------------------------------
 */
#include "postgres.h"
 
#include <math.h>
 
#include "access/htup_details.h"
#include "catalog/pg_operator.h"
#include "catalog/pg_statistic.h"
#include "utils/float.h"
#include "utils/fmgrprotos.h"
#include "utils/lsyscache.h"
#include "utils/rangetypes.h"
#include "utils/selfuncs.h"
#include "utils/typcache.h"
 
static double calc_rangesel(TypeCacheEntry *typcache, VariableStatData *vardata,
                            const RangeType *constval, Oid operator);
static double default_range_selectivity(Oid operator);
static double calc_hist_selectivity(TypeCacheEntry *typcache,
                                    VariableStatData *vardata, const RangeType *constval,
                                    Oid operator);
static double calc_hist_selectivity_scalar(TypeCacheEntry *typcache,
                                           const RangeBound *constbound,
                                           const RangeBound *hist, int hist_nvalues,
                                           bool equal);
static int  rbound_bsearch(TypeCacheEntry *typcache, const RangeBound *value,
                           const RangeBound *hist, int hist_length, bool equal);
static float8 get_position(TypeCacheEntry *typcache, const RangeBound *value,
                           const RangeBound *hist1, const RangeBound *hist2);
static float8 get_len_position(double value, double hist1, double hist2);
static float8 get_distance(TypeCacheEntry *typcache, const RangeBound *bound1,
                           const RangeBound *bound2);
static int  length_hist_bsearch(const Datum *length_hist_values,
                                int length_hist_nvalues, double value, bool equal);
static double calc_length_hist_frac(const Datum *length_hist_values,
                                    int length_hist_nvalues, double length1, double length2, bool equal);
static double calc_hist_selectivity_contained(TypeCacheEntry *typcache,
                                              const RangeBound *lower, RangeBound *upper,
                                              const RangeBound *hist_lower, int hist_nvalues,
                                              const Datum *length_hist_values, int length_hist_nvalues);
static double calc_hist_selectivity_contains(TypeCacheEntry *typcache,
                                             const RangeBound *lower, const RangeBound *upper,
                                             const RangeBound *hist_lower, int hist_nvalues,
                                             const Datum *length_hist_values, int length_hist_nvalues);
 
/*
 * Returns a default selectivity estimate for given operator, when we don't
 * have statistics or cannot use them for some reason.
 */
static double
default_range_selectivity(Oid operator)
{
    switch (operator)
    {
        case OID_RANGE_OVERLAP_OP:
            return 0.01;
 
        case OID_RANGE_CONTAINS_OP:
        case OID_RANGE_CONTAINED_OP:
            return 0.005;
 
        case OID_RANGE_CONTAINS_ELEM_OP:
        case OID_RANGE_ELEM_CONTAINED_OP:
 
            /*
             * "range @> elem" is more or less identical to a scalar
             * inequality "A >= b AND A <= c".
             */
            return DEFAULT_RANGE_INEQ_SEL;
 
        case OID_RANGE_LESS_OP:
        case OID_RANGE_LESS_EQUAL_OP:
        case OID_RANGE_GREATER_OP:
        case OID_RANGE_GREATER_EQUAL_OP:
        case OID_RANGE_LEFT_OP:
        case OID_RANGE_RIGHT_OP:
        case OID_RANGE_OVERLAPS_LEFT_OP:
        case OID_RANGE_OVERLAPS_RIGHT_OP:
            /* these are similar to regular scalar inequalities */
            return DEFAULT_INEQ_SEL;
 
        default:
            /* all range operators should be handled above, but just in case */
            return 0.01;
    }
}
 
/*
 * rangesel -- restriction selectivity for range operators
 */
Datum
rangesel(PG_FUNCTION_ARGS)
{
    PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
    Oid         operator = PG_GETARG_OID(1);
    List       *args = (List *) PG_GETARG_POINTER(2);
    int         varRelid = PG_GETARG_INT32(3);
    VariableStatData vardata;
    Node       *other;
    bool        varonleft;
    Selectivity selec;
    TypeCacheEntry *typcache = NULL;
    RangeType  *constrange = NULL;
 
    /*
     * If expression is not (variable op something) or (something op
     * variable), then punt and return a default estimate.
     */
    if (!get_restriction_variable(root, args, varRelid,
                                  &vardata, &other, &varonleft))
        PG_RETURN_FLOAT8(default_range_selectivity(operator));
 
    /*
     * Can't do anything useful if the something is not a constant, either.
     */
    if (!IsA(other, Const))
    {
        ReleaseVariableStats(vardata);
        PG_RETURN_FLOAT8(default_range_selectivity(operator));
    }
 
    /*
     * All the range operators are strict, so we can cope with a NULL constant
     * right away.
     */
    if (((Const *) other)->constisnull)
    {
        ReleaseVariableStats(vardata);
        PG_RETURN_FLOAT8(0.0);
    }
 
