* network_selfuncs.c
* Functions for selectivity estimation of inet/cidr operators
*
- * Currently these are just stubs, but we hope to do better soon.
+ * 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) 1996-2015, PostgreSQL Global Development Group
* Portions Copyright (c) 1994, Regents of the University of California
*/
#include "postgres.h"
+#include <math.h>
+
+#include "access/htup_details.h"
+#include "catalog/pg_operator.h"
+#include "catalog/pg_statistic.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)
{
- PG_RETURN_FLOAT8(0.001);
+ 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,
+ *hist_values;
+ int hist_nvalues;
+ Form_pg_statistic stats;
+ 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(vardata.statsTuple,
+ vardata.atttype, vardata.atttypmod,
+ STATISTIC_KIND_HISTOGRAM, InvalidOid,
+ NULL,
+ &hist_values, &hist_nvalues,
+ NULL, NULL))
+ {
+ 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(hist_values, hist_nvalues,
+ constvalue, opr_codenum);
+
+ free_attstatsslot(vardata.atttype, hist_values, hist_nvalues, NULL, 0);
+ }
+ 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)
{
- PG_RETURN_FLOAT8(0.001);
+ 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_nvalues,
+ mcv2_nvalues,
+ mcv1_nnumbers,
+ mcv2_nnumbers,
+ hist1_nvalues,
+ hist2_nvalues,
+ mcv1_length = 0,
+ mcv2_length = 0;
+ Datum *mcv1_values,
+ *mcv2_values,
+ *hist1_values,
+ *hist2_values;
+ float4 *mcv1_numbers,
+ *mcv2_numbers;
+
+ if (HeapTupleIsValid(vardata1->statsTuple))
+ {
+ stats = (Form_pg_statistic) GETSTRUCT(vardata1->statsTuple);
+ nullfrac1 = stats->stanullfrac;
+
+ mcv1_exists = get_attstatsslot(vardata1->statsTuple,
+ vardata1->atttype, vardata1->atttypmod,
+ STATISTIC_KIND_MCV, InvalidOid,
+ NULL,
+ &mcv1_values, &mcv1_nvalues,
+ &mcv1_numbers, &mcv1_nnumbers);
+ hist1_exists = get_attstatsslot(vardata1->statsTuple,
+ vardata1->atttype, vardata1->atttypmod,
+ STATISTIC_KIND_HISTOGRAM, InvalidOid,
+ NULL,
+ &hist1_values, &hist1_nvalues,
+ NULL, NULL);
+ /* Arbitrarily limit number of MCVs considered */
+ mcv1_length = Min(mcv1_nvalues, MAX_CONSIDERED_ELEMS);
+ if (mcv1_exists)
+ sumcommon1 = mcv_population(mcv1_numbers, mcv1_length);
+ }
+
+ if (HeapTupleIsValid(vardata2->statsTuple))
+ {
+ stats = (Form_pg_statistic) GETSTRUCT(vardata2->statsTuple);
+ nullfrac2 = stats->stanullfrac;
+
+ mcv2_exists = get_attstatsslot(vardata2->statsTuple,
+ vardata2->atttype, vardata2->atttypmod,
+ STATISTIC_KIND_MCV, InvalidOid,
+ NULL,
+ &mcv2_values, &mcv2_nvalues,
+ &mcv2_numbers, &mcv2_nnumbers);
+ hist2_exists = get_attstatsslot(vardata2->statsTuple,
+ vardata2->atttype, vardata2->atttypmod,
+ STATISTIC_KIND_HISTOGRAM, InvalidOid,
+ NULL,
+ &hist2_values, &hist2_nvalues,
+ NULL, NULL);
+ /* Arbitrarily limit number of MCVs considered */
+ mcv2_length = Min(mcv2_nvalues, MAX_CONSIDERED_ELEMS);
+ if (mcv2_exists)
+ sumcommon2 = mcv_population(mcv2_numbers, mcv2_length);
+ }
+
+ opr_codenum = inet_opr_codenum(operator);
+
+ /*
+ * Calculate selectivity for MCV vs MCV matches.
