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1 : : /*-------------------------------------------------------------------------
2 : : *
3 : : * network_selfuncs.c
4 : : * Functions for selectivity estimation of inet/cidr operators
5 : : *
6 : : * This module provides estimators for the subnet inclusion and overlap
7 : : * operators. Estimates are based on null fraction, most common values,
8 : : * and histogram of inet/cidr columns.
9 : : *
10 : : * Portions Copyright (c) 1996-2026, PostgreSQL Global Development Group
11 : : * Portions Copyright (c) 1994, Regents of the University of California
12 : : *
13 : : *
14 : : * IDENTIFICATION
15 : : * src/backend/utils/adt/network_selfuncs.c
16 : : *
17 : : *-------------------------------------------------------------------------
18 : : */
19 : : #include "postgres.h"
20 : :
21 : : #include <math.h>
22 : :
23 : : #include "access/htup_details.h"
24 : : #include "catalog/pg_operator.h"
25 : : #include "catalog/pg_statistic.h"
26 : : #include "utils/fmgrprotos.h"
27 : : #include "utils/inet.h"
28 : : #include "utils/lsyscache.h"
29 : : #include "utils/selfuncs.h"
30 : :
31 : :
32 : : /* Default selectivity for the inet overlap operator */
33 : : #define DEFAULT_OVERLAP_SEL 0.01
34 : :
35 : : /* Default selectivity for the various inclusion operators */
36 : : #define DEFAULT_INCLUSION_SEL 0.005
37 : :
38 : : /* Default selectivity for specified operator */
39 : : #define DEFAULT_SEL(operator) \
40 : : ((operator) == OID_INET_OVERLAP_OP ? \
41 : : DEFAULT_OVERLAP_SEL : DEFAULT_INCLUSION_SEL)
42 : :
43 : : /* Maximum number of items to consider in join selectivity calculations */
44 : : #define MAX_CONSIDERED_ELEMS 1024
45 : :
46 : : static Selectivity networkjoinsel_inner(Oid operator, int opr_codenum,
47 : : VariableStatData *vardata1, VariableStatData *vardata2);
48 : : static Selectivity networkjoinsel_semi(Oid operator, int opr_codenum,
49 : : VariableStatData *vardata1, VariableStatData *vardata2);
50 : : static Selectivity mcv_population(float4 *mcv_numbers, int mcv_nvalues);
51 : : static Selectivity inet_hist_value_sel(const Datum *values, int nvalues,
52 : : Datum constvalue, int opr_codenum);
53 : : static Selectivity inet_mcv_join_sel(Datum *mcv1_values,
54 : : float4 *mcv1_numbers, int mcv1_nvalues, Datum *mcv2_values,
55 : : float4 *mcv2_numbers, int mcv2_nvalues, Oid operator);
56 : : static Selectivity inet_mcv_hist_sel(const Datum *mcv_values, float4 *mcv_numbers,
57 : : int mcv_nvalues, const Datum *hist_values, int hist_nvalues,
58 : : int opr_codenum);
59 : : static Selectivity inet_hist_inclusion_join_sel(const Datum *hist1_values,
60 : : int hist1_nvalues,
61 : : const Datum *hist2_values, int hist2_nvalues,
62 : : int opr_codenum);
63 : : static Selectivity inet_semi_join_sel(Datum lhs_value,
64 : : bool mcv_exists, Datum *mcv_values, int mcv_nvalues,
65 : : bool hist_exists, Datum *hist_values, int hist_nvalues,
66 : : double hist_weight,
67 : : FmgrInfo *proc, int opr_codenum);
68 : : static int inet_opr_codenum(Oid operator);
69 : : static int inet_inclusion_cmp(inet *left, inet *right, int opr_codenum);
70 : : static int inet_masklen_inclusion_cmp(inet *left, inet *right,
71 : : int opr_codenum);
72 : : static int inet_hist_match_divider(inet *boundary, inet *query,
73 : : int opr_codenum);
74 : :
75 : : /*
76 : : * Selectivity estimation for the subnet inclusion/overlap operators
77 : : */
78 : : Datum
79 : 750 : networksel(PG_FUNCTION_ARGS)
80 : : {
81 : 750 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
82 : 750 : Oid operator = PG_GETARG_OID(1);
83 : 750 : List *args = (List *) PG_GETARG_POINTER(2);
84 : 750 : int varRelid = PG_GETARG_INT32(3);
85 : : int opr_codenum;
86 : : VariableStatData vardata;
87 : : Node *other;
88 : : bool varonleft;
89 : : Selectivity selec,
90 : : mcv_selec,
91 : : non_mcv_selec;
92 : : Datum constvalue;
93 : : Form_pg_statistic stats;
94 : : AttStatsSlot hslot;
95 : : double sumcommon,
96 : : nullfrac;
97 : : FmgrInfo proc;
98 : :
99 : : /*
100 : : * Before all else, verify that the operator is one of the ones supported
101 : : * by this function, which in turn proves that the input datatypes are
102 : : * what we expect. Otherwise, attaching this selectivity function to some
103 : : * unexpected operator could cause trouble.
104 : : */
105 : 750 : opr_codenum = inet_opr_codenum(operator);
106 : :
107 : : /*
108 : : * If expression is not (variable op something) or (something op
109 : : * variable), then punt and return a default estimate.
110 : : */
111 [ - + ]: 750 : if (!get_restriction_variable(root, args, varRelid,
112 : : &vardata, &other, &varonleft))
113 [ # # ]: 0 : PG_RETURN_FLOAT8(DEFAULT_SEL(operator));
114 : :
115 : : /*
116 : : * Can't do anything useful if the something is not a constant, either.