    /*
     * If var is on the right, commute the operator, so that we can assume the
     * var is on the left in what follows.
     */
    if (!varonleft)
    {
        /* we have other Op var, commute to make var Op other */
        operator = get_commutator(operator);
        if (!operator)
        {
            /* Use default selectivity (should we raise an error instead?) */
            ReleaseVariableStats(vardata);
            PG_RETURN_FLOAT8(default_range_selectivity(operator));
        }
    }
 
    /*
     * OK, there's a Var and a Const we're dealing with here.  We need the
     * Const to be of same range type as the column, else we can't do anything
     * useful. (Such cases will likely fail at runtime, but here we'd rather
     * just return a default estimate.)
     *
     * If the operator is "range @> element", the constant should be of the
     * element type of the range column. Convert it to a range that includes
     * only that single point, so that we don't need special handling for that
     * in what follows.
     */
    if (operator == OID_RANGE_CONTAINS_ELEM_OP)
    {
        typcache = range_get_typcache(fcinfo, vardata.vartype);
 
        if (((Const *) other)->consttype == typcache->rngelemtype->type_id)
        {
            RangeBound  lower,
                        upper;
 
            lower.inclusive = true;
            lower.val = ((Const *) other)->constvalue;
            lower.infinite = false;
            lower.lower = true;
            upper.inclusive = true;
            upper.val = ((Const *) other)->constvalue;
            upper.infinite = false;
            upper.lower = false;
            constrange = range_serialize(typcache, &lower, &upper, false, NULL);
        }
    }
    else if (operator == OID_RANGE_ELEM_CONTAINED_OP)
    {
        /*
         * Here, the Var is the elem, not the range.  In typical cases
         * elem_contained_by_range_support will have simplified this case, so
         * that we won't get here.  If we do get here we'll fall back on a
         * default estimate.
         */
    }
    else if (((Const *) other)->consttype == vardata.vartype)
    {
        /* Both sides are the same range type */
        typcache = range_get_typcache(fcinfo, vardata.vartype);
 
        constrange = DatumGetRangeTypeP(((Const *) other)->constvalue);
    }
 
    /*
     * If we got a valid constant on one side of the operator, proceed to
     * estimate using statistics. Otherwise punt and return a default constant
     * estimate.  Note that calc_rangesel need not handle
     * OID_RANGE_ELEM_CONTAINED_OP.
     */
    if (constrange)
        selec = calc_rangesel(typcache, &vardata, constrange, operator);
    else
        selec = default_range_selectivity(operator);
 
    ReleaseVariableStats(vardata);
 
    CLAMP_PROBABILITY(selec);
 
    PG_RETURN_FLOAT8((float8) selec);
}
 
static double
calc_rangesel(TypeCacheEntry *typcache, VariableStatData *vardata,
              const RangeType *constval, Oid operator)
{
    double      hist_selec;
    double      selec;
    float4      empty_frac,
                null_frac;
 
    /*
     * First look up the fraction of NULLs and empty ranges from pg_statistic.
     */
    if (HeapTupleIsValid(vardata->statsTuple))
    {
        Form_pg_statistic stats;
        AttStatsSlot sslot;
 
        stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
        null_frac = stats->stanullfrac;
 
        /* Try to get fraction of empty ranges */
        if (get_attstatsslot(&sslot, vardata->statsTuple,
                             STATISTIC_KIND_RANGE_LENGTH_HISTOGRAM,
                             InvalidOid,
                             ATTSTATSSLOT_NUMBERS))
        {
            if (sslot.nnumbers != 1)
                elog(ERROR, "invalid empty fraction statistic");    /* shouldn't happen */
            empty_frac = sslot.numbers[0];
            free_attstatsslot(&sslot);
        }
        else
        {
            /* No empty fraction statistic. Assume no empty ranges. */
            empty_frac = 0.0;
        }
    }
    else
    {
        /*
         * No stats are available. Follow through the calculations below
         * anyway, assuming no NULLs and no empty ranges. This still allows us
         * to give a better-than-nothing estimate based on whether the
         * constant is an empty range or not.
         */
        null_frac = 0.0;
        empty_frac = 0.0;
    }
 
    if (RangeIsEmpty(constval))
    {
        /*
         * An empty range matches all ranges, all empty ranges, or nothing,
         * depending on the operator
         */
        switch (operator)
        {
                /* these return false if either argument is empty */
            case OID_RANGE_OVERLAP_OP:
            case OID_RANGE_OVERLAPS_LEFT_OP:
            case OID_RANGE_OVERLAPS_RIGHT_OP:
            case OID_RANGE_LEFT_OP:
            case OID_RANGE_RIGHT_OP:
                /* nothing is less than an empty range */
            case OID_RANGE_LESS_OP:
                selec = 0.0;
                break;
 
                /* only empty ranges can be contained by an empty range */
            case OID_RANGE_CONTAINED_OP:
                /* only empty ranges are <= an empty range */
            case OID_RANGE_LESS_EQUAL_OP:
                selec = empty_frac;
                break;
 
