Line data Source code
1 : /*-------------------------------------------------------------------------
2 : *
3 : * costsize.c
4 : * Routines to compute (and set) relation sizes and path costs
5 : *
6 : * Path costs are measured in arbitrary units established by these basic
7 : * parameters:
8 : *
9 : * seq_page_cost Cost of a sequential page fetch
10 : * random_page_cost Cost of a non-sequential page fetch
11 : * cpu_tuple_cost Cost of typical CPU time to process a tuple
12 : * cpu_index_tuple_cost Cost of typical CPU time to process an index tuple
13 : * cpu_operator_cost Cost of CPU time to execute an operator or function
14 : * parallel_tuple_cost Cost of CPU time to pass a tuple from worker to master backend
15 : * parallel_setup_cost Cost of setting up shared memory for parallelism
16 : *
17 : * We expect that the kernel will typically do some amount of read-ahead
18 : * optimization; this in conjunction with seek costs means that seq_page_cost
19 : * is normally considerably less than random_page_cost. (However, if the
20 : * database is fully cached in RAM, it is reasonable to set them equal.)
21 : *
22 : * We also use a rough estimate "effective_cache_size" of the number of
23 : * disk pages in Postgres + OS-level disk cache. (We can't simply use
24 : * NBuffers for this purpose because that would ignore the effects of
25 : * the kernel's disk cache.)
26 : *
27 : * Obviously, taking constants for these values is an oversimplification,
28 : * but it's tough enough to get any useful estimates even at this level of
29 : * detail. Note that all of these parameters are user-settable, in case
30 : * the default values are drastically off for a particular platform.
31 : *
32 : * seq_page_cost and random_page_cost can also be overridden for an individual
33 : * tablespace, in case some data is on a fast disk and other data is on a slow
34 : * disk. Per-tablespace overrides never apply to temporary work files such as
35 : * an external sort or a materialize node that overflows work_mem.
36 : *
37 : * We compute two separate costs for each path:
38 : * total_cost: total estimated cost to fetch all tuples
39 : * startup_cost: cost that is expended before first tuple is fetched
40 : * In some scenarios, such as when there is a LIMIT or we are implementing
41 : * an EXISTS(...) sub-select, it is not necessary to fetch all tuples of the
42 : * path's result. A caller can estimate the cost of fetching a partial
43 : * result by interpolating between startup_cost and total_cost. In detail:
44 : * actual_cost = startup_cost +
45 : * (total_cost - startup_cost) * tuples_to_fetch / path->rows;
46 : * Note that a base relation's rows count (and, by extension, plan_rows for
47 : * plan nodes below the LIMIT node) are set without regard to any LIMIT, so
48 : * that this equation works properly. (Note: while path->rows is never zero
49 : * for ordinary relations, it is zero for paths for provably-empty relations,
50 : * so beware of division-by-zero.) The LIMIT is applied as a top-level
51 : * plan node.
52 : *
53 : * For largely historical reasons, most of the routines in this module use
54 : * the passed result Path only to store their results (rows, startup_cost and
55 : * total_cost) into. All the input data they need is passed as separate
56 : * parameters, even though much of it could be extracted from the Path.
57 : * An exception is made for the cost_XXXjoin() routines, which expect all
58 : * the other fields of the passed XXXPath to be filled in, and similarly
59 : * cost_index() assumes the passed IndexPath is valid except for its output
60 : * values.
61 : *
62 : *
63 : * Portions Copyright (c) 1996-2019, PostgreSQL Global Development Group
64 : * Portions Copyright (c) 1994, Regents of the University of California
65 : *
66 : * IDENTIFICATION
67 : * src/backend/optimizer/path/costsize.c
68 : *
69 : *-------------------------------------------------------------------------
70 : */
71 :
72 : #include "postgres.h"
73 :
74 : #include <math.h>
75 :
76 : #include "access/amapi.h"
77 : #include "access/htup_details.h"
78 : #include "access/tsmapi.h"
79 : #include "executor/executor.h"
80 : #include "executor/nodeHash.h"
81 : #include "miscadmin.h"
82 : #include "nodes/makefuncs.h"
83 : #include "nodes/nodeFuncs.h"
84 : #include "optimizer/clauses.h"
85 : #include "optimizer/cost.h"
86 : #include "optimizer/optimizer.h"
87 : #include "optimizer/pathnode.h"
88 : #include "optimizer/paths.h"
89 : #include "optimizer/placeholder.h"
90 : #include "optimizer/plancat.h"
91 : #include "optimizer/planmain.h"
92 : #include "optimizer/restrictinfo.h"
93 : #include "parser/parsetree.h"
94 : #include "utils/lsyscache.h"
95 : #include "utils/selfuncs.h"
96 : #include "utils/spccache.h"
97 : #include "utils/tuplesort.h"
98 :
99 :
100 : #define LOG2(x) (log(x) / 0.693147180559945)
101 :
102 : /*
103 : * Append and MergeAppend nodes are less expensive than some other operations
104 : * which use cpu_tuple_cost; instead of adding a separate GUC, estimate the
105 : * per-tuple cost as cpu_tuple_cost multiplied by this value.
106 : */
107 : #define APPEND_CPU_COST_MULTIPLIER 0.5
108 :
109 :
110 : double seq_page_cost = DEFAULT_SEQ_PAGE_COST;
111 : double random_page_cost = DEFAULT_RANDOM_PAGE_COST;
112 : double cpu_tuple_cost = DEFAULT_CPU_TUPLE_COST;
113 : double cpu_index_tuple_cost = DEFAULT_CPU_INDEX_TUPLE_COST;
114 : double cpu_operator_cost = DEFAULT_CPU_OPERATOR_COST;
115 : double parallel_tuple_cost = DEFAULT_PARALLEL_TUPLE_COST;
116 : double parallel_setup_cost = DEFAULT_PARALLEL_SETUP_COST;
117 :
118 : int effective_cache_size = DEFAULT_EFFECTIVE_CACHE_SIZE;
119 :
120 : Cost disable_cost = 1.0e10;
121 :
122 : int max_parallel_workers_per_gather = 2;
123 :
124 : bool enable_seqscan = true;
125 : bool enable_indexscan = true;
126 : bool enable_indexonlyscan = true;
127 : bool enable_bitmapscan = true;
128 : bool enable_tidscan = true;
129 : bool enable_sort = true;
130 : bool enable_hashagg = true;
131 : bool enable_nestloop = true;
132 : bool enable_material = true;
133 : bool enable_mergejoin = true;
134 : bool enable_hashjoin = true;
135 : bool enable_gathermerge = true;
136 : bool enable_partitionwise_join = false;
137 : bool enable_partitionwise_aggregate = false;
138 : bool enable_parallel_append = true;
139 : bool enable_parallel_hash = true;
140 : bool enable_partition_pruning = true;
141 :
142 : typedef struct
143 : {
144 : PlannerInfo *root;
145 : QualCost total;
146 : } cost_qual_eval_context;
147 :
148 : static List *extract_nonindex_conditions(List *qual_clauses, List *indexclauses);
149 : static MergeScanSelCache *cached_scansel(PlannerInfo *root,
150 : RestrictInfo *rinfo,
151 : PathKey *pathkey);
152 : static void cost_rescan(PlannerInfo *root, Path *path,
153 : Cost *rescan_startup_cost, Cost *rescan_total_cost);
154 : static bool cost_qual_eval_walker(Node *node, cost_qual_eval_context *context);
155 : static void get_restriction_qual_cost(PlannerInfo *root, RelOptInfo *baserel,
156 : ParamPathInfo *param_info,
157 : QualCost *qpqual_cost);
158 : static bool has_indexed_join_quals(NestPath *joinpath);
159 : static double approx_tuple_count(PlannerInfo *root, JoinPath *path,
160 : List *quals);
161 : static double calc_joinrel_size_estimate(PlannerInfo *root,
162 : RelOptInfo *joinrel,
163 : RelOptInfo *outer_rel,
164 : RelOptInfo *inner_rel,
165 : double outer_rows,
166 : double inner_rows,
167 : SpecialJoinInfo *sjinfo,
168 : List *restrictlist);
169 : static Selectivity get_foreign_key_join_selectivity(PlannerInfo *root,
170 : Relids outer_relids,
171 : Relids inner_relids,
172 : SpecialJoinInfo *sjinfo,
173 : List **restrictlist);
174 : static Cost append_nonpartial_cost(List *subpaths, int numpaths,
175 : int parallel_workers);
176 : static void set_rel_width(PlannerInfo *root, RelOptInfo *rel);
177 : static double relation_byte_size(double tuples, int width);
178 : static double page_size(double tuples, int width);
179 : static double get_parallel_divisor(Path *path);
180 :
181 :
182 : /*
183 : * clamp_row_est
184 : * Force a row-count estimate to a sane value.
185 : */
186 : double
187 3151086 : clamp_row_est(double nrows)
188 : {
189 : /*
190 : * Force estimate to be at least one row, to make explain output look
191 : * better and to avoid possible divide-by-zero when interpolating costs.
192 : * Make it an integer, too.
193 : */
194 3151086 : if (nrows <= 1.0)
195 1268660 : nrows = 1.0;
196 : else
197 1882426 : nrows = rint(nrows);
198 :
199 3151086 : return nrows;
200 : }
201 :
202 :
203 : /*
204 : * cost_seqscan
205 : * Determines and returns the cost of scanning a relation sequentially.
206 : *
207 : * 'baserel' is the relation to be scanned
208 : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
209 : */
210 : void
211 228666 : cost_seqscan(Path *path, PlannerInfo *root,
212 : RelOptInfo *baserel, ParamPathInfo *param_info)
213 : {
214 228666 : Cost startup_cost = 0;
215 : Cost cpu_run_cost;
216 : Cost disk_run_cost;
217 : double spc_seq_page_cost;
218 : QualCost qpqual_cost;
219 : Cost cpu_per_tuple;
220 :
221 : /* Should only be applied to base relations */
222 : Assert(baserel->relid > 0);
223 : Assert(baserel->rtekind == RTE_RELATION);
224 :
225 : /* Mark the path with the correct row estimate */
226 228666 : if (param_info)
227 340 : path->rows = param_info->ppi_rows;
228 : else
229 228326 : path->rows = baserel->rows;
230 :
231 228666 : if (!enable_seqscan)
232 6726 : startup_cost += disable_cost;
233 :
234 : /* fetch estimated page cost for tablespace containing table */
235 228666 : get_tablespace_page_costs(baserel->reltablespace,
236 : NULL,
237 : &spc_seq_page_cost);
238 :
239 : /*
240 : * disk costs
241 : */
242 228666 : disk_run_cost = spc_seq_page_cost * baserel->pages;
243 :
244 : /* CPU costs */
245 228666 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
246 :
247 228666 : startup_cost += qpqual_cost.startup;
248 228666 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
249 228666 : cpu_run_cost = cpu_per_tuple * baserel->tuples;
250 : /* tlist eval costs are paid per output row, not per tuple scanned */
251 228666 : startup_cost += path->pathtarget->cost.startup;
252 228666 : cpu_run_cost += path->pathtarget->cost.per_tuple * path->rows;
253 :
254 : /* Adjust costing for parallelism, if used. */
255 228666 : if (path->parallel_workers > 0)
256 : {
257 12012 : double parallel_divisor = get_parallel_divisor(path);
258 :
259 : /* The CPU cost is divided among all the workers. */
260 12012 : cpu_run_cost /= parallel_divisor;
261 :
262 : /*
263 : * It may be possible to amortize some of the I/O cost, but probably
264 : * not very much, because most operating systems already do aggressive
265 : * prefetching. For now, we assume that the disk run cost can't be
266 : * amortized at all.
267 : */
268 :
269 : /*
270 : * In the case of a parallel plan, the row count needs to represent
271 : * the number of tuples processed per worker.
272 : */
273 12012 : path->rows = clamp_row_est(path->rows / parallel_divisor);
274 : }
275 :
276 228666 : path->startup_cost = startup_cost;
277 228666 : path->total_cost = startup_cost + cpu_run_cost + disk_run_cost;
278 228666 : }
279 :
280 : /*
281 : * cost_samplescan
282 : * Determines and returns the cost of scanning a relation using sampling.
283 : *
284 : * 'baserel' is the relation to be scanned
285 : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
286 : */
287 : void
288 180 : cost_samplescan(Path *path, PlannerInfo *root,
289 : RelOptInfo *baserel, ParamPathInfo *param_info)
290 : {
291 180 : Cost startup_cost = 0;
292 180 : Cost run_cost = 0;
293 : RangeTblEntry *rte;
294 : TableSampleClause *tsc;
295 : TsmRoutine *tsm;
296 : double spc_seq_page_cost,
297 : spc_random_page_cost,
298 : spc_page_cost;
299 : QualCost qpqual_cost;
300 : Cost cpu_per_tuple;
301 :
302 : /* Should only be applied to base relations with tablesample clauses */
303 : Assert(baserel->relid > 0);
304 180 : rte = planner_rt_fetch(baserel->relid, root);
305 : Assert(rte->rtekind == RTE_RELATION);
306 180 : tsc = rte->tablesample;
307 : Assert(tsc != NULL);
308 180 : tsm = GetTsmRoutine(tsc->tsmhandler);
309 :
310 : /* Mark the path with the correct row estimate */
311 180 : if (param_info)
312 12 : path->rows = param_info->ppi_rows;
313 : else
314 168 : path->rows = baserel->rows;
315 :
316 : /* fetch estimated page cost for tablespace containing table */
317 180 : get_tablespace_page_costs(baserel->reltablespace,
318 : &spc_random_page_cost,
319 : &spc_seq_page_cost);
320 :
321 : /* if NextSampleBlock is used, assume random access, else sequential */
322 360 : spc_page_cost = (tsm->NextSampleBlock != NULL) ?
323 180 : spc_random_page_cost : spc_seq_page_cost;
324 :
325 : /*
326 : * disk costs (recall that baserel->pages has already been set to the
327 : * number of pages the sampling method will visit)
328 : */
329 180 : run_cost += spc_page_cost * baserel->pages;
330 :
331 : /*
332 : * CPU costs (recall that baserel->tuples has already been set to the
333 : * number of tuples the sampling method will select). Note that we ignore
334 : * execution cost of the TABLESAMPLE parameter expressions; they will be
335 : * evaluated only once per scan, and in most usages they'll likely be
336 : * simple constants anyway. We also don't charge anything for the
337 : * calculations the sampling method might do internally.
338 : */
339 180 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
340 :
341 180 : startup_cost += qpqual_cost.startup;
342 180 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
343 180 : run_cost += cpu_per_tuple * baserel->tuples;
344 : /* tlist eval costs are paid per output row, not per tuple scanned */
345 180 : startup_cost += path->pathtarget->cost.startup;
346 180 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
347 :
348 180 : path->startup_cost = startup_cost;
349 180 : path->total_cost = startup_cost + run_cost;
350 180 : }
351 :
352 : /*
353 : * cost_gather
354 : * Determines and returns the cost of gather path.
355 : *
356 : * 'rel' is the relation to be operated upon
357 : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
358 : * 'rows' may be used to point to a row estimate; if non-NULL, it overrides
359 : * both 'rel' and 'param_info'. This is useful when the path doesn't exactly
360 : * correspond to any particular RelOptInfo.
361 : */
362 : void
363 7720 : cost_gather(GatherPath *path, PlannerInfo *root,
364 : RelOptInfo *rel, ParamPathInfo *param_info,
365 : double *rows)
366 : {
367 7720 : Cost startup_cost = 0;
368 7720 : Cost run_cost = 0;
369 :
370 : /* Mark the path with the correct row estimate */
371 7720 : if (rows)
372 966 : path->path.rows = *rows;
373 6754 : else if (param_info)
374 0 : path->path.rows = param_info->ppi_rows;
375 : else
376 6754 : path->path.rows = rel->rows;
377 :
378 7720 : startup_cost = path->subpath->startup_cost;
379 :
380 7720 : run_cost = path->subpath->total_cost - path->subpath->startup_cost;
381 :
382 : /* Parallel setup and communication cost. */
383 7720 : startup_cost += parallel_setup_cost;
384 7720 : run_cost += parallel_tuple_cost * path->path.rows;
385 :
386 7720 : path->path.startup_cost = startup_cost;
387 7720 : path->path.total_cost = (startup_cost + run_cost);
388 7720 : }
389 :
390 : /*
391 : * cost_gather_merge
392 : * Determines and returns the cost of gather merge path.
393 : *
394 : * GatherMerge merges several pre-sorted input streams, using a heap that at
395 : * any given instant holds the next tuple from each stream. If there are N
396 : * streams, we need about N*log2(N) tuple comparisons to construct the heap at
397 : * startup, and then for each output tuple, about log2(N) comparisons to
398 : * replace the top heap entry with the next tuple from the same stream.
399 : */
400 : void
401 2844 : cost_gather_merge(GatherMergePath *path, PlannerInfo *root,
402 : RelOptInfo *rel, ParamPathInfo *param_info,
403 : Cost input_startup_cost, Cost input_total_cost,
404 : double *rows)
405 : {
406 2844 : Cost startup_cost = 0;
407 2844 : Cost run_cost = 0;
408 : Cost comparison_cost;
409 : double N;
410 : double logN;
411 :
412 : /* Mark the path with the correct row estimate */
413 2844 : if (rows)
414 2640 : path->path.rows = *rows;
415 204 : else if (param_info)
416 0 : path->path.rows = param_info->ppi_rows;
417 : else
418 204 : path->path.rows = rel->rows;
419 :
420 2844 : if (!enable_gathermerge)
421 0 : startup_cost += disable_cost;
422 :
423 : /*
424 : * Add one to the number of workers to account for the leader. This might
425 : * be overgenerous since the leader will do less work than other workers
426 : * in typical cases, but we'll go with it for now.
427 : */
428 : Assert(path->num_workers > 0);
429 2844 : N = (double) path->num_workers + 1;
430 2844 : logN = LOG2(N);
431 :
432 : /* Assumed cost per tuple comparison */
433 2844 : comparison_cost = 2.0 * cpu_operator_cost;
434 :
435 : /* Heap creation cost */
436 2844 : startup_cost += comparison_cost * N * logN;
437 :
438 : /* Per-tuple heap maintenance cost */
439 2844 : run_cost += path->path.rows * comparison_cost * logN;
440 :
441 : /* small cost for heap management, like cost_merge_append */
442 2844 : run_cost += cpu_operator_cost * path->path.rows;
443 :
444 : /*
445 : * Parallel setup and communication cost. Since Gather Merge, unlike
446 : * Gather, requires us to block until a tuple is available from every
447 : * worker, we bump the IPC cost up a little bit as compared with Gather.
448 : * For lack of a better idea, charge an extra 5%.
449 : */
450 2844 : startup_cost += parallel_setup_cost;
451 2844 : run_cost += parallel_tuple_cost * path->path.rows * 1.05;
452 :
453 2844 : path->path.startup_cost = startup_cost + input_startup_cost;
454 2844 : path->path.total_cost = (startup_cost + run_cost + input_total_cost);
455 2844 : }
456 :
457 : /*
458 : * cost_index
459 : * Determines and returns the cost of scanning a relation using an index.
460 : *
461 : * 'path' describes the indexscan under consideration, and is complete
462 : * except for the fields to be set by this routine
463 : * 'loop_count' is the number of repetitions of the indexscan to factor into
464 : * estimates of caching behavior
465 : *
466 : * In addition to rows, startup_cost and total_cost, cost_index() sets the
467 : * path's indextotalcost and indexselectivity fields. These values will be
468 : * needed if the IndexPath is used in a BitmapIndexScan.
469 : *
470 : * NOTE: path->indexquals must contain only clauses usable as index
471 : * restrictions. Any additional quals evaluated as qpquals may reduce the
472 : * number of returned tuples, but they won't reduce the number of tuples
473 : * we have to fetch from the table, so they don't reduce the scan cost.
474 : */
475 : void
476 366158 : cost_index(IndexPath *path, PlannerInfo *root, double loop_count,
477 : bool partial_path)
478 : {
479 366158 : IndexOptInfo *index = path->indexinfo;
480 366158 : RelOptInfo *baserel = index->rel;
481 366158 : bool indexonly = (path->path.pathtype == T_IndexOnlyScan);
482 : amcostestimate_function amcostestimate;
483 : List *qpquals;
484 366158 : Cost startup_cost = 0;
485 366158 : Cost run_cost = 0;
486 366158 : Cost cpu_run_cost = 0;
487 : Cost indexStartupCost;
488 : Cost indexTotalCost;
489 : Selectivity indexSelectivity;
490 : double indexCorrelation,
491 : csquared;
492 : double spc_seq_page_cost,
493 : spc_random_page_cost;
494 : Cost min_IO_cost,
495 : max_IO_cost;
496 : QualCost qpqual_cost;
497 : Cost cpu_per_tuple;
498 : double tuples_fetched;
499 : double pages_fetched;
500 : double rand_heap_pages;
501 : double index_pages;
502 :
503 : /* Should only be applied to base relations */
504 : Assert(IsA(baserel, RelOptInfo) &&
505 : IsA(index, IndexOptInfo));
506 : Assert(baserel->relid > 0);
507 : Assert(baserel->rtekind == RTE_RELATION);
508 :
509 : /*
510 : * Mark the path with the correct row estimate, and identify which quals
511 : * will need to be enforced as qpquals. We need not check any quals that
512 : * are implied by the index's predicate, so we can use indrestrictinfo not
513 : * baserestrictinfo as the list of relevant restriction clauses for the
514 : * rel.
515 : */
516 366158 : if (path->path.param_info)
517 : {
518 59040 : path->path.rows = path->path.param_info->ppi_rows;
519 : /* qpquals come from the rel's restriction clauses and ppi_clauses */
520 118080 : qpquals = list_concat(extract_nonindex_conditions(path->indexinfo->indrestrictinfo,
521 : path->indexclauses),
522 59040 : extract_nonindex_conditions(path->path.param_info->ppi_clauses,
523 : path->indexclauses));
524 : }
525 : else
526 : {
527 307118 : path->path.rows = baserel->rows;
528 : /* qpquals come from just the rel's restriction clauses */
529 307118 : qpquals = extract_nonindex_conditions(path->indexinfo->indrestrictinfo,
530 : path->indexclauses);
531 : }
532 :
533 366158 : if (!enable_indexscan)
534 1432 : startup_cost += disable_cost;
535 : /* we don't need to check enable_indexonlyscan; indxpath.c does that */
536 :
537 : /*
538 : * Call index-access-method-specific code to estimate the processing cost
539 : * for scanning the index, as well as the selectivity of the index (ie,
540 : * the fraction of main-table tuples we will have to retrieve) and its
541 : * correlation to the main-table tuple order. We need a cast here because
542 : * pathnodes.h uses a weak function type to avoid including amapi.h.
543 : */
544 366158 : amcostestimate = (amcostestimate_function) index->amcostestimate;
545 366158 : amcostestimate(root, path, loop_count,
546 : &indexStartupCost, &indexTotalCost,
547 : &indexSelectivity, &indexCorrelation,
548 : &index_pages);
549 :
550 : /*
551 : * Save amcostestimate's results for possible use in bitmap scan planning.
552 : * We don't bother to save indexStartupCost or indexCorrelation, because a
553 : * bitmap scan doesn't care about either.
554 : */
555 366158 : path->indextotalcost = indexTotalCost;
556 366158 : path->indexselectivity = indexSelectivity;
557 :
558 : /* all costs for touching index itself included here */
559 366158 : startup_cost += indexStartupCost;
560 366158 : run_cost += indexTotalCost - indexStartupCost;
561 :
562 : /* estimate number of main-table tuples fetched */
563 366158 : tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);
564 :
565 : /* fetch estimated page costs for tablespace containing table */
566 366158 : get_tablespace_page_costs(baserel->reltablespace,
567 : &spc_random_page_cost,
568 : &spc_seq_page_cost);
569 :
570 : /*----------
571 : * Estimate number of main-table pages fetched, and compute I/O cost.
572 : *
573 : * When the index ordering is uncorrelated with the table ordering,
574 : * we use an approximation proposed by Mackert and Lohman (see
575 : * index_pages_fetched() for details) to compute the number of pages
576 : * fetched, and then charge spc_random_page_cost per page fetched.