117 : : */
118 [ - + ]: 750 : if (!IsA(other, Const))
119 : : {
120 [ # # ]: 0 : ReleaseVariableStats(vardata);
121 [ # # ]: 0 : PG_RETURN_FLOAT8(DEFAULT_SEL(operator));
122 : : }
123 : :
124 : : /* All of the operators handled here are strict. */
125 [ - + ]: 750 : if (((Const *) other)->constisnull)
126 : : {
127 [ # # ]: 0 : ReleaseVariableStats(vardata);
128 : 0 : PG_RETURN_FLOAT8(0.0);
129 : : }
130 : 750 : constvalue = ((Const *) other)->constvalue;
131 : :
132 : : /* Otherwise, we need stats in order to produce a non-default estimate. */
133 [ + - ]: 750 : if (!HeapTupleIsValid(vardata.statsTuple))
134 : : {
135 [ - + ]: 750 : ReleaseVariableStats(vardata);
136 [ + + ]: 750 : PG_RETURN_FLOAT8(DEFAULT_SEL(operator));
137 : : }
138 : :
139 : 0 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
140 : 0 : nullfrac = stats->stanullfrac;
141 : :
142 : : /*
143 : : * If we have most-common-values info, add up the fractions of the MCV
144 : : * entries that satisfy MCV OP CONST. These fractions contribute directly
145 : : * to the result selectivity. Also add up the total fraction represented
146 : : * by MCV entries.
147 : : */
148 : 0 : fmgr_info(get_opcode(operator), &proc);
149 : 0 : mcv_selec = mcv_selectivity(&vardata, &proc, InvalidOid,
150 : : constvalue, varonleft,
151 : : &sumcommon);
152 : :
153 : : /*
154 : : * If we have a histogram, use it to estimate the proportion of the
155 : : * non-MCV population that satisfies the clause. If we don't, apply the
156 : : * default selectivity to that population.
157 : : */
158 [ # # ]: 0 : if (get_attstatsslot(&hslot, vardata.statsTuple,
159 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
160 : : ATTSTATSSLOT_VALUES))
161 : : {
162 : : int h_codenum;
163 : :
164 : : /* Commute if needed, so we can consider histogram to be on the left */
165 [ # # ]: 0 : h_codenum = varonleft ? opr_codenum : -opr_codenum;
166 : 0 : non_mcv_selec = inet_hist_value_sel(hslot.values, hslot.nvalues,
167 : : constvalue, h_codenum);
168 : :
169 : 0 : free_attstatsslot(&hslot);
170 : : }
171 : : else
172 [ # # ]: 0 : non_mcv_selec = DEFAULT_SEL(operator);
173 : :
174 : : /* Combine selectivities for MCV and non-MCV populations */
175 : 0 : selec = mcv_selec + (1.0 - nullfrac - sumcommon) * non_mcv_selec;
176 : :
177 : : /* Result should be in range, but make sure... */
178 [ # # # # ]: 0 : CLAMP_PROBABILITY(selec);
179 : :
180 [ # # ]: 0 : ReleaseVariableStats(vardata);
181 : :
182 : 0 : PG_RETURN_FLOAT8(selec);
183 : : }
184 : :
185 : : /*
186 : : * Join selectivity estimation for the subnet inclusion/overlap operators
187 : : *
188 : : * This function has the same structure as eqjoinsel() in selfuncs.c.
189 : : *
190 : : * Throughout networkjoinsel and its subroutines, we have a performance issue
191 : : * in that the amount of work to be done is O(N^2) in the length of the MCV
192 : : * and histogram arrays. To keep the runtime from getting out of hand when
193 : : * large statistics targets have been set, we arbitrarily limit the number of
194 : : * values considered to 1024 (MAX_CONSIDERED_ELEMS). For the MCV arrays, this
195 : : * is easy: just consider at most the first N elements. (Since the MCVs are
196 : : * sorted by decreasing frequency, this correctly gets us the first N MCVs.)
197 : : * For the histogram arrays, we decimate; that is consider only every k'th
198 : : * element, where k is chosen so that no more than MAX_CONSIDERED_ELEMS
199 : : * elements are considered. This should still give us a good random sample of
200 : : * the non-MCV population. Decimation is done on-the-fly in the loops that
201 : : * iterate over the histogram arrays.
202 : : */
203 : : Datum
204 : 0 : networkjoinsel(PG_FUNCTION_ARGS)
205 : : {
206 : 0 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
207 : 0 : Oid operator = PG_GETARG_OID(1);
208 : 0 : List *args = (List *) PG_GETARG_POINTER(2);
209 : : #ifdef NOT_USED
210 : : JoinType jointype = (JoinType) PG_GETARG_INT16(3);
211 : : #endif
212 : 0 : SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
213 : : double selec;
214 : : int opr_codenum;
215 : : VariableStatData vardata1;
216 : : VariableStatData vardata2;
217 : : bool join_is_reversed;
218 : :
219 : : /*
220 : : * Before all else, verify that the operator is one of the ones supported
221 : : * by this function, which in turn proves that the input datatypes are
222 : : * what we expect. Otherwise, attaching this selectivity function to some
223 : : * unexpected operator could cause trouble.
224 : : */
225 : 0 : opr_codenum = inet_opr_codenum(operator);
226 : :
227 : 0 : get_join_variables(root, args, sjinfo,
228 : : &vardata1, &vardata2, &join_is_reversed);
229 : :
230 [ # # # ]: 0 : switch (sjinfo->jointype)
231 : : {
232 : 0 : case JOIN_INNER:
233 : : case JOIN_LEFT:
234 : : case JOIN_FULL:
235 : :
236 : : /*
237 : : * Selectivity for left/full join is not exactly the same as inner
238 : : * join, but we neglect the difference, as eqjoinsel does.