                /* everything contains an empty range */
            case OID_RANGE_CONTAINS_OP:
                /* everything is >= an empty range */
            case OID_RANGE_GREATER_EQUAL_OP:
                selec = 1.0;
                break;
 
                /* all non-empty ranges are > an empty range */
            case OID_RANGE_GREATER_OP:
                selec = 1.0 - empty_frac;
                break;
 
                /* an element cannot be empty */
            case OID_RANGE_CONTAINS_ELEM_OP:
            default:
                elog(ERROR, "unexpected operator %u", operator);
                selec = 0.0;    /* keep compiler quiet */
                break;
        }
    }
    else
    {
        /*
         * Calculate selectivity using bound histograms. If that fails for
         * some reason, e.g no histogram in pg_statistic, use the default
         * constant estimate for the fraction of non-empty values. This is
         * still somewhat better than just returning the default estimate,
         * because this still takes into account the fraction of empty and
         * NULL tuples, if we had statistics for them.
         */
        hist_selec = calc_hist_selectivity(typcache, vardata, constval,
                                           operator);
        if (hist_selec < 0.0)
            hist_selec = default_range_selectivity(operator);
 
        /*
         * Now merge the results for the empty ranges and histogram
         * calculations, realizing that the histogram covers only the
         * non-null, non-empty values.
         */
        if (operator == OID_RANGE_CONTAINED_OP)
        {
            /* empty is contained by anything non-empty */
            selec = (1.0 - empty_frac) * hist_selec + empty_frac;
        }
        else
        {
            /* with any other operator, empty Op non-empty matches nothing */
            selec = (1.0 - empty_frac) * hist_selec;
        }
    }
 
    /* all range operators are strict */
    selec *= (1.0 - null_frac);
 
    /* result should be in range, but make sure... */
    CLAMP_PROBABILITY(selec);
 
    return selec;
}
 
/*
 * Calculate range operator selectivity using histograms of range bounds.
 *
 * This estimate is for the portion of values that are not empty and not
 * NULL.
 */
static double
calc_hist_selectivity(TypeCacheEntry *typcache, VariableStatData *vardata,
                      const RangeType *constval, Oid operator)
{
    AttStatsSlot hslot;
    AttStatsSlot lslot;
    int         nhist;
    RangeBound *hist_lower;
    RangeBound *hist_upper;
    int         i;
    RangeBound  const_lower;
    RangeBound  const_upper;
    bool        empty;
    double      hist_selec;
 
    /* Can't use the histogram with insecure range support functions */
    if (!statistic_proc_security_check(vardata,
                                       typcache->rng_cmp_proc_finfo.fn_oid))
        return -1;
    if (OidIsValid(typcache->rng_subdiff_finfo.fn_oid) &&
        !statistic_proc_security_check(vardata,
                                       typcache->rng_subdiff_finfo.fn_oid))
        return -1;
 
    /* Try to get histogram of ranges */
    if (!(HeapTupleIsValid(vardata->statsTuple) &&
          get_attstatsslot(&hslot, vardata->statsTuple,
                           STATISTIC_KIND_BOUNDS_HISTOGRAM, InvalidOid,
                           ATTSTATSSLOT_VALUES)))
        return -1.0;
 
    /* check that it's a histogram, not just a dummy entry */
    if (hslot.nvalues < 2)
    {
        free_attstatsslot(&hslot);
        return -1.0;
    }
 
    /*
     * Convert histogram of ranges into histograms of its lower and upper
     * bounds.
     */
    nhist = hslot.nvalues;
    hist_lower = palloc_array(RangeBound, nhist);
    hist_upper = palloc_array(RangeBound, nhist);
    for (i = 0; i < nhist; i++)
    {
        range_deserialize(typcache, DatumGetRangeTypeP(hslot.values[i]),
                          &hist_lower[i], &hist_upper[i], &empty);
        /* The histogram should not contain any empty ranges */
        if (empty)
            elog(ERROR, "bounds histogram contains an empty range");
    }
 
    /* @> and @< also need a histogram of range lengths */
    if (operator == OID_RANGE_CONTAINS_OP ||
        operator == OID_RANGE_CONTAINED_OP)
    {
        if (!(HeapTupleIsValid(vardata->statsTuple) &&
              get_attstatsslot(&lslot, vardata->statsTuple,
                               STATISTIC_KIND_RANGE_LENGTH_HISTOGRAM,
                               InvalidOid,
                               ATTSTATSSLOT_VALUES)))
        {
            free_attstatsslot(&hslot);
            return -1.0;
        }
 
        /* check that it's a histogram, not just a dummy entry */
        if (lslot.nvalues < 2)
        {
            free_attstatsslot(&lslot);
            free_attstatsslot(&hslot);
            return -1.0;
        }
    }
    else
        memset(&lslot, 0, sizeof(lslot));
 
