greenplumn network_selfuncs 源码
greenplumn network_selfuncs 代码
文件路径：/src/backend/utils/adt/network_selfuncs.c
/*-------------------------------------------------------------------------
*
* network_selfuncs.c
* Functions for selectivity estimation of inet/cidr operators
*
* This module provides estimators for the subnet inclusion and overlap
* operators. Estimates are based on null fraction, most common values,
* and histogram of inet/cidr columns.
*
* Portions Copyright (c) 1996-2019, PostgreSQL Global Development Group
* Portions Copyright (c) 1994, Regents of the University of California
*
*
* IDENTIFICATION
* src/backend/utils/adt/network_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/builtins.h"
#include "utils/inet.h"
#include "utils/lsyscache.h"
#include "utils/selfuncs.h"
/* Default selectivity for the inet overlap operator */
#define DEFAULT_OVERLAP_SEL 0.01
/* Default selectivity for the various inclusion operators */
#define DEFAULT_INCLUSION_SEL 0.005
/* Default selectivity for specified operator */
#define DEFAULT_SEL(operator) \
((operator) == OID_INET_OVERLAP_OP ? \
DEFAULT_OVERLAP_SEL : DEFAULT_INCLUSION_SEL)
/* Maximum number of items to consider in join selectivity calculations */
#define MAX_CONSIDERED_ELEMS 1024
static Selectivity networkjoinsel_inner(Oid operator,
VariableStatData *vardata1, VariableStatData *vardata2);
static Selectivity networkjoinsel_semi(Oid operator,
VariableStatData *vardata1, VariableStatData *vardata2);
static Selectivity mcv_population(float4 *mcv_numbers, int mcv_nvalues);
static Selectivity inet_hist_value_sel(Datum *values, int nvalues,
Datum constvalue, int opr_codenum);
static Selectivity inet_mcv_join_sel(Datum *mcv1_values,
float4 *mcv1_numbers, int mcv1_nvalues, Datum *mcv2_values,
float4 *mcv2_numbers, int mcv2_nvalues, Oid operator);
static Selectivity inet_mcv_hist_sel(Datum *mcv_values, float4 *mcv_numbers,
int mcv_nvalues, Datum *hist_values, int hist_nvalues,
int opr_codenum);
static Selectivity inet_hist_inclusion_join_sel(Datum *hist1_values,
int hist1_nvalues,
Datum *hist2_values, int hist2_nvalues,
int opr_codenum);
static Selectivity inet_semi_join_sel(Datum lhs_value,
bool mcv_exists, Datum *mcv_values, int mcv_nvalues,
bool hist_exists, Datum *hist_values, int hist_nvalues,
double hist_weight,
FmgrInfo *proc, int opr_codenum);
static int inet_opr_codenum(Oid operator);
static int inet_inclusion_cmp(inet *left, inet *right, int opr_codenum);
static int inet_masklen_inclusion_cmp(inet *left, inet *right,
int opr_codenum);
static int inet_hist_match_divider(inet *boundary, inet *query,
int opr_codenum);
/*
* Selectivity estimation for the subnet inclusion/overlap operators
*/
Datum
networksel(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,
mcv_selec,
non_mcv_selec;
Datum constvalue;
Form_pg_statistic stats;
AttStatsSlot hslot;
double sumcommon,
nullfrac;
FmgrInfo proc;
/*
* 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_SEL(operator));
/*
* Can't do anything useful if the something is not a constant, either.
*/
if (!IsA(other, Const))
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(DEFAULT_SEL(operator));
}
/* All of the operators handled here are strict. */
if (((Const *) other)->constisnull)
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(0.0);
}
constvalue = ((Const *) other)->constvalue;
/* Otherwise, we need stats in order to produce a non-default estimate. */
if (!HeapTupleIsValid(vardata.statsTuple))
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(DEFAULT_SEL(operator));
}
stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
nullfrac = stats->stanullfrac;
/*
* If we have most-common-values info, add up the fractions of the MCV
* entries that satisfy MCV OP CONST. These fractions contribute directly
* to the result selectivity. Also add up the total fraction represented
* by MCV entries.