239 : : */
240 : 0 : selec = networkjoinsel_inner(operator, opr_codenum,
241 : : &vardata1, &vardata2);
242 : 0 : break;
243 : 0 : case JOIN_SEMI:
244 : : case JOIN_ANTI:
245 : : /* Here, it's important that we pass the outer var on the left. */
246 [ # # ]: 0 : if (!join_is_reversed)
247 : 0 : selec = networkjoinsel_semi(operator, opr_codenum,
248 : : &vardata1, &vardata2);
249 : : else
250 : 0 : selec = networkjoinsel_semi(get_commutator(operator),
251 : : -opr_codenum,
252 : : &vardata2, &vardata1);
253 : 0 : break;
254 : 0 : default:
255 : : /* other values not expected here */
256 [ # # ]: 0 : elog(ERROR, "unrecognized join type: %d",
257 : : (int) sjinfo->jointype);
258 : : selec = 0; /* keep compiler quiet */
259 : : break;
260 : : }
261 : :
262 [ # # ]: 0 : ReleaseVariableStats(vardata1);
263 [ # # ]: 0 : ReleaseVariableStats(vardata2);
264 : :
265 [ # # # # ]: 0 : CLAMP_PROBABILITY(selec);
266 : :
267 : 0 : PG_RETURN_FLOAT8((float8) selec);
268 : : }
269 : :
270 : : /*
271 : : * Inner join selectivity estimation for subnet inclusion/overlap operators
272 : : *
273 : : * Calculates MCV vs MCV, MCV vs histogram and histogram vs histogram
274 : : * selectivity for join using the subnet inclusion operators. Unlike the
275 : : * join selectivity function for the equality operator, eqjoinsel_inner(),
276 : : * one to one matching of the values is not enough. Network inclusion
277 : : * operators are likely to match many to many, so we must check all pairs.
278 : : * (Note: it might be possible to exploit understanding of the histogram's
279 : : * btree ordering to reduce the work needed, but we don't currently try.)
280 : : * Also, MCV vs histogram selectivity is not neglected as in eqjoinsel_inner().
281 : : */
282 : : static Selectivity
283 : 0 : networkjoinsel_inner(Oid operator, int opr_codenum,
284 : : VariableStatData *vardata1, VariableStatData *vardata2)
285 : : {
286 : : Form_pg_statistic stats;
287 : 0 : double nullfrac1 = 0.0,
288 : 0 : nullfrac2 = 0.0;
289 : 0 : Selectivity selec = 0.0,
290 : 0 : sumcommon1 = 0.0,
291 : 0 : sumcommon2 = 0.0;
292 : 0 : bool mcv1_exists = false,
293 : 0 : mcv2_exists = false,
294 : 0 : hist1_exists = false,
295 : 0 : hist2_exists = false;
296 : 0 : int mcv1_length = 0,
297 : 0 : mcv2_length = 0;
298 : : AttStatsSlot mcv1_slot;
299 : : AttStatsSlot mcv2_slot;
300 : : AttStatsSlot hist1_slot;
301 : : AttStatsSlot hist2_slot;
302 : :
303 [ # # ]: 0 : if (HeapTupleIsValid(vardata1->statsTuple))
304 : : {
305 : 0 : stats = (Form_pg_statistic) GETSTRUCT(vardata1->statsTuple);
306 : 0 : nullfrac1 = stats->stanullfrac;
307 : :
308 : 0 : mcv1_exists = get_attstatsslot(&mcv1_slot, vardata1->statsTuple,
309 : : STATISTIC_KIND_MCV, InvalidOid,
310 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
311 : 0 : hist1_exists = get_attstatsslot(&hist1_slot, vardata1->statsTuple,
312 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
313 : : ATTSTATSSLOT_VALUES);
314 : : /* Arbitrarily limit number of MCVs considered */
315 : 0 : mcv1_length = Min(mcv1_slot.nvalues, MAX_CONSIDERED_ELEMS);
316 [ # # ]: 0 : if (mcv1_exists)
317 : 0 : sumcommon1 = mcv_population(mcv1_slot.numbers, mcv1_length);
318 : : }
319 : : else
320 : : {
321 : 0 : memset(&mcv1_slot, 0, sizeof(mcv1_slot));
322 : 0 : memset(&hist1_slot, 0, sizeof(hist1_slot));
323 : : }
324 : :
325 [ # # ]: 0 : if (HeapTupleIsValid(vardata2->statsTuple))
326 : : {
327 : 0 : stats = (Form_pg_statistic) GETSTRUCT(vardata2->statsTuple);
328 : 0 : nullfrac2 = stats->stanullfrac;
329 : :
330 : 0 : mcv2_exists = get_attstatsslot(&mcv2_slot, vardata2->statsTuple,
331 : : STATISTIC_KIND_MCV, InvalidOid,
332 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
333 : 0 : hist2_exists = get_attstatsslot(&hist2_slot, vardata2->statsTuple,
334 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
335 : : ATTSTATSSLOT_VALUES);
336 : : /* Arbitrarily limit number of MCVs considered */
337 : 0 : mcv2_length = Min(mcv2_slot.nvalues, MAX_CONSIDERED_ELEMS);
338 [ # # ]: 0 : if (mcv2_exists)
339 : 0 : sumcommon2 = mcv_population(mcv2_slot.numbers, mcv2_length);
340 : : }
341 : : else
342 : : {
343 : 0 : memset(&mcv2_slot, 0, sizeof(mcv2_slot));
344 : 0 : memset(&hist2_slot, 0, sizeof(hist2_slot));
345 : : }
346 : :
347 : : /*
348 : : * Calculate selectivity for MCV vs MCV matches.
349 : : */
350 [ # # # # ]: 0 : if (mcv1_exists && mcv2_exists)
351 : 0 : selec += inet_mcv_join_sel(mcv1_slot.values, mcv1_slot.numbers,
352 : : mcv1_length,
353 : : mcv2_slot.values, mcv2_slot.numbers,
354 : : mcv2_length,
355 : : operator);
356 : :
357 : : /*
358 : : * Add in selectivities for MCV vs histogram matches, scaling according to
359 : : * the fractions of the populations represented by the histograms. Note
360 : : * that the second case needs to commute the operator.