577 : *
578 : * When the index ordering is exactly correlated with the table ordering
579 : * (just after a CLUSTER, for example), the number of pages fetched should
580 : * be exactly selectivity * table_size. What's more, all but the first
581 : * will be sequential fetches, not the random fetches that occur in the
582 : * uncorrelated case. So if the number of pages is more than 1, we
583 : * ought to charge
584 : * spc_random_page_cost + (pages_fetched - 1) * spc_seq_page_cost
585 : * For partially-correlated indexes, we ought to charge somewhere between
586 : * these two estimates. We currently interpolate linearly between the
587 : * estimates based on the correlation squared (XXX is that appropriate?).
588 : *
589 : * If it's an index-only scan, then we will not need to fetch any heap
590 : * pages for which the visibility map shows all tuples are visible.
591 : * Hence, reduce the estimated number of heap fetches accordingly.
592 : * We use the measured fraction of the entire heap that is all-visible,
593 : * which might not be particularly relevant to the subset of the heap
594 : * that this query will fetch; but it's not clear how to do better.
595 : *----------
596 : */
597 366158 : if (loop_count > 1)
598 : {
599 : /*
600 : * For repeated indexscans, the appropriate estimate for the
601 : * uncorrelated case is to scale up the number of tuples fetched in
602 : * the Mackert and Lohman formula by the number of scans, so that we
603 : * estimate the number of pages fetched by all the scans; then
604 : * pro-rate the costs for one scan. In this case we assume all the
605 : * fetches are random accesses.
606 : */
607 40356 : pages_fetched = index_pages_fetched(tuples_fetched * loop_count,
608 : baserel->pages,
609 40356 : (double) index->pages,
610 : root);
611 :
612 40356 : if (indexonly)
613 4902 : pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
614 :
615 40356 : rand_heap_pages = pages_fetched;
616 :
617 40356 : max_IO_cost = (pages_fetched * spc_random_page_cost) / loop_count;
618 :
619 : /*
620 : * In the perfectly correlated case, the number of pages touched by
621 : * each scan is selectivity * table_size, and we can use the Mackert
622 : * and Lohman formula at the page level to estimate how much work is
623 : * saved by caching across scans. We still assume all the fetches are
624 : * random, though, which is an overestimate that's hard to correct for
625 : * without double-counting the cache effects. (But in most cases
626 : * where such a plan is actually interesting, only one page would get
627 : * fetched per scan anyway, so it shouldn't matter much.)
628 : */
629 40356 : pages_fetched = ceil(indexSelectivity * (double) baserel->pages);
630 :
631 40356 : pages_fetched = index_pages_fetched(pages_fetched * loop_count,
632 : baserel->pages,
633 40356 : (double) index->pages,
634 : root);
635 :
636 40356 : if (indexonly)
637 4902 : pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
638 :
639 40356 : min_IO_cost = (pages_fetched * spc_random_page_cost) / loop_count;
640 : }
641 : else
642 : {
643 : /*
644 : * Normal case: apply the Mackert and Lohman formula, and then
645 : * interpolate between that and the correlation-derived result.
646 : */
647 325802 : pages_fetched = index_pages_fetched(tuples_fetched,
648 : baserel->pages,
649 325802 : (double) index->pages,
650 : root);
651 :
652 325802 : if (indexonly)
653 27136 : pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
654 :
655 325802 : rand_heap_pages = pages_fetched;
656 :
657 : /* max_IO_cost is for the perfectly uncorrelated case (csquared=0) */
658 325802 : max_IO_cost = pages_fetched * spc_random_page_cost;
659 :
660 : /* min_IO_cost is for the perfectly correlated case (csquared=1) */
661 325802 : pages_fetched = ceil(indexSelectivity * (double) baserel->pages);
662 :
663 325802 : if (indexonly)
664 27136 : pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
665 :
666 325802 : if (pages_fetched > 0)
667 : {
668 301126 : min_IO_cost = spc_random_page_cost;
669 301126 : if (pages_fetched > 1)
670 93174 : min_IO_cost += (pages_fetched - 1) * spc_seq_page_cost;
671 : }
672 : else
673 24676 : min_IO_cost = 0;
674 : }
675 :
676 366158 : if (partial_path)
677 : {
678 : /*
679 : * For index only scans compute workers based on number of index pages
680 : * fetched; the number of heap pages we fetch might be so small as to
681 : * effectively rule out parallelism, which we don't want to do.
682 : */
683 122632 : if (indexonly)
684 10372 : rand_heap_pages = -1;
685 :
686 : /*
687 : * Estimate the number of parallel workers required to scan index. Use
688 : * the number of heap pages computed considering heap fetches won't be
689 : * sequential as for parallel scans the pages are accessed in random
690 : * order.
691 : */
692 122632 : path->path.parallel_workers = compute_parallel_worker(baserel,
693 : rand_heap_pages,
694 : index_pages,
695 : max_parallel_workers_per_gather);
696 :
697 : /*
698 : * Fall out if workers can't be assigned for parallel scan, because in
699 : * such a case this path will be rejected. So there is no benefit in
700 : * doing extra computation.
701 : */
702 122632 : if (path->path.parallel_workers <= 0)
703 119432 : return;
704 :
705 3200 : path->path.parallel_aware = true;
706 : }
707 :
708 : /*
709 : * Now interpolate based on estimated index order correlation to get total
710 : * disk I/O cost for main table accesses.
711 : */
712 246726 : csquared = indexCorrelation * indexCorrelation;
713 :
714 246726 : run_cost += max_IO_cost + csquared * (min_IO_cost - max_IO_cost);
715 :
716 : /*
717 : * Estimate CPU costs per tuple.
718 : *
719 : * What we want here is cpu_tuple_cost plus the evaluation costs of any
720 : * qual clauses that we have to evaluate as qpquals.
721 : */
722 246726 : cost_qual_eval(&qpqual_cost, qpquals, root);
723 :
724 246726 : startup_cost += qpqual_cost.startup;
725 246726 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
726 :
727 246726 : cpu_run_cost += cpu_per_tuple * tuples_fetched;
728 :
729 : /* tlist eval costs are paid per output row, not per tuple scanned */
730 246726 : startup_cost += path->path.pathtarget->cost.startup;
731 246726 : cpu_run_cost += path->path.pathtarget->cost.per_tuple * path->path.rows;
732 :
733 : /* Adjust costing for parallelism, if used. */
734 246726 : if (path->path.parallel_workers > 0)
735 : {
736 3200 : double parallel_divisor = get_parallel_divisor(&path->path);
737 :
738 3200 : path->path.rows = clamp_row_est(path->path.rows / parallel_divisor);
739 :
740 : /* The CPU cost is divided among all the workers. */
741 3200 : cpu_run_cost /= parallel_divisor;
742 : }
743 :
744 246726 : run_cost += cpu_run_cost;
745 :
746 246726 : path->path.startup_cost = startup_cost;
747 246726 : path->path.total_cost = startup_cost + run_cost;
748 : }
749 :
750 : /*
751 : * extract_nonindex_conditions
752 : *
753 : * Given a list of quals to be enforced in an indexscan, extract the ones that
754 : * will have to be applied as qpquals (ie, the index machinery won't handle
755 : * them). Here we detect only whether a qual clause is directly redundant
756 : * with some indexclause. If the index path is chosen for use, createplan.c
757 : * will try a bit harder to get rid of redundant qual conditions; specifically
758 : * it will see if quals can be proven to be implied by the indexquals. But
759 : * it does not seem worth the cycles to try to factor that in at this stage,
760 : * since we're only trying to estimate qual eval costs. Otherwise this must
761 : * match the logic in create_indexscan_plan().
762 : *
763 : * qual_clauses, and the result, are lists of RestrictInfos.
764 : * indexclauses is a list of IndexClauses.
765 : */
766 : static List *
767 425198 : extract_nonindex_conditions(List *qual_clauses, List *indexclauses)
768 : {
769 425198 : List *result = NIL;
770 : ListCell *lc;
771 :
772 867662 : foreach(lc, qual_clauses)
773 : {
774 442464 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc);
775 :
776 442464 : if (rinfo->pseudoconstant)
777 1364 : continue; /* we may drop pseudoconstants here */
778 441100 : if (is_redundant_with_indexclauses(rinfo, indexclauses))
779 300948 : continue; /* dup or derived from same EquivalenceClass */
780 : /* ... skip the predicate proof attempt createplan.c will try ... */
781 140152 : result = lappend(result, rinfo);
782 : }
783 425198 : return result;
784 : }
785 :
786 : /*
787 : * index_pages_fetched
788 : * Estimate the number of pages actually fetched after accounting for
789 : * cache effects.
790 : *
791 : * We use an approximation proposed by Mackert and Lohman, "Index Scans
792 : * Using a Finite LRU Buffer: A Validated I/O Model", ACM Transactions
793 : * on Database Systems, Vol. 14, No. 3, September 1989, Pages 401-424.
794 : * The Mackert and Lohman approximation is that the number of pages
795 : * fetched is
796 : * PF =
797 : * min(2TNs/(2T+Ns), T) when T <= b
798 : * 2TNs/(2T+Ns) when T > b and Ns <= 2Tb/(2T-b)
799 : * b + (Ns - 2Tb/(2T-b))*(T-b)/T when T > b and Ns > 2Tb/(2T-b)
800 : * where
801 : * T = # pages in table
802 : * N = # tuples in table
803 : * s = selectivity = fraction of table to be scanned
804 : * b = # buffer pages available (we include kernel space here)
805 : *
806 : * We assume that effective_cache_size is the total number of buffer pages
807 : * available for the whole query, and pro-rate that space across all the
808 : * tables in the query and the index currently under consideration. (This
809 : * ignores space needed for other indexes used by the query, but since we
810 : * don't know which indexes will get used, we can't estimate that very well;
811 : * and in any case counting all the tables may well be an overestimate, since
812 : * depending on the join plan not all the tables may be scanned concurrently.)
813 : *
814 : * The product Ns is the number of tuples fetched; we pass in that
815 : * product rather than calculating it here. "pages" is the number of pages
816 : * in the object under consideration (either an index or a table).
817 : * "index_pages" is the amount to add to the total table space, which was
818 : * computed for us by make_one_rel.
819 : *
820 : * Caller is expected to have ensured that tuples_fetched is greater than zero
821 : * and rounded to integer (see clamp_row_est). The result will likewise be
822 : * greater than zero and integral.
823 : */
824 : double
825 515422 : index_pages_fetched(double tuples_fetched, BlockNumber pages,
826 : double index_pages, PlannerInfo *root)
827 : {
828 : double pages_fetched;
829 : double total_pages;
830 : double T,
831 : b;
832 :
833 : /* T is # pages in table, but don't allow it to be zero */
834 515422 : T = (pages > 1) ? (double) pages : 1.0;
835 :
836 : /* Compute number of pages assumed to be competing for cache space */
837 515422 : total_pages = root->total_table_pages + index_pages;
838 515422 : total_pages = Max(total_pages, 1.0);
839 : Assert(T <= total_pages);
840 :
841 : /* b is pro-rated share of effective_cache_size */
842 515422 : b = (double) effective_cache_size * T / total_pages;
843 :
844 : /* force it positive and integral */
845 515422 : if (b <= 1.0)
846 0 : b = 1.0;
847 : else
848 515422 : b = ceil(b);
849 :
850 : /* This part is the Mackert and Lohman formula */
851 515422 : if (T <= b)
852 : {
853 515422 : pages_fetched =
854 515422 : (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
855 515422 : if (pages_fetched >= T)
856 259024 : pages_fetched = T;
857 : else
858 256398 : pages_fetched = ceil(pages_fetched);
859 : }
860 : else
861 : {
862 : double lim;
863 :
864 0 : lim = (2.0 * T * b) / (2.0 * T - b);
865 0 : if (tuples_fetched <= lim)
866 : {
867 0 : pages_fetched =
868 0 : (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
869 : }
870 : else
871 : {
872 0 : pages_fetched =
873 0 : b + (tuples_fetched - lim) * (T - b) / T;
874 : }
875 0 : pages_fetched = ceil(pages_fetched);
876 : }
877 515422 : return pages_fetched;
878 : }
879 :
880 : /*
881 : * get_indexpath_pages
882 : * Determine the total size of the indexes used in a bitmap index path.
883 : *
884 : * Note: if the same index is used more than once in a bitmap tree, we will
885 : * count it multiple times, which perhaps is the wrong thing ... but it's
886 : * not completely clear, and detecting duplicates is difficult, so ignore it
887 : * for now.
888 : */
889 : static double
890 74252 : get_indexpath_pages(Path *bitmapqual)
891 : {
892 74252 : double result = 0;
893 : ListCell *l;
894 :
895 74252 : if (IsA(bitmapqual, BitmapAndPath))
896 : {
897 6324 : BitmapAndPath *apath = (BitmapAndPath *) bitmapqual;
898 :
899 18972 : foreach(l, apath->bitmapquals)
900 : {
901 12648 : result += get_indexpath_pages((Path *) lfirst(l));
902 : }
903 : }
904 67928 : else if (IsA(bitmapqual, BitmapOrPath))
905 : {
906 36 : BitmapOrPath *opath = (BitmapOrPath *) bitmapqual;
907 :
908 108 : foreach(l, opath->bitmapquals)
909 : {
910 72 : result += get_indexpath_pages((Path *) lfirst(l));
911 : }
912 : }
913 67892 : else if (IsA(bitmapqual, IndexPath))
914 : {
915 67892 : IndexPath *ipath = (IndexPath *) bitmapqual;
916 :
917 67892 : result = (double) ipath->indexinfo->pages;
918 : }
919 : else
920 0 : elog(ERROR, "unrecognized node type: %d", nodeTag(bitmapqual));
921 :
922 74252 : return result;
923 : }
924 :
925 : /*
926 : * cost_bitmap_heap_scan
927 : * Determines and returns the cost of scanning a relation using a bitmap
928 : * index-then-heap plan.
929 : *
930 : * 'baserel' is the relation to be scanned
931 : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
932 : * 'bitmapqual' is a tree of IndexPaths, BitmapAndPaths, and BitmapOrPaths
933 : * 'loop_count' is the number of repetitions of the indexscan to factor into
934 : * estimates of caching behavior
935 : *
936 : * Note: the component IndexPaths in bitmapqual should have been costed
937 : * using the same loop_count.
938 : */
939 : void
940 244328 : cost_bitmap_heap_scan(Path *path, PlannerInfo *root, RelOptInfo *baserel,
941 : ParamPathInfo *param_info,
942 : Path *bitmapqual, double loop_count)
943 : {
944 244328 : Cost startup_cost = 0;
945 244328 : Cost run_cost = 0;
946 : Cost indexTotalCost;
947 : QualCost qpqual_cost;
948 : Cost cpu_per_tuple;
949 : Cost cost_per_page;
950 : Cost cpu_run_cost;
951 : double tuples_fetched;
952 : double pages_fetched;
953 : double spc_seq_page_cost,
954 : spc_random_page_cost;
955 : double T;
956 :
957 : /* Should only be applied to base relations */
958 : Assert(IsA(baserel, RelOptInfo));
959 : Assert(baserel->relid > 0);
960 : Assert(baserel->rtekind == RTE_RELATION);
961 :
962 : /* Mark the path with the correct row estimate */
963 244328 : if (param_info)
964 82530 : path->rows = param_info->ppi_rows;
965 : else
966 161798 : path->rows = baserel->rows;
967 :
968 244328 : if (!enable_bitmapscan)
969 3896 : startup_cost += disable_cost;
970 :
971 244328 : pages_fetched = compute_bitmap_pages(root, baserel, bitmapqual,
972 : loop_count, &indexTotalCost,
973 : &tuples_fetched);
974 :
975 244328 : startup_cost += indexTotalCost;
976 244328 : T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
977 :
978 : /* Fetch estimated page costs for tablespace containing table. */
979 244328 : get_tablespace_page_costs(baserel->reltablespace,
980 : &spc_random_page_cost,
981 : &spc_seq_page_cost);
982 :
983 : /*
984 : * For small numbers of pages we should charge spc_random_page_cost
985 : * apiece, while if nearly all the table's pages are being read, it's more
986 : * appropriate to charge spc_seq_page_cost apiece. The effect is
987 : * nonlinear, too. For lack of a better idea, interpolate like this to
988 : * determine the cost per page.
989 : */
990 244328 : if (pages_fetched >= 2.0)
991 141084 : cost_per_page = spc_random_page_cost -
992 70542 : (spc_random_page_cost - spc_seq_page_cost)
993 70542 : * sqrt(pages_fetched / T);
994 : else
995 173786 : cost_per_page = spc_random_page_cost;
996 :
997 244328 : run_cost += pages_fetched * cost_per_page;
998 :
999 : /*
1000 : * Estimate CPU costs per tuple.
1001 : *
1002 : * Often the indexquals don't need to be rechecked at each tuple ... but
1003 : * not always, especially not if there are enough tuples involved that the
1004 : * bitmaps become lossy. For the moment, just assume they will be
1005 : * rechecked always. This means we charge the full freight for all the
1006 : * scan clauses.
1007 : */
1008 244328 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1009 :
1010 244328 : startup_cost += qpqual_cost.startup;
1011 244328 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1012 244328 : cpu_run_cost = cpu_per_tuple * tuples_fetched;
1013 :
1014 : /* Adjust costing for parallelism, if used. */
1015 244328 : if (path->parallel_workers > 0)
1016 : {
1017 900 : double parallel_divisor = get_parallel_divisor(path);
1018 :
1019 : /* The CPU cost is divided among all the workers. */
1020 900 : cpu_run_cost /= parallel_divisor;
1021 :
1022 900 : path->rows = clamp_row_est(path->rows / parallel_divisor);
1023 : }
1024 :
1025 :
1026 244328 : run_cost += cpu_run_cost;
1027 :
1028 : /* tlist eval costs are paid per output row, not per tuple scanned */
1029 244328 : startup_cost += path->pathtarget->cost.startup;
1030 244328 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1031 :
1032 244328 : path->startup_cost = startup_cost;
1033 244328 : path->total_cost = startup_cost + run_cost;
1034 244328 : }
1035 :
1036 : /*
1037 : * cost_bitmap_tree_node
1038 : * Extract cost and selectivity from a bitmap tree node (index/and/or)
1039 : */
1040 : void
1041 419242 : cost_bitmap_tree_node(Path *path, Cost *cost, Selectivity *selec)
1042 : {
1043 419242 : if (IsA(path, IndexPath))
1044 : {
1045 407042 : *cost = ((IndexPath *) path)->indextotalcost;
1046 407042 : *selec = ((IndexPath *) path)->indexselectivity;
1047 :
1048 : /*
1049 : * Charge a small amount per retrieved tuple to reflect the costs of
1050 : * manipulating the bitmap. This is mostly to make sure that a bitmap
1051 : * scan doesn't look to be the same cost as an indexscan to retrieve a
1052 : * single tuple.
1053 : */
1054 407042 : *cost += 0.1 * cpu_operator_cost * path->rows;
1055 : }
1056 12200 : else if (IsA(path, BitmapAndPath))
1057 : {
1058 11160 : *cost = path->total_cost;
1059 11160 : *selec = ((BitmapAndPath *) path)->bitmapselectivity;
1060 : }
1061 1040 : else if (IsA(path, BitmapOrPath))
1062 : {
1063 1040 : *cost = path->total_cost;
1064 1040 : *selec = ((BitmapOrPath *) path)->bitmapselectivity;
1065 : }
1066 : else
1067 : {
1068 0 : elog(ERROR, "unrecognized node type: %d", nodeTag(path));
1069 : *cost = *selec = 0; /* keep compiler quiet */
1070 : }
1071 419242 : }
1072 :
1073 : /*
1074 : * cost_bitmap_and_node
1075 : * Estimate the cost of a BitmapAnd node
1076 : *
1077 : * Note that this considers only the costs of index scanning and bitmap
1078 : * creation, not the eventual heap access. In that sense the object isn't
1079 : * truly a Path, but it has enough path-like properties (costs in particular)
1080 : * to warrant treating it as one. We don't bother to set the path rows field,
1081 : * however.
1082 : */
1083 : void
1084 11116 : cost_bitmap_and_node(BitmapAndPath *path, PlannerInfo *root)
1085 : {
1086 : Cost totalCost;
1087 : Selectivity selec;
1088 : ListCell *l;
1089 :
1090 : /*
1091 : * We estimate AND selectivity on the assumption that the inputs are
1092 : * independent. This is probably often wrong, but we don't have the info
1093 : * to do better.
1094 : *
1095 : * The runtime cost of the BitmapAnd itself is estimated at 100x
1096 : * cpu_operator_cost for each tbm_intersect needed. Probably too small,
1097 : * definitely too simplistic?
1098 : */
1099 11116 : totalCost = 0.0;
1100 11116 : selec = 1.0;
1101 33348 : foreach(l, path->bitmapquals)
1102 : {
1103 22232 : Path *subpath = (Path *) lfirst(l);
1104 : Cost subCost;
1105 : Selectivity subselec;
1106 :
1107 22232 : cost_bitmap_tree_node(subpath, &subCost, &subselec);
1108 :
1109 22232 : selec *= subselec;
1110 :
1111 22232 : totalCost += subCost;
1112 22232 : if (l != list_head(path->bitmapquals))
1113 11116 : totalCost += 100.0 * cpu_operator_cost;
1114 : }
1115 11116 : path->bitmapselectivity = selec;
1116 11116 : path->path.rows = 0; /* per above, not used */
1117 11116 : path->path.startup_cost = totalCost;
1118 11116 : path->path.total_cost = totalCost;
1119 11116 : }
1120 :
1121 : /*
1122 : * cost_bitmap_or_node
1123 : * Estimate the cost of a BitmapOr node
1124 : *
1125 : * See comments for cost_bitmap_and_node.
1126 : */
1127 : void
1128 318 : cost_bitmap_or_node(BitmapOrPath *path, PlannerInfo *root)
1129 : {
1130 : Cost totalCost;
1131 : Selectivity selec;
1132 : ListCell *l;
1133 :
1134 : /*
1135 : * We estimate OR selectivity on the assumption that the inputs are
1136 : * non-overlapping, since that's often the case in "x IN (list)" type
1137 : * situations. Of course, we clamp to 1.0 at the end.
1138 : *
1139 : * The runtime cost of the BitmapOr itself is estimated at 100x
1140 : * cpu_operator_cost for each tbm_union needed. Probably too small,
1141 : * definitely too simplistic? We are aware that the tbm_unions are
1142 : * optimized out when the inputs are BitmapIndexScans.
1143 : */
1144 318 : totalCost = 0.0;
1145 318 : selec = 0.0;
1146 978 : foreach(l, path->bitmapquals)
1147 : {
1148 660 : Path *subpath = (Path *) lfirst(l);
1149 : Cost subCost;
1150 : Selectivity subselec;
1151 :
1152 660 : cost_bitmap_tree_node(subpath, &subCost, &subselec);
1153 :
1154 660 : selec += subselec;
1155 :
1156 660 : totalCost += subCost;
1157 1002 : if (l != list_head(path->bitmapquals) &&
1158 342 : !IsA(subpath, IndexPath))
1159 20 : totalCost += 100.0 * cpu_operator_cost;
1160 : }
1161 318 : path->bitmapselectivity = Min(selec, 1.0);
1162 318 : path->path.rows = 0; /* per above, not used */
1163 318 : path->path.startup_cost = totalCost;
1164 318 : path->path.total_cost = totalCost;
1165 318 : }
1166 :
1167 : /*
1168 : * cost_tidscan
1169 : * Determines and returns the cost of scanning a relation using TIDs.