*/
fmgr_info(get_opcode(operator), &proc);
mcv_selec = mcv_selectivity(&vardata, &proc, constvalue, varonleft,
&sumcommon);
/*
* If we have a histogram, use it to estimate the proportion of the
* non-MCV population that satisfies the clause. If we don't, apply the
* default selectivity to that population.
*/
if (get_attstatsslot(&hslot, vardata.statsTuple,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
ATTSTATSSLOT_VALUES))
{
int opr_codenum = inet_opr_codenum(operator);
/* Commute if needed, so we can consider histogram to be on the left */
if (!varonleft)
opr_codenum = -opr_codenum;
non_mcv_selec = inet_hist_value_sel(hslot.values, hslot.nvalues,
constvalue, opr_codenum);
free_attstatsslot(&hslot);
}
else
non_mcv_selec = DEFAULT_SEL(operator);
/* Combine selectivities for MCV and non-MCV populations */
selec = mcv_selec + (1.0 - nullfrac - sumcommon) * non_mcv_selec;
/* Result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(selec);
}
/*
* Join selectivity estimation for the subnet inclusion/overlap operators
*
* This function has the same structure as eqjoinsel() in selfuncs.c.
*
* Throughout networkjoinsel and its subroutines, we have a performance issue
* in that the amount of work to be done is O(N^2) in the length of the MCV
* and histogram arrays. To keep the runtime from getting out of hand when
* large statistics targets have been set, we arbitrarily limit the number of
* values considered to 1024 (MAX_CONSIDERED_ELEMS). For the MCV arrays, this
* is easy: just consider at most the first N elements. (Since the MCVs are
* sorted by decreasing frequency, this correctly gets us the first N MCVs.)
* For the histogram arrays, we decimate; that is consider only every k'th
* element, where k is chosen so that no more than MAX_CONSIDERED_ELEMS
* elements are considered. This should still give us a good random sample of
* the non-MCV population. Decimation is done on-the-fly in the loops that
* iterate over the histogram arrays.
*/
Datum
networkjoinsel(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
Oid operator = PG_GETARG_OID(1);
List *args = (List *) PG_GETARG_POINTER(2);
#ifdef NOT_USED
JoinType jointype = (JoinType) PG_GETARG_INT16(3);
#endif
SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
double selec;
VariableStatData vardata1;
VariableStatData vardata2;
bool join_is_reversed;
get_join_variables(root, args, sjinfo,
&vardata1, &vardata2, &join_is_reversed);
switch (sjinfo->jointype)
{
case JOIN_INNER:
case JOIN_LEFT:
case JOIN_FULL:
/*
* Selectivity for left/full join is not exactly the same as inner
* join, but we neglect the difference, as eqjoinsel does.
*/
selec = networkjoinsel_inner(operator, &vardata1, &vardata2);
break;
case JOIN_SEMI:
case JOIN_ANTI:
/* Here, it's important that we pass the outer var on the left. */
if (!join_is_reversed)
selec = networkjoinsel_semi(operator, &vardata1, &vardata2);
else
selec = networkjoinsel_semi(get_commutator(operator),
&vardata2, &vardata1);
break;
default:
/* other values not expected here */
elog(ERROR, "unrecognized join type: %d",
(int) sjinfo->jointype);
selec = 0; /* keep compiler quiet */
break;
}
ReleaseVariableStats(vardata1);
ReleaseVariableStats(vardata2);
CLAMP_PROBABILITY(selec);
PG_RETURN_FLOAT8((float8) selec);
}
/*
* Inner join selectivity estimation for subnet inclusion/overlap operators
*
* Calculates MCV vs MCV, MCV vs histogram and histogram vs histogram
* selectivity for join using the subnet inclusion operators. Unlike the
* join selectivity function for the equality operator, eqjoinsel_inner(),
* one to one matching of the values is not enough. Network inclusion
* operators are likely to match many to many, so we must check all pairs.