361 : : */
362 [ # # # # ]: 0 : if (mcv1_exists && hist2_exists)
363 : 0 : selec += (1.0 - nullfrac2 - sumcommon2) *
364 : 0 : inet_mcv_hist_sel(mcv1_slot.values, mcv1_slot.numbers, mcv1_length,
365 : 0 : hist2_slot.values, hist2_slot.nvalues,
366 : : opr_codenum);
367 [ # # # # ]: 0 : if (mcv2_exists && hist1_exists)
368 : 0 : selec += (1.0 - nullfrac1 - sumcommon1) *
369 : 0 : inet_mcv_hist_sel(mcv2_slot.values, mcv2_slot.numbers, mcv2_length,
370 : 0 : hist1_slot.values, hist1_slot.nvalues,
371 : : -opr_codenum);
372 : :
373 : : /*
374 : : * Add in selectivity for histogram vs histogram matches, again scaling
375 : : * appropriately.
376 : : */
377 [ # # # # ]: 0 : if (hist1_exists && hist2_exists)
378 : 0 : selec += (1.0 - nullfrac1 - sumcommon1) *
379 : 0 : (1.0 - nullfrac2 - sumcommon2) *
380 : 0 : inet_hist_inclusion_join_sel(hist1_slot.values, hist1_slot.nvalues,
381 : 0 : hist2_slot.values, hist2_slot.nvalues,
382 : : opr_codenum);
383 : :
384 : : /*
385 : : * If useful statistics are not available then use the default estimate.
386 : : * We can apply null fractions if known, though.
387 : : */
388 [ # # # # : 0 : if ((!mcv1_exists && !hist1_exists) || (!mcv2_exists && !hist2_exists))
# # # # ]
389 [ # # ]: 0 : selec = (1.0 - nullfrac1) * (1.0 - nullfrac2) * DEFAULT_SEL(operator);
390 : :
391 : : /* Release stats. */
392 : 0 : free_attstatsslot(&mcv1_slot);
393 : 0 : free_attstatsslot(&mcv2_slot);
394 : 0 : free_attstatsslot(&hist1_slot);
395 : 0 : free_attstatsslot(&hist2_slot);
396 : :
397 : 0 : return selec;
398 : : }
399 : :
400 : : /*
401 : : * Semi join selectivity estimation for subnet inclusion/overlap operators
402 : : *
403 : : * Calculates MCV vs MCV, MCV vs histogram, histogram vs MCV, and histogram vs
404 : : * histogram selectivity for semi/anti join cases.
405 : : */
406 : : static Selectivity
407 : 0 : networkjoinsel_semi(Oid operator, int opr_codenum,
408 : : VariableStatData *vardata1, VariableStatData *vardata2)
409 : : {
410 : : Form_pg_statistic stats;
411 : 0 : Selectivity selec = 0.0,
412 : 0 : sumcommon1 = 0.0,
413 : 0 : sumcommon2 = 0.0;
414 : 0 : double nullfrac1 = 0.0,
415 : 0 : nullfrac2 = 0.0,
416 : 0 : hist2_weight = 0.0;
417 : 0 : bool mcv1_exists = false,
418 : 0 : mcv2_exists = false,
419 : 0 : hist1_exists = false,
420 : 0 : hist2_exists = false;
421 : : FmgrInfo proc;
422 : : int i,
423 : 0 : mcv1_length = 0,
424 : 0 : mcv2_length = 0;
425 : : AttStatsSlot mcv1_slot;
426 : : AttStatsSlot mcv2_slot;
427 : : AttStatsSlot hist1_slot;
428 : : AttStatsSlot hist2_slot;
429 : :
430 [ # # ]: 0 : if (HeapTupleIsValid(vardata1->statsTuple))
431 : : {
432 : 0 : stats = (Form_pg_statistic) GETSTRUCT(vardata1->statsTuple);
433 : 0 : nullfrac1 = stats->stanullfrac;
434 : :
435 : 0 : mcv1_exists = get_attstatsslot(&mcv1_slot, vardata1->statsTuple,
436 : : STATISTIC_KIND_MCV, InvalidOid,
437 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
438 : 0 : hist1_exists = get_attstatsslot(&hist1_slot, vardata1->statsTuple,
439 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
440 : : ATTSTATSSLOT_VALUES);
441 : : /* Arbitrarily limit number of MCVs considered */
442 : 0 : mcv1_length = Min(mcv1_slot.nvalues, MAX_CONSIDERED_ELEMS);
443 [ # # ]: 0 : if (mcv1_exists)
444 : 0 : sumcommon1 = mcv_population(mcv1_slot.numbers, mcv1_length);
445 : : }
446 : : else
447 : : {
448 : 0 : memset(&mcv1_slot, 0, sizeof(mcv1_slot));
449 : 0 : memset(&hist1_slot, 0, sizeof(hist1_slot));
450 : : }
451 : :
452 [ # # ]: 0 : if (HeapTupleIsValid(vardata2->statsTuple))
453 : : {
454 : 0 : stats = (Form_pg_statistic) GETSTRUCT(vardata2->statsTuple);
455 : 0 : nullfrac2 = stats->stanullfrac;
456 : :
457 : 0 : mcv2_exists = get_attstatsslot(&mcv2_slot, vardata2->statsTuple,
458 : : STATISTIC_KIND_MCV, InvalidOid,
459 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
460 : 0 : hist2_exists = get_attstatsslot(&hist2_slot, vardata2->statsTuple,
461 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
462 : : ATTSTATSSLOT_VALUES);
463 : : /* Arbitrarily limit number of MCVs considered */
464 : 0 : mcv2_length = Min(mcv2_slot.nvalues, MAX_CONSIDERED_ELEMS);
465 [ # # ]: 0 : if (mcv2_exists)
466 : 0 : sumcommon2 = mcv_population(mcv2_slot.numbers, mcv2_length);
467 : : }
468 : : else
469 : : {
470 : 0 : memset(&mcv2_slot, 0, sizeof(mcv2_slot));
471 : 0 : memset(&hist2_slot, 0, sizeof(hist2_slot));
472 : : }
473 : :
474 : 0 : fmgr_info(get_opcode(operator), &proc);
475 : :
476 : : /* Estimate number of input rows represented by RHS histogram. */
477 [ # # # # ]: 0 : if (hist2_exists && vardata2->rel)
478 : 0 : hist2_weight = (1.0 - nullfrac2 - sumcommon2) * vardata2->rel->rows;
479 : :
480 : : /*
481 : : * Consider each element of the LHS MCV list, matching it to whatever RHS
482 : : * stats we have. Scale according to the known frequency of the MCV.