1170 : *
1171 : * 'baserel' is the relation to be scanned
1172 : * 'tidquals' is the list of TID-checkable quals
1173 : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1174 : */
1175 : void
1176 594 : cost_tidscan(Path *path, PlannerInfo *root,
1177 : RelOptInfo *baserel, List *tidquals, ParamPathInfo *param_info)
1178 : {
1179 594 : Cost startup_cost = 0;
1180 594 : Cost run_cost = 0;
1181 594 : bool isCurrentOf = false;
1182 : QualCost qpqual_cost;
1183 : Cost cpu_per_tuple;
1184 : QualCost tid_qual_cost;
1185 : int ntuples;
1186 : ListCell *l;
1187 : double spc_random_page_cost;
1188 :
1189 : /* Should only be applied to base relations */
1190 : Assert(baserel->relid > 0);
1191 : Assert(baserel->rtekind == RTE_RELATION);
1192 :
1193 : /* Mark the path with the correct row estimate */
1194 594 : if (param_info)
1195 78 : path->rows = param_info->ppi_rows;
1196 : else
1197 516 : path->rows = baserel->rows;
1198 :
1199 : /* Count how many tuples we expect to retrieve */
1200 594 : ntuples = 0;
1201 1204 : foreach(l, tidquals)
1202 : {
1203 610 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, l);
1204 610 : Expr *qual = rinfo->clause;
1205 :
1206 610 : if (IsA(qual, ScalarArrayOpExpr))
1207 : {
1208 : /* Each element of the array yields 1 tuple */
1209 20 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) qual;
1210 20 : Node *arraynode = (Node *) lsecond(saop->args);
1211 :
1212 20 : ntuples += estimate_array_length(arraynode);
1213 : }
1214 590 : else if (IsA(qual, CurrentOfExpr))
1215 : {
1216 : /* CURRENT OF yields 1 tuple */
1217 380 : isCurrentOf = true;
1218 380 : ntuples++;
1219 : }
1220 : else
1221 : {
1222 : /* It's just CTID = something, count 1 tuple */
1223 210 : ntuples++;
1224 : }
1225 : }
1226 :
1227 : /*
1228 : * We must force TID scan for WHERE CURRENT OF, because only nodeTidscan.c
1229 : * understands how to do it correctly. Therefore, honor enable_tidscan
1230 : * only when CURRENT OF isn't present. Also note that cost_qual_eval
1231 : * counts a CurrentOfExpr as having startup cost disable_cost, which we
1232 : * subtract off here; that's to prevent other plan types such as seqscan
1233 : * from winning.
1234 : */
1235 594 : if (isCurrentOf)
1236 : {
1237 : Assert(baserel->baserestrictcost.startup >= disable_cost);
1238 380 : startup_cost -= disable_cost;
1239 : }
1240 214 : else if (!enable_tidscan)
1241 0 : startup_cost += disable_cost;
1242 :
1243 : /*
1244 : * The TID qual expressions will be computed once, any other baserestrict
1245 : * quals once per retrieved tuple.
1246 : */
1247 594 : cost_qual_eval(&tid_qual_cost, tidquals, root);
1248 :
1249 : /* fetch estimated page cost for tablespace containing table */
1250 594 : get_tablespace_page_costs(baserel->reltablespace,
1251 : &spc_random_page_cost,
1252 : NULL);
1253 :
1254 : /* disk costs --- assume each tuple on a different page */
1255 594 : run_cost += spc_random_page_cost * ntuples;
1256 :
1257 : /* Add scanning CPU costs */
1258 594 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1259 :
1260 : /* XXX currently we assume TID quals are a subset of qpquals */
1261 594 : startup_cost += qpqual_cost.startup + tid_qual_cost.per_tuple;
1262 1188 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple -
1263 594 : tid_qual_cost.per_tuple;
1264 594 : run_cost += cpu_per_tuple * ntuples;
1265 :
1266 : /* tlist eval costs are paid per output row, not per tuple scanned */
1267 594 : startup_cost += path->pathtarget->cost.startup;
1268 594 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1269 :
1270 594 : path->startup_cost = startup_cost;
1271 594 : path->total_cost = startup_cost + run_cost;
1272 594 : }
1273 :
1274 : /*
1275 : * cost_subqueryscan
1276 : * Determines and returns the cost of scanning a subquery RTE.
1277 : *
1278 : * 'baserel' is the relation to be scanned
1279 : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1280 : */
1281 : void
1282 7916 : cost_subqueryscan(SubqueryScanPath *path, PlannerInfo *root,
1283 : RelOptInfo *baserel, ParamPathInfo *param_info)
1284 : {
1285 : Cost startup_cost;
1286 : Cost run_cost;
1287 : QualCost qpqual_cost;
1288 : Cost cpu_per_tuple;
1289 :
1290 : /* Should only be applied to base relations that are subqueries */
1291 : Assert(baserel->relid > 0);
1292 : Assert(baserel->rtekind == RTE_SUBQUERY);
1293 :
1294 : /* Mark the path with the correct row estimate */
1295 7916 : if (param_info)
1296 212 : path->path.rows = param_info->ppi_rows;
1297 : else
1298 7704 : path->path.rows = baserel->rows;
1299 :
1300 : /*
1301 : * Cost of path is cost of evaluating the subplan, plus cost of evaluating
1302 : * any restriction clauses and tlist that will be attached to the
1303 : * SubqueryScan node, plus cpu_tuple_cost to account for selection and
1304 : * projection overhead.
1305 : */
1306 7916 : path->path.startup_cost = path->subpath->startup_cost;
1307 7916 : path->path.total_cost = path->subpath->total_cost;
1308 :
1309 7916 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1310 :
1311 7916 : startup_cost = qpqual_cost.startup;
1312 7916 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1313 7916 : run_cost = cpu_per_tuple * baserel->tuples;
1314 :
1315 : /* tlist eval costs are paid per output row, not per tuple scanned */
1316 7916 : startup_cost += path->path.pathtarget->cost.startup;
1317 7916 : run_cost += path->path.pathtarget->cost.per_tuple * path->path.rows;
1318 :
1319 7916 : path->path.startup_cost += startup_cost;
1320 7916 : path->path.total_cost += startup_cost + run_cost;
1321 7916 : }
1322 :
1323 : /*
1324 : * cost_functionscan
1325 : * Determines and returns the cost of scanning a function RTE.
1326 : *
1327 : * 'baserel' is the relation to be scanned
1328 : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1329 : */
1330 : void
1331 28720 : cost_functionscan(Path *path, PlannerInfo *root,
1332 : RelOptInfo *baserel, ParamPathInfo *param_info)
1333 : {
1334 28720 : Cost startup_cost = 0;
1335 28720 : Cost run_cost = 0;
1336 : QualCost qpqual_cost;
1337 : Cost cpu_per_tuple;
1338 : RangeTblEntry *rte;
1339 : QualCost exprcost;
1340 :
1341 : /* Should only be applied to base relations that are functions */
1342 : Assert(baserel->relid > 0);
1343 28720 : rte = planner_rt_fetch(baserel->relid, root);
1344 : Assert(rte->rtekind == RTE_FUNCTION);
1345 :
1346 : /* Mark the path with the correct row estimate */
1347 28720 : if (param_info)
1348 336 : path->rows = param_info->ppi_rows;
1349 : else
1350 28384 : path->rows = baserel->rows;
1351 :
1352 : /*
1353 : * Estimate costs of executing the function expression(s).
1354 : *
1355 : * Currently, nodeFunctionscan.c always executes the functions to
1356 : * completion before returning any rows, and caches the results in a
1357 : * tuplestore. So the function eval cost is all startup cost, and per-row
1358 : * costs are minimal.
1359 : *
1360 : * XXX in principle we ought to charge tuplestore spill costs if the
1361 : * number of rows is large. However, given how phony our rowcount
1362 : * estimates for functions tend to be, there's not a lot of point in that
1363 : * refinement right now.
1364 : */
1365 28720 : cost_qual_eval_node(&exprcost, (Node *) rte->functions, root);
1366 :
1367 28720 : startup_cost += exprcost.startup + exprcost.per_tuple;
1368 :
1369 : /* Add scanning CPU costs */
1370 28720 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1371 :
1372 28720 : startup_cost += qpqual_cost.startup;
1373 28720 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1374 28720 : run_cost += cpu_per_tuple * baserel->tuples;
1375 :
1376 : /* tlist eval costs are paid per output row, not per tuple scanned */
1377 28720 : startup_cost += path->pathtarget->cost.startup;
1378 28720 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1379 :
1380 28720 : path->startup_cost = startup_cost;
1381 28720 : path->total_cost = startup_cost + run_cost;
1382 28720 : }
1383 :
1384 : /*
1385 : * cost_tablefuncscan
1386 : * Determines and returns the cost of scanning a table function.
1387 : *
1388 : * 'baserel' is the relation to be scanned
1389 : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1390 : */
1391 : void
1392 140 : cost_tablefuncscan(Path *path, PlannerInfo *root,
1393 : RelOptInfo *baserel, ParamPathInfo *param_info)
1394 : {
1395 140 : Cost startup_cost = 0;
1396 140 : Cost run_cost = 0;
1397 : QualCost qpqual_cost;
1398 : Cost cpu_per_tuple;
1399 : RangeTblEntry *rte;
1400 : QualCost exprcost;
1401 :
1402 : /* Should only be applied to base relations that are functions */
1403 : Assert(baserel->relid > 0);
1404 140 : rte = planner_rt_fetch(baserel->relid, root);
1405 : Assert(rte->rtekind == RTE_TABLEFUNC);
1406 :
1407 : /* Mark the path with the correct row estimate */
1408 140 : if (param_info)
1409 96 : path->rows = param_info->ppi_rows;
1410 : else
1411 44 : path->rows = baserel->rows;
1412 :
1413 : /*
1414 : * Estimate costs of executing the table func expression(s).
1415 : *
1416 : * XXX in principle we ought to charge tuplestore spill costs if the
1417 : * number of rows is large. However, given how phony our rowcount
1418 : * estimates for tablefuncs tend to be, there's not a lot of point in that
1419 : * refinement right now.
1420 : */
1421 140 : cost_qual_eval_node(&exprcost, (Node *) rte->tablefunc, root);
1422 :
1423 140 : startup_cost += exprcost.startup + exprcost.per_tuple;
1424 :
1425 : /* Add scanning CPU costs */
1426 140 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1427 :
1428 140 : startup_cost += qpqual_cost.startup;
1429 140 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1430 140 : run_cost += cpu_per_tuple * baserel->tuples;
1431 :
1432 : /* tlist eval costs are paid per output row, not per tuple scanned */
1433 140 : startup_cost += path->pathtarget->cost.startup;
1434 140 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1435 :
1436 140 : path->startup_cost = startup_cost;
1437 140 : path->total_cost = startup_cost + run_cost;
1438 140 : }
1439 :
1440 : /*
1441 : * cost_valuesscan
1442 : * Determines and returns the cost of scanning a VALUES RTE.
1443 : *
1444 : * 'baserel' is the relation to be scanned
1445 : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1446 : */
1447 : void
1448 3770 : cost_valuesscan(Path *path, PlannerInfo *root,
1449 : RelOptInfo *baserel, ParamPathInfo *param_info)
1450 : {
1451 3770 : Cost startup_cost = 0;
1452 3770 : Cost run_cost = 0;
1453 : QualCost qpqual_cost;
1454 : Cost cpu_per_tuple;
1455 :
1456 : /* Should only be applied to base relations that are values lists */
1457 : Assert(baserel->relid > 0);
1458 : Assert(baserel->rtekind == RTE_VALUES);
1459 :
1460 : /* Mark the path with the correct row estimate */
1461 3770 : if (param_info)
1462 28 : path->rows = param_info->ppi_rows;
1463 : else
1464 3742 : path->rows = baserel->rows;
1465 :
1466 : /*
1467 : * For now, estimate list evaluation cost at one operator eval per list
1468 : * (probably pretty bogus, but is it worth being smarter?)
1469 : */
1470 3770 : cpu_per_tuple = cpu_operator_cost;
1471 :
1472 : /* Add scanning CPU costs */
1473 3770 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1474 :
1475 3770 : startup_cost += qpqual_cost.startup;
1476 3770 : cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
1477 3770 : run_cost += cpu_per_tuple * baserel->tuples;
1478 :
1479 : /* tlist eval costs are paid per output row, not per tuple scanned */
1480 3770 : startup_cost += path->pathtarget->cost.startup;
1481 3770 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1482 :
1483 3770 : path->startup_cost = startup_cost;
1484 3770 : path->total_cost = startup_cost + run_cost;
1485 3770 : }
1486 :
1487 : /*
1488 : * cost_ctescan
1489 : * Determines and returns the cost of scanning a CTE RTE.
1490 : *
1491 : * Note: this is used for both self-reference and regular CTEs; the
1492 : * possible cost differences are below the threshold of what we could
1493 : * estimate accurately anyway. Note that the costs of evaluating the
1494 : * referenced CTE query are added into the final plan as initplan costs,
1495 : * and should NOT be counted here.
1496 : */
1497 : void
1498 1220 : cost_ctescan(Path *path, PlannerInfo *root,
1499 : RelOptInfo *baserel, ParamPathInfo *param_info)
1500 : {
1501 1220 : Cost startup_cost = 0;
1502 1220 : Cost run_cost = 0;
1503 : QualCost qpqual_cost;
1504 : Cost cpu_per_tuple;
1505 :
1506 : /* Should only be applied to base relations that are CTEs */
1507 : Assert(baserel->relid > 0);
1508 : Assert(baserel->rtekind == RTE_CTE);
1509 :
1510 : /* Mark the path with the correct row estimate */
1511 1220 : if (param_info)
1512 0 : path->rows = param_info->ppi_rows;
1513 : else
1514 1220 : path->rows = baserel->rows;
1515 :
1516 : /* Charge one CPU tuple cost per row for tuplestore manipulation */
1517 1220 : cpu_per_tuple = cpu_tuple_cost;
1518 :
1519 : /* Add scanning CPU costs */
1520 1220 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1521 :
1522 1220 : startup_cost += qpqual_cost.startup;
1523 1220 : cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
1524 1220 : run_cost += cpu_per_tuple * baserel->tuples;
1525 :
1526 : /* tlist eval costs are paid per output row, not per tuple scanned */
1527 1220 : startup_cost += path->pathtarget->cost.startup;
1528 1220 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1529 :
1530 1220 : path->startup_cost = startup_cost;
1531 1220 : path->total_cost = startup_cost + run_cost;
1532 1220 : }
1533 :
1534 : /*
1535 : * cost_namedtuplestorescan
1536 : * Determines and returns the cost of scanning a named tuplestore.
1537 : */
1538 : void
1539 256 : cost_namedtuplestorescan(Path *path, PlannerInfo *root,
1540 : RelOptInfo *baserel, ParamPathInfo *param_info)
1541 : {
1542 256 : Cost startup_cost = 0;
1543 256 : Cost run_cost = 0;
1544 : QualCost qpqual_cost;
1545 : Cost cpu_per_tuple;
1546 :
1547 : /* Should only be applied to base relations that are Tuplestores */
1548 : Assert(baserel->relid > 0);
1549 : Assert(baserel->rtekind == RTE_NAMEDTUPLESTORE);
1550 :
1551 : /* Mark the path with the correct row estimate */
1552 256 : if (param_info)
1553 0 : path->rows = param_info->ppi_rows;
1554 : else
1555 256 : path->rows = baserel->rows;
1556 :
1557 : /* Charge one CPU tuple cost per row for tuplestore manipulation */
1558 256 : cpu_per_tuple = cpu_tuple_cost;
1559 :
1560 : /* Add scanning CPU costs */
1561 256 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1562 :
1563 256 : startup_cost += qpqual_cost.startup;
1564 256 : cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
1565 256 : run_cost += cpu_per_tuple * baserel->tuples;
1566 :
1567 256 : path->startup_cost = startup_cost;
1568 256 : path->total_cost = startup_cost + run_cost;
1569 256 : }
1570 :
1571 : /*
1572 : * cost_resultscan
1573 : * Determines and returns the cost of scanning an RTE_RESULT relation.
1574 : */
1575 : void
1576 598 : cost_resultscan(Path *path, PlannerInfo *root,
1577 : RelOptInfo *baserel, ParamPathInfo *param_info)
1578 : {
1579 598 : Cost startup_cost = 0;
1580 598 : Cost run_cost = 0;
1581 : QualCost qpqual_cost;
1582 : Cost cpu_per_tuple;
1583 :
1584 : /* Should only be applied to RTE_RESULT base relations */
1585 : Assert(baserel->relid > 0);
1586 : Assert(baserel->rtekind == RTE_RESULT);
1587 :
1588 : /* Mark the path with the correct row estimate */
1589 598 : if (param_info)
1590 60 : path->rows = param_info->ppi_rows;
1591 : else
1592 538 : path->rows = baserel->rows;
1593 :
1594 : /* We charge qual cost plus cpu_tuple_cost */
1595 598 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1596 :
1597 598 : startup_cost += qpqual_cost.startup;
1598 598 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1599 598 : run_cost += cpu_per_tuple * baserel->tuples;
1600 :
1601 598 : path->startup_cost = startup_cost;
1602 598 : path->total_cost = startup_cost + run_cost;
1603 598 : }
1604 :
1605 : /*
1606 : * cost_recursive_union
1607 : * Determines and returns the cost of performing a recursive union,
1608 : * and also the estimated output size.
1609 : *
1610 : * We are given Paths for the nonrecursive and recursive terms.
1611 : */
1612 : void
1613 326 : cost_recursive_union(Path *runion, Path *nrterm, Path *rterm)
1614 : {
1615 : Cost startup_cost;
1616 : Cost total_cost;
1617 : double total_rows;
1618 :
1619 : /* We probably have decent estimates for the non-recursive term */
1620 326 : startup_cost = nrterm->startup_cost;
1621 326 : total_cost = nrterm->total_cost;
1622 326 : total_rows = nrterm->rows;
1623 :
1624 : /*
1625 : * We arbitrarily assume that about 10 recursive iterations will be
1626 : * needed, and that we've managed to get a good fix on the cost and output
1627 : * size of each one of them. These are mighty shaky assumptions but it's
1628 : * hard to see how to do better.
1629 : */
1630 326 : total_cost += 10 * rterm->total_cost;
1631 326 : total_rows += 10 * rterm->rows;
1632 :
1633 : /*
1634 : * Also charge cpu_tuple_cost per row to account for the costs of
1635 : * manipulating the tuplestores. (We don't worry about possible
1636 : * spill-to-disk costs.)
1637 : */
1638 326 : total_cost += cpu_tuple_cost * total_rows;
1639 :
1640 326 : runion->startup_cost = startup_cost;
1641 326 : runion->total_cost = total_cost;
1642 326 : runion->rows = total_rows;
1643 326 : runion->pathtarget->width = Max(nrterm->pathtarget->width,
1644 : rterm->pathtarget->width);
1645 326 : }
1646 :
1647 : /*
1648 : * cost_sort
1649 : * Determines and returns the cost of sorting a relation, including
1650 : * the cost of reading the input data.
1651 : *
1652 : * If the total volume of data to sort is less than sort_mem, we will do
1653 : * an in-memory sort, which requires no I/O and about t*log2(t) tuple
1654 : * comparisons for t tuples.
1655 : *
1656 : * If the total volume exceeds sort_mem, we switch to a tape-style merge
1657 : * algorithm. There will still be about t*log2(t) tuple comparisons in
1658 : * total, but we will also need to write and read each tuple once per
1659 : * merge pass. We expect about ceil(logM(r)) merge passes where r is the
1660 : * number of initial runs formed and M is the merge order used by tuplesort.c.
1661 : * Since the average initial run should be about sort_mem, we have
1662 : * disk traffic = 2 * relsize * ceil(logM(p / sort_mem))
1663 : * cpu = comparison_cost * t * log2(t)
1664 : *
1665 : * If the sort is bounded (i.e., only the first k result tuples are needed)
1666 : * and k tuples can fit into sort_mem, we use a heap method that keeps only
1667 : * k tuples in the heap; this will require about t*log2(k) tuple comparisons.
1668 : *
1669 : * The disk traffic is assumed to be 3/4ths sequential and 1/4th random
1670 : * accesses (XXX can't we refine that guess?)
1671 : *
1672 : * By default, we charge two operator evals per tuple comparison, which should
1673 : * be in the right ballpark in most cases. The caller can tweak this by
1674 : * specifying nonzero comparison_cost; typically that's used for any extra
1675 : * work that has to be done to prepare the inputs to the comparison operators.
1676 : *
1677 : * 'pathkeys' is a list of sort keys
1678 : * 'input_cost' is the total cost for reading the input data
1679 : * 'tuples' is the number of tuples in the relation
1680 : * 'width' is the average tuple width in bytes
1681 : * 'comparison_cost' is the extra cost per comparison, if any
1682 : * 'sort_mem' is the number of kilobytes of work memory allowed for the sort
1683 : * 'limit_tuples' is the bound on the number of output tuples; -1 if no bound
1684 : *
1685 : * NOTE: some callers currently pass NIL for pathkeys because they
1686 : * can't conveniently supply the sort keys. Since this routine doesn't
1687 : * currently do anything with pathkeys anyway, that doesn't matter...
1688 : * but if it ever does, it should react gracefully to lack of key data.
1689 : * (Actually, the thing we'd most likely be interested in is just the number
1690 : * of sort keys, which all callers *could* supply.)
1691 : */
1692 : void
1693 623394 : cost_sort(Path *path, PlannerInfo *root,
1694 : List *pathkeys, Cost input_cost, double tuples, int width,
1695 : Cost comparison_cost, int sort_mem,
1696 : double limit_tuples)
1697 : {
1698 623394 : Cost startup_cost = input_cost;
1699 623394 : Cost run_cost = 0;
1700 623394 : double input_bytes = relation_byte_size(tuples, width);
1701 : double output_bytes;
1702 : double output_tuples;
1703 623394 : long sort_mem_bytes = sort_mem * 1024L;
1704 :
1705 623394 : if (!enable_sort)
1706 278 : startup_cost += disable_cost;
1707 :
1708 623394 : path->rows = tuples;
1709 :
1710 : /*
1711 : * We want to be sure the cost of a sort is never estimated as zero, even
1712 : * if passed-in tuple count is zero. Besides, mustn't do log(0)...
1713 : */
1714 623394 : if (tuples < 2.0)
1715 190646 : tuples = 2.0;
1716 :
1717 : /* Include the default cost-per-comparison */
1718 623394 : comparison_cost += 2.0 * cpu_operator_cost;
1719 :
1720 : /* Do we have a useful LIMIT? */
1721 623394 : if (limit_tuples > 0 && limit_tuples < tuples)
1722 : {
1723 1104 : output_tuples = limit_tuples;
1724 1104 : output_bytes = relation_byte_size(output_tuples, width);
1725 : }
1726 : else
1727 : {
1728 622290 : output_tuples = tuples;
1729 622290 : output_bytes = input_bytes;
1730 : }
1731 :
1732 623394 : if (output_bytes > sort_mem_bytes)
1733 : {
1734 : /*
1735 : * We'll have to use a disk-based sort of all the tuples
1736 : */
1737 7220 : double npages = ceil(input_bytes / BLCKSZ);
1738 7220 : double nruns = input_bytes / sort_mem_bytes;
1739 7220 : double mergeorder = tuplesort_merge_order(sort_mem_bytes);
1740 : double log_runs;
1741 : double npageaccesses;
1742 :
1743 : /*
1744 : * CPU costs
1745 : *
1746 : * Assume about N log2 N comparisons
1747 : */
1748 7220 : startup_cost += comparison_cost * tuples * LOG2(tuples);
1749 :
1750 : /* Disk costs */
1751 :
1752 : /* Compute logM(r) as log(r) / log(M) */
1753 7220 : if (nruns > mergeorder)
1754 3416 : log_runs = ceil(log(nruns) / log(mergeorder));
1755 : else
1756 3804 : log_runs = 1.0;
1757 7220 : npageaccesses = 2.0 * npages * log_runs;
1758 : /* Assume 3/4ths of accesses are sequential, 1/4th are not */
1759 7220 : startup_cost += npageaccesses *
1760 7220 : (seq_page_cost * 0.75 + random_page_cost * 0.25);
1761 : }
1762 616174 : else if (tuples > 2 * output_tuples || input_bytes > sort_mem_bytes)
1763 : {
1764 : /*
1765 : * We'll use a bounded heap-sort keeping just K tuples in memory, for
1766 : * a total number of tuple comparisons of N log2 K; but the constant
1767 : * factor is a bit higher than for quicksort. Tweak it so that the
1768 : * cost curve is continuous at the crossover point.
1769 : */
1770 716 : startup_cost += comparison_cost * tuples * LOG2(2.0 * output_tuples);
1771 : }
1772 : else
1773 : {
1774 : /* We'll use plain quicksort on all the input tuples */
1775 615458 : startup_cost += comparison_cost * tuples * LOG2(tuples);
1776 : }
1777 :
1778 : /*
1779 : * Also charge a small amount (arbitrarily set equal to operator cost) per
1780 : * extracted tuple. We don't charge cpu_tuple_cost because a Sort node
1781 : * doesn't do qual-checking or projection, so it has less overhead than
1782 : * most plan nodes. Note it's correct to use tuples not output_tuples
1783 : * here --- the upper LIMIT will pro-rate the run cost so we'd be double
1784 : * counting the LIMIT otherwise.