    /* Extract the bounds of the constant value. */
    range_deserialize(typcache, constval, &const_lower, &const_upper, &empty);
    Assert(!empty);
 
    /*
     * Calculate selectivity comparing the lower or upper bound of the
     * constant with the histogram of lower or upper bounds.
     */
    switch (operator)
    {
        case OID_RANGE_LESS_OP:
 
            /*
             * The regular b-tree comparison operators (<, <=, >, >=) compare
             * the lower bounds first, and the upper bounds for values with
             * equal lower bounds. Estimate that by comparing the lower bounds
             * only. This gives a fairly accurate estimate assuming there
             * aren't many rows with a lower bound equal to the constant's
             * lower bound.
             */
            hist_selec =
                calc_hist_selectivity_scalar(typcache, &const_lower,
                                             hist_lower, nhist, false);
            break;
 
        case OID_RANGE_LESS_EQUAL_OP:
            hist_selec =
                calc_hist_selectivity_scalar(typcache, &const_lower,
                                             hist_lower, nhist, true);
            break;
 
        case OID_RANGE_GREATER_OP:
            hist_selec =
                1 - calc_hist_selectivity_scalar(typcache, &const_lower,
                                                 hist_lower, nhist, false);
            break;
 
        case OID_RANGE_GREATER_EQUAL_OP:
            hist_selec =
                1 - calc_hist_selectivity_scalar(typcache, &const_lower,
                                                 hist_lower, nhist, true);
            break;
 
        case OID_RANGE_LEFT_OP:
            /* var << const when upper(var) < lower(const) */
            hist_selec =
                calc_hist_selectivity_scalar(typcache, &const_lower,
                                             hist_upper, nhist, false);
            break;
 
        case OID_RANGE_RIGHT_OP:
            /* var >> const when lower(var) > upper(const) */
            hist_selec =
                1 - calc_hist_selectivity_scalar(typcache, &const_upper,
                                                 hist_lower, nhist, true);
            break;
 
        case OID_RANGE_OVERLAPS_RIGHT_OP:
            /* compare lower bounds */
            hist_selec =
                1 - calc_hist_selectivity_scalar(typcache, &const_lower,
                                                 hist_lower, nhist, false);
            break;
 
        case OID_RANGE_OVERLAPS_LEFT_OP:
            /* compare upper bounds */
            hist_selec =
                calc_hist_selectivity_scalar(typcache, &const_upper,
                                             hist_upper, nhist, true);
            break;
 
        case OID_RANGE_OVERLAP_OP:
        case OID_RANGE_CONTAINS_ELEM_OP:
 
            /*
             * A && B <=> NOT (A << B OR A >> B).
             *
             * Since A << B and A >> B are mutually exclusive events we can
             * sum their probabilities to find probability of (A << B OR A >>
             * B).
             *
             * "range @> elem" is equivalent to "range && [elem,elem]". The
             * caller already constructed the singular range from the element
             * constant, so just treat it the same as &&.
             */
            hist_selec =
                calc_hist_selectivity_scalar(typcache, &const_lower, hist_upper,
                                             nhist, false);
            hist_selec +=
                (1.0 - calc_hist_selectivity_scalar(typcache, &const_upper, hist_lower,
                                                    nhist, true));
            hist_selec = 1.0 - hist_selec;
            break;
 
        case OID_RANGE_CONTAINS_OP:
            hist_selec =
                calc_hist_selectivity_contains(typcache, &const_lower,
                                               &const_upper, hist_lower, nhist,
                                               lslot.values, lslot.nvalues);
            break;
 
        case OID_RANGE_CONTAINED_OP:
            if (const_lower.infinite)
            {
                /*
                 * Lower bound no longer matters. Just estimate the fraction
                 * with an upper bound <= const upper bound
                 */
                hist_selec =
                    calc_hist_selectivity_scalar(typcache, &const_upper,
                                                 hist_upper, nhist, true);
            }
            else if (const_upper.infinite)
            {
                hist_selec =
                    1.0 - calc_hist_selectivity_scalar(typcache, &const_lower,
                                                       hist_lower, nhist, false);
            }
            else
            {
                hist_selec =
                    calc_hist_selectivity_contained(typcache, &const_lower,
                                                    &const_upper, hist_lower, nhist,
                                                    lslot.values, lslot.nvalues);
            }
            break;
 
        default:
            elog(ERROR, "unknown range operator %u", operator);
            hist_selec = -1.0;  /* keep compiler quiet */
            break;
    }
 
    free_attstatsslot(&lslot);
    free_attstatsslot(&hslot);
 
    return hist_selec;
}
 
 
/*
 * Look up the fraction of values less than (or equal, if 'equal' argument
 * is true) a given const in a histogram of range bounds.
 */
static double
calc_hist_selectivity_scalar(TypeCacheEntry *typcache, const RangeBound *constbound,
                             const RangeBound *hist, int hist_nvalues, bool equal)
{
    Selectivity selec;
    int         index;
 