483 : : */
484 [ # # # # : 0 : if (mcv1_exists && (mcv2_exists || hist2_exists))
# # ]
485 : : {
486 [ # # ]: 0 : for (i = 0; i < mcv1_length; i++)
487 : : {
488 : 0 : selec += mcv1_slot.numbers[i] *
489 : 0 : inet_semi_join_sel(mcv1_slot.values[i],
490 : : mcv2_exists, mcv2_slot.values, mcv2_length,
491 : : hist2_exists,
492 : : hist2_slot.values, hist2_slot.nvalues,
493 : : hist2_weight,
494 : : &proc, opr_codenum);
495 : : }
496 : : }
497 : :
498 : : /*
499 : : * Consider each element of the LHS histogram, except for the first and
500 : : * last elements, which we exclude on the grounds that they're outliers
501 : : * and thus not very representative. Scale on the assumption that each
502 : : * such histogram element represents an equal share of the LHS histogram
503 : : * population (which is a bit bogus, because the members of its bucket may
504 : : * not all act the same with respect to the join clause, but it's hard to
505 : : * do better).
506 : : *
507 : : * If there are too many histogram elements, decimate to limit runtime.
508 : : */
509 [ # # # # : 0 : if (hist1_exists && hist1_slot.nvalues > 2 && (mcv2_exists || hist2_exists))
# # # # ]
510 : : {
511 : 0 : double hist_selec_sum = 0.0;
512 : : int k,
513 : : n;
514 : :
515 : 0 : k = (hist1_slot.nvalues - 3) / MAX_CONSIDERED_ELEMS + 1;
516 : :
517 : 0 : n = 0;
518 [ # # ]: 0 : for (i = 1; i < hist1_slot.nvalues - 1; i += k)
519 : : {
520 : 0 : hist_selec_sum +=
521 : 0 : inet_semi_join_sel(hist1_slot.values[i],
522 : : mcv2_exists, mcv2_slot.values, mcv2_length,
523 : : hist2_exists,
524 : : hist2_slot.values, hist2_slot.nvalues,
525 : : hist2_weight,
526 : : &proc, opr_codenum);
527 : 0 : n++;
528 : : }
529 : :
530 : 0 : selec += (1.0 - nullfrac1 - sumcommon1) * hist_selec_sum / n;
531 : : }
532 : :
533 : : /*
534 : : * If useful statistics are not available then use the default estimate.
535 : : * We can apply null fractions if known, though.
536 : : */
537 [ # # # # : 0 : if ((!mcv1_exists && !hist1_exists) || (!mcv2_exists && !hist2_exists))
# # # # ]
538 [ # # ]: 0 : selec = (1.0 - nullfrac1) * (1.0 - nullfrac2) * DEFAULT_SEL(operator);
539 : :
540 : : /* Release stats. */
541 : 0 : free_attstatsslot(&mcv1_slot);
542 : 0 : free_attstatsslot(&mcv2_slot);
543 : 0 : free_attstatsslot(&hist1_slot);
544 : 0 : free_attstatsslot(&hist2_slot);
545 : :
546 : 0 : return selec;
547 : : }
548 : :
549 : : /*
550 : : * Compute the fraction of a relation's population that is represented
551 : : * by the MCV list.
552 : : */
553 : : static Selectivity
554 : 0 : mcv_population(float4 *mcv_numbers, int mcv_nvalues)
555 : : {
556 : 0 : Selectivity sumcommon = 0.0;
557 : : int i;
558 : :
559 [ # # ]: 0 : for (i = 0; i < mcv_nvalues; i++)
560 : : {
561 : 0 : sumcommon += mcv_numbers[i];
562 : : }
563 : :
564 : 0 : return sumcommon;
565 : : }
566 : :
567 : : /*
568 : : * Inet histogram vs single value selectivity estimation
569 : : *
570 : : * Estimate the fraction of the histogram population that satisfies
571 : : * "value OPR CONST". (The result needs to be scaled to reflect the
572 : : * proportion of the total population represented by the histogram.)
573 : : *
574 : : * The histogram is originally for the inet btree comparison operators.
575 : : * Only the common bits of the network part and the length of the network part
576 : : * (masklen) are interesting for the subnet inclusion operators. Fortunately,
577 : : * btree comparison treats the network part as the major sort key. Even so,
578 : : * the length of the network part would not really be significant in the
579 : : * histogram. This would lead to big mistakes for data sets with uneven
580 : : * masklen distribution. To reduce this problem, comparisons with the left
581 : : * and the right sides of the buckets are used together.
582 : : *
583 : : * Histogram bucket matches are calculated in two forms. If the constant
584 : : * matches both bucket endpoints the bucket is considered as fully matched.
585 : : * The second form is to match the bucket partially; we recognize this when
586 : : * the constant matches just one endpoint, or the two endpoints fall on
587 : : * opposite sides of the constant. (Note that when the constant matches an
588 : : * interior histogram element, it gets credit for partial matches to the
589 : : * buckets on both sides, while a match to a histogram endpoint gets credit
590 : : * for only one partial match. This is desirable.)