* (Note: it might be possible to exploit understanding of the histogram's
* btree ordering to reduce the work needed, but we don't currently try.)
* Also, MCV vs histogram selectivity is not neglected as in eqjoinsel_inner().
*/
static Selectivity
networkjoinsel_inner(Oid operator,
VariableStatData *vardata1, VariableStatData *vardata2)
{
Form_pg_statistic stats;
double nullfrac1 = 0.0,
nullfrac2 = 0.0;
Selectivity selec = 0.0,
sumcommon1 = 0.0,
sumcommon2 = 0.0;
bool mcv1_exists = false,
mcv2_exists = false,
hist1_exists = false,
hist2_exists = false;
int opr_codenum;
int mcv1_length = 0,
mcv2_length = 0;
AttStatsSlot mcv1_slot;
AttStatsSlot mcv2_slot;
AttStatsSlot hist1_slot;
AttStatsSlot hist2_slot;
if (HeapTupleIsValid(vardata1->statsTuple))
{
stats = (Form_pg_statistic) GETSTRUCT(vardata1->statsTuple);
nullfrac1 = stats->stanullfrac;
mcv1_exists = get_attstatsslot(&mcv1_slot, vardata1->statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
hist1_exists = get_attstatsslot(&hist1_slot, vardata1->statsTuple,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
ATTSTATSSLOT_VALUES);
/* Arbitrarily limit number of MCVs considered */
mcv1_length = Min(mcv1_slot.nvalues, MAX_CONSIDERED_ELEMS);
if (mcv1_exists)
sumcommon1 = mcv_population(mcv1_slot.numbers, mcv1_length);
}
else
{
memset(&mcv1_slot, 0, sizeof(mcv1_slot));
memset(&hist1_slot, 0, sizeof(hist1_slot));
}
if (HeapTupleIsValid(vardata2->statsTuple))
{
stats = (Form_pg_statistic) GETSTRUCT(vardata2->statsTuple);
nullfrac2 = stats->stanullfrac;
mcv2_exists = get_attstatsslot(&mcv2_slot, vardata2->statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
hist2_exists = get_attstatsslot(&hist2_slot, vardata2->statsTuple,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
ATTSTATSSLOT_VALUES);
/* Arbitrarily limit number of MCVs considered */
mcv2_length = Min(mcv2_slot.nvalues, MAX_CONSIDERED_ELEMS);
if (mcv2_exists)
sumcommon2 = mcv_population(mcv2_slot.numbers, mcv2_length);
}
else
{
memset(&mcv2_slot, 0, sizeof(mcv2_slot));
memset(&hist2_slot, 0, sizeof(hist2_slot));
}
opr_codenum = inet_opr_codenum(operator);
/*
* Calculate selectivity for MCV vs MCV matches.
*/
if (mcv1_exists && mcv2_exists)
selec += inet_mcv_join_sel(mcv1_slot.values, mcv1_slot.numbers,
mcv1_length,
mcv2_slot.values, mcv2_slot.numbers,
mcv2_length,
operator);
/*
* Add in selectivities for MCV vs histogram matches, scaling according to
* the fractions of the populations represented by the histograms. Note
* that the second case needs to commute the operator.
*/
if (mcv1_exists && hist2_exists)
selec += (1.0 - nullfrac2 - sumcommon2) *
inet_mcv_hist_sel(mcv1_slot.values, mcv1_slot.numbers, mcv1_length,
hist2_slot.values, hist2_slot.nvalues,
opr_codenum);
if (mcv2_exists && hist1_exists)
selec += (1.0 - nullfrac1 - sumcommon1) *
inet_mcv_hist_sel(mcv2_slot.values, mcv2_slot.numbers, mcv2_length,
hist1_slot.values, hist1_slot.nvalues,
-opr_codenum);
/*
* Add in selectivity for histogram vs histogram matches, again scaling
* appropriately.