    /*
     * Find the histogram bin the given constant falls into. Estimate
     * selectivity as the number of preceding whole bins.
     */
    index = rbound_bsearch(typcache, constbound, hist, hist_nvalues, equal);
    selec = (Selectivity) (Max(index, 0)) / (Selectivity) (hist_nvalues - 1);
 
    /* Adjust using linear interpolation within the bin */
    if (index >= 0 && index < hist_nvalues - 1)
        selec += get_position(typcache, constbound, &hist[index],
                              &hist[index + 1]) / (Selectivity) (hist_nvalues - 1);
 
    return selec;
}
 
/*
 * Binary search on an array of range bounds. Returns greatest index of range
 * bound in array which is less(less or equal) than given range bound. If all
 * range bounds in array are greater or equal(greater) than given range bound,
 * return -1. When "equal" flag is set conditions in brackets are used.
 *
 * This function is used in scalar operator selectivity estimation. Another
 * goal of this function is to find a histogram bin where to stop
 * interpolation of portion of bounds which are less than or equal to given bound.
 */
static int
rbound_bsearch(TypeCacheEntry *typcache, const RangeBound *value, const RangeBound *hist,
               int hist_length, bool equal)
{
    int         lower = -1,
                upper = hist_length - 1,
                cmp,
                middle;
 
    while (lower < upper)
    {
        middle = (lower + upper + 1) / 2;
        cmp = range_cmp_bounds(typcache, &hist[middle], value);
 
        if (cmp < 0 || (equal && cmp == 0))
            lower = middle;
        else
            upper = middle - 1;
    }
    return lower;
}
 
 
/*
 * Binary search on length histogram. Returns greatest index of range length in
 * histogram which is less than (less than or equal) the given length value. If
 * all lengths in the histogram are greater than (greater than or equal) the
 * given length, returns -1.
 */
static int
length_hist_bsearch(const Datum *length_hist_values, int length_hist_nvalues,
                    double value, bool equal)
{
    int         lower = -1,
                upper = length_hist_nvalues - 1,
                middle;
 
    while (lower < upper)
    {
        double      middleval;
 
        middle = (lower + upper + 1) / 2;
 
        middleval = DatumGetFloat8(length_hist_values[middle]);
        if (middleval < value || (equal && middleval <= value))
            lower = middle;
        else
            upper = middle - 1;
    }
    return lower;
}
 
/*
 * Get relative position of value in histogram bin in [0,1] range.
 */
static float8
get_position(TypeCacheEntry *typcache, const RangeBound *value, const RangeBound *hist1,
             const RangeBound *hist2)
{
    bool        has_subdiff = OidIsValid(typcache->rng_subdiff_finfo.fn_oid);
    float8      position;
 
    if (!hist1->infinite && !hist2->infinite)
    {
        float8      bin_width;
 
        /*
         * Both bounds are finite. Assuming the subtype's comparison function
         * works sanely, the value must be finite, too, because it lies
         * somewhere between the bounds.  If it doesn't, arbitrarily return
         * 0.5.
         */
        if (value->infinite)
            return 0.5;
 
        /* Can't interpolate without subdiff function */
        if (!has_subdiff)
            return 0.5;
 
        /* Calculate relative position using subdiff function. */
        bin_width = DatumGetFloat8(FunctionCall2Coll(&typcache->rng_subdiff_finfo,
                                                     typcache->rng_collation,
                                                     hist2->val,
                                                     hist1->val));
        if (isnan(bin_width) || bin_width <= 0.0)
            return 0.5;         /* punt for NaN or zero-width bin */
 
        position = DatumGetFloat8(FunctionCall2Coll(&typcache->rng_subdiff_finfo,
                                                    typcache->rng_collation,
                                                    value->val,
                                                    hist1->val))
            / bin_width;
 
        if (isnan(position))
            return 0.5;         /* punt for NaN from subdiff, Inf/Inf, etc */
 
        /* Relative position must be in [0,1] range */
        position = Max(position, 0.0);
        position = Min(position, 1.0);
        return position;
    }
    else if (hist1->infinite && !hist2->infinite)
    {
        /*
         * Lower bin boundary is -infinite, upper is finite. If the value is
         * -infinite, return 0.0 to indicate it's equal to the lower bound.
         * Otherwise return 1.0 to indicate it's infinitely far from the lower
         * bound.
         */
        return ((value->infinite && value->lower) ? 0.0 : 1.0);
    }
    else if (!hist1->infinite && hist2->infinite)
    {
        /* same as above, but in reverse */
        return ((value->infinite && !value->lower) ? 1.0 : 0.0);
    }
    else
    {
        /*
         * If both bin boundaries are infinite, they should be equal to each
         * other, and the value should also be infinite and equal to both
         * bounds. (But don't Assert that, to avoid crashing if a user creates
         * a datatype with a broken comparison function).
         *
         * Assume the value to lie in the middle of the infinite bounds.
         */
        return 0.5;
    }
}
 