591 : : *
592 : : * The divider in the partial bucket match is imagined as the distance
593 : : * between the decisive bits and the common bits of the addresses. It will
594 : : * be used as a power of two as it is the natural scale for the IP network
595 : : * inclusion. This partial bucket match divider calculation is an empirical
596 : : * formula and subject to change with more experiment.
597 : : *
598 : : * For a partial match, we try to calculate dividers for both of the
599 : : * boundaries. If the address family of a boundary value does not match the
600 : : * constant or comparison of the length of the network parts is not correct
601 : : * for the operator, the divider for that boundary will not be taken into
602 : : * account. If both of the dividers are valid, the greater one will be used
603 : : * to minimize the mistake in buckets that have disparate masklens. This
604 : : * calculation is unfair when dividers can be calculated for both of the
605 : : * boundaries but they are far from each other; but it is not a common
606 : : * situation as the boundaries are expected to share most of their significant
607 : : * bits of their masklens. The mistake would be greater, if we would use the
608 : : * minimum instead of the maximum, and we don't know a sensible way to combine
609 : : * them.
610 : : *
611 : : * For partial match in buckets that have different address families on the
612 : : * left and right sides, only the boundary with the same address family is
613 : : * taken into consideration. This can cause more mistakes for these buckets
614 : : * if the masklens of their boundaries are also disparate. But this can only
615 : : * happen in one bucket, since only two address families exist. It seems a
616 : : * better option than not considering these buckets at all.
617 : : */
618 : : static Selectivity
619 : 0 : inet_hist_value_sel(const Datum *values, int nvalues, Datum constvalue,
620 : : int opr_codenum)
621 : : {
622 : 0 : Selectivity match = 0.0;
623 : : inet *query,
624 : : *left,
625 : : *right;
626 : : int i,
627 : : k,
628 : : n;
629 : : int left_order,
630 : : right_order,
631 : : left_divider,
632 : : right_divider;
633 : :
634 : : /* guard against zero-divide below */
635 [ # # ]: 0 : if (nvalues <= 1)
636 : 0 : return 0.0;
637 : :
638 : : /* if there are too many histogram elements, decimate to limit runtime */
639 : 0 : k = (nvalues - 2) / MAX_CONSIDERED_ELEMS + 1;
640 : :
641 : 0 : query = DatumGetInetPP(constvalue);
642 : :
643 : : /* "left" is the left boundary value of the current bucket ... */
644 : 0 : left = DatumGetInetPP(values[0]);
645 : 0 : left_order = inet_inclusion_cmp(left, query, opr_codenum);
646 : :
647 : 0 : n = 0;
648 [ # # ]: 0 : for (i = k; i < nvalues; i += k)
649 : : {
650 : : /* ... and "right" is the right boundary value */
651 : 0 : right = DatumGetInetPP(values[i]);
652 : 0 : right_order = inet_inclusion_cmp(right, query, opr_codenum);
653 : :
654 [ # # # # ]: 0 : if (left_order == 0 && right_order == 0)
655 : : {
656 : : /* The whole bucket matches, since both endpoints do. */
657 : 0 : match += 1.0;
658 : : }
659 [ # # # # : 0 : else if ((left_order <= 0 && right_order >= 0) ||
# # ]
660 [ # # ]: 0 : (left_order >= 0 && right_order <= 0))
661 : : {
662 : : /* Partial bucket match. */
663 : 0 : left_divider = inet_hist_match_divider(left, query, opr_codenum);
664 : 0 : right_divider = inet_hist_match_divider(right, query, opr_codenum);
665 : :
666 [ # # # # ]: 0 : if (left_divider >= 0 || right_divider >= 0)
667 : 0 : match += 1.0 / pow(2.0, Max(left_divider, right_divider));
668 : : }
669 : :
670 : : /* Shift the variables. */
671 : 0 : left = right;
672 : 0 : left_order = right_order;
673 : :
674 : : /* Count the number of buckets considered. */
675 : 0 : n++;
676 : : }
677 : :
678 : 0 : return match / n;
679 : : }
680 : :
681 : : /*
682 : : * Inet MCV vs MCV join selectivity estimation
683 : : *
684 : : * We simply add up the fractions of the populations that satisfy the clause.
685 : : * The result is exact and does not need to be scaled further.
686 : : */
687 : : static Selectivity
688 : 0 : inet_mcv_join_sel(Datum *mcv1_values, float4 *mcv1_numbers, int mcv1_nvalues,
689 : : Datum *mcv2_values, float4 *mcv2_numbers, int mcv2_nvalues,
690 : : Oid operator)
691 : : {
692 : 0 : Selectivity selec = 0.0;
693 : : FmgrInfo proc;
694 : : int i,
695 : : j;
696 : :
697 : 0 : fmgr_info(get_opcode(operator), &proc);
698 : :
699 [ # # ]: 0 : for (i = 0; i < mcv1_nvalues; i++)
700 : : {
701 [ # # ]: 0 : for (j = 0; j < mcv2_nvalues; j++)
702 [ # # ]: 0 : if (DatumGetBool(FunctionCall2(&proc,
703 : : mcv1_values[i],
704 : : mcv2_values[j])))
705 : 0 : selec += mcv1_numbers[i] * mcv2_numbers[j];
706 : : }
707 : 0 : return selec;
708 : : }
709 : :
710 : : /*
711 : : * Inet MCV vs histogram join selectivity estimation
712 : : *
713 : : * For each MCV on the lefthand side, estimate the fraction of the righthand's
714 : : * histogram population that satisfies the join clause, and add those up,
715 : : * scaling by the MCV's frequency. The result still needs to be scaled
716 : : * according to the fraction of the righthand's population represented by
717 : : * the histogram.