*/
if (hist1_exists && hist2_exists)
selec += (1.0 - nullfrac1 - sumcommon1) *
(1.0 - nullfrac2 - sumcommon2) *
inet_hist_inclusion_join_sel(hist1_slot.values, hist1_slot.nvalues,
hist2_slot.values, hist2_slot.nvalues,
opr_codenum);
/*
* If useful statistics are not available then use the default estimate.
* We can apply null fractions if known, though.
*/
if ((!mcv1_exists && !hist1_exists) || (!mcv2_exists && !hist2_exists))
selec = (1.0 - nullfrac1) * (1.0 - nullfrac2) * DEFAULT_SEL(operator);
/* Release stats. */
free_attstatsslot(&mcv1_slot);
free_attstatsslot(&mcv2_slot);
free_attstatsslot(&hist1_slot);
free_attstatsslot(&hist2_slot);
return selec;
}
/*
* Semi join selectivity estimation for subnet inclusion/overlap operators
*
* Calculates MCV vs MCV, MCV vs histogram, histogram vs MCV, and histogram vs
* histogram selectivity for semi/anti join cases.
*/
static Selectivity
networkjoinsel_semi(Oid operator,
VariableStatData *vardata1, VariableStatData *vardata2)
{
Form_pg_statistic stats;
Selectivity selec = 0.0,
sumcommon1 = 0.0,
sumcommon2 = 0.0;
double nullfrac1 = 0.0,
nullfrac2 = 0.0,
hist2_weight = 0.0;
bool mcv1_exists = false,
mcv2_exists = false,
hist1_exists = false,
hist2_exists = false;
int opr_codenum;
FmgrInfo proc;
int i,
mcv1_length = 0,
mcv2_length = 0;
AttStatsSlot mcv1_slot;
AttStatsSlot mcv2_slot;
AttStatsSlot hist1_slot;
AttStatsSlot hist2_slot;
if (HeapTupleIsValid(vardata1->statsTuple))
{
stats = (Form_pg_statistic) GETSTRUCT(vardata1->statsTuple);
nullfrac1 = stats->stanullfrac;
mcv1_exists = get_attstatsslot(&mcv1_slot, vardata1->statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
hist1_exists = get_attstatsslot(&hist1_slot, vardata1->statsTuple,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
ATTSTATSSLOT_VALUES);
/* Arbitrarily limit number of MCVs considered */
mcv1_length = Min(mcv1_slot.nvalues, MAX_CONSIDERED_ELEMS);
if (mcv1_exists)
sumcommon1 = mcv_population(mcv1_slot.numbers, mcv1_length);
}
else
{
memset(&mcv1_slot, 0, sizeof(mcv1_slot));
memset(&hist1_slot, 0, sizeof(hist1_slot));
}
if (HeapTupleIsValid(vardata2->statsTuple))
{
stats = (Form_pg_statistic) GETSTRUCT(vardata2->statsTuple);
nullfrac2 = stats->stanullfrac;
mcv2_exists = get_attstatsslot(&mcv2_slot, vardata2->statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
hist2_exists = get_attstatsslot(&hist2_slot, vardata2->statsTuple,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
ATTSTATSSLOT_VALUES);
/* Arbitrarily limit number of MCVs considered */
mcv2_length = Min(mcv2_slot.nvalues, MAX_CONSIDERED_ELEMS);
if (mcv2_exists)
sumcommon2 = mcv_population(mcv2_slot.numbers, mcv2_length);
}
else
{
memset(&mcv2_slot, 0, sizeof(mcv2_slot));
memset(&hist2_slot, 0, sizeof(hist2_slot));
}
opr_codenum = inet_opr_codenum(operator);
fmgr_info(get_opcode(operator), &proc);
/* Estimate number of input rows represented by RHS histogram. */
if (hist2_exists && vardata2->rel)
hist2_weight = (1.0 - nullfrac2 - sumcommon2) * vardata2->rel->rows;
/*
* Consider each element of the LHS MCV list, matching it to whatever RHS
* stats we have. Scale according to the known frequency of the MCV.