1785 : */
1786 623394 : run_cost += cpu_operator_cost * tuples;
1787 :
1788 623394 : path->startup_cost = startup_cost;
1789 623394 : path->total_cost = startup_cost + run_cost;
1790 623394 : }
1791 :
1792 : /*
1793 : * append_nonpartial_cost
1794 : * Estimate the cost of the non-partial paths in a Parallel Append.
1795 : * The non-partial paths are assumed to be the first "numpaths" paths
1796 : * from the subpaths list, and to be in order of decreasing cost.
1797 : */
1798 : static Cost
1799 8196 : append_nonpartial_cost(List *subpaths, int numpaths, int parallel_workers)
1800 : {
1801 : Cost *costarr;
1802 : int arrlen;
1803 : ListCell *l;
1804 : ListCell *cell;
1805 : int i;
1806 : int path_index;
1807 : int min_index;
1808 : int max_index;
1809 :
1810 8196 : if (numpaths == 0)
1811 6526 : return 0;
1812 :
1813 : /*
1814 : * Array length is number of workers or number of relevants paths,
1815 : * whichever is less.
1816 : */
1817 1670 : arrlen = Min(parallel_workers, numpaths);
1818 1670 : costarr = (Cost *) palloc(sizeof(Cost) * arrlen);
1819 :
1820 : /* The first few paths will each be claimed by a different worker. */
1821 1670 : path_index = 0;
1822 4798 : foreach(cell, subpaths)
1823 : {
1824 3648 : Path *subpath = (Path *) lfirst(cell);
1825 :
1826 3648 : if (path_index == arrlen)
1827 520 : break;
1828 3128 : costarr[path_index++] = subpath->total_cost;
1829 : }
1830 :
1831 : /*
1832 : * Since subpaths are sorted by decreasing cost, the last one will have
1833 : * the minimum cost.
1834 : */
1835 1670 : min_index = arrlen - 1;
1836 :
1837 : /*
1838 : * For each of the remaining subpaths, add its cost to the array element
1839 : * with minimum cost.
1840 : */
1841 2032 : for_each_cell(l, cell)
1842 : {
1843 630 : Path *subpath = (Path *) lfirst(l);
1844 : int i;
1845 :
1846 : /* Consider only the non-partial paths */
1847 630 : if (path_index++ == numpaths)
1848 268 : break;
1849 :
1850 362 : costarr[min_index] += subpath->total_cost;
1851 :
1852 : /* Update the new min cost array index */
1853 1110 : for (min_index = i = 0; i < arrlen; i++)
1854 : {
1855 748 : if (costarr[i] < costarr[min_index])
1856 114 : min_index = i;
1857 : }
1858 : }
1859 :
1860 : /* Return the highest cost from the array */
1861 4798 : for (max_index = i = 0; i < arrlen; i++)
1862 : {
1863 3128 : if (costarr[i] > costarr[max_index])
1864 186 : max_index = i;
1865 : }
1866 :
1867 1670 : return costarr[max_index];
1868 : }
1869 :
1870 : /*
1871 : * cost_append
1872 : * Determines and returns the cost of an Append node.
1873 : */
1874 : void
1875 23114 : cost_append(AppendPath *apath)
1876 : {
1877 : ListCell *l;
1878 :
1879 23114 : apath->path.startup_cost = 0;
1880 23114 : apath->path.total_cost = 0;
1881 23114 : apath->path.rows = 0;
1882 :
1883 23114 : if (apath->subpaths == NIL)
1884 748 : return;
1885 :
1886 22366 : if (!apath->path.parallel_aware)
1887 : {
1888 14170 : List *pathkeys = apath->path.pathkeys;
1889 :
1890 14170 : if (pathkeys == NIL)
1891 : {
1892 13400 : Path *subpath = (Path *) linitial(apath->subpaths);
1893 :
1894 : /*
1895 : * For an unordered, non-parallel-aware Append we take the startup
1896 : * cost as the startup cost of the first subpath.
1897 : */
1898 13400 : apath->path.startup_cost = subpath->startup_cost;
1899 :
1900 : /* Compute rows and costs as sums of subplan rows and costs. */
1901 54092 : foreach(l, apath->subpaths)
1902 : {
1903 40692 : Path *subpath = (Path *) lfirst(l);
1904 :
1905 40692 : apath->path.rows += subpath->rows;
1906 40692 : apath->path.total_cost += subpath->total_cost;
1907 : }
1908 : }
1909 : else
1910 : {
1911 : /*
1912 : * For an ordered, non-parallel-aware Append we take the startup
1913 : * cost as the sum of the subpath startup costs. This ensures
1914 : * that we don't underestimate the startup cost when a query's
1915 : * LIMIT is such that several of the children have to be run to
1916 : * satisfy it. This might be overkill --- another plausible hack
1917 : * would be to take the Append's startup cost as the maximum of
1918 : * the child startup costs. But we don't want to risk believing
1919 : * that an ORDER BY LIMIT query can be satisfied at small cost
1920 : * when the first child has small startup cost but later ones
1921 : * don't. (If we had the ability to deal with nonlinear cost
1922 : * interpolation for partial retrievals, we would not need to be
1923 : * so conservative about this.)
1924 : *
1925 : * This case is also different from the above in that we have to
1926 : * account for possibly injecting sorts into subpaths that aren't
1927 : * natively ordered.
1928 : */
1929 2984 : foreach(l, apath->subpaths)
1930 : {
1931 2214 : Path *subpath = (Path *) lfirst(l);
1932 : Path sort_path; /* dummy for result of cost_sort */
1933 :
1934 2214 : if (!pathkeys_contained_in(pathkeys, subpath->pathkeys))
1935 : {
1936 : /*
1937 : * We'll need to insert a Sort node, so include costs for
1938 : * that. We can use the parent's LIMIT if any, since we
1939 : * certainly won't pull more than that many tuples from
1940 : * any child.
1941 : */
1942 40 : cost_sort(&sort_path,
1943 : NULL, /* doesn't currently need root */
1944 : pathkeys,
1945 : subpath->total_cost,
1946 : subpath->rows,
1947 20 : subpath->pathtarget->width,
1948 : 0.0,
1949 : work_mem,
1950 : apath->limit_tuples);
1951 20 : subpath = &sort_path;
1952 : }
1953 :
1954 2214 : apath->path.rows += subpath->rows;
1955 2214 : apath->path.startup_cost += subpath->startup_cost;
1956 2214 : apath->path.total_cost += subpath->total_cost;
1957 : }
1958 : }
1959 : }
1960 : else /* parallel-aware */
1961 : {
1962 8196 : int i = 0;
1963 8196 : double parallel_divisor = get_parallel_divisor(&apath->path);
1964 :
1965 : /* Parallel-aware Append never produces ordered output. */
1966 : Assert(apath->path.pathkeys == NIL);
1967 :
1968 : /* Calculate startup cost. */
1969 34862 : foreach(l, apath->subpaths)
1970 : {
1971 26666 : Path *subpath = (Path *) lfirst(l);
1972 :
1973 : /*
1974 : * Append will start returning tuples when the child node having
1975 : * lowest startup cost is done setting up. We consider only the
1976 : * first few subplans that immediately get a worker assigned.
1977 : */
1978 26666 : if (i == 0)
1979 8196 : apath->path.startup_cost = subpath->startup_cost;
1980 18470 : else if (i < apath->path.parallel_workers)
1981 8140 : apath->path.startup_cost = Min(apath->path.startup_cost,
1982 : subpath->startup_cost);
1983 :
1984 : /*
1985 : * Apply parallel divisor to subpaths. Scale the number of rows
1986 : * for each partial subpath based on the ratio of the parallel
1987 : * divisor originally used for the subpath to the one we adopted.
1988 : * Also add the cost of partial paths to the total cost, but
1989 : * ignore non-partial paths for now.
1990 : */
1991 26666 : if (i < apath->first_partial_path)
1992 3490 : apath->path.rows += subpath->rows / parallel_divisor;
1993 : else
1994 : {
1995 : double subpath_parallel_divisor;
1996 :
1997 23176 : subpath_parallel_divisor = get_parallel_divisor(subpath);
1998 23176 : apath->path.rows += subpath->rows * (subpath_parallel_divisor /
1999 : parallel_divisor);
2000 23176 : apath->path.total_cost += subpath->total_cost;
2001 : }
2002 :
2003 26666 : apath->path.rows = clamp_row_est(apath->path.rows);
2004 :
2005 26666 : i++;
2006 : }
2007 :
2008 : /* Add cost for non-partial subpaths. */
2009 8196 : apath->path.total_cost +=
2010 8196 : append_nonpartial_cost(apath->subpaths,
2011 : apath->first_partial_path,
2012 : apath->path.parallel_workers);
2013 : }
2014 :
2015 : /*
2016 : * Although Append does not do any selection or projection, it's not free;
2017 : * add a small per-tuple overhead.
2018 : */
2019 44732 : apath->path.total_cost +=
2020 22366 : cpu_tuple_cost * APPEND_CPU_COST_MULTIPLIER * apath->path.rows;
2021 : }
2022 :
2023 : /*
2024 : * cost_merge_append
2025 : * Determines and returns the cost of a MergeAppend node.
2026 : *
2027 : * MergeAppend merges several pre-sorted input streams, using a heap that
2028 : * at any given instant holds the next tuple from each stream. If there
2029 : * are N streams, we need about N*log2(N) tuple comparisons to construct
2030 : * the heap at startup, and then for each output tuple, about log2(N)
2031 : * comparisons to replace the top entry.
2032 : *
2033 : * (The effective value of N will drop once some of the input streams are
2034 : * exhausted, but it seems unlikely to be worth trying to account for that.)
2035 : *
2036 : * The heap is never spilled to disk, since we assume N is not very large.
2037 : * So this is much simpler than cost_sort.
2038 : *
2039 : * As in cost_sort, we charge two operator evals per tuple comparison.
2040 : *
2041 : * 'pathkeys' is a list of sort keys
2042 : * 'n_streams' is the number of input streams
2043 : * 'input_startup_cost' is the sum of the input streams' startup costs
2044 : * 'input_total_cost' is the sum of the input streams' total costs
2045 : * 'tuples' is the number of tuples in all the streams
2046 : */
2047 : void
2048 1306 : cost_merge_append(Path *path, PlannerInfo *root,
2049 : List *pathkeys, int n_streams,
2050 : Cost input_startup_cost, Cost input_total_cost,
2051 : double tuples)
2052 : {
2053 1306 : Cost startup_cost = 0;
2054 1306 : Cost run_cost = 0;
2055 : Cost comparison_cost;
2056 : double N;
2057 : double logN;
2058 :
2059 : /*
2060 : * Avoid log(0)...
2061 : */
2062 1306 : N = (n_streams < 2) ? 2.0 : (double) n_streams;
2063 1306 : logN = LOG2(N);
2064 :
2065 : /* Assumed cost per tuple comparison */
2066 1306 : comparison_cost = 2.0 * cpu_operator_cost;
2067 :
2068 : /* Heap creation cost */
2069 1306 : startup_cost += comparison_cost * N * logN;
2070 :
2071 : /* Per-tuple heap maintenance cost */
2072 1306 : run_cost += tuples * comparison_cost * logN;
2073 :
2074 : /*
2075 : * Although MergeAppend does not do any selection or projection, it's not
2076 : * free; add a small per-tuple overhead.
2077 : */
2078 1306 : run_cost += cpu_tuple_cost * APPEND_CPU_COST_MULTIPLIER * tuples;
2079 :
2080 1306 : path->startup_cost = startup_cost + input_startup_cost;
2081 1306 : path->total_cost = startup_cost + run_cost + input_total_cost;
2082 1306 : }
2083 :
2084 : /*
2085 : * cost_material
2086 : * Determines and returns the cost of materializing a relation, including
2087 : * the cost of reading the input data.
2088 : *
2089 : * If the total volume of data to materialize exceeds work_mem, we will need
2090 : * to write it to disk, so the cost is much higher in that case.
2091 : *
2092 : * Note that here we are estimating the costs for the first scan of the
2093 : * relation, so the materialization is all overhead --- any savings will
2094 : * occur only on rescan, which is estimated in cost_rescan.
2095 : */
2096 : void
2097 214652 : cost_material(Path *path,
2098 : Cost input_startup_cost, Cost input_total_cost,
2099 : double tuples, int width)
2100 : {
2101 214652 : Cost startup_cost = input_startup_cost;
2102 214652 : Cost run_cost = input_total_cost - input_startup_cost;
2103 214652 : double nbytes = relation_byte_size(tuples, width);
2104 214652 : long work_mem_bytes = work_mem * 1024L;
2105 :
2106 214652 : path->rows = tuples;
2107 :
2108 : /*
2109 : * Whether spilling or not, charge 2x cpu_operator_cost per tuple to
2110 : * reflect bookkeeping overhead. (This rate must be more than what
2111 : * cost_rescan charges for materialize, ie, cpu_operator_cost per tuple;
2112 : * if it is exactly the same then there will be a cost tie between
2113 : * nestloop with A outer, materialized B inner and nestloop with B outer,
2114 : * materialized A inner. The extra cost ensures we'll prefer
2115 : * materializing the smaller rel.) Note that this is normally a good deal
2116 : * less than cpu_tuple_cost; which is OK because a Material plan node
2117 : * doesn't do qual-checking or projection, so it's got less overhead than
2118 : * most plan nodes.
2119 : */
2120 214652 : run_cost += 2 * cpu_operator_cost * tuples;
2121 :
2122 : /*
2123 : * If we will spill to disk, charge at the rate of seq_page_cost per page.
2124 : * This cost is assumed to be evenly spread through the plan run phase,
2125 : * which isn't exactly accurate but our cost model doesn't allow for
2126 : * nonuniform costs within the run phase.
2127 : */
2128 214652 : if (nbytes > work_mem_bytes)
2129 : {
2130 1984 : double npages = ceil(nbytes / BLCKSZ);
2131 :
2132 1984 : run_cost += seq_page_cost * npages;
2133 : }
2134 :
2135 214652 : path->startup_cost = startup_cost;
2136 214652 : path->total_cost = startup_cost + run_cost;
2137 214652 : }
2138 :
2139 : /*
2140 : * cost_agg
2141 : * Determines and returns the cost of performing an Agg plan node,
2142 : * including the cost of its input.
2143 : *
2144 : * aggcosts can be NULL when there are no actual aggregate functions (i.e.,
2145 : * we are using a hashed Agg node just to do grouping).
2146 : *
2147 : * Note: when aggstrategy == AGG_SORTED, caller must ensure that input costs
2148 : * are for appropriately-sorted input.
2149 : */
2150 : void
2151 35988 : cost_agg(Path *path, PlannerInfo *root,
2152 : AggStrategy aggstrategy, const AggClauseCosts *aggcosts,
2153 : int numGroupCols, double numGroups,
2154 : List *quals,
2155 : Cost input_startup_cost, Cost input_total_cost,
2156 : double input_tuples)
2157 : {
2158 : double output_tuples;
2159 : Cost startup_cost;
2160 : Cost total_cost;
2161 : AggClauseCosts dummy_aggcosts;
2162 :
2163 : /* Use all-zero per-aggregate costs if NULL is passed */
2164 35988 : if (aggcosts == NULL)
2165 : {
2166 : Assert(aggstrategy == AGG_HASHED);
2167 2878 : MemSet(&dummy_aggcosts, 0, sizeof(AggClauseCosts));
2168 2878 : aggcosts = &dummy_aggcosts;
2169 : }
2170 :
2171 : /*
2172 : * The transCost.per_tuple component of aggcosts should be charged once
2173 : * per input tuple, corresponding to the costs of evaluating the aggregate
2174 : * transfns and their input expressions. The finalCost.per_tuple component
2175 : * is charged once per output tuple, corresponding to the costs of
2176 : * evaluating the finalfns. Startup costs are of course charged but once.
2177 : *
2178 : * If we are grouping, we charge an additional cpu_operator_cost per
2179 : * grouping column per input tuple for grouping comparisons.
2180 : *
2181 : * We will produce a single output tuple if not grouping, and a tuple per
2182 : * group otherwise. We charge cpu_tuple_cost for each output tuple.
2183 : *
2184 : * Note: in this cost model, AGG_SORTED and AGG_HASHED have exactly the
2185 : * same total CPU cost, but AGG_SORTED has lower startup cost. If the
2186 : * input path is already sorted appropriately, AGG_SORTED should be
2187 : * preferred (since it has no risk of memory overflow). This will happen
2188 : * as long as the computed total costs are indeed exactly equal --- but if
2189 : * there's roundoff error we might do the wrong thing. So be sure that
2190 : * the computations below form the same intermediate values in the same
2191 : * order.
2192 : */
2193 35988 : if (aggstrategy == AGG_PLAIN)
2194 : {
2195 23312 : startup_cost = input_total_cost;
2196 23312 : startup_cost += aggcosts->transCost.startup;
2197 23312 : startup_cost += aggcosts->transCost.per_tuple * input_tuples;
2198 23312 : startup_cost += aggcosts->finalCost.startup;
2199 23312 : startup_cost += aggcosts->finalCost.per_tuple;
2200 : /* we aren't grouping */
2201 23312 : total_cost = startup_cost + cpu_tuple_cost;
2202 23312 : output_tuples = 1;
2203 : }
2204 12676 : else if (aggstrategy == AGG_SORTED || aggstrategy == AGG_MIXED)
2205 : {
2206 : /* Here we are able to deliver output on-the-fly */
2207 5614 : startup_cost = input_startup_cost;
2208 5614 : total_cost = input_total_cost;
2209 5614 : if (aggstrategy == AGG_MIXED && !enable_hashagg)
2210 : {
2211 228 : startup_cost += disable_cost;
2212 228 : total_cost += disable_cost;
2213 : }
2214 : /* calcs phrased this way to match HASHED case, see note above */
2215 5614 : total_cost += aggcosts->transCost.startup;
2216 5614 : total_cost += aggcosts->transCost.per_tuple * input_tuples;
2217 5614 : total_cost += (cpu_operator_cost * numGroupCols) * input_tuples;
2218 5614 : total_cost += aggcosts->finalCost.startup;
2219 5614 : total_cost += aggcosts->finalCost.per_tuple * numGroups;
2220 5614 : total_cost += cpu_tuple_cost * numGroups;
2221 5614 : output_tuples = numGroups;
2222 : }
2223 : else
2224 : {
2225 : /* must be AGG_HASHED */
2226 7062 : startup_cost = input_total_cost;
2227 7062 : if (!enable_hashagg)
2228 794 : startup_cost += disable_cost;
2229 7062 : startup_cost += aggcosts->transCost.startup;
2230 7062 : startup_cost += aggcosts->transCost.per_tuple * input_tuples;
2231 7062 : startup_cost += (cpu_operator_cost * numGroupCols) * input_tuples;
2232 7062 : startup_cost += aggcosts->finalCost.startup;
2233 7062 : total_cost = startup_cost;
2234 7062 : total_cost += aggcosts->finalCost.per_tuple * numGroups;
2235 7062 : total_cost += cpu_tuple_cost * numGroups;
2236 7062 : output_tuples = numGroups;
2237 : }
2238 :
2239 : /*
2240 : * If there are quals (HAVING quals), account for their cost and
2241 : * selectivity.
2242 : */
2243 35988 : if (quals)
2244 : {
2245 : QualCost qual_cost;
2246 :
2247 2358 : cost_qual_eval(&qual_cost, quals, root);
2248 2358 : startup_cost += qual_cost.startup;
2249 2358 : total_cost += qual_cost.startup + output_tuples * qual_cost.per_tuple;
2250 :
2251 2358 : output_tuples = clamp_row_est(output_tuples *
2252 2358 : clauselist_selectivity(root,
2253 : quals,
2254 : 0,
2255 : JOIN_INNER,
2256 : NULL));
2257 : }
2258 :
2259 35988 : path->rows = output_tuples;
2260 35988 : path->startup_cost = startup_cost;
2261 35988 : path->total_cost = total_cost;
2262 35988 : }
2263 :
2264 : /*
2265 : * cost_windowagg
2266 : * Determines and returns the cost of performing a WindowAgg plan node,
2267 : * including the cost of its input.
2268 : *
2269 : * Input is assumed already properly sorted.
2270 : */
2271 : void
2272 1160 : cost_windowagg(Path *path, PlannerInfo *root,
2273 : List *windowFuncs, int numPartCols, int numOrderCols,
2274 : Cost input_startup_cost, Cost input_total_cost,
2275 : double input_tuples)
2276 : {
2277 : Cost startup_cost;
2278 : Cost total_cost;
2279 : ListCell *lc;
2280 :
2281 1160 : startup_cost = input_startup_cost;
2282 1160 : total_cost = input_total_cost;
2283 :
2284 : /*
2285 : * Window functions are assumed to cost their stated execution cost, plus
2286 : * the cost of evaluating their input expressions, per tuple. Since they
2287 : * may in fact evaluate their inputs at multiple rows during each cycle,
2288 : * this could be a drastic underestimate; but without a way to know how
2289 : * many rows the window function will fetch, it's hard to do better. In
2290 : * any case, it's a good estimate for all the built-in window functions,
2291 : * so we'll just do this for now.
2292 : */
2293 2572 : foreach(lc, windowFuncs)
2294 : {
2295 1412 : WindowFunc *wfunc = lfirst_node(WindowFunc, lc);
2296 : Cost wfunccost;
2297 : QualCost argcosts;
2298 :
2299 1412 : argcosts.startup = argcosts.per_tuple = 0;
2300 1412 : add_function_cost(root, wfunc->winfnoid, (Node *) wfunc,
2301 : &argcosts);
2302 1412 : startup_cost += argcosts.startup;
2303 1412 : wfunccost = argcosts.per_tuple;
2304 :
2305 : /* also add the input expressions' cost to per-input-row costs */
2306 1412 : cost_qual_eval_node(&argcosts, (Node *) wfunc->args, root);
2307 1412 : startup_cost += argcosts.startup;
2308 1412 : wfunccost += argcosts.per_tuple;
2309 :
2310 : /*
2311 : * Add the filter's cost to per-input-row costs. XXX We should reduce
2312 : * input expression costs according to filter selectivity.
2313 : */
2314 1412 : cost_qual_eval_node(&argcosts, (Node *) wfunc->aggfilter, root);
2315 1412 : startup_cost += argcosts.startup;
2316 1412 : wfunccost += argcosts.per_tuple;
2317 :
2318 1412 : total_cost += wfunccost * input_tuples;
2319 : }
2320 :
2321 : /*
2322 : * We also charge cpu_operator_cost per grouping column per tuple for
2323 : * grouping comparisons, plus cpu_tuple_cost per tuple for general
2324 : * overhead.
2325 : *
2326 : * XXX this neglects costs of spooling the data to disk when it overflows
2327 : * work_mem. Sooner or later that should get accounted for.
2328 : */
2329 1160 : total_cost += cpu_operator_cost * (numPartCols + numOrderCols) * input_tuples;
2330 1160 : total_cost += cpu_tuple_cost * input_tuples;
2331 :
2332 1160 : path->rows = input_tuples;
2333 1160 : path->startup_cost = startup_cost;
2334 1160 : path->total_cost = total_cost;
2335 1160 : }
2336 :
2337 : /*
2338 : * cost_group
2339 : * Determines and returns the cost of performing a Group plan node,
2340 : * including the cost of its input.
2341 : *
2342 : * Note: caller must ensure that input costs are for appropriately-sorted
2343 : * input.
2344 : */
2345 : void
2346 822 : cost_group(Path *path, PlannerInfo *root,
2347 : int numGroupCols, double numGroups,
2348 : List *quals,
2349 : Cost input_startup_cost, Cost input_total_cost,
2350 : double input_tuples)
2351 : {
2352 : double output_tuples;
2353 : Cost startup_cost;
2354 : Cost total_cost;
2355 :
2356 822 : output_tuples = numGroups;
2357 822 : startup_cost = input_startup_cost;
2358 822 : total_cost = input_total_cost;
2359 :
2360 : /*
2361 : * Charge one cpu_operator_cost per comparison per input tuple. We assume
2362 : * all columns get compared at most of the tuples.
2363 : */
2364 822 : total_cost += cpu_operator_cost * input_tuples * numGroupCols;
2365 :
2366 : /*
2367 : * If there are quals (HAVING quals), account for their cost and
2368 : * selectivity.