718 : : */
719 : : static Selectivity
720 : 0 : inet_mcv_hist_sel(const Datum *mcv_values, float4 *mcv_numbers, int mcv_nvalues,
721 : : const Datum *hist_values, int hist_nvalues,
722 : : int opr_codenum)
723 : : {
724 : 0 : Selectivity selec = 0.0;
725 : : int i;
726 : :
727 : : /*
728 : : * We'll call inet_hist_value_selec with the histogram on the left, so we
729 : : * must commute the operator.
730 : : */
731 : 0 : opr_codenum = -opr_codenum;
732 : :
733 [ # # ]: 0 : for (i = 0; i < mcv_nvalues; i++)
734 : : {
735 : 0 : selec += mcv_numbers[i] *
736 : 0 : inet_hist_value_sel(hist_values, hist_nvalues, mcv_values[i],
737 : : opr_codenum);
738 : : }
739 : 0 : return selec;
740 : : }
741 : :
742 : : /*
743 : : * Inet histogram vs histogram join selectivity estimation
744 : : *
745 : : * Here, we take all values listed in the second histogram (except for the
746 : : * first and last elements, which are excluded on the grounds of possibly
747 : : * not being very representative) and treat them as a uniform sample of
748 : : * the non-MCV population for that relation. For each one, we apply
749 : : * inet_hist_value_selec to see what fraction of the first histogram
750 : : * it matches.
751 : : *
752 : : * We could alternatively do this the other way around using the operator's
753 : : * commutator. XXX would it be worthwhile to do it both ways and take the
754 : : * average? That would at least avoid non-commutative estimation results.
755 : : */
756 : : static Selectivity
757 : 0 : inet_hist_inclusion_join_sel(const Datum *hist1_values, int hist1_nvalues,
758 : : const Datum *hist2_values, int hist2_nvalues,
759 : : int opr_codenum)
760 : : {
761 : 0 : double match = 0.0;
762 : : int i,
763 : : k,
764 : : n;
765 : :
766 [ # # ]: 0 : if (hist2_nvalues <= 2)
767 : 0 : return 0.0; /* no interior histogram elements */
768 : :
769 : : /* if there are too many histogram elements, decimate to limit runtime */
770 : 0 : k = (hist2_nvalues - 3) / MAX_CONSIDERED_ELEMS + 1;
771 : :
772 : 0 : n = 0;
773 [ # # ]: 0 : for (i = 1; i < hist2_nvalues - 1; i += k)
774 : : {
775 : 0 : match += inet_hist_value_sel(hist1_values, hist1_nvalues,
776 : 0 : hist2_values[i], opr_codenum);
777 : 0 : n++;
778 : : }
779 : :
780 : 0 : return match / n;
781 : : }
782 : :
783 : : /*
784 : : * Inet semi join selectivity estimation for one value
785 : : *
786 : : * The function calculates the probability that there is at least one row
787 : : * in the RHS table that satisfies the "lhs_value op column" condition.
788 : : * It is used in semi join estimation to check a sample from the left hand
789 : : * side table.
790 : : *
791 : : * The MCV and histogram from the right hand side table should be provided as
792 : : * arguments with the lhs_value from the left hand side table for the join.
793 : : * hist_weight is the total number of rows represented by the histogram.
794 : : * For example, if the table has 1000 rows, and 10% of the rows are in the MCV
795 : : * list, and another 10% are NULLs, hist_weight would be 800.
796 : : *
797 : : * First, the lhs_value will be matched to the most common values. If it
798 : : * matches any of them, 1.0 will be returned, because then there is surely
799 : : * a match.
800 : : *
801 : : * Otherwise, the histogram will be used to estimate the number of rows in
802 : : * the second table that match the condition. If the estimate is greater
803 : : * than 1.0, 1.0 will be returned, because it means there is a greater chance
804 : : * that the lhs_value will match more than one row in the table. If it is
805 : : * between 0.0 and 1.0, it will be returned as the probability.
806 : : */
807 : : static Selectivity
808 : 0 : inet_semi_join_sel(Datum lhs_value,
809 : : bool mcv_exists, Datum *mcv_values, int mcv_nvalues,
810 : : bool hist_exists, Datum *hist_values, int hist_nvalues,
811 : : double hist_weight,
812 : : FmgrInfo *proc, int opr_codenum)
813 : : {
814 [ # # ]: 0 : if (mcv_exists)
815 : : {
816 : : int i;
817 : :
818 [ # # ]: 0 : for (i = 0; i < mcv_nvalues; i++)
819 : : {
820 [ # # ]: 0 : if (DatumGetBool(FunctionCall2(proc,
821 : : lhs_value,
822 : : mcv_values[i])))
823 : 0 : return 1.0;
824 : : }
825 : : }
826 : :
827 [ # # # # ]: 0 : if (hist_exists && hist_weight > 0)
828 : : {
829 : : Selectivity hist_selec;
830 : :
831 : : /* Commute operator, since we're passing lhs_value on the right */
832 : 0 : hist_selec = inet_hist_value_sel(hist_values, hist_nvalues,
833 : : lhs_value, -opr_codenum);
834 : :
835 [ # # ]: 0 : if (hist_selec > 0)
836 [ # # ]: 0 : return Min(1.0, hist_weight * hist_selec);
837 : : }
838 : :
839 : 0 : return 0.0;
840 : : }
841 : :
842 : : /*
843 : : * Assign useful code numbers for the subnet inclusion/overlap operators
844 : : *
845 : : * This will throw an error if the operator is not one of the ones we
846 : : * support in networksel() and networkjoinsel().
847 : : *
848 : : * Only inet_masklen_inclusion_cmp() and inet_hist_match_divider() depend
849 : : * on the exact codes assigned here; but many other places in this file
850 : : * know that they can negate a code to obtain the code for the commutator
851 : : * operator.