*/
if (mcv1_exists && (mcv2_exists || hist2_exists))
{
for (i = 0; i < mcv1_length; i++)
{
selec += mcv1_slot.numbers[i] *
inet_semi_join_sel(mcv1_slot.values[i],
mcv2_exists, mcv2_slot.values, mcv2_length,
hist2_exists,
hist2_slot.values, hist2_slot.nvalues,
hist2_weight,
&proc, opr_codenum);
}
}
/*
* Consider each element of the LHS histogram, except for the first and
* last elements, which we exclude on the grounds that they're outliers
* and thus not very representative. Scale on the assumption that each
* such histogram element represents an equal share of the LHS histogram
* population (which is a bit bogus, because the members of its bucket may
* not all act the same with respect to the join clause, but it's hard to
* do better).
*
* If there are too many histogram elements, decimate to limit runtime.
*/
if (hist1_exists && hist1_slot.nvalues > 2 && (mcv2_exists || hist2_exists))
{
double hist_selec_sum = 0.0;
int k,
n;
k = (hist1_slot.nvalues - 3) / MAX_CONSIDERED_ELEMS + 1;
n = 0;
for (i = 1; i < hist1_slot.nvalues - 1; i += k)
{
hist_selec_sum +=
inet_semi_join_sel(hist1_slot.values[i],
mcv2_exists, mcv2_slot.values, mcv2_length,
hist2_exists,
hist2_slot.values, hist2_slot.nvalues,
hist2_weight,
&proc, opr_codenum);
n++;
}
selec += (1.0 - nullfrac1 - sumcommon1) * hist_selec_sum / n;
}
/*
* If useful statistics are not available then use the default estimate.
* We can apply null fractions if known, though.
*/
if ((!mcv1_exists && !hist1_exists) || (!mcv2_exists && !hist2_exists))
selec = (1.0 - nullfrac1) * (1.0 - nullfrac2) * DEFAULT_SEL(operator);
/* Release stats. */
free_attstatsslot(&mcv1_slot);
free_attstatsslot(&mcv2_slot);
free_attstatsslot(&hist1_slot);
free_attstatsslot(&hist2_slot);
return selec;
}
/*
* Compute the fraction of a relation's population that is represented
* by the MCV list.
*/
static Selectivity
mcv_population(float4 *mcv_numbers, int mcv_nvalues)
{
Selectivity sumcommon = 0.0;
int i;
for (i = 0; i < mcv_nvalues; i++)
{
sumcommon += mcv_numbers[i];
}
return sumcommon;
}
/*
* Inet histogram vs single value selectivity estimation
*
* Estimate the fraction of the histogram population that satisfies
* "value OPR CONST". (The result needs to be scaled to reflect the
* proportion of the total population represented by the histogram.)
*
* The histogram is originally for the inet btree comparison operators.
* Only the common bits of the network part and the length of the network part
* (masklen) are interesting for the subnet inclusion operators. Fortunately,
* btree comparison treats the network part as the major sort key. Even so,
* the length of the network part would not really be significant in the
* histogram. This would lead to big mistakes for data sets with uneven
* masklen distribution. To reduce this problem, comparisons with the left
* and the right sides of the buckets are used together.
*
* Histogram bucket matches are calculated in two forms. If the constant
* matches both bucket endpoints the bucket is considered as fully matched.
* The second form is to match the bucket partially; we recognize this when
* the constant matches just one endpoint, or the two endpoints fall on
* opposite sides of the constant. (Note that when the constant matches an
* interior histogram element, it gets credit for partial matches to the
* buckets on both sides, while a match to a histogram endpoint gets credit
* for only one partial match. This is desirable.)
*
* The divider in the partial bucket match is imagined as the distance
* between the decisive bits and the common bits of the addresses. It will
* be used as a power of two as it is the natural scale for the IP network
* inclusion. This partial bucket match divider calculation is an empirical
* formula and subject to change with more experiment.