2369 : */
2370 822 : if (quals)
2371 : {
2372 : QualCost qual_cost;
2373 :
2374 0 : cost_qual_eval(&qual_cost, quals, root);
2375 0 : startup_cost += qual_cost.startup;
2376 0 : total_cost += qual_cost.startup + output_tuples * qual_cost.per_tuple;
2377 :
2378 0 : output_tuples = clamp_row_est(output_tuples *
2379 0 : clauselist_selectivity(root,
2380 : quals,
2381 : 0,
2382 : JOIN_INNER,
2383 : NULL));
2384 : }
2385 :
2386 822 : path->rows = output_tuples;
2387 822 : path->startup_cost = startup_cost;
2388 822 : path->total_cost = total_cost;
2389 822 : }
2390 :
2391 : /*
2392 : * initial_cost_nestloop
2393 : * Preliminary estimate of the cost of a nestloop join path.
2394 : *
2395 : * This must quickly produce lower-bound estimates of the path's startup and
2396 : * total costs. If we are unable to eliminate the proposed path from
2397 : * consideration using the lower bounds, final_cost_nestloop will be called
2398 : * to obtain the final estimates.
2399 : *
2400 : * The exact division of labor between this function and final_cost_nestloop
2401 : * is private to them, and represents a tradeoff between speed of the initial
2402 : * estimate and getting a tight lower bound. We choose to not examine the
2403 : * join quals here, since that's by far the most expensive part of the
2404 : * calculations. The end result is that CPU-cost considerations must be
2405 : * left for the second phase; and for SEMI/ANTI joins, we must also postpone
2406 : * incorporation of the inner path's run cost.
2407 : *
2408 : * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
2409 : * other data to be used by final_cost_nestloop
2410 : * 'jointype' is the type of join to be performed
2411 : * 'outer_path' is the outer input to the join
2412 : * 'inner_path' is the inner input to the join
2413 : * 'extra' contains miscellaneous information about the join
2414 : */
2415 : void
2416 1017014 : initial_cost_nestloop(PlannerInfo *root, JoinCostWorkspace *workspace,
2417 : JoinType jointype,
2418 : Path *outer_path, Path *inner_path,
2419 : JoinPathExtraData *extra)
2420 : {
2421 1017014 : Cost startup_cost = 0;
2422 1017014 : Cost run_cost = 0;
2423 1017014 : double outer_path_rows = outer_path->rows;
2424 : Cost inner_rescan_start_cost;
2425 : Cost inner_rescan_total_cost;
2426 : Cost inner_run_cost;
2427 : Cost inner_rescan_run_cost;
2428 :
2429 : /* estimate costs to rescan the inner relation */
2430 1017014 : cost_rescan(root, inner_path,
2431 : &inner_rescan_start_cost,
2432 : &inner_rescan_total_cost);
2433 :
2434 : /* cost of source data */
2435 :
2436 : /*
2437 : * NOTE: clearly, we must pay both outer and inner paths' startup_cost
2438 : * before we can start returning tuples, so the join's startup cost is
2439 : * their sum. We'll also pay the inner path's rescan startup cost
2440 : * multiple times.
2441 : */
2442 1017014 : startup_cost += outer_path->startup_cost + inner_path->startup_cost;
2443 1017014 : run_cost += outer_path->total_cost - outer_path->startup_cost;
2444 1017014 : if (outer_path_rows > 1)
2445 616212 : run_cost += (outer_path_rows - 1) * inner_rescan_start_cost;
2446 :
2447 1017014 : inner_run_cost = inner_path->total_cost - inner_path->startup_cost;
2448 1017014 : inner_rescan_run_cost = inner_rescan_total_cost - inner_rescan_start_cost;
2449 :
2450 2000228 : if (jointype == JOIN_SEMI || jointype == JOIN_ANTI ||
2451 983214 : extra->inner_unique)
2452 : {
2453 : /*
2454 : * With a SEMI or ANTI join, or if the innerrel is known unique, the
2455 : * executor will stop after the first match.
2456 : *
2457 : * Getting decent estimates requires inspection of the join quals,
2458 : * which we choose to postpone to final_cost_nestloop.
2459 : */
2460 :
2461 : /* Save private data for final_cost_nestloop */
2462 468374 : workspace->inner_run_cost = inner_run_cost;
2463 468374 : workspace->inner_rescan_run_cost = inner_rescan_run_cost;
2464 : }
2465 : else
2466 : {
2467 : /* Normal case; we'll scan whole input rel for each outer row */
2468 548640 : run_cost += inner_run_cost;
2469 548640 : if (outer_path_rows > 1)
2470 317770 : run_cost += (outer_path_rows - 1) * inner_rescan_run_cost;
2471 : }
2472 :
2473 : /* CPU costs left for later */
2474 :
2475 : /* Public result fields */
2476 1017014 : workspace->startup_cost = startup_cost;
2477 1017014 : workspace->total_cost = startup_cost + run_cost;
2478 : /* Save private data for final_cost_nestloop */
2479 1017014 : workspace->run_cost = run_cost;
2480 1017014 : }
2481 :
2482 : /*
2483 : * final_cost_nestloop
2484 : * Final estimate of the cost and result size of a nestloop join path.
2485 : *
2486 : * 'path' is already filled in except for the rows and cost fields
2487 : * 'workspace' is the result from initial_cost_nestloop
2488 : * 'extra' contains miscellaneous information about the join
2489 : */
2490 : void
2491 561086 : final_cost_nestloop(PlannerInfo *root, NestPath *path,
2492 : JoinCostWorkspace *workspace,
2493 : JoinPathExtraData *extra)
2494 : {
2495 561086 : Path *outer_path = path->outerjoinpath;
2496 561086 : Path *inner_path = path->innerjoinpath;
2497 561086 : double outer_path_rows = outer_path->rows;
2498 561086 : double inner_path_rows = inner_path->rows;
2499 561086 : Cost startup_cost = workspace->startup_cost;
2500 561086 : Cost run_cost = workspace->run_cost;
2501 : Cost cpu_per_tuple;
2502 : QualCost restrict_qual_cost;
2503 : double ntuples;
2504 :
2505 : /* Protect some assumptions below that rowcounts aren't zero or NaN */
2506 561086 : if (outer_path_rows <= 0 || isnan(outer_path_rows))
2507 0 : outer_path_rows = 1;
2508 561086 : if (inner_path_rows <= 0 || isnan(inner_path_rows))
2509 88 : inner_path_rows = 1;
2510 :
2511 : /* Mark the path with the correct row estimate */
2512 561086 : if (path->path.param_info)
2513 4660 : path->path.rows = path->path.param_info->ppi_rows;
2514 : else
2515 556426 : path->path.rows = path->path.parent->rows;
2516 :
2517 : /* For partial paths, scale row estimate. */
2518 561086 : if (path->path.parallel_workers > 0)
2519 : {
2520 1660 : double parallel_divisor = get_parallel_divisor(&path->path);
2521 :
2522 1660 : path->path.rows =
2523 1660 : clamp_row_est(path->path.rows / parallel_divisor);
2524 : }
2525 :
2526 : /*
2527 : * We could include disable_cost in the preliminary estimate, but that
2528 : * would amount to optimizing for the case where the join method is
2529 : * disabled, which doesn't seem like the way to bet.
2530 : */
2531 561086 : if (!enable_nestloop)
2532 1608 : startup_cost += disable_cost;
2533 :
2534 : /* cost of inner-relation source data (we already dealt with outer rel) */
2535 :
2536 1100190 : if (path->jointype == JOIN_SEMI || path->jointype == JOIN_ANTI ||
2537 539104 : extra->inner_unique)
2538 351624 : {
2539 : /*
2540 : * With a SEMI or ANTI join, or if the innerrel is known unique, the
2541 : * executor will stop after the first match.
2542 : */
2543 351624 : Cost inner_run_cost = workspace->inner_run_cost;
2544 351624 : Cost inner_rescan_run_cost = workspace->inner_rescan_run_cost;
2545 : double outer_matched_rows;
2546 : double outer_unmatched_rows;
2547 : Selectivity inner_scan_frac;
2548 :
2549 : /*
2550 : * For an outer-rel row that has at least one match, we can expect the
2551 : * inner scan to stop after a fraction 1/(match_count+1) of the inner
2552 : * rows, if the matches are evenly distributed. Since they probably
2553 : * aren't quite evenly distributed, we apply a fuzz factor of 2.0 to
2554 : * that fraction. (If we used a larger fuzz factor, we'd have to
2555 : * clamp inner_scan_frac to at most 1.0; but since match_count is at
2556 : * least 1, no such clamp is needed now.)
2557 : */
2558 351624 : outer_matched_rows = rint(outer_path_rows * extra->semifactors.outer_match_frac);
2559 351624 : outer_unmatched_rows = outer_path_rows - outer_matched_rows;
2560 351624 : inner_scan_frac = 2.0 / (extra->semifactors.match_count + 1.0);
2561 :
2562 : /*
2563 : * Compute number of tuples processed (not number emitted!). First,
2564 : * account for successfully-matched outer rows.
2565 : */
2566 351624 : ntuples = outer_matched_rows * inner_path_rows * inner_scan_frac;
2567 :
2568 : /*
2569 : * Now we need to estimate the actual costs of scanning the inner
2570 : * relation, which may be quite a bit less than N times inner_run_cost
2571 : * due to early scan stops. We consider two cases. If the inner path
2572 : * is an indexscan using all the joinquals as indexquals, then an
2573 : * unmatched outer row results in an indexscan returning no rows,
2574 : * which is probably quite cheap. Otherwise, the executor will have
2575 : * to scan the whole inner rel for an unmatched row; not so cheap.
2576 : */
2577 351624 : if (has_indexed_join_quals(path))
2578 : {
2579 : /*
2580 : * Successfully-matched outer rows will only require scanning
2581 : * inner_scan_frac of the inner relation. In this case, we don't
2582 : * need to charge the full inner_run_cost even when that's more
2583 : * than inner_rescan_run_cost, because we can assume that none of
2584 : * the inner scans ever scan the whole inner relation. So it's
2585 : * okay to assume that all the inner scan executions can be
2586 : * fractions of the full cost, even if materialization is reducing
2587 : * the rescan cost. At this writing, it's impossible to get here
2588 : * for a materialized inner scan, so inner_run_cost and
2589 : * inner_rescan_run_cost will be the same anyway; but just in
2590 : * case, use inner_run_cost for the first matched tuple and
2591 : * inner_rescan_run_cost for additional ones.
2592 : */
2593 73580 : run_cost += inner_run_cost * inner_scan_frac;
2594 73580 : if (outer_matched_rows > 1)
2595 4026 : run_cost += (outer_matched_rows - 1) * inner_rescan_run_cost * inner_scan_frac;
2596 :
2597 : /*
2598 : * Add the cost of inner-scan executions for unmatched outer rows.
2599 : * We estimate this as the same cost as returning the first tuple
2600 : * of a nonempty scan. We consider that these are all rescans,
2601 : * since we used inner_run_cost once already.
2602 : */
2603 147160 : run_cost += outer_unmatched_rows *
2604 73580 : inner_rescan_run_cost / inner_path_rows;
2605 :
2606 : /*
2607 : * We won't be evaluating any quals at all for unmatched rows, so
2608 : * don't add them to ntuples.
2609 : */
2610 : }
2611 : else
2612 : {
2613 : /*
2614 : * Here, a complicating factor is that rescans may be cheaper than
2615 : * first scans. If we never scan all the way to the end of the
2616 : * inner rel, it might be (depending on the plan type) that we'd
2617 : * never pay the whole inner first-scan run cost. However it is
2618 : * difficult to estimate whether that will happen (and it could
2619 : * not happen if there are any unmatched outer rows!), so be
2620 : * conservative and always charge the whole first-scan cost once.
2621 : * We consider this charge to correspond to the first unmatched
2622 : * outer row, unless there isn't one in our estimate, in which
2623 : * case blame it on the first matched row.
2624 : */
2625 :
2626 : /* First, count all unmatched join tuples as being processed */
2627 278044 : ntuples += outer_unmatched_rows * inner_path_rows;
2628 :
2629 : /* Now add the forced full scan, and decrement appropriate count */
2630 278044 : run_cost += inner_run_cost;
2631 278044 : if (outer_unmatched_rows >= 1)
2632 211660 : outer_unmatched_rows -= 1;
2633 : else
2634 66384 : outer_matched_rows -= 1;
2635 :
2636 : /* Add inner run cost for additional outer tuples having matches */
2637 278044 : if (outer_matched_rows > 0)
2638 72584 : run_cost += outer_matched_rows * inner_rescan_run_cost * inner_scan_frac;
2639 :
2640 : /* Add inner run cost for additional unmatched outer tuples */
2641 278044 : if (outer_unmatched_rows > 0)
2642 133954 : run_cost += outer_unmatched_rows * inner_rescan_run_cost;
2643 : }
2644 : }
2645 : else
2646 : {
2647 : /* Normal-case source costs were included in preliminary estimate */
2648 :
2649 : /* Compute number of tuples processed (not number emitted!) */
2650 209462 : ntuples = outer_path_rows * inner_path_rows;
2651 : }
2652 :
2653 : /* CPU costs */
2654 561086 : cost_qual_eval(&restrict_qual_cost, path->joinrestrictinfo, root);
2655 561086 : startup_cost += restrict_qual_cost.startup;
2656 561086 : cpu_per_tuple = cpu_tuple_cost + restrict_qual_cost.per_tuple;
2657 561086 : run_cost += cpu_per_tuple * ntuples;
2658 :
2659 : /* tlist eval costs are paid per output row, not per tuple scanned */
2660 561086 : startup_cost += path->path.pathtarget->cost.startup;
2661 561086 : run_cost += path->path.pathtarget->cost.per_tuple * path->path.rows;
2662 :
2663 561086 : path->path.startup_cost = startup_cost;
2664 561086 : path->path.total_cost = startup_cost + run_cost;
2665 561086 : }
2666 :
2667 : /*
2668 : * initial_cost_mergejoin
2669 : * Preliminary estimate of the cost of a mergejoin path.
2670 : *
2671 : * This must quickly produce lower-bound estimates of the path's startup and
2672 : * total costs. If we are unable to eliminate the proposed path from
2673 : * consideration using the lower bounds, final_cost_mergejoin will be called
2674 : * to obtain the final estimates.
2675 : *
2676 : * The exact division of labor between this function and final_cost_mergejoin
2677 : * is private to them, and represents a tradeoff between speed of the initial
2678 : * estimate and getting a tight lower bound. We choose to not examine the
2679 : * join quals here, except for obtaining the scan selectivity estimate which
2680 : * is really essential (but fortunately, use of caching keeps the cost of
2681 : * getting that down to something reasonable).
2682 : * We also assume that cost_sort is cheap enough to use here.
2683 : *
2684 : * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
2685 : * other data to be used by final_cost_mergejoin
2686 : * 'jointype' is the type of join to be performed
2687 : * 'mergeclauses' is the list of joinclauses to be used as merge clauses
2688 : * 'outer_path' is the outer input to the join
2689 : * 'inner_path' is the inner input to the join
2690 : * 'outersortkeys' is the list of sort keys for the outer path
2691 : * 'innersortkeys' is the list of sort keys for the inner path
2692 : * 'extra' contains miscellaneous information about the join
2693 : *
2694 : * Note: outersortkeys and innersortkeys should be NIL if no explicit
2695 : * sort is needed because the respective source path is already ordered.
2696 : */
2697 : void
2698 478862 : initial_cost_mergejoin(PlannerInfo *root, JoinCostWorkspace *workspace,
2699 : JoinType jointype,
2700 : List *mergeclauses,
2701 : Path *outer_path, Path *inner_path,
2702 : List *outersortkeys, List *innersortkeys,
2703 : JoinPathExtraData *extra)
2704 : {
2705 478862 : Cost startup_cost = 0;
2706 478862 : Cost run_cost = 0;
2707 478862 : double outer_path_rows = outer_path->rows;
2708 478862 : double inner_path_rows = inner_path->rows;
2709 : Cost inner_run_cost;
2710 : double outer_rows,
2711 : inner_rows,
2712 : outer_skip_rows,
2713 : inner_skip_rows;
2714 : Selectivity outerstartsel,
2715 : outerendsel,
2716 : innerstartsel,
2717 : innerendsel;
2718 : Path sort_path; /* dummy for result of cost_sort */
2719 :
2720 : /* Protect some assumptions below that rowcounts aren't zero or NaN */
2721 478862 : if (outer_path_rows <= 0 || isnan(outer_path_rows))
2722 32 : outer_path_rows = 1;
2723 478862 : if (inner_path_rows <= 0 || isnan(inner_path_rows))
2724 76 : inner_path_rows = 1;
2725 :
2726 : /*
2727 : * A merge join will stop as soon as it exhausts either input stream
2728 : * (unless it's an outer join, in which case the outer side has to be
2729 : * scanned all the way anyway). Estimate fraction of the left and right
2730 : * inputs that will actually need to be scanned. Likewise, we can
2731 : * estimate the number of rows that will be skipped before the first join
2732 : * pair is found, which should be factored into startup cost. We use only
2733 : * the first (most significant) merge clause for this purpose. Since
2734 : * mergejoinscansel() is a fairly expensive computation, we cache the
2735 : * results in the merge clause RestrictInfo.
2736 : */
2737 478862 : if (mergeclauses && jointype != JOIN_FULL)
2738 476532 : {
2739 476532 : RestrictInfo *firstclause = (RestrictInfo *) linitial(mergeclauses);
2740 : List *opathkeys;
2741 : List *ipathkeys;
2742 : PathKey *opathkey;
2743 : PathKey *ipathkey;
2744 : MergeScanSelCache *cache;
2745 :
2746 : /* Get the input pathkeys to determine the sort-order details */
2747 476532 : opathkeys = outersortkeys ? outersortkeys : outer_path->pathkeys;
2748 476532 : ipathkeys = innersortkeys ? innersortkeys : inner_path->pathkeys;
2749 : Assert(opathkeys);
2750 : Assert(ipathkeys);
2751 476532 : opathkey = (PathKey *) linitial(opathkeys);
2752 476532 : ipathkey = (PathKey *) linitial(ipathkeys);
2753 : /* debugging check */
2754 953064 : if (opathkey->pk_opfamily != ipathkey->pk_opfamily ||
2755 953064 : opathkey->pk_eclass->ec_collation != ipathkey->pk_eclass->ec_collation ||
2756 953064 : opathkey->pk_strategy != ipathkey->pk_strategy ||
2757 476532 : opathkey->pk_nulls_first != ipathkey->pk_nulls_first)
2758 0 : elog(ERROR, "left and right pathkeys do not match in mergejoin");
2759 :
2760 : /* Get the selectivity with caching */
2761 476532 : cache = cached_scansel(root, firstclause, opathkey);
2762 :
2763 476532 : if (bms_is_subset(firstclause->left_relids,
2764 476532 : outer_path->parent->relids))
2765 : {
2766 : /* left side of clause is outer */
2767 237290 : outerstartsel = cache->leftstartsel;
2768 237290 : outerendsel = cache->leftendsel;
2769 237290 : innerstartsel = cache->rightstartsel;
2770 237290 : innerendsel = cache->rightendsel;
2771 : }
2772 : else
2773 : {
2774 : /* left side of clause is inner */
2775 239242 : outerstartsel = cache->rightstartsel;
2776 239242 : outerendsel = cache->rightendsel;
2777 239242 : innerstartsel = cache->leftstartsel;
2778 239242 : innerendsel = cache->leftendsel;
2779 : }
2780 476532 : if (jointype == JOIN_LEFT ||
2781 : jointype == JOIN_ANTI)
2782 : {
2783 115552 : outerstartsel = 0.0;
2784 115552 : outerendsel = 1.0;
2785 : }
2786 360980 : else if (jointype == JOIN_RIGHT)
2787 : {
2788 85986 : innerstartsel = 0.0;
2789 85986 : innerendsel = 1.0;
2790 : }
2791 : }
2792 : else
2793 : {
2794 : /* cope with clauseless or full mergejoin */
2795 2330 : outerstartsel = innerstartsel = 0.0;
2796 2330 : outerendsel = innerendsel = 1.0;
2797 : }
2798 :
2799 : /*
2800 : * Convert selectivities to row counts. We force outer_rows and
2801 : * inner_rows to be at least 1, but the skip_rows estimates can be zero.
2802 : */
2803 478862 : outer_skip_rows = rint(outer_path_rows * outerstartsel);
2804 478862 : inner_skip_rows = rint(inner_path_rows * innerstartsel);
2805 478862 : outer_rows = clamp_row_est(outer_path_rows * outerendsel);
2806 478862 : inner_rows = clamp_row_est(inner_path_rows * innerendsel);
2807 :
2808 : Assert(outer_skip_rows <= outer_rows);
2809 : Assert(inner_skip_rows <= inner_rows);
2810 :
2811 : /*
2812 : * Readjust scan selectivities to account for above rounding. This is
2813 : * normally an insignificant effect, but when there are only a few rows in
2814 : * the inputs, failing to do this makes for a large percentage error.
2815 : */
2816 478862 : outerstartsel = outer_skip_rows / outer_path_rows;
2817 478862 : innerstartsel = inner_skip_rows / inner_path_rows;
2818 478862 : outerendsel = outer_rows / outer_path_rows;
2819 478862 : innerendsel = inner_rows / inner_path_rows;
2820 :
2821 : Assert(outerstartsel <= outerendsel);
2822 : Assert(innerstartsel <= innerendsel);
2823 :
2824 : /* cost of source data */
2825 :
2826 478862 : if (outersortkeys) /* do we need to sort outer? */
2827 : {
2828 427344 : cost_sort(&sort_path,
2829 : root,
2830 : outersortkeys,
2831 : outer_path->total_cost,
2832 : outer_path_rows,
2833 213672 : outer_path->pathtarget->width,
2834 : 0.0,
2835 : work_mem,
2836 : -1.0);
2837 213672 : startup_cost += sort_path.startup_cost;
2838 427344 : startup_cost += (sort_path.total_cost - sort_path.startup_cost)
2839 213672 : * outerstartsel;
2840 427344 : run_cost += (sort_path.total_cost - sort_path.startup_cost)
2841 213672 : * (outerendsel - outerstartsel);
2842 : }
2843 : else
2844 : {
2845 265190 : startup_cost += outer_path->startup_cost;
2846 530380 : startup_cost += (outer_path->total_cost - outer_path->startup_cost)
2847 265190 : * outerstartsel;
2848 530380 : run_cost += (outer_path->total_cost - outer_path->startup_cost)
2849 265190 : * (outerendsel - outerstartsel);
2850 : }
2851 :
2852 478862 : if (innersortkeys) /* do we need to sort inner? */
2853 : {
2854 737996 : cost_sort(&sort_path,
2855 : root,
2856 : innersortkeys,
2857 : inner_path->total_cost,
2858 : inner_path_rows,
2859 368998 : inner_path->pathtarget->width,
2860 : 0.0,
2861 : work_mem,
2862 : -1.0);
2863 368998 : startup_cost += sort_path.startup_cost;
2864 737996 : startup_cost += (sort_path.total_cost - sort_path.startup_cost)
2865 368998 : * innerstartsel;
2866 737996 : inner_run_cost = (sort_path.total_cost - sort_path.startup_cost)
2867 368998 : * (innerendsel - innerstartsel);
2868 : }
2869 : else
2870 : {
2871 109864 : startup_cost += inner_path->startup_cost;
2872 219728 : startup_cost += (inner_path->total_cost - inner_path->startup_cost)
2873 109864 : * innerstartsel;
2874 219728 : inner_run_cost = (inner_path->total_cost - inner_path->startup_cost)
2875 109864 : * (innerendsel - innerstartsel);
2876 : }
2877 :
2878 : /*
2879 : * We can't yet determine whether rescanning occurs, or whether
2880 : * materialization of the inner input should be done. The minimum
2881 : * possible inner input cost, regardless of rescan and materialization
2882 : * considerations, is inner_run_cost. We include that in
2883 : * workspace->total_cost, but not yet in run_cost.