 
/*
 * Get relative position of value in a length histogram bin in [0,1] range.
 */
static double
get_len_position(double value, double hist1, double hist2)
{
    if (!isinf(hist1) && !isinf(hist2))
    {
        /*
         * Both bounds are finite. The value should be finite too, because it
         * lies somewhere between the bounds. If it doesn't, just return
         * something.
         */
        if (isinf(value))
            return 0.5;
 
        return 1.0 - (hist2 - value) / (hist2 - hist1);
    }
    else if (isinf(hist1) && !isinf(hist2))
    {
        /*
         * Lower bin boundary is -infinite, upper is finite. Return 1.0 to
         * indicate the value is infinitely far from the lower bound.
         */
        return 1.0;
    }
    else if (isinf(hist1) && isinf(hist2))
    {
        /* same as above, but in reverse */
        return 0.0;
    }
    else
    {
        /*
         * If both bin boundaries are infinite, they should be equal to each
         * other, and the value should also be infinite and equal to both
         * bounds. (But don't Assert that, to avoid crashing unnecessarily if
         * the caller messes up)
         *
         * Assume the value to lie in the middle of the infinite bounds.
         */
        return 0.5;
    }
}
 
/*
 * Measure distance between two range bounds.
 */
static float8
get_distance(TypeCacheEntry *typcache, const RangeBound *bound1, const RangeBound *bound2)
{
    bool        has_subdiff = OidIsValid(typcache->rng_subdiff_finfo.fn_oid);
 
    if (!bound1->infinite && !bound2->infinite)
    {
        /*
         * Neither bound is infinite, use subdiff function or return default
         * value of 1.0 if no subdiff is available.
         */
        if (has_subdiff)
        {
            float8      res;
 
            res = DatumGetFloat8(FunctionCall2Coll(&typcache->rng_subdiff_finfo,
                                                   typcache->rng_collation,
                                                   bound2->val,
                                                   bound1->val));
            /* Reject possible NaN result, also negative result */
            if (isnan(res) || res < 0.0)
                return 1.0;
            else
                return res;
        }
        else
            return 1.0;
    }
    else if (bound1->infinite && bound2->infinite)
    {
        /* Both bounds are infinite */
        if (bound1->lower == bound2->lower)
            return 0.0;
        else
            return get_float8_infinity();
    }
    else
    {
        /* One bound is infinite, the other is not */
        return get_float8_infinity();
    }
}
 
/*
 * Calculate the average of function P(x), in the interval [length1, length2],
 * where P(x) is the fraction of tuples with length < x (or length <= x if
 * 'equal' is true).
 */
static double
calc_length_hist_frac(const Datum *length_hist_values, int length_hist_nvalues,
                      double length1, double length2, bool equal)
{
    double      frac;
    double      A,
                B,
                PA,
                PB;
    double      pos;
    int         i;
    double      area;
 
    Assert(length2 >= length1);
 
    if (length2 < 0.0)
        return 0.0;             /* shouldn't happen, but doesn't hurt to check */
 
    /* All lengths in the table are <= infinite. */
    if (isinf(length2) && equal)
        return 1.0;
 
    /*----------
     * The average of a function between A and B can be calculated by the
     * formula:
     *
     *          B
     *    1     /
     * -------  | P(x)dx
     *  B - A   /
     *          A
     *
     * The geometrical interpretation of the integral is the area under the
     * graph of P(x). P(x) is defined by the length histogram. We calculate
     * the area in a piecewise fashion, iterating through the length histogram
     * bins. Each bin is a trapezoid:
     *
     *       P(x2)
     *        /|
     *       / |
     * P(x1)/  |
     *     |   |
     *     |   |
     *  ---+---+--
     *     x1  x2
     *
     * where x1 and x2 are the boundaries of the current histogram, and P(x1)
     * and P(x1) are the cumulative fraction of tuples at the boundaries.
     *
     * The area of each trapezoid is 1/2 * (P(x2) + P(x1)) * (x2 - x1)
     *
     * The first bin contains the lower bound passed by the caller, so we
     * use linear interpolation between the previous and next histogram bin
     * boundary to calculate P(x1). Likewise for the last bin: we use linear
     * interpolation to calculate P(x2). For the bins in between, x1 and x2
     * lie on histogram bin boundaries, so P(x1) and P(x2) are simply:
     * P(x1) =    (bin index) / (number of bins)
     * P(x2) = (bin index + 1 / (number of bins)
     */
 
    /* First bin, the one that contains lower bound */
    i = length_hist_bsearch(length_hist_values, length_hist_nvalues, length1, equal);
    if (i >= length_hist_nvalues - 1)
        return 1.0;
 
    if (i < 0)
    {
        i = 0;
        pos = 0.0;
    }
    else
    {
        /* interpolate length1's position in the bin */
        pos = get_len_position(length1,
                               DatumGetFloat8(length_hist_values[i]),
                               DatumGetFloat8(length_hist_values[i + 1]));
    }
    PB = (((double) i) + pos) / (double) (length_hist_nvalues - 1);
    B = length1;
 