+ */
+ if (mcv1_exists && mcv2_exists)
+ selec += inet_mcv_join_sel(mcv1_values, mcv1_numbers, mcv1_length,
+ mcv2_values, mcv2_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_values, mcv1_numbers, mcv1_length,
+ hist2_values, hist2_nvalues,
+ opr_codenum);
+ if (mcv2_exists && hist1_exists)
+ selec += (1.0 - nullfrac1 - sumcommon1) *
+ inet_mcv_hist_sel(mcv2_values, mcv2_numbers, mcv2_length,
+ hist1_values, hist1_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_values, hist1_nvalues,
+ hist2_values, hist2_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. */
+ if (mcv1_exists)
+ free_attstatsslot(vardata1->atttype, mcv1_values, mcv1_nvalues,
+ mcv1_numbers, mcv1_nnumbers);
+ if (mcv2_exists)
+ free_attstatsslot(vardata2->atttype, mcv2_values, mcv2_nvalues,
+ mcv2_numbers, mcv2_nnumbers);
+ if (hist1_exists)
+ free_attstatsslot(vardata1->atttype, hist1_values, hist1_nvalues,
+ NULL, 0);
+ if (hist2_exists)
+ free_attstatsslot(vardata2->atttype, hist2_values, hist2_nvalues,
+ NULL, 0);
+
+ 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_nvalues,
+ mcv2_nvalues,
+ mcv1_nnumbers,
+ mcv2_nnumbers,
+ hist1_nvalues,
+ hist2_nvalues,
+ mcv1_length = 0,
+ mcv2_length = 0;
+ Datum *mcv1_values,
+ *mcv2_values,
+ *hist1_values,
+ *hist2_values;
+ float4 *mcv1_numbers,
+ *mcv2_numbers;
+
+ if (HeapTupleIsValid(vardata1->statsTuple))
+ {
+ stats = (Form_pg_statistic) GETSTRUCT(vardata1->statsTuple);
+ nullfrac1 = stats->stanullfrac;
+
+ mcv1_exists = get_attstatsslot(vardata1->statsTuple,
+ vardata1->atttype, vardata1->atttypmod,
+ STATISTIC_KIND_MCV, InvalidOid,
+ NULL,
+ &mcv1_values, &mcv1_nvalues,
+ &mcv1_numbers, &mcv1_nnumbers);
+ hist1_exists = get_attstatsslot(vardata1->statsTuple,
+ vardata1->atttype, vardata1->atttypmod,
+ STATISTIC_KIND_HISTOGRAM, InvalidOid,
+ NULL,
+ &hist1_values, &hist1_nvalues,
+ NULL, NULL);
+ /* Arbitrarily limit number of MCVs considered */
+ mcv1_length = Min(mcv1_nvalues, MAX_CONSIDERED_ELEMS);
+ if (mcv1_exists)
+ sumcommon1 = mcv_population(mcv1_numbers, mcv1_length);
+ }
+
+ if (HeapTupleIsValid(vardata2->statsTuple))
+ {
+ stats = (Form_pg_statistic) GETSTRUCT(vardata2->statsTuple);
+ nullfrac2 = stats->stanullfrac;
+
+ mcv2_exists = get_attstatsslot(vardata2->statsTuple,
+ vardata2->atttype, vardata2->atttypmod,
+ STATISTIC_KIND_MCV, InvalidOid,
+ NULL,
+ &mcv2_values, &mcv2_nvalues,
+ &mcv2_numbers, &mcv2_nnumbers);
+ hist2_exists = get_attstatsslot(vardata2->statsTuple,
+ vardata2->atttype, vardata2->atttypmod,
+ STATISTIC_KIND_HISTOGRAM, InvalidOid,
+ NULL,
+ &hist2_values, &hist2_nvalues,
+ NULL, NULL);
+ /* Arbitrarily limit number of MCVs considered */
+ mcv2_length = Min(mcv2_nvalues, MAX_CONSIDERED_ELEMS);
+ if (mcv2_exists)
+ sumcommon2 = mcv_population(mcv2_numbers, mcv2_length);
+ }
+
+ 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_numbers[i] *
+ inet_semi_join_sel(mcv1_values[i],
+ mcv2_exists, mcv2_values, mcv2_length,
+ hist2_exists, hist2_values, hist2_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_nvalues > 2 && (mcv2_exists || hist2_exists))
+ {
+ double hist_selec_sum = 0.0;
+ int k,
+ n;
+
+ k = (hist1_nvalues - 3) / MAX_CONSIDERED_ELEMS + 1;
+
+ n = 0;
+ for (i = 1; i < hist1_nvalues - 1; i += k)
+ {
+ hist_selec_sum +=
+ inet_semi_join_sel(hist1_values[i],
+ mcv2_exists, mcv2_values, mcv2_length,
+ hist2_exists, hist2_values, hist2_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. */
+ if (mcv1_exists)
+ free_attstatsslot(vardata1->atttype, mcv1_values, mcv1_nvalues,
+ mcv1_numbers, mcv1_nnumbers);
+ if (mcv2_exists)
+ free_attstatsslot(vardata2->atttype, mcv2_values, mcv2_nvalues,
+ mcv2_numbers, mcv2_nnumbers);
+ if (hist1_exists)
+ free_attstatsslot(vardata1->atttype, hist1_values, hist1_nvalues,
+ NULL, 0);
+ if (hist2_exists)
+ free_attstatsslot(vardata2->atttype, hist2_values, hist2_nvalues,
+ NULL, 0);
+
+ 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;
}
projects / postgresql / commitdiff