2884 : */
2885 :
2886 : /* CPU costs left for later */
2887 :
2888 : /* Public result fields */
2889 478862 : workspace->startup_cost = startup_cost;
2890 478862 : workspace->total_cost = startup_cost + run_cost + inner_run_cost;
2891 : /* Save private data for final_cost_mergejoin */
2892 478862 : workspace->run_cost = run_cost;
2893 478862 : workspace->inner_run_cost = inner_run_cost;
2894 478862 : workspace->outer_rows = outer_rows;
2895 478862 : workspace->inner_rows = inner_rows;
2896 478862 : workspace->outer_skip_rows = outer_skip_rows;
2897 478862 : workspace->inner_skip_rows = inner_skip_rows;
2898 478862 : }
2899 :
2900 : /*
2901 : * final_cost_mergejoin
2902 : * Final estimate of the cost and result size of a mergejoin path.
2903 : *
2904 : * Unlike other costsize functions, this routine makes two actual decisions:
2905 : * whether the executor will need to do mark/restore, and whether we should
2906 : * materialize the inner path. It would be logically cleaner to build
2907 : * separate paths testing these alternatives, but that would require repeating
2908 : * most of the cost calculations, which are not all that cheap. Since the
2909 : * choice will not affect output pathkeys or startup cost, only total cost,
2910 : * there is no possibility of wanting to keep more than one path. So it seems
2911 : * best to make the decisions here and record them in the path's
2912 : * skip_mark_restore and materialize_inner fields.
2913 : *
2914 : * Mark/restore overhead is usually required, but can be skipped if we know
2915 : * that the executor need find only one match per outer tuple, and that the
2916 : * mergeclauses are sufficient to identify a match.
2917 : *
2918 : * We materialize the inner path if we need mark/restore and either the inner
2919 : * path can't support mark/restore, or it's cheaper to use an interposed
2920 : * Material node to handle mark/restore.
2921 : *
2922 : * 'path' is already filled in except for the rows and cost fields and
2923 : * skip_mark_restore and materialize_inner
2924 : * 'workspace' is the result from initial_cost_mergejoin
2925 : * 'extra' contains miscellaneous information about the join
2926 : */
2927 : void
2928 116928 : final_cost_mergejoin(PlannerInfo *root, MergePath *path,
2929 : JoinCostWorkspace *workspace,
2930 : JoinPathExtraData *extra)
2931 : {
2932 116928 : Path *outer_path = path->jpath.outerjoinpath;
2933 116928 : Path *inner_path = path->jpath.innerjoinpath;
2934 116928 : double inner_path_rows = inner_path->rows;
2935 116928 : List *mergeclauses = path->path_mergeclauses;
2936 116928 : List *innersortkeys = path->innersortkeys;
2937 116928 : Cost startup_cost = workspace->startup_cost;
2938 116928 : Cost run_cost = workspace->run_cost;
2939 116928 : Cost inner_run_cost = workspace->inner_run_cost;
2940 116928 : double outer_rows = workspace->outer_rows;
2941 116928 : double inner_rows = workspace->inner_rows;
2942 116928 : double outer_skip_rows = workspace->outer_skip_rows;
2943 116928 : double inner_skip_rows = workspace->inner_skip_rows;
2944 : Cost cpu_per_tuple,
2945 : bare_inner_cost,
2946 : mat_inner_cost;
2947 : QualCost merge_qual_cost;
2948 : QualCost qp_qual_cost;
2949 : double mergejointuples,
2950 : rescannedtuples;
2951 : double rescanratio;
2952 :
2953 : /* Protect some assumptions below that rowcounts aren't zero or NaN */
2954 116928 : if (inner_path_rows <= 0 || isnan(inner_path_rows))
2955 52 : inner_path_rows = 1;
2956 :
2957 : /* Mark the path with the correct row estimate */
2958 116928 : if (path->jpath.path.param_info)
2959 392 : path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
2960 : else
2961 116536 : path->jpath.path.rows = path->jpath.path.parent->rows;
2962 :
2963 : /* For partial paths, scale row estimate. */
2964 116928 : if (path->jpath.path.parallel_workers > 0)
2965 : {
2966 2524 : double parallel_divisor = get_parallel_divisor(&path->jpath.path);
2967 :
2968 2524 : path->jpath.path.rows =
2969 2524 : clamp_row_est(path->jpath.path.rows / parallel_divisor);
2970 : }
2971 :
2972 : /*
2973 : * We could include disable_cost in the preliminary estimate, but that
2974 : * would amount to optimizing for the case where the join method is
2975 : * disabled, which doesn't seem like the way to bet.
2976 : */
2977 116928 : if (!enable_mergejoin)
2978 0 : startup_cost += disable_cost;
2979 :
2980 : /*
2981 : * Compute cost of the mergequals and qpquals (other restriction clauses)
2982 : * separately.
2983 : */
2984 116928 : cost_qual_eval(&merge_qual_cost, mergeclauses, root);
2985 116928 : cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo, root);
2986 116928 : qp_qual_cost.startup -= merge_qual_cost.startup;
2987 116928 : qp_qual_cost.per_tuple -= merge_qual_cost.per_tuple;
2988 :
2989 : /*
2990 : * With a SEMI or ANTI join, or if the innerrel is known unique, the
2991 : * executor will stop scanning for matches after the first match. When
2992 : * all the joinclauses are merge clauses, this means we don't ever need to
2993 : * back up the merge, and so we can skip mark/restore overhead.
2994 : */
2995 232874 : if ((path->jpath.jointype == JOIN_SEMI ||
2996 227742 : path->jpath.jointype == JOIN_ANTI ||
2997 166902 : extra->inner_unique) &&
2998 55106 : (list_length(path->jpath.joinrestrictinfo) ==
2999 55106 : list_length(path->path_mergeclauses)))
3000 49124 : path->skip_mark_restore = true;
3001 : else
3002 67804 : path->skip_mark_restore = false;
3003 :
3004 : /*
3005 : * Get approx # tuples passing the mergequals. We use approx_tuple_count
3006 : * here because we need an estimate done with JOIN_INNER semantics.
3007 : */
3008 116928 : mergejointuples = approx_tuple_count(root, &path->jpath, mergeclauses);
3009 :
3010 : /*
3011 : * When there are equal merge keys in the outer relation, the mergejoin
3012 : * must rescan any matching tuples in the inner relation. This means
3013 : * re-fetching inner tuples; we have to estimate how often that happens.
3014 : *
3015 : * For regular inner and outer joins, the number of re-fetches can be
3016 : * estimated approximately as size of merge join output minus size of
3017 : * inner relation. Assume that the distinct key values are 1, 2, ..., and
3018 : * denote the number of values of each key in the outer relation as m1,
3019 : * m2, ...; in the inner relation, n1, n2, ... Then we have
3020 : *
3021 : * size of join = m1 * n1 + m2 * n2 + ...
3022 : *
3023 : * number of rescanned tuples = (m1 - 1) * n1 + (m2 - 1) * n2 + ... = m1 *
3024 : * n1 + m2 * n2 + ... - (n1 + n2 + ...) = size of join - size of inner
3025 : * relation
3026 : *
3027 : * This equation works correctly for outer tuples having no inner match
3028 : * (nk = 0), but not for inner tuples having no outer match (mk = 0); we
3029 : * are effectively subtracting those from the number of rescanned tuples,
3030 : * when we should not. Can we do better without expensive selectivity
3031 : * computations?
3032 : *
3033 : * The whole issue is moot if we are working from a unique-ified outer
3034 : * input, or if we know we don't need to mark/restore at all.
3035 : */
3036 116928 : if (IsA(outer_path, UniquePath) ||path->skip_mark_restore)
3037 49392 : rescannedtuples = 0;
3038 : else
3039 : {
3040 67536 : rescannedtuples = mergejointuples - inner_path_rows;
3041 : /* Must clamp because of possible underestimate */
3042 67536 : if (rescannedtuples < 0)
3043 32212 : rescannedtuples = 0;
3044 : }
3045 :
3046 : /*
3047 : * We'll inflate various costs this much to account for rescanning. Note
3048 : * that this is to be multiplied by something involving inner_rows, or
3049 : * another number related to the portion of the inner rel we'll scan.
3050 : */
3051 116928 : rescanratio = 1.0 + (rescannedtuples / inner_rows);
3052 :
3053 : /*
3054 : * Decide whether we want to materialize the inner input to shield it from
3055 : * mark/restore and performing re-fetches. Our cost model for regular
3056 : * re-fetches is that a re-fetch costs the same as an original fetch,
3057 : * which is probably an overestimate; but on the other hand we ignore the
3058 : * bookkeeping costs of mark/restore. Not clear if it's worth developing
3059 : * a more refined model. So we just need to inflate the inner run cost by
3060 : * rescanratio.
3061 : */
3062 116928 : bare_inner_cost = inner_run_cost * rescanratio;
3063 :
3064 : /*
3065 : * When we interpose a Material node the re-fetch cost is assumed to be
3066 : * just cpu_operator_cost per tuple, independently of the underlying
3067 : * plan's cost; and we charge an extra cpu_operator_cost per original
3068 : * fetch as well. Note that we're assuming the materialize node will
3069 : * never spill to disk, since it only has to remember tuples back to the
3070 : * last mark. (If there are a huge number of duplicates, our other cost
3071 : * factors will make the path so expensive that it probably won't get
3072 : * chosen anyway.) So we don't use cost_rescan here.
3073 : *
3074 : * Note: keep this estimate in sync with create_mergejoin_plan's labeling
3075 : * of the generated Material node.
3076 : */
3077 116928 : mat_inner_cost = inner_run_cost +
3078 116928 : cpu_operator_cost * inner_rows * rescanratio;
3079 :
3080 : /*
3081 : * If we don't need mark/restore at all, we don't need materialization.
3082 : */
3083 116928 : if (path->skip_mark_restore)
3084 49124 : path->materialize_inner = false;
3085 :
3086 : /*
3087 : * Prefer materializing if it looks cheaper, unless the user has asked to
3088 : * suppress materialization.
3089 : */
3090 67804 : else if (enable_material && mat_inner_cost < bare_inner_cost)
3091 956 : path->materialize_inner = true;
3092 :
3093 : /*
3094 : * Even if materializing doesn't look cheaper, we *must* do it if the
3095 : * inner path is to be used directly (without sorting) and it doesn't
3096 : * support mark/restore.
3097 : *
3098 : * Since the inner side must be ordered, and only Sorts and IndexScans can
3099 : * create order to begin with, and they both support mark/restore, you
3100 : * might think there's no problem --- but you'd be wrong. Nestloop and
3101 : * merge joins can *preserve* the order of their inputs, so they can be
3102 : * selected as the input of a mergejoin, and they don't support
3103 : * mark/restore at present.
3104 : *
3105 : * We don't test the value of enable_material here, because
3106 : * materialization is required for correctness in this case, and turning
3107 : * it off does not entitle us to deliver an invalid plan.
3108 : */
3109 69416 : else if (innersortkeys == NIL &&
3110 2568 : !ExecSupportsMarkRestore(inner_path))
3111 692 : path->materialize_inner = true;
3112 :
3113 : /*
3114 : * Also, force materializing if the inner path is to be sorted and the
3115 : * sort is expected to spill to disk. This is because the final merge
3116 : * pass can be done on-the-fly if it doesn't have to support mark/restore.
3117 : * We don't try to adjust the cost estimates for this consideration,
3118 : * though.
3119 : *
3120 : * Since materialization is a performance optimization in this case,
3121 : * rather than necessary for correctness, we skip it if enable_material is
3122 : * off.
3123 : */
3124 130436 : else if (enable_material && innersortkeys != NIL &&
3125 64280 : relation_byte_size(inner_path_rows,
3126 128560 : inner_path->pathtarget->width) >
3127 64280 : (work_mem * 1024L))
3128 140 : path->materialize_inner = true;
3129 : else
3130 66016 : path->materialize_inner = false;
3131 :
3132 : /* Charge the right incremental cost for the chosen case */
3133 116928 : if (path->materialize_inner)
3134 1788 : run_cost += mat_inner_cost;
3135 : else
3136 115140 : run_cost += bare_inner_cost;
3137 :
3138 : /* CPU costs */
3139 :
3140 : /*
3141 : * The number of tuple comparisons needed is approximately number of outer
3142 : * rows plus number of inner rows plus number of rescanned tuples (can we
3143 : * refine this?). At each one, we need to evaluate the mergejoin quals.
3144 : */
3145 116928 : startup_cost += merge_qual_cost.startup;
3146 233856 : startup_cost += merge_qual_cost.per_tuple *
3147 116928 : (outer_skip_rows + inner_skip_rows * rescanratio);
3148 233856 : run_cost += merge_qual_cost.per_tuple *
3149 233856 : ((outer_rows - outer_skip_rows) +
3150 116928 : (inner_rows - inner_skip_rows) * rescanratio);
3151 :
3152 : /*
3153 : * For each tuple that gets through the mergejoin proper, we charge
3154 : * cpu_tuple_cost plus the cost of evaluating additional restriction
3155 : * clauses that are to be applied at the join. (This is pessimistic since
3156 : * not all of the quals may get evaluated at each tuple.)
3157 : *
3158 : * Note: we could adjust for SEMI/ANTI joins skipping some qual
3159 : * evaluations here, but it's probably not worth the trouble.
3160 : */
3161 116928 : startup_cost += qp_qual_cost.startup;
3162 116928 : cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
3163 116928 : run_cost += cpu_per_tuple * mergejointuples;
3164 :
3165 : /* tlist eval costs are paid per output row, not per tuple scanned */
3166 116928 : startup_cost += path->jpath.path.pathtarget->cost.startup;
3167 116928 : run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows;
3168 :
3169 116928 : path->jpath.path.startup_cost = startup_cost;
3170 116928 : path->jpath.path.total_cost = startup_cost + run_cost;
3171 116928 : }
3172 :
3173 : /*
3174 : * run mergejoinscansel() with caching
3175 : */
3176 : static MergeScanSelCache *
3177 476532 : cached_scansel(PlannerInfo *root, RestrictInfo *rinfo, PathKey *pathkey)
3178 : {
3179 : MergeScanSelCache *cache;
3180 : ListCell *lc;
3181 : Selectivity leftstartsel,
3182 : leftendsel,
3183 : rightstartsel,
3184 : rightendsel;
3185 : MemoryContext oldcontext;
3186 :
3187 : /* Do we have this result already? */
3188 476536 : foreach(lc, rinfo->scansel_cache)
3189 : {
3190 425518 : cache = (MergeScanSelCache *) lfirst(lc);
3191 851036 : if (cache->opfamily == pathkey->pk_opfamily &&
3192 851036 : cache->collation == pathkey->pk_eclass->ec_collation &&
3193 851032 : cache->strategy == pathkey->pk_strategy &&
3194 425514 : cache->nulls_first == pathkey->pk_nulls_first)
3195 425514 : return cache;
3196 : }
3197 :
3198 : /* Nope, do the computation */
3199 102036 : mergejoinscansel(root,
3200 51018 : (Node *) rinfo->clause,
3201 : pathkey->pk_opfamily,
3202 : pathkey->pk_strategy,
3203 51018 : pathkey->pk_nulls_first,
3204 : &leftstartsel,
3205 : &leftendsel,
3206 : &rightstartsel,
3207 : &rightendsel);
3208 :
3209 : /* Cache the result in suitably long-lived workspace */
3210 51018 : oldcontext = MemoryContextSwitchTo(root->planner_cxt);
3211 :
3212 51018 : cache = (MergeScanSelCache *) palloc(sizeof(MergeScanSelCache));
3213 51018 : cache->opfamily = pathkey->pk_opfamily;
3214 51018 : cache->collation = pathkey->pk_eclass->ec_collation;
3215 51018 : cache->strategy = pathkey->pk_strategy;
3216 51018 : cache->nulls_first = pathkey->pk_nulls_first;
3217 51018 : cache->leftstartsel = leftstartsel;
3218 51018 : cache->leftendsel = leftendsel;
3219 51018 : cache->rightstartsel = rightstartsel;
3220 51018 : cache->rightendsel = rightendsel;
3221 :
3222 51018 : rinfo->scansel_cache = lappend(rinfo->scansel_cache, cache);
3223 :
3224 51018 : MemoryContextSwitchTo(oldcontext);
3225 :
3226 51018 : return cache;
3227 : }
3228 :
3229 : /*
3230 : * initial_cost_hashjoin
3231 : * Preliminary estimate of the cost of a hashjoin path.
3232 : *
3233 : * This must quickly produce lower-bound estimates of the path's startup and
3234 : * total costs. If we are unable to eliminate the proposed path from
3235 : * consideration using the lower bounds, final_cost_hashjoin will be called
3236 : * to obtain the final estimates.
3237 : *
3238 : * The exact division of labor between this function and final_cost_hashjoin
3239 : * is private to them, and represents a tradeoff between speed of the initial
3240 : * estimate and getting a tight lower bound. We choose to not examine the
3241 : * join quals here (other than by counting the number of hash clauses),
3242 : * so we can't do much with CPU costs. We do assume that
3243 : * ExecChooseHashTableSize is cheap enough to use here.
3244 : *
3245 : * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
3246 : * other data to be used by final_cost_hashjoin
3247 : * 'jointype' is the type of join to be performed
3248 : * 'hashclauses' is the list of joinclauses to be used as hash clauses
3249 : * 'outer_path' is the outer input to the join
3250 : * 'inner_path' is the inner input to the join
3251 : * 'extra' contains miscellaneous information about the join
3252 : * 'parallel_hash' indicates that inner_path is partial and that a shared
3253 : * hash table will be built in parallel
3254 : */
3255 : void
3256 260526 : initial_cost_hashjoin(PlannerInfo *root, JoinCostWorkspace *workspace,
3257 : JoinType jointype,
3258 : List *hashclauses,
3259 : Path *outer_path, Path *inner_path,
3260 : JoinPathExtraData *extra,
3261 : bool parallel_hash)
3262 : {
3263 260526 : Cost startup_cost = 0;
3264 260526 : Cost run_cost = 0;
3265 260526 : double outer_path_rows = outer_path->rows;
3266 260526 : double inner_path_rows = inner_path->rows;
3267 260526 : double inner_path_rows_total = inner_path_rows;
3268 260526 : int num_hashclauses = list_length(hashclauses);
3269 : int numbuckets;
3270 : int numbatches;
3271 : int num_skew_mcvs;
3272 : size_t space_allowed; /* unused */
3273 :
3274 : /* cost of source data */
3275 260526 : startup_cost += outer_path->startup_cost;
3276 260526 : run_cost += outer_path->total_cost - outer_path->startup_cost;
3277 260526 : startup_cost += inner_path->total_cost;
3278 :
3279 : /*
3280 : * Cost of computing hash function: must do it once per input tuple. We
3281 : * charge one cpu_operator_cost for each column's hash function. Also,
3282 : * tack on one cpu_tuple_cost per inner row, to model the costs of
3283 : * inserting the row into the hashtable.
3284 : *
3285 : * XXX when a hashclause is more complex than a single operator, we really
3286 : * should charge the extra eval costs of the left or right side, as
3287 : * appropriate, here. This seems more work than it's worth at the moment.
3288 : */
3289 521052 : startup_cost += (cpu_operator_cost * num_hashclauses + cpu_tuple_cost)
3290 260526 : * inner_path_rows;
3291 260526 : run_cost += cpu_operator_cost * num_hashclauses * outer_path_rows;
3292 :
3293 : /*
3294 : * If this is a parallel hash build, then the value we have for
3295 : * inner_rows_total currently refers only to the rows returned by each
3296 : * participant. For shared hash table size estimation, we need the total
3297 : * number, so we need to undo the division.
3298 : */
3299 260526 : if (parallel_hash)
3300 2664 : inner_path_rows_total *= get_parallel_divisor(inner_path);
3301 :
3302 : /*
3303 : * Get hash table size that executor would use for inner relation.
3304 : *
3305 : * XXX for the moment, always assume that skew optimization will be
3306 : * performed. As long as SKEW_WORK_MEM_PERCENT is small, it's not worth
3307 : * trying to determine that for sure.
3308 : *
3309 : * XXX at some point it might be interesting to try to account for skew
3310 : * optimization in the cost estimate, but for now, we don't.
3311 : */
3312 521052 : ExecChooseHashTableSize(inner_path_rows_total,
3313 260526 : inner_path->pathtarget->width,
3314 : true, /* useskew */
3315 : parallel_hash, /* try_combined_work_mem */
3316 : outer_path->parallel_workers,
3317 : &space_allowed,
3318 : &numbuckets,
3319 : &numbatches,
3320 : &num_skew_mcvs);
3321 :
3322 : /*
3323 : * If inner relation is too big then we will need to "batch" the join,
3324 : * which implies writing and reading most of the tuples to disk an extra
3325 : * time. Charge seq_page_cost per page, since the I/O should be nice and
3326 : * sequential. Writing the inner rel counts as startup cost, all the rest
3327 : * as run cost.
3328 : */
3329 260526 : if (numbatches > 1)
3330 : {
3331 2160 : double outerpages = page_size(outer_path_rows,
3332 2160 : outer_path->pathtarget->width);
3333 2160 : double innerpages = page_size(inner_path_rows,
3334 2160 : inner_path->pathtarget->width);
3335 :
3336 2160 : startup_cost += seq_page_cost * innerpages;
3337 2160 : run_cost += seq_page_cost * (innerpages + 2 * outerpages);
3338 : }
3339 :
3340 : /* CPU costs left for later */
3341 :
3342 : /* Public result fields */
3343 260526 : workspace->startup_cost = startup_cost;
3344 260526 : workspace->total_cost = startup_cost + run_cost;
3345 : /* Save private data for final_cost_hashjoin */
3346 260526 : workspace->run_cost = run_cost;
3347 260526 : workspace->numbuckets = numbuckets;
3348 260526 : workspace->numbatches = numbatches;
3349 260526 : workspace->inner_rows_total = inner_path_rows_total;
3350 260526 : }
3351 :
3352 : /*
3353 : * final_cost_hashjoin
3354 : * Final estimate of the cost and result size of a hashjoin path.
3355 : *
3356 : * Note: the numbatches estimate is also saved into 'path' for use later
3357 : *
3358 : * 'path' is already filled in except for the rows and cost fields and
3359 : * num_batches
3360 : * 'workspace' is the result from initial_cost_hashjoin
3361 : * 'extra' contains miscellaneous information about the join
3362 : */
3363 : void
3364 134394 : final_cost_hashjoin(PlannerInfo *root, HashPath *path,
3365 : JoinCostWorkspace *workspace,
3366 : JoinPathExtraData *extra)
3367 : {
3368 134394 : Path *outer_path = path->jpath.outerjoinpath;
3369 134394 : Path *inner_path = path->jpath.innerjoinpath;
3370 134394 : double outer_path_rows = outer_path->rows;
3371 134394 : double inner_path_rows = inner_path->rows;
3372 134394 : double inner_path_rows_total = workspace->inner_rows_total;
3373 134394 : List *hashclauses = path->path_hashclauses;
3374 134394 : Cost startup_cost = workspace->startup_cost;
3375 134394 : Cost run_cost = workspace->run_cost;
3376 134394 : int numbuckets = workspace->numbuckets;
3377 134394 : int numbatches = workspace->numbatches;
3378 : Cost cpu_per_tuple;
3379 : QualCost hash_qual_cost;
3380 : QualCost qp_qual_cost;
3381 : double hashjointuples;
3382 : double virtualbuckets;
3383 : Selectivity innerbucketsize;
3384 : Selectivity innermcvfreq;
3385 : ListCell *hcl;
3386 :
3387 : /* Mark the path with the correct row estimate */
3388 134394 : if (path->jpath.path.param_info)
3389 762 : path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
3390 : else
3391 133632 : path->jpath.path.rows = path->jpath.path.parent->rows;
3392 :
3393 : /* For partial paths, scale row estimate. */
3394 134394 : if (path->jpath.path.parallel_workers > 0)
3395 : {
3396 2440 : double parallel_divisor = get_parallel_divisor(&path->jpath.path);
3397 :
3398 2440 : path->jpath.path.rows =
3399 2440 : clamp_row_est(path->jpath.path.rows / parallel_divisor);
3400 : }
3401 :
3402 : /*
3403 : * We could include disable_cost in the preliminary estimate, but that
3404 : * would amount to optimizing for the case where the join method is
3405 : * disabled, which doesn't seem like the way to bet.
3406 : */
3407 134394 : if (!enable_hashjoin)
3408 12 : startup_cost += disable_cost;
3409 :
3410 : /* mark the path with estimated # of batches */
3411 134394 : path->num_batches = numbatches;
3412 :
3413 : /* store the total number of tuples (sum of partial row estimates) */
3414 134394 : path->inner_rows_total = inner_path_rows_total;
3415 :
3416 : /* and compute the number of "virtual" buckets in the whole join */
3417 134394 : virtualbuckets = (double) numbuckets * (double) numbatches;
3418 :
3419 : /*
3420 : * Determine bucketsize fraction and MCV frequency for the inner relation.