*
* For a partial match, we try to calculate dividers for both of the
* boundaries. If the address family of a boundary value does not match the
* constant or comparison of the length of the network parts is not correct
* for the operator, the divider for that boundary will not be taken into
* account. If both of the dividers are valid, the greater one will be used
* to minimize the mistake in buckets that have disparate masklens. This
* calculation is unfair when dividers can be calculated for both of the
* boundaries but they are far from each other; but it is not a common
* situation as the boundaries are expected to share most of their significant
* bits of their masklens. The mistake would be greater, if we would use the
* minimum instead of the maximum, and we don't know a sensible way to combine
* them.
*
* For partial match in buckets that have different address families on the
* left and right sides, only the boundary with the same address family is
* taken into consideration. This can cause more mistakes for these buckets
* if the masklens of their boundaries are also disparate. But this can only
* happen in one bucket, since only two address families exist. It seems a
* better option than not considering these buckets at all.
*/
static Selectivity
inet_hist_value_sel(Datum *values, int nvalues, Datum constvalue,
int opr_codenum)
{
Selectivity match = 0.0;
inet *query,
*left,
*right;
int i,
k,
n;
int left_order,
right_order,
left_divider,
right_divider;
/* guard against zero-divide below */
if (nvalues <= 1)
return 0.0;
/* if there are too many histogram elements, decimate to limit runtime */
k = (nvalues - 2) / MAX_CONSIDERED_ELEMS + 1;
query = DatumGetInetPP(constvalue);
/* "left" is the left boundary value of the current bucket ... */
left = DatumGetInetPP(values[0]);
left_order = inet_inclusion_cmp(left, query, opr_codenum);
n = 0;
for (i = k; i < nvalues; i += k)
{
/* ... and "right" is the right boundary value */
right = DatumGetInetPP(values[i]);
right_order = inet_inclusion_cmp(right, query, opr_codenum);
if (left_order == 0 && right_order == 0)
{
/* The whole bucket matches, since both endpoints do. */
match += 1.0;
}
else if ((left_order <= 0 && right_order >= 0) ||
(left_order >= 0 && right_order <= 0))
{
/* Partial bucket match. */
left_divider = inet_hist_match_divider(left, query, opr_codenum);
right_divider = inet_hist_match_divider(right, query, opr_codenum);
if (left_divider >= 0 || right_divider >= 0)
match += 1.0 / pow(2.0, Max(left_divider, right_divider));
}
/* Shift the variables. */
left = right;
left_order = right_order;
/* Count the number of buckets considered. */
n++;
}
return match / n;
}
/*
* Inet MCV vs MCV join selectivity estimation
*
* We simply add up the fractions of the populations that satisfy the clause.
* The result is exact and does not need to be scaled further.
*/
static Selectivity
inet_mcv_join_sel(Datum *mcv1_values, float4 *mcv1_numbers, int mcv1_nvalues,
Datum *mcv2_values, float4 *mcv2_numbers, int mcv2_nvalues,
Oid operator)
{
Selectivity selec = 0.0;
FmgrInfo proc;
int i,
j;
fmgr_info(get_opcode(operator), &proc);
for (i = 0; i < mcv1_nvalues; i++)
{
for (j = 0; j < mcv2_nvalues; j++)
if (DatumGetBool(FunctionCall2(&proc,
mcv1_values[i],
mcv2_values[j])))
selec += mcv1_numbers[i] * mcv2_numbers[j];
}
return selec;
}
/*
* Inet MCV vs histogram join selectivity estimation
*
* For each MCV on the lefthand side, estimate the fraction of the righthand's
* histogram population that satisfies the join clause, and add those up,
* scaling by the MCV's frequency. The result still needs to be scaled
* according to the fraction of the righthand's population represented by
* the histogram.
*/
static Selectivity
inet_mcv_hist_sel(Datum *mcv_values, float4 *mcv_numbers, int mcv_nvalues,
Datum *hist_values, int hist_nvalues,
int opr_codenum)
{
Selectivity selec = 0.0;
int i;
/*
* We'll call inet_hist_value_selec with the histogram on the left, so we
* must commute the operator.