    /*
     * In the degenerate case that length1 == length2, simply return
     * P(length1). This is not merely an optimization: if length1 == length2,
     * we'd divide by zero later on.
     */
    if (length2 == length1)
        return PB;
 
    /*
     * Loop through all the bins, until we hit the last bin, the one that
     * contains the upper bound. (if lower and upper bounds are in the same
     * bin, this falls out immediately)
     */
    area = 0.0;
    for (; i < length_hist_nvalues - 1; i++)
    {
        double      bin_upper = DatumGetFloat8(length_hist_values[i + 1]);
 
        /* check if we've reached the last bin */
        if (!(bin_upper < length2 || (equal && bin_upper <= length2)))
            break;
 
        /* the upper bound of previous bin is the lower bound of this bin */
        A = B;
        PA = PB;
 
        B = bin_upper;
        PB = (double) i / (double) (length_hist_nvalues - 1);
 
        /*
         * Add the area of this trapezoid to the total. The point of the
         * if-check is to avoid NaN, in the corner case that PA == PB == 0,
         * and B - A == Inf. The area of a zero-height trapezoid (PA == PB ==
         * 0) is zero, regardless of the width (B - A).
         */
        if (PA > 0 || PB > 0)
            area += 0.5 * (PB + PA) * (B - A);
    }
 
    /* Last bin */
    A = B;
    PA = PB;
 
    B = length2;                /* last bin ends at the query upper bound */
    if (i >= length_hist_nvalues - 1)
        pos = 0.0;
    else
    {
        if (DatumGetFloat8(length_hist_values[i]) == DatumGetFloat8(length_hist_values[i + 1]))
            pos = 0.0;
        else
            pos = get_len_position(length2, DatumGetFloat8(length_hist_values[i]), DatumGetFloat8(length_hist_values[i + 1]));
    }
    PB = (((double) i) + pos) / (double) (length_hist_nvalues - 1);
 
    if (PA > 0 || PB > 0)
        area += 0.5 * (PB + PA) * (B - A);
 
    /*
     * Ok, we have calculated the area, ie. the integral. Divide by width to
     * get the requested average.
     *
     * Avoid NaN arising from infinite / infinite. This happens at least if
     * length2 is infinite. It's not clear what the correct value would be in
     * that case, so 0.5 seems as good as any value.
     */
    if (isinf(area) && isinf(length2))
        frac = 0.5;
    else
        frac = area / (length2 - length1);
 
    return frac;
}
 
/*
 * Calculate selectivity of "var <@ const" operator, ie. estimate the fraction
 * of ranges that fall within the constant lower and upper bounds. This uses
 * the histograms of range lower bounds and range lengths, on the assumption
 * that the range lengths are independent of the lower bounds.
 *
 * The caller has already checked that constant lower and upper bounds are
 * finite.
 */
static double
calc_hist_selectivity_contained(TypeCacheEntry *typcache,
                                const RangeBound *lower, RangeBound *upper,
                                const RangeBound *hist_lower, int hist_nvalues,
                                const Datum *length_hist_values, int length_hist_nvalues)
{
    int         i,
                upper_index;
    float8      prev_dist;
    double      bin_width;
    double      upper_bin_width;
    double      sum_frac;
 
    /*
     * Begin by finding the bin containing the upper bound, in the lower bound
     * histogram. Any range with a lower bound > constant upper bound can't
     * match, ie. there are no matches in bins greater than upper_index.
     */
    upper->inclusive = !upper->inclusive;
    upper->lower = true;
    upper_index = rbound_bsearch(typcache, upper, hist_lower, hist_nvalues,
                                 false);
 
    /*
     * If the upper bound value is below the histogram's lower limit, there
     * are no matches.
     */
    if (upper_index < 0)
        return 0.0;
 
    /*
     * If the upper bound value is at or beyond the histogram's upper limit,
     * start our loop at the last actual bin, as though the upper bound were
     * within that bin; get_position will clamp its result to 1.0 anyway.
     * (This corresponds to assuming that the data population above the
     * histogram's upper limit is empty, exactly like what we just assumed for
     * the lower limit.)
     */
    upper_index = Min(upper_index, hist_nvalues - 2);
 
    /*
     * Calculate upper_bin_width, ie. the fraction of the (upper_index,
     * upper_index + 1) bin which is greater than upper bound of query range
     * using linear interpolation of subdiff function.
     */
    upper_bin_width = get_position(typcache, upper,
                                   &hist_lower[upper_index],
                                   &hist_lower[upper_index + 1]);
 