3421 : * We use the smallest bucketsize or MCV frequency estimated for any
3422 : * individual hashclause; this is undoubtedly conservative.
3423 : *
3424 : * BUT: if inner relation has been unique-ified, we can assume it's good
3425 : * for hashing. This is important both because it's the right answer, and
3426 : * because we avoid contaminating the cache with a value that's wrong for
3427 : * non-unique-ified paths.
3428 : */
3429 134394 : if (IsA(inner_path, UniquePath))
3430 : {
3431 396 : innerbucketsize = 1.0 / virtualbuckets;
3432 396 : innermcvfreq = 0.0;
3433 : }
3434 : else
3435 : {
3436 133998 : innerbucketsize = 1.0;
3437 133998 : innermcvfreq = 1.0;
3438 303144 : foreach(hcl, hashclauses)
3439 : {
3440 169146 : RestrictInfo *restrictinfo = lfirst_node(RestrictInfo, hcl);
3441 : Selectivity thisbucketsize;
3442 : Selectivity thismcvfreq;
3443 :
3444 : /*
3445 : * First we have to figure out which side of the hashjoin clause
3446 : * is the inner side.
3447 : *
3448 : * Since we tend to visit the same clauses over and over when
3449 : * planning a large query, we cache the bucket stats estimates in
3450 : * the RestrictInfo node to avoid repeated lookups of statistics.
3451 : */
3452 169146 : if (bms_is_subset(restrictinfo->right_relids,
3453 169146 : inner_path->parent->relids))
3454 : {
3455 : /* righthand side is inner */
3456 91102 : thisbucketsize = restrictinfo->right_bucketsize;
3457 91102 : if (thisbucketsize < 0)
3458 : {
3459 : /* not cached yet */
3460 85616 : estimate_hash_bucket_stats(root,
3461 42808 : get_rightop(restrictinfo->clause),
3462 : virtualbuckets,
3463 : &restrictinfo->right_mcvfreq,
3464 : &restrictinfo->right_bucketsize);
3465 42808 : thisbucketsize = restrictinfo->right_bucketsize;
3466 : }
3467 91102 : thismcvfreq = restrictinfo->right_mcvfreq;
3468 : }
3469 : else
3470 : {
3471 : Assert(bms_is_subset(restrictinfo->left_relids,
3472 : inner_path->parent->relids));
3473 : /* lefthand side is inner */
3474 78044 : thisbucketsize = restrictinfo->left_bucketsize;
3475 78044 : if (thisbucketsize < 0)
3476 : {
3477 : /* not cached yet */
3478 64708 : estimate_hash_bucket_stats(root,
3479 32354 : get_leftop(restrictinfo->clause),
3480 : virtualbuckets,
3481 : &restrictinfo->left_mcvfreq,
3482 : &restrictinfo->left_bucketsize);
3483 32354 : thisbucketsize = restrictinfo->left_bucketsize;
3484 : }
3485 78044 : thismcvfreq = restrictinfo->left_mcvfreq;
3486 : }
3487 :
3488 169146 : if (innerbucketsize > thisbucketsize)
3489 78486 : innerbucketsize = thisbucketsize;
3490 169146 : if (innermcvfreq > thismcvfreq)
3491 143704 : innermcvfreq = thismcvfreq;
3492 : }
3493 : }
3494 :
3495 : /*
3496 : * If the bucket holding the inner MCV would exceed work_mem, we don't
3497 : * want to hash unless there is really no other alternative, so apply
3498 : * disable_cost. (The executor normally copes with excessive memory usage
3499 : * by splitting batches, but obviously it cannot separate equal values
3500 : * that way, so it will be unable to drive the batch size below work_mem
3501 : * when this is true.)
3502 : */
3503 268788 : if (relation_byte_size(clamp_row_est(inner_path_rows * innermcvfreq),
3504 268788 : inner_path->pathtarget->width) >
3505 134394 : (work_mem * 1024L))
3506 0 : startup_cost += disable_cost;
3507 :
3508 : /*
3509 : * Compute cost of the hashquals and qpquals (other restriction clauses)
3510 : * separately.
3511 : */
3512 134394 : cost_qual_eval(&hash_qual_cost, hashclauses, root);
3513 134394 : cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo, root);
3514 134394 : qp_qual_cost.startup -= hash_qual_cost.startup;
3515 134394 : qp_qual_cost.per_tuple -= hash_qual_cost.per_tuple;
3516 :
3517 : /* CPU costs */
3518 :
3519 268092 : if (path->jpath.jointype == JOIN_SEMI ||
3520 257734 : path->jpath.jointype == JOIN_ANTI ||
3521 124036 : extra->inner_unique)
3522 54642 : {
3523 : double outer_matched_rows;
3524 : Selectivity inner_scan_frac;
3525 :
3526 : /*
3527 : * With a SEMI or ANTI join, or if the innerrel is known unique, the
3528 : * executor will stop after the first match.
3529 : *
3530 : * For an outer-rel row that has at least one match, we can expect the
3531 : * bucket scan to stop after a fraction 1/(match_count+1) of the
3532 : * bucket's rows, if the matches are evenly distributed. Since they
3533 : * probably aren't quite evenly distributed, we apply a fuzz factor of
3534 : * 2.0 to that fraction. (If we used a larger fuzz factor, we'd have
3535 : * to clamp inner_scan_frac to at most 1.0; but since match_count is
3536 : * at least 1, no such clamp is needed now.)
3537 : */
3538 54642 : outer_matched_rows = rint(outer_path_rows * extra->semifactors.outer_match_frac);
3539 54642 : inner_scan_frac = 2.0 / (extra->semifactors.match_count + 1.0);
3540 :
3541 54642 : startup_cost += hash_qual_cost.startup;
3542 163926 : run_cost += hash_qual_cost.per_tuple * outer_matched_rows *
3543 109284 : clamp_row_est(inner_path_rows * innerbucketsize * inner_scan_frac) * 0.5;
3544 :
3545 : /*
3546 : * For unmatched outer-rel rows, the picture is quite a lot different.
3547 : * In the first place, there is no reason to assume that these rows
3548 : * preferentially hit heavily-populated buckets; instead assume they
3549 : * are uncorrelated with the inner distribution and so they see an
3550 : * average bucket size of inner_path_rows / virtualbuckets. In the
3551 : * second place, it seems likely that they will have few if any exact
3552 : * hash-code matches and so very few of the tuples in the bucket will
3553 : * actually require eval of the hash quals. We don't have any good
3554 : * way to estimate how many will, but for the moment assume that the
3555 : * effective cost per bucket entry is one-tenth what it is for
3556 : * matchable tuples.
3557 : */
3558 163926 : run_cost += hash_qual_cost.per_tuple *
3559 109284 : (outer_path_rows - outer_matched_rows) *
3560 109284 : clamp_row_est(inner_path_rows / virtualbuckets) * 0.05;
3561 :
3562 : /* Get # of tuples that will pass the basic join */
3563 54642 : if (path->jpath.jointype == JOIN_ANTI)
3564 9662 : hashjointuples = outer_path_rows - outer_matched_rows;
3565 : else
3566 44980 : hashjointuples = outer_matched_rows;
3567 : }
3568 : else
3569 : {
3570 : /*
3571 : * The number of tuple comparisons needed is the number of outer
3572 : * tuples times the typical number of tuples in a hash bucket, which
3573 : * is the inner relation size times its bucketsize fraction. At each
3574 : * one, we need to evaluate the hashjoin quals. But actually,
3575 : * charging the full qual eval cost at each tuple is pessimistic,
3576 : * since we don't evaluate the quals unless the hash values match
3577 : * exactly. For lack of a better idea, halve the cost estimate to
3578 : * allow for that.
3579 : */
3580 79752 : startup_cost += hash_qual_cost.startup;
3581 239256 : run_cost += hash_qual_cost.per_tuple * outer_path_rows *
3582 159504 : clamp_row_est(inner_path_rows * innerbucketsize) * 0.5;
3583 :
3584 : /*
3585 : * Get approx # tuples passing the hashquals. We use
3586 : * approx_tuple_count here because we need an estimate done with
3587 : * JOIN_INNER semantics.
3588 : */
3589 79752 : hashjointuples = approx_tuple_count(root, &path->jpath, hashclauses);
3590 : }
3591 :
3592 : /*
3593 : * For each tuple that gets through the hashjoin proper, we charge
3594 : * cpu_tuple_cost plus the cost of evaluating additional restriction
3595 : * clauses that are to be applied at the join. (This is pessimistic since
3596 : * not all of the quals may get evaluated at each tuple.)
3597 : */
3598 134394 : startup_cost += qp_qual_cost.startup;
3599 134394 : cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
3600 134394 : run_cost += cpu_per_tuple * hashjointuples;
3601 :
3602 : /* tlist eval costs are paid per output row, not per tuple scanned */
3603 134394 : startup_cost += path->jpath.path.pathtarget->cost.startup;
3604 134394 : run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows;
3605 :
3606 134394 : path->jpath.path.startup_cost = startup_cost;
3607 134394 : path->jpath.path.total_cost = startup_cost + run_cost;
3608 134394 : }
3609 :
3610 :
3611 : /*
3612 : * cost_subplan
3613 : * Figure the costs for a SubPlan (or initplan).
3614 : *
3615 : * Note: we could dig the subplan's Plan out of the root list, but in practice
3616 : * all callers have it handy already, so we make them pass it.
3617 : */
3618 : void
3619 45954 : cost_subplan(PlannerInfo *root, SubPlan *subplan, Plan *plan)
3620 : {
3621 : QualCost sp_cost;
3622 :
3623 : /* Figure any cost for evaluating the testexpr */
3624 45954 : cost_qual_eval(&sp_cost,
3625 45954 : make_ands_implicit((Expr *) subplan->testexpr),
3626 : root);
3627 :
3628 45954 : if (subplan->useHashTable)
3629 : {
3630 : /*
3631 : * If we are using a hash table for the subquery outputs, then the
3632 : * cost of evaluating the query is a one-time cost. We charge one
3633 : * cpu_operator_cost per tuple for the work of loading the hashtable,
3634 : * too.
3635 : */
3636 2472 : sp_cost.startup += plan->total_cost +
3637 1236 : cpu_operator_cost * plan->plan_rows;
3638 :
3639 : /*
3640 : * The per-tuple costs include the cost of evaluating the lefthand
3641 : * expressions, plus the cost of probing the hashtable. We already
3642 : * accounted for the lefthand expressions as part of the testexpr, and
3643 : * will also have counted one cpu_operator_cost for each comparison
3644 : * operator. That is probably too low for the probing cost, but it's
3645 : * hard to make a better estimate, so live with it for now.
3646 : */
3647 : }
3648 : else
3649 : {
3650 : /*
3651 : * Otherwise we will be rescanning the subplan output on each
3652 : * evaluation. We need to estimate how much of the output we will
3653 : * actually need to scan. NOTE: this logic should agree with the
3654 : * tuple_fraction estimates used by make_subplan() in
3655 : * plan/subselect.c.
3656 : */
3657 44718 : Cost plan_run_cost = plan->total_cost - plan->startup_cost;
3658 :
3659 44718 : if (subplan->subLinkType == EXISTS_SUBLINK)
3660 : {
3661 : /* we only need to fetch 1 tuple; clamp to avoid zero divide */
3662 1594 : sp_cost.per_tuple += plan_run_cost / clamp_row_est(plan->plan_rows);
3663 : }
3664 86236 : else if (subplan->subLinkType == ALL_SUBLINK ||
3665 43112 : subplan->subLinkType == ANY_SUBLINK)
3666 : {
3667 : /* assume we need 50% of the tuples */
3668 60 : sp_cost.per_tuple += 0.50 * plan_run_cost;
3669 : /* also charge a cpu_operator_cost per row examined */
3670 60 : sp_cost.per_tuple += 0.50 * plan->plan_rows * cpu_operator_cost;
3671 : }
3672 : else
3673 : {
3674 : /* assume we need all tuples */
3675 43064 : sp_cost.per_tuple += plan_run_cost;
3676 : }
3677 :
3678 : /*
3679 : * Also account for subplan's startup cost. If the subplan is
3680 : * uncorrelated or undirect correlated, AND its topmost node is one
3681 : * that materializes its output, assume that we'll only need to pay
3682 : * its startup cost once; otherwise assume we pay the startup cost
3683 : * every time.
3684 : */
3685 54432 : if (subplan->parParam == NIL &&
3686 9714 : ExecMaterializesOutput(nodeTag(plan)))
3687 170 : sp_cost.startup += plan->startup_cost;
3688 : else
3689 44548 : sp_cost.per_tuple += plan->startup_cost;
3690 : }
3691 :
3692 45954 : subplan->startup_cost = sp_cost.startup;
3693 45954 : subplan->per_call_cost = sp_cost.per_tuple;
3694 45954 : }
3695 :
3696 :
3697 : /*
3698 : * cost_rescan
3699 : * Given a finished Path, estimate the costs of rescanning it after
3700 : * having done so the first time. For some Path types a rescan is
3701 : * cheaper than an original scan (if no parameters change), and this
3702 : * function embodies knowledge about that. The default is to return
3703 : * the same costs stored in the Path. (Note that the cost estimates
3704 : * actually stored in Paths are always for first scans.)
3705 : *
3706 : * This function is not currently intended to model effects such as rescans
3707 : * being cheaper due to disk block caching; what we are concerned with is
3708 : * plan types wherein the executor caches results explicitly, or doesn't
3709 : * redo startup calculations, etc.
3710 : */
3711 : static void
3712 1017014 : cost_rescan(PlannerInfo *root, Path *path,
3713 : Cost *rescan_startup_cost, /* output parameters */
3714 : Cost *rescan_total_cost)
3715 : {
3716 1017014 : switch (path->pathtype)
3717 : {
3718 : case T_FunctionScan:
3719 :
3720 : /*
3721 : * Currently, nodeFunctionscan.c always executes the function to
3722 : * completion before returning any rows, and caches the results in
3723 : * a tuplestore. So the function eval cost is all startup cost
3724 : * and isn't paid over again on rescans. However, all run costs
3725 : * will be paid over again.
3726 : */
3727 9708 : *rescan_startup_cost = 0;
3728 9708 : *rescan_total_cost = path->total_cost - path->startup_cost;
3729 9708 : break;
3730 : case T_HashJoin:
3731 :
3732 : /*
3733 : * If it's a single-batch join, we don't need to rebuild the hash
3734 : * table during a rescan.
3735 : */
3736 34248 : if (((HashPath *) path)->num_batches == 1)
3737 : {
3738 : /* Startup cost is exactly the cost of hash table building */
3739 34248 : *rescan_startup_cost = 0;
3740 34248 : *rescan_total_cost = path->total_cost - path->startup_cost;
3741 : }
3742 : else
3743 : {
3744 : /* Otherwise, no special treatment */
3745 0 : *rescan_startup_cost = path->startup_cost;
3746 0 : *rescan_total_cost = path->total_cost;
3747 : }
3748 34248 : break;
3749 : case T_CteScan:
3750 : case T_WorkTableScan:
3751 : {
3752 : /*
3753 : * These plan types materialize their final result in a
3754 : * tuplestore or tuplesort object. So the rescan cost is only
3755 : * cpu_tuple_cost per tuple, unless the result is large enough
3756 : * to spill to disk.
3757 : */
3758 1976 : Cost run_cost = cpu_tuple_cost * path->rows;
3759 1976 : double nbytes = relation_byte_size(path->rows,
3760 1976 : path->pathtarget->width);
3761 1976 : long work_mem_bytes = work_mem * 1024L;
3762 :
3763 1976 : if (nbytes > work_mem_bytes)
3764 : {
3765 : /* It will spill, so account for re-read cost */
3766 0 : double npages = ceil(nbytes / BLCKSZ);
3767 :
3768 0 : run_cost += seq_page_cost * npages;
3769 : }
3770 1976 : *rescan_startup_cost = 0;
3771 1976 : *rescan_total_cost = run_cost;
3772 : }
3773 1976 : break;
3774 : case T_Material:
3775 : case T_Sort:
3776 : {
3777 : /*
3778 : * These plan types not only materialize their results, but do
3779 : * not implement qual filtering or projection. So they are
3780 : * even cheaper to rescan than the ones above. We charge only
3781 : * cpu_operator_cost per tuple. (Note: keep that in sync with
3782 : * the run_cost charge in cost_sort, and also see comments in
3783 : * cost_material before you change it.)
3784 : */
3785 394924 : Cost run_cost = cpu_operator_cost * path->rows;
3786 394924 : double nbytes = relation_byte_size(path->rows,
3787 394924 : path->pathtarget->width);
3788 394924 : long work_mem_bytes = work_mem * 1024L;
3789 :
3790 394924 : if (nbytes > work_mem_bytes)
3791 : {
3792 : /* It will spill, so account for re-read cost */
3793 3772 : double npages = ceil(nbytes / BLCKSZ);
3794 :
3795 3772 : run_cost += seq_page_cost * npages;
3796 : }
3797 394924 : *rescan_startup_cost = 0;
3798 394924 : *rescan_total_cost = run_cost;
3799 : }
3800 394924 : break;
3801 : default:
3802 576158 : *rescan_startup_cost = path->startup_cost;
3803 576158 : *rescan_total_cost = path->total_cost;
3804 576158 : break;
3805 : }
3806 1017014 : }
3807 :
3808 :
3809 : /*
3810 : * cost_qual_eval
3811 : * Estimate the CPU costs of evaluating a WHERE clause.
3812 : * The input can be either an implicitly-ANDed list of boolean
3813 : * expressions, or a list of RestrictInfo nodes. (The latter is
3814 : * preferred since it allows caching of the results.)
3815 : * The result includes both a one-time (startup) component,
3816 : * and a per-evaluation component.
3817 : */
3818 : void
3819 1709912 : cost_qual_eval(QualCost *cost, List *quals, PlannerInfo *root)
3820 : {
3821 : cost_qual_eval_context context;
3822 : ListCell *l;
3823 :
3824 1709912 : context.root = root;
3825 1709912 : context.total.startup = 0;
3826 1709912 : context.total.per_tuple = 0;
3827 :
3828 : /* We don't charge any cost for the implicit ANDing at top level ... */
3829 :
3830 3198954 : foreach(l, quals)
3831 : {
3832 1489042 : Node *qual = (Node *) lfirst(l);
3833 :
3834 1489042 : cost_qual_eval_walker(qual, &context);
3835 : }
3836 :
3837 1709912 : *cost = context.total;
3838 1709912 : }
3839 :
3840 : /*
3841 : * cost_qual_eval_node
3842 : * As above, for a single RestrictInfo or expression.
3843 : */
3844 : void
3845 1101004 : cost_qual_eval_node(QualCost *cost, Node *qual, PlannerInfo *root)
3846 : {
3847 : cost_qual_eval_context context;
3848 :
3849 1101004 : context.root = root;
3850 1101004 : context.total.startup = 0;
3851 1101004 : context.total.per_tuple = 0;
3852 :
3853 1101004 : cost_qual_eval_walker(qual, &context);
3854 :
3855 1101004 : *cost = context.total;
3856 1101004 : }
3857 :
3858 : static bool
3859 4438624 : cost_qual_eval_walker(Node *node, cost_qual_eval_context *context)
3860 : {
3861 4438624 : if (node == NULL)
3862 49220 : return false;
3863 :
3864 : /*
3865 : * RestrictInfo nodes contain an eval_cost field reserved for this
3866 : * routine's use, so that it's not necessary to evaluate the qual clause's
3867 : * cost more than once. If the clause's cost hasn't been computed yet,
3868 : * the field's startup value will contain -1.
3869 : */
3870 4389404 : if (IsA(node, RestrictInfo))
3871 : {
3872 1553396 : RestrictInfo *rinfo = (RestrictInfo *) node;
3873 :
3874 1553396 : if (rinfo->eval_cost.startup < 0)
3875 : {
3876 : cost_qual_eval_context locContext;
3877 :
3878 292568 : locContext.root = context->root;
3879 292568 : locContext.total.startup = 0;
3880 292568 : locContext.total.per_tuple = 0;
3881 :
3882 : /*
3883 : * For an OR clause, recurse into the marked-up tree so that we
3884 : * set the eval_cost for contained RestrictInfos too.
3885 : */
3886 292568 : if (rinfo->orclause)
3887 4630 : cost_qual_eval_walker((Node *) rinfo->orclause, &locContext);
3888 : else
3889 287938 : cost_qual_eval_walker((Node *) rinfo->clause, &locContext);
3890 :
3891 : /*
3892 : * If the RestrictInfo is marked pseudoconstant, it will be tested
3893 : * only once, so treat its cost as all startup cost.
3894 : */
3895 292568 : if (rinfo->pseudoconstant)
3896 : {
3897 : /* count one execution during startup */
3898 2412 : locContext.total.startup += locContext.total.per_tuple;
3899 2412 : locContext.total.per_tuple = 0;
3900 : }
3901 292568 : rinfo->eval_cost = locContext.total;
3902 : }
3903 1553396 : context->total.startup += rinfo->eval_cost.startup;
3904 1553396 : context->total.per_tuple += rinfo->eval_cost.per_tuple;
3905 : /* do NOT recurse into children */
3906 1553396 : return false;
3907 : }
3908 :
3909 : /*
3910 : * For each operator or function node in the given tree, we charge the
3911 : * estimated execution cost given by pg_proc.procost (remember to multiply
3912 : * this by cpu_operator_cost).
3913 : *
3914 : * Vars and Consts are charged zero, and so are boolean operators (AND,
3915 : * OR, NOT). Simplistic, but a lot better than no model at all.
3916 : *
3917 : * Should we try to account for the possibility of short-circuit
3918 : * evaluation of AND/OR? Probably *not*, because that would make the
3919 : * results depend on the clause ordering, and we are not in any position
3920 : * to expect that the current ordering of the clauses is the one that's
3921 : * going to end up being used. The above per-RestrictInfo caching would
3922 : * not mix well with trying to re-order clauses anyway.
3923 : *
3924 : * Another issue that is entirely ignored here is that if a set-returning
3925 : * function is below top level in the tree, the functions/operators above
3926 : * it will need to be evaluated multiple times. In practical use, such
3927 : * cases arise so seldom as to not be worth the added complexity needed;
3928 : * moreover, since our rowcount estimates for functions tend to be pretty
3929 : * phony, the results would also be pretty phony.
3930 : */
3931 2836008 : if (IsA(node, FuncExpr))
3932 : {
3933 244406 : add_function_cost(context->root, ((FuncExpr *) node)->funcid, node,
3934 : &context->total);
3935 : }
3936 4846254 : else if (IsA(node, OpExpr) ||
3937 4508840 : IsA(node, DistinctExpr) ||
3938 2254188 : IsA(node, NullIfExpr))
3939 : {
3940 : /* rely on struct equivalence to treat these all alike */
3941 337466 : set_opfuncid((OpExpr *) node);
3942 337466 : add_function_cost(context->root, ((OpExpr *) node)->opfuncid, node,
3943 : &context->total);
3944 : }
3945 2254136 : else if (IsA(node, ScalarArrayOpExpr))
3946 : {
3947 : /*
3948 : * Estimate that the operator will be applied to about half of the
3949 : * array elements before the answer is determined.
3950 : */
3951 20568 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) node;
3952 20568 : Node *arraynode = (Node *) lsecond(saop->args);
3953 : QualCost sacosts;
3954 :
3955 20568 : set_sa_opfuncid(saop);
3956 20568 : sacosts.startup = sacosts.per_tuple = 0;
3957 20568 : add_function_cost(context->root, saop->opfuncid, NULL,
3958 : &sacosts);
3959 20568 : context->total.startup += sacosts.startup;
3960 61704 : context->total.per_tuple += sacosts.per_tuple *
3961 41136 : estimate_array_length(arraynode) * 0.5;
3962 : }
3963 4434966 : else if (IsA(node, Aggref) ||
3964 2201398 : IsA(node, WindowFunc))
3965 : {
3966 : /*
3967 : * Aggref and WindowFunc nodes are (and should be) treated like Vars,
3968 : * ie, zero execution cost in the current model, because they behave
3969 : * essentially like Vars at execution. We disregard the costs of
3970 : * their input expressions for the same reason. The actual execution
3971 : * costs of the aggregate/window functions and their arguments have to
3972 : * be factored into plan-node-specific costing of the Agg or WindowAgg
3973 : * plan node.