852 : : */
853 : : static int
854 : 750 : inet_opr_codenum(Oid operator)
855 : : {
856 [ + + + + : 750 : switch (operator)
+ - ]
857 : : {
858 : 100 : case OID_INET_SUP_OP:
859 : 100 : return -2;
860 : 180 : case OID_INET_SUPEQ_OP:
861 : 180 : return -1;
862 : 170 : case OID_INET_OVERLAP_OP:
863 : 170 : return 0;
864 : 180 : case OID_INET_SUBEQ_OP:
865 : 180 : return 1;
866 : 120 : case OID_INET_SUB_OP:
867 : 120 : return 2;
868 : 0 : default:
869 [ # # ]: 0 : elog(ERROR, "unrecognized operator %u for inet selectivity",
870 : : operator);
871 : : }
872 : : return 0; /* unreached, but keep compiler quiet */
873 : : }
874 : :
875 : : /*
876 : : * Comparison function for the subnet inclusion/overlap operators
877 : : *
878 : : * If the comparison is okay for the specified inclusion operator, the return
879 : : * value will be 0. Otherwise the return value will be less than or greater
880 : : * than 0 as appropriate for the operator.
881 : : *
882 : : * Comparison is compatible with the basic comparison function for the inet
883 : : * type. See network_cmp_internal() in network.c for the original. Basic
884 : : * comparison operators are implemented with the network_cmp_internal()
885 : : * function. It is possible to implement the subnet inclusion operators with
886 : : * this function.
887 : : *
888 : : * Comparison is first on the common bits of the network part, then on the
889 : : * length of the network part (masklen) as in the network_cmp_internal()
890 : : * function. Only the first part is in this function. The second part is
891 : : * separated to another function for reusability. The difference between the
892 : : * second part and the original network_cmp_internal() is that the inclusion
893 : : * operator is considered while comparing the lengths of the network parts.
894 : : * See the inet_masklen_inclusion_cmp() function below.
895 : : */
896 : : static int
897 : 0 : inet_inclusion_cmp(inet *left, inet *right, int opr_codenum)
898 : : {
899 [ # # ]: 0 : if (ip_family(left) == ip_family(right))
900 : : {
901 : : int order;
902 : :
903 : 0 : order = bitncmp(ip_addr(left), ip_addr(right),
904 [ # # ]: 0 : Min(ip_bits(left), ip_bits(right)));
905 [ # # ]: 0 : if (order != 0)
906 : 0 : return order;
907 : :
908 : 0 : return inet_masklen_inclusion_cmp(left, right, opr_codenum);
909 : : }
910 : :
911 : 0 : return ip_family(left) - ip_family(right);
912 : : }
913 : :
914 : : /*
915 : : * Masklen comparison function for the subnet inclusion/overlap operators
916 : : *
917 : : * Compares the lengths of the network parts of the inputs. If the comparison
918 : : * is okay for the specified inclusion operator, the return value will be 0.
919 : : * Otherwise the return value will be less than or greater than 0 as
920 : : * appropriate for the operator.
921 : : */
922 : : static int
923 : 0 : inet_masklen_inclusion_cmp(inet *left, inet *right, int opr_codenum)
924 : : {
925 : : int order;
926 : :
927 : 0 : order = (int) ip_bits(left) - (int) ip_bits(right);
928 : :
929 : : /*
930 : : * Return 0 if the operator would accept this combination of masklens.
931 : : * Note that opr_codenum zero (overlaps) will accept all cases.
932 : : */
933 [ # # # # : 0 : if ((order > 0 && opr_codenum >= 0) ||
# # ]
934 [ # # # # : 0 : (order == 0 && opr_codenum >= -1 && opr_codenum <= 1) ||
# # ]
935 [ # # ]: 0 : (order < 0 && opr_codenum <= 0))
936 : 0 : return 0;
937 : :
938 : : /*
939 : : * Otherwise, return a negative value for sup/supeq (notionally, the RHS
940 : : * needs to have a larger masklen than it has, which would make it sort
941 : : * later), or a positive value for sub/subeq (vice versa).
942 : : */
943 : 0 : return opr_codenum;
944 : : }
945 : :
946 : : /*
947 : : * Inet histogram partial match divider calculation
948 : : *
949 : : * First the families and the lengths of the network parts are compared using
950 : : * the subnet inclusion operator. If those are acceptable for the operator,
951 : : * the divider will be calculated using the masklens and the common bits of
952 : : * the addresses. -1 will be returned if it cannot be calculated.
953 : : *
954 : : * See commentary for inet_hist_value_sel() for some rationale for this.
955 : : */
956 : : static int
957 : 0 : inet_hist_match_divider(inet *boundary, inet *query, int opr_codenum)
958 : : {
959 [ # # # # ]: 0 : if (ip_family(boundary) == ip_family(query) &&
960 : 0 : inet_masklen_inclusion_cmp(boundary, query, opr_codenum) == 0)
961 : : {
962 : : int min_bits,
963 : : decisive_bits;
964 : :
965 [ # # ]: 0 : min_bits = Min(ip_bits(boundary), ip_bits(query));
966 : :
967 : : /*
968 : : * Set decisive_bits to the masklen of the one that should contain the
969 : : * other according to the operator.
970 : : */
971 [ # # ]: 0 : if (opr_codenum < 0)
972 : 0 : decisive_bits = ip_bits(boundary);
973 [ # # ]: 0 : else if (opr_codenum > 0)
974 : 0 : decisive_bits = ip_bits(query);
975 : : else
976 : 0 : decisive_bits = min_bits;
977 : :
978 : : /*
979 : : * Now return the number of non-common decisive bits. (This will be
980 : : * zero if the boundary and query in fact match, else positive.)
981 : : */
982 [ # # ]: 0 : if (min_bits > 0)
983 : 0 : return decisive_bits - bitncommon(ip_addr(boundary),
984 : 0 : ip_addr(query),
985 : : min_bits);
986 : 0 : return decisive_bits;
987 : : }
988 : :
989 : 0 : return -1;
990 : : }
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