    /*
     * In the loop, dist and prev_dist are the distance of the "current" bin's
     * lower and upper bounds from the constant upper bound.
     *
     * bin_width represents the width of the current bin. Normally it is 1.0,
     * meaning a full width bin, but can be less in the corner cases: start
     * and end of the loop. We start with bin_width = upper_bin_width, because
     * we begin at the bin containing the upper bound.
     */
    prev_dist = 0.0;
    bin_width = upper_bin_width;
 
    sum_frac = 0.0;
    for (i = upper_index; i >= 0; i--)
    {
        double      dist;
        double      length_hist_frac;
        bool        final_bin = false;
 
        /*
         * dist -- distance from upper bound of query range to lower bound of
         * the current bin in the lower bound histogram. Or to the lower bound
         * of the constant range, if this is the final bin, containing the
         * constant lower bound.
         */
        if (range_cmp_bounds(typcache, &hist_lower[i], lower) < 0)
        {
            dist = get_distance(typcache, lower, upper);
 
            /*
             * Subtract from bin_width the portion of this bin that we want to
             * ignore.
             */
            bin_width -= get_position(typcache, lower, &hist_lower[i],
                                      &hist_lower[i + 1]);
            if (bin_width < 0.0)
                bin_width = 0.0;
            final_bin = true;
        }
        else
            dist = get_distance(typcache, &hist_lower[i], upper);
 
        /*
         * Estimate the fraction of tuples in this bin that are narrow enough
         * to not exceed the distance to the upper bound of the query range.
         */
        length_hist_frac = calc_length_hist_frac(length_hist_values,
                                                 length_hist_nvalues,
                                                 prev_dist, dist, true);
 
        /*
         * Add the fraction of tuples in this bin, with a suitable length, to
         * the total.
         */
        sum_frac += length_hist_frac * bin_width / (double) (hist_nvalues - 1);
 
        if (final_bin)
            break;
 
        bin_width = 1.0;
        prev_dist = dist;
    }
 
    return sum_frac;
}
 
/*
 * Calculate selectivity of "var @> const" operator, ie. estimate the fraction
 * of ranges that contain the constant lower and upper bounds. This uses
 * the histograms of range lower bounds and range lengths, on the assumption
 * that the range lengths are independent of the lower bounds.
 */
static double
calc_hist_selectivity_contains(TypeCacheEntry *typcache,
                               const RangeBound *lower, const RangeBound *upper,
                               const RangeBound *hist_lower, int hist_nvalues,
                               const Datum *length_hist_values, int length_hist_nvalues)
{
    int         i,
                lower_index;
    double      bin_width,
                lower_bin_width;
    double      sum_frac;
    float8      prev_dist;
 
    /* Find the bin containing the lower bound of query range. */
    lower_index = rbound_bsearch(typcache, lower, hist_lower, hist_nvalues,
                                 true);
 
    /*
     * If the lower bound value is below the histogram's lower limit, there
     * are no matches.
     */
    if (lower_index < 0)
        return 0.0;
 
    /*
     * If the lower bound value is at or beyond the histogram's upper limit,
     * start our loop at the last actual bin, as though the upper bound were
     * within that bin; get_position will clamp its result to 1.0 anyway.
     * (This corresponds to assuming that the data population above the
     * histogram's upper limit is empty, exactly like what we just assumed for
     * the lower limit.)
     */
    lower_index = Min(lower_index, hist_nvalues - 2);
 
    /*
     * Calculate lower_bin_width, ie. the fraction of the of (lower_index,
     * lower_index + 1) bin which is greater than lower bound of query range
     * using linear interpolation of subdiff function.
     */
    lower_bin_width = get_position(typcache, lower, &hist_lower[lower_index],
                                   &hist_lower[lower_index + 1]);
 
    /*
     * Loop through all the lower bound bins, smaller than the query lower
     * bound. In the loop, dist and prev_dist are the distance of the
     * "current" bin's lower and upper bounds from the constant upper bound.
     * We begin from query lower bound, and walk backwards, so the first bin's
     * upper bound is the query lower bound, and its distance to the query
     * upper bound is the length of the query range.
     *
     * bin_width represents the width of the current bin. Normally it is 1.0,
     * meaning a full width bin, except for the first bin, which is only
     * counted up to the constant lower bound.
     */
    prev_dist = get_distance(typcache, lower, upper);
    sum_frac = 0.0;
    bin_width = lower_bin_width;
    for (i = lower_index; i >= 0; i--)
    {
        float8      dist;
        double      length_hist_frac;
 
        /*
         * dist -- distance from upper bound of query range to current value
         * of lower bound histogram or lower bound of query range (if we've
         * reach it).
         */
        dist = get_distance(typcache, &hist_lower[i], upper);
 
        /*
         * Get average fraction of length histogram which covers intervals
         * longer than (or equal to) distance to upper bound of query range.
         */
        length_hist_frac =
            1.0 - calc_length_hist_frac(length_hist_values,
                                        length_hist_nvalues,
                                        prev_dist, dist, false);
 
        sum_frac += length_hist_frac * bin_width / (double) (hist_nvalues - 1);
 
        bin_width = 1.0;
        prev_dist = dist;
    }
 
    return sum_frac;
}