*/
opr_codenum = -opr_codenum;
for (i = 0; i < mcv_nvalues; i++)
{
selec += mcv_numbers[i] *
inet_hist_value_sel(hist_values, hist_nvalues, mcv_values[i],
opr_codenum);
}
return selec;
}
/*
* Inet histogram vs histogram join selectivity estimation
*
* Here, we take all values listed in the second histogram (except for the
* first and last elements, which are excluded on the grounds of possibly
* not being very representative) and treat them as a uniform sample of
* the non-MCV population for that relation. For each one, we apply
* inet_hist_value_selec to see what fraction of the first histogram
* it matches.
*
* We could alternatively do this the other way around using the operator's
* commutator. XXX would it be worthwhile to do it both ways and take the
* average? That would at least avoid non-commutative estimation results.
*/
static Selectivity
inet_hist_inclusion_join_sel(Datum *hist1_values, int hist1_nvalues,
Datum *hist2_values, int hist2_nvalues,
int opr_codenum)
{
double match = 0.0;
int i,
k,
n;
if (hist2_nvalues <= 2)
return 0.0; /* no interior histogram elements */
/* if there are too many histogram elements, decimate to limit runtime */
k = (hist2_nvalues - 3) / MAX_CONSIDERED_ELEMS + 1;
n = 0;
for (i = 1; i < hist2_nvalues - 1; i += k)
{
match += inet_hist_value_sel(hist1_values, hist1_nvalues,
hist2_values[i], opr_codenum);
n++;
}
return match / n;
}
/*
* Inet semi join selectivity estimation for one value
*
* The function calculates the probability that there is at least one row
* in the RHS table that satisfies the "lhs_value op column" condition.
* It is used in semi join estimation to check a sample from the left hand
* side table.
*
* The MCV and histogram from the right hand side table should be provided as
* arguments with the lhs_value from the left hand side table for the join.
* hist_weight is the total number of rows represented by the histogram.
* For example, if the table has 1000 rows, and 10% of the rows are in the MCV
* list, and another 10% are NULLs, hist_weight would be 800.
*
* First, the lhs_value will be matched to the most common values. If it
* matches any of them, 1.0 will be returned, because then there is surely
* a match.
*
* Otherwise, the histogram will be used to estimate the number of rows in
* the second table that match the condition. If the estimate is greater
* than 1.0, 1.0 will be returned, because it means there is a greater chance
* that the lhs_value will match more than one row in the table. If it is
* between 0.0 and 1.0, it will be returned as the probability.
*/
static Selectivity
inet_semi_join_sel(Datum lhs_value,
bool mcv_exists, Datum *mcv_values, int mcv_nvalues,
bool hist_exists, Datum *hist_values, int hist_nvalues,
double hist_weight,
FmgrInfo *proc, int opr_codenum)
{
if (mcv_exists)
{
int i;
for (i = 0; i < mcv_nvalues; i++)
{
if (DatumGetBool(FunctionCall2(proc,
lhs_value,
mcv_values[i])))
return 1.0;
}
}
if (hist_exists && hist_weight > 0)
{
Selectivity hist_selec;
/* Commute operator, since we're passing lhs_value on the right */
hist_selec = inet_hist_value_sel(hist_values, hist_nvalues,
lhs_value, -opr_codenum);
if (hist_selec > 0)
return Min(1.0, hist_weight * hist_selec);
}
return 0.0;
}
/*
* Assign useful code numbers for the subnet inclusion/overlap operators
*
* Only inet_masklen_inclusion_cmp() and inet_hist_match_divider() depend
* on the exact codes assigned here; but many other places in this file
* know that they can negate a code to obtain the code for the commutator
* operator.
*/
static int
inet_opr_codenum(Oid operator)
{
switch (operator)
{
case OID_INET_SUP_OP:
return -2;
case OID_INET_SUPEQ_OP:
return -1;
case OID_INET_OVERLAP_OP:
return 0;
case OID_INET_SUBEQ_OP:
return 1;
case OID_INET_SUB_OP:
return 2;
default:
elog(ERROR, "unrecognized operator %u for inet selectivity",
operator);
}
return 0; /* unreached, but keep compiler quiet */
}
/*
* Comparison function for the subnet inclusion/overlap operators
*
* If the comparison is okay for the specified inclusion operator, the return
* value will be 0. Otherwise the return value will be less than or greater
* than 0 as appropriate for the operator.
*
* Comparison is compatible with the basic comparison function for the inet
* type. See network_cmp_internal() in network.c for the original. Basic
* comparison operators are implemented with the network_cmp_internal()
* function. It is possible to implement the subnet inclusion operators with
* this function.
*
* Comparison is first on the common bits of the network part, then on the
* length of the network part (masklen) as in the network_cmp_internal()
* function. Only the first part is in this function. The second part is
* separated to another function for reusability. The difference between the
* second part and the original network_cmp_internal() is that the inclusion
* operator is considered while comparing the lengths of the network parts.
* See the inet_masklen_inclusion_cmp() function below.
*/
static int
inet_inclusion_cmp(inet *left, inet *right, int opr_codenum)
{
if (ip_family(left) == ip_family(right))
{
int order;
order = bitncmp(ip_addr(left), ip_addr(right),
Min(ip_bits(left), ip_bits(right)));
if (order != 0)
return order;
return inet_masklen_inclusion_cmp(left, right, opr_codenum);
}
return ip_family(left) - ip_family(right);
}
/*
* Masklen comparison function for the subnet inclusion/overlap operators
*
* Compares the lengths of the network parts of the inputs. If the comparison
* is okay for the specified inclusion operator, the return value will be 0.
* Otherwise the return value will be less than or greater than 0 as
* appropriate for the operator.
*/
static int
inet_masklen_inclusion_cmp(inet *left, inet *right, int opr_codenum)
{
int order;
order = (int) ip_bits(left) - (int) ip_bits(right);
/*
* Return 0 if the operator would accept this combination of masklens.
* Note that opr_codenum zero (overlaps) will accept all cases.
*/
if ((order > 0 && opr_codenum >= 0) ||
(order == 0 && opr_codenum >= -1 && opr_codenum <= 1) ||
(order < 0 && opr_codenum <= 0))
return 0;
/*
* Otherwise, return a negative value for sup/supeq (notionally, the RHS
* needs to have a larger masklen than it has, which would make it sort
* later), or a positive value for sub/subeq (vice versa).
*/
return opr_codenum;
}
/*
* Inet histogram partial match divider calculation
*
* First the families and the lengths of the network parts are compared using
* the subnet inclusion operator. If those are acceptable for the operator,
* the divider will be calculated using the masklens and the common bits of
* the addresses. -1 will be returned if it cannot be calculated.
*
* See commentary for inet_hist_value_sel() for some rationale for this.
*/
static int
inet_hist_match_divider(inet *boundary, inet *query, int opr_codenum)
{
if (ip_family(boundary) == ip_family(query) &&
inet_masklen_inclusion_cmp(boundary, query, opr_codenum) == 0)
{
int min_bits,
decisive_bits;
min_bits = Min(ip_bits(boundary), ip_bits(query));
/*
* Set decisive_bits to the masklen of the one that should contain the
* other according to the operator.
*/
if (opr_codenum < 0)
decisive_bits = ip_bits(boundary);
else if (opr_codenum > 0)
decisive_bits = ip_bits(query);
else
decisive_bits = min_bits;
/*
* Now return the number of non-common decisive bits. (This will be
* zero if the boundary and query in fact match, else positive.)
*/
if (min_bits > 0)
return decisive_bits - bitncommon(ip_addr(boundary),
ip_addr(query),
min_bits);
return decisive_bits;
}
return -1;
}
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