3974 : */
3975 33654 : return false; /* don't recurse into children */
3976 : }
3977 2199914 : else if (IsA(node, CoerceViaIO))
3978 : {
3979 6728 : CoerceViaIO *iocoerce = (CoerceViaIO *) node;
3980 : Oid iofunc;
3981 : Oid typioparam;
3982 : bool typisvarlena;
3983 :
3984 : /* check the result type's input function */
3985 6728 : getTypeInputInfo(iocoerce->resulttype,
3986 : &iofunc, &typioparam);
3987 6728 : add_function_cost(context->root, iofunc, NULL,
3988 : &context->total);
3989 : /* check the input type's output function */
3990 6728 : getTypeOutputInfo(exprType((Node *) iocoerce->arg),
3991 : &iofunc, &typisvarlena);
3992 6728 : add_function_cost(context->root, iofunc, NULL,
3993 : &context->total);
3994 : }
3995 2193186 : else if (IsA(node, ArrayCoerceExpr))
3996 : {
3997 254 : ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node;
3998 : QualCost perelemcost;
3999 :
4000 254 : cost_qual_eval_node(&perelemcost, (Node *) acoerce->elemexpr,
4001 : context->root);
4002 254 : context->total.startup += perelemcost.startup;
4003 254 : if (perelemcost.per_tuple > 0)
4004 76 : context->total.per_tuple += perelemcost.per_tuple *
4005 38 : estimate_array_length((Node *) acoerce->arg);
4006 : }
4007 2192932 : else if (IsA(node, RowCompareExpr))
4008 : {
4009 : /* Conservatively assume we will check all the columns */
4010 104 : RowCompareExpr *rcexpr = (RowCompareExpr *) node;
4011 : ListCell *lc;
4012 :
4013 348 : foreach(lc, rcexpr->opnos)
4014 : {
4015 244 : Oid opid = lfirst_oid(lc);
4016 :
4017 244 : add_function_cost(context->root, get_opcode(opid), NULL,
4018 : &context->total);
4019 : }
4020 : }
4021 4385574 : else if (IsA(node, MinMaxExpr) ||
4022 4383624 : IsA(node, SQLValueFunction) ||
4023 4381400 : IsA(node, XmlExpr) ||
4024 4354266 : IsA(node, CoerceToDomain) ||
4025 2163744 : IsA(node, NextValueExpr))
4026 : {
4027 : /* Treat all these as having cost 1 */
4028 29214 : context->total.per_tuple += cpu_operator_cost;
4029 : }
4030 2163614 : else if (IsA(node, CurrentOfExpr))
4031 : {
4032 : /* Report high cost to prevent selection of anything but TID scan */
4033 384 : context->total.startup += disable_cost;
4034 : }
4035 2163230 : else if (IsA(node, SubLink))
4036 : {
4037 : /* This routine should not be applied to un-planned expressions */
4038 0 : elog(ERROR, "cannot handle unplanned sub-select");
4039 : }
4040 2163230 : else if (IsA(node, SubPlan))
4041 : {
4042 : /*
4043 : * A subplan node in an expression typically indicates that the
4044 : * subplan will be executed on each evaluation, so charge accordingly.
4045 : * (Sub-selects that can be executed as InitPlans have already been
4046 : * removed from the expression.)
4047 : */
4048 52460 : SubPlan *subplan = (SubPlan *) node;
4049 :
4050 52460 : context->total.startup += subplan->startup_cost;
4051 52460 : context->total.per_tuple += subplan->per_call_cost;
4052 :
4053 : /*
4054 : * We don't want to recurse into the testexpr, because it was already
4055 : * counted in the SubPlan node's costs. So we're done.
4056 : */
4057 52460 : return false;
4058 : }
4059 2110770 : else if (IsA(node, AlternativeSubPlan))
4060 : {
4061 : /*
4062 : * Arbitrarily use the first alternative plan for costing. (We should
4063 : * certainly only include one alternative, and we don't yet have
4064 : * enough information to know which one the executor is most likely to
4065 : * use.)
4066 : */
4067 1422 : AlternativeSubPlan *asplan = (AlternativeSubPlan *) node;
4068 :
4069 1422 : return cost_qual_eval_walker((Node *) linitial(asplan->subplans),
4070 : context);
4071 : }
4072 2109348 : else if (IsA(node, PlaceHolderVar))
4073 : {
4074 : /*
4075 : * A PlaceHolderVar should be given cost zero when considering general
4076 : * expression evaluation costs. The expense of doing the contained
4077 : * expression is charged as part of the tlist eval costs of the scan
4078 : * or join where the PHV is first computed (see set_rel_width and
4079 : * add_placeholders_to_joinrel). If we charged it again here, we'd be
4080 : * double-counting the cost for each level of plan that the PHV
4081 : * bubbles up through. Hence, return without recursing into the
4082 : * phexpr.
4083 : */
4084 1012 : return false;
4085 : }
4086 :
4087 : /* recurse into children */
4088 2747460 : return expression_tree_walker(node, cost_qual_eval_walker,
4089 : (void *) context);
4090 : }
4091 :
4092 : /*
4093 : * get_restriction_qual_cost
4094 : * Compute evaluation costs of a baserel's restriction quals, plus any
4095 : * movable join quals that have been pushed down to the scan.
4096 : * Results are returned into *qpqual_cost.
4097 : *
4098 : * This is a convenience subroutine that works for seqscans and other cases
4099 : * where all the given quals will be evaluated the hard way. It's not useful
4100 : * for cost_index(), for example, where the index machinery takes care of
4101 : * some of the quals. We assume baserestrictcost was previously set by
4102 : * set_baserel_size_estimates().
4103 : */
4104 : static void
4105 516388 : get_restriction_qual_cost(PlannerInfo *root, RelOptInfo *baserel,
4106 : ParamPathInfo *param_info,
4107 : QualCost *qpqual_cost)
4108 : {
4109 516388 : if (param_info)
4110 : {
4111 : /* Include costs of pushed-down clauses */
4112 83692 : cost_qual_eval(qpqual_cost, param_info->ppi_clauses, root);
4113 :
4114 83692 : qpqual_cost->startup += baserel->baserestrictcost.startup;
4115 83692 : qpqual_cost->per_tuple += baserel->baserestrictcost.per_tuple;
4116 : }
4117 : else
4118 432696 : *qpqual_cost = baserel->baserestrictcost;
4119 516388 : }
4120 :
4121 :
4122 : /*
4123 : * compute_semi_anti_join_factors
4124 : * Estimate how much of the inner input a SEMI, ANTI, or inner_unique join
4125 : * can be expected to scan.
4126 : *
4127 : * In a hash or nestloop SEMI/ANTI join, the executor will stop scanning
4128 : * inner rows as soon as it finds a match to the current outer row.
4129 : * The same happens if we have detected the inner rel is unique.
4130 : * We should therefore adjust some of the cost components for this effect.
4131 : * This function computes some estimates needed for these adjustments.
4132 : * These estimates will be the same regardless of the particular paths used
4133 : * for the outer and inner relation, so we compute these once and then pass
4134 : * them to all the join cost estimation functions.
4135 : *
4136 : * Input parameters:
4137 : * joinrel: join relation under consideration
4138 : * outerrel: outer relation under consideration
4139 : * innerrel: inner relation under consideration
4140 : * jointype: if not JOIN_SEMI or JOIN_ANTI, we assume it's inner_unique
4141 : * sjinfo: SpecialJoinInfo relevant to this join
4142 : * restrictlist: join quals
4143 : * Output parameters:
4144 : * *semifactors is filled in (see pathnodes.h for field definitions)
4145 : */
4146 : void
4147 106684 : compute_semi_anti_join_factors(PlannerInfo *root,
4148 : RelOptInfo *joinrel,
4149 : RelOptInfo *outerrel,
4150 : RelOptInfo *innerrel,
4151 : JoinType jointype,
4152 : SpecialJoinInfo *sjinfo,
4153 : List *restrictlist,
4154 : SemiAntiJoinFactors *semifactors)
4155 : {
4156 : Selectivity jselec;
4157 : Selectivity nselec;
4158 : Selectivity avgmatch;
4159 : SpecialJoinInfo norm_sjinfo;
4160 : List *joinquals;
4161 : ListCell *l;
4162 :
4163 : /*
4164 : * In an ANTI join, we must ignore clauses that are "pushed down", since
4165 : * those won't affect the match logic. In a SEMI join, we do not
4166 : * distinguish joinquals from "pushed down" quals, so just use the whole
4167 : * restrictinfo list. For other outer join types, we should consider only
4168 : * non-pushed-down quals, so that this devolves to an IS_OUTER_JOIN check.
4169 : */
4170 106684 : if (IS_OUTER_JOIN(jointype))
4171 : {
4172 44094 : joinquals = NIL;
4173 98692 : foreach(l, restrictlist)
4174 : {
4175 54598 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, l);
4176 :
4177 54598 : if (!RINFO_IS_PUSHED_DOWN(rinfo, joinrel->relids))
4178 52730 : joinquals = lappend(joinquals, rinfo);
4179 : }
4180 : }
4181 : else
4182 62590 : joinquals = restrictlist;
4183 :
4184 : /*
4185 : * Get the JOIN_SEMI or JOIN_ANTI selectivity of the join clauses.
4186 : */
4187 106684 : jselec = clauselist_selectivity(root,
4188 : joinquals,
4189 : 0,
4190 : (jointype == JOIN_ANTI) ? JOIN_ANTI : JOIN_SEMI,
4191 : sjinfo);
4192 :
4193 : /*
4194 : * Also get the normal inner-join selectivity of the join clauses.
4195 : */
4196 106684 : norm_sjinfo.type = T_SpecialJoinInfo;
4197 106684 : norm_sjinfo.min_lefthand = outerrel->relids;
4198 106684 : norm_sjinfo.min_righthand = innerrel->relids;
4199 106684 : norm_sjinfo.syn_lefthand = outerrel->relids;
4200 106684 : norm_sjinfo.syn_righthand = innerrel->relids;
4201 106684 : norm_sjinfo.jointype = JOIN_INNER;
4202 : /* we don't bother trying to make the remaining fields valid */
4203 106684 : norm_sjinfo.lhs_strict = false;
4204 106684 : norm_sjinfo.delay_upper_joins = false;
4205 106684 : norm_sjinfo.semi_can_btree = false;
4206 106684 : norm_sjinfo.semi_can_hash = false;
4207 106684 : norm_sjinfo.semi_operators = NIL;
4208 106684 : norm_sjinfo.semi_rhs_exprs = NIL;
4209 :
4210 106684 : nselec = clauselist_selectivity(root,
4211 : joinquals,
4212 : 0,
4213 : JOIN_INNER,
4214 : &norm_sjinfo);
4215 :
4216 : /* Avoid leaking a lot of ListCells */
4217 106684 : if (IS_OUTER_JOIN(jointype))
4218 44094 : list_free(joinquals);
4219 :
4220 : /*
4221 : * jselec can be interpreted as the fraction of outer-rel rows that have
4222 : * any matches (this is true for both SEMI and ANTI cases). And nselec is
4223 : * the fraction of the Cartesian product that matches. So, the average
4224 : * number of matches for each outer-rel row that has at least one match is
4225 : * nselec * inner_rows / jselec.
4226 : *
4227 : * Note: it is correct to use the inner rel's "rows" count here, even
4228 : * though we might later be considering a parameterized inner path with
4229 : * fewer rows. This is because we have included all the join clauses in
4230 : * the selectivity estimate.
4231 : */
4232 106684 : if (jselec > 0) /* protect against zero divide */
4233 : {
4234 106526 : avgmatch = nselec * innerrel->rows / jselec;
4235 : /* Clamp to sane range */
4236 106526 : avgmatch = Max(1.0, avgmatch);
4237 : }
4238 : else
4239 158 : avgmatch = 1.0;
4240 :
4241 106684 : semifactors->outer_match_frac = jselec;
4242 106684 : semifactors->match_count = avgmatch;
4243 106684 : }
4244 :
4245 : /*
4246 : * has_indexed_join_quals
4247 : * Check whether all the joinquals of a nestloop join are used as
4248 : * inner index quals.
4249 : *
4250 : * If the inner path of a SEMI/ANTI join is an indexscan (including bitmap
4251 : * indexscan) that uses all the joinquals as indexquals, we can assume that an
4252 : * unmatched outer tuple is cheap to process, whereas otherwise it's probably
4253 : * expensive.
4254 : */
4255 : static bool
4256 351624 : has_indexed_join_quals(NestPath *joinpath)
4257 : {
4258 351624 : Relids joinrelids = joinpath->path.parent->relids;
4259 351624 : Path *innerpath = joinpath->innerjoinpath;
4260 : List *indexclauses;
4261 : bool found_one;
4262 : ListCell *lc;
4263 :
4264 : /* If join still has quals to evaluate, it's not fast */
4265 351624 : if (joinpath->joinrestrictinfo != NIL)
4266 273806 : return false;
4267 : /* Nor if the inner path isn't parameterized at all */
4268 77818 : if (innerpath->param_info == NULL)
4269 3200 : return false;
4270 :
4271 : /* Find the indexclauses list for the inner scan */
4272 74618 : switch (innerpath->pathtype)
4273 : {
4274 : case T_IndexScan:
4275 : case T_IndexOnlyScan:
4276 74270 : indexclauses = ((IndexPath *) innerpath)->indexclauses;
4277 74270 : break;
4278 : case T_BitmapHeapScan:
4279 : {
4280 : /* Accept only a simple bitmap scan, not AND/OR cases */
4281 4 : Path *bmqual = ((BitmapHeapPath *) innerpath)->bitmapqual;
4282 :
4283 4 : if (IsA(bmqual, IndexPath))
4284 4 : indexclauses = ((IndexPath *) bmqual)->indexclauses;
4285 : else
4286 0 : return false;
4287 4 : break;
4288 : }
4289 : default:
4290 :
4291 : /*
4292 : * If it's not a simple indexscan, it probably doesn't run quickly
4293 : * for zero rows out, even if it's a parameterized path using all
4294 : * the joinquals.
4295 : */
4296 344 : return false;
4297 : }
4298 :
4299 : /*
4300 : * Examine the inner path's param clauses. Any that are from the outer
4301 : * path must be found in the indexclauses list, either exactly or in an
4302 : * equivalent form generated by equivclass.c. Also, we must find at least
4303 : * one such clause, else it's a clauseless join which isn't fast.
4304 : */
4305 74274 : found_one = false;
4306 149468 : foreach(lc, innerpath->param_info->ppi_clauses)
4307 : {
4308 75888 : RestrictInfo *rinfo = (RestrictInfo *) lfirst(lc);
4309 :
4310 75888 : if (join_clause_is_movable_into(rinfo,
4311 75888 : innerpath->parent->relids,
4312 : joinrelids))
4313 : {
4314 75888 : if (!is_redundant_with_indexclauses(rinfo, indexclauses))
4315 694 : return false;
4316 75194 : found_one = true;
4317 : }
4318 : }
4319 73580 : return found_one;
4320 : }
4321 :
4322 :
4323 : /*
4324 : * approx_tuple_count
4325 : * Quick-and-dirty estimation of the number of join rows passing
4326 : * a set of qual conditions.
4327 : *
4328 : * The quals can be either an implicitly-ANDed list of boolean expressions,
4329 : * or a list of RestrictInfo nodes (typically the latter).
4330 : *
4331 : * We intentionally compute the selectivity under JOIN_INNER rules, even
4332 : * if it's some type of outer join. This is appropriate because we are
4333 : * trying to figure out how many tuples pass the initial merge or hash
4334 : * join step.
4335 : *
4336 : * This is quick-and-dirty because we bypass clauselist_selectivity, and
4337 : * simply multiply the independent clause selectivities together. Now
4338 : * clauselist_selectivity often can't do any better than that anyhow, but
4339 : * for some situations (such as range constraints) it is smarter. However,
4340 : * we can't effectively cache the results of clauselist_selectivity, whereas
4341 : * the individual clause selectivities can be and are cached.
4342 : *
4343 : * Since we are only using the results to estimate how many potential
4344 : * output tuples are generated and passed through qpqual checking, it
4345 : * seems OK to live with the approximation.
4346 : */
4347 : static double
4348 196680 : approx_tuple_count(PlannerInfo *root, JoinPath *path, List *quals)
4349 : {
4350 : double tuples;
4351 196680 : double outer_tuples = path->outerjoinpath->rows;
4352 196680 : double inner_tuples = path->innerjoinpath->rows;
4353 : SpecialJoinInfo sjinfo;
4354 196680 : Selectivity selec = 1.0;
4355 : ListCell *l;
4356 :
4357 : /*
4358 : * Make up a SpecialJoinInfo for JOIN_INNER semantics.
4359 : */
4360 196680 : sjinfo.type = T_SpecialJoinInfo;
4361 196680 : sjinfo.min_lefthand = path->outerjoinpath->parent->relids;
4362 196680 : sjinfo.min_righthand = path->innerjoinpath->parent->relids;
4363 196680 : sjinfo.syn_lefthand = path->outerjoinpath->parent->relids;
4364 196680 : sjinfo.syn_righthand = path->innerjoinpath->parent->relids;
4365 196680 : sjinfo.jointype = JOIN_INNER;
4366 : /* we don't bother trying to make the remaining fields valid */
4367 196680 : sjinfo.lhs_strict = false;
4368 196680 : sjinfo.delay_upper_joins = false;
4369 196680 : sjinfo.semi_can_btree = false;
4370 196680 : sjinfo.semi_can_hash = false;
4371 196680 : sjinfo.semi_operators = NIL;
4372 196680 : sjinfo.semi_rhs_exprs = NIL;
4373 :
4374 : /* Get the approximate selectivity */
4375 432690 : foreach(l, quals)
4376 : {
4377 236010 : Node *qual = (Node *) lfirst(l);
4378 :
4379 : /* Note that clause_selectivity will be able to cache its result */
4380 236010 : selec *= clause_selectivity(root, qual, 0, JOIN_INNER, &sjinfo);
4381 : }
4382 :
4383 : /* Apply it to the input relation sizes */
4384 196680 : tuples = selec * outer_tuples * inner_tuples;
4385 :
4386 196680 : return clamp_row_est(tuples);
4387 : }
4388 :
4389 :
4390 : /*
4391 : * set_baserel_size_estimates
4392 : * Set the size estimates for the given base relation.
4393 : *
4394 : * The rel's targetlist and restrictinfo list must have been constructed
4395 : * already, and rel->tuples must be set.
4396 : *
4397 : * We set the following fields of the rel node:
4398 : * rows: the estimated number of output tuples (after applying
4399 : * restriction clauses).
4400 : * width: the estimated average output tuple width in bytes.
4401 : * baserestrictcost: estimated cost of evaluating baserestrictinfo clauses.
4402 : */
4403 : void
4404 260256 : set_baserel_size_estimates(PlannerInfo *root, RelOptInfo *rel)
4405 : {
4406 : double nrows;
4407 :
4408 : /* Should only be applied to base relations */
4409 : Assert(rel->relid > 0);
4410 :
4411 520512 : nrows = rel->tuples *
4412 260256 : clauselist_selectivity(root,
4413 : rel->baserestrictinfo,
4414 : 0,
4415 : JOIN_INNER,
4416 : NULL);
4417 :
4418 260256 : rel->rows = clamp_row_est(nrows);
4419 :
4420 260256 : cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo, root);
4421 :
4422 260256 : set_rel_width(root, rel);
4423 260256 : }
4424 :
4425 : /*
4426 : * get_parameterized_baserel_size
4427 : * Make a size estimate for a parameterized scan of a base relation.
4428 : *
4429 : * 'param_clauses' lists the additional join clauses to be used.
4430 : *
4431 : * set_baserel_size_estimates must have been applied already.
4432 : */
4433 : double
4434 59666 : get_parameterized_baserel_size(PlannerInfo *root, RelOptInfo *rel,
4435 : List *param_clauses)
4436 : {
4437 : List *allclauses;
4438 : double nrows;
4439 :
4440 : /*
4441 : * Estimate the number of rows returned by the parameterized scan, knowing
4442 : * that it will apply all the extra join clauses as well as the rel's own
4443 : * restriction clauses. Note that we force the clauses to be treated as
4444 : * non-join clauses during selectivity estimation.
4445 : */
4446 59666 : allclauses = list_concat(list_copy(param_clauses),
4447 : rel->baserestrictinfo);
4448 119332 : nrows = rel->tuples *
4449 59666 : clauselist_selectivity(root,
4450 : allclauses,
4451 59666 : rel->relid, /* do not use 0! */
4452 : JOIN_INNER,
4453 : NULL);
4454 59666 : nrows = clamp_row_est(nrows);
4455 : /* For safety, make sure result is not more than the base estimate */
4456 59666 : if (nrows > rel->rows)
4457 0 : nrows = rel->rows;
4458 59666 : return nrows;
4459 : }
4460 :
4461 : /*
4462 : * set_joinrel_size_estimates
4463 : * Set the size estimates for the given join relation.
4464 : *
4465 : * The rel's targetlist must have been constructed already, and a
4466 : * restriction clause list that matches the given component rels must
4467 : * be provided.
4468 : *
4469 : * Since there is more than one way to make a joinrel for more than two
4470 : * base relations, the results we get here could depend on which component
4471 : * rel pair is provided. In theory we should get the same answers no matter
4472 : * which pair is provided; in practice, since the selectivity estimation
4473 : * routines don't handle all cases equally well, we might not. But there's
4474 : * not much to be done about it. (Would it make sense to repeat the
4475 : * calculations for each pair of input rels that's encountered, and somehow
4476 : * average the results? Probably way more trouble than it's worth, and
4477 : * anyway we must keep the rowcount estimate the same for all paths for the
4478 : * joinrel.)
4479 : *
4480 : * We set only the rows field here. The reltarget field was already set by
4481 : * build_joinrel_tlist, and baserestrictcost is not used for join rels.
4482 : */
4483 : void
4484 94458 : set_joinrel_size_estimates(PlannerInfo *root, RelOptInfo *rel,
4485 : RelOptInfo *outer_rel,
4486 : RelOptInfo *inner_rel,
4487 : SpecialJoinInfo *sjinfo,
4488 : List *restrictlist)
4489 : {
4490 94458 : rel->rows = calc_joinrel_size_estimate(root,
4491 : rel,
4492 : outer_rel,
4493 : inner_rel,
4494 : outer_rel->rows,
4495 : inner_rel->rows,
4496 : sjinfo,
4497 : restrictlist);
4498 94458 : }
4499 :
4500 : /*
4501 : * get_parameterized_joinrel_size
4502 : * Make a size estimate for a parameterized scan of a join relation.
4503 : *
4504 : * 'rel' is the joinrel under consideration.
4505 : * 'outer_path', 'inner_path' are (probably also parameterized) Paths that
4506 : * produce the relations being joined.
4507 : * 'sjinfo' is any SpecialJoinInfo relevant to this join.
4508 : * 'restrict_clauses' lists the join clauses that need to be applied at the
4509 : * join node (including any movable clauses that were moved down to this join,
4510 : * and not including any movable clauses that were pushed down into the
4511 : * child paths).
4512 : *
4513 : * set_joinrel_size_estimates must have been applied already.
4514 : */
4515 : double
4516 1756 : get_parameterized_joinrel_size(PlannerInfo *root, RelOptInfo *rel,
4517 : Path *outer_path,
4518 : Path *inner_path,
4519 : SpecialJoinInfo *sjinfo,
4520 : List *restrict_clauses)
4521 : {
4522 : double nrows;
4523 :
4524 : /*
4525 : * Estimate the number of rows returned by the parameterized join as the
4526 : * sizes of the input paths times the selectivity of the clauses that have
4527 : * ended up at this join node.
4528 : *
4529 : * As with set_joinrel_size_estimates, the rowcount estimate could depend
4530 : * on the pair of input paths provided, though ideally we'd get the same
4531 : * estimate for any pair with the same parameterization.
4532 : */
4533 1756 : nrows = calc_joinrel_size_estimate(root,
4534 : rel,
4535 : outer_path->parent,
4536 : inner_path->parent,
4537 : outer_path->rows,
4538 : inner_path->rows,
4539 : sjinfo,
4540 : restrict_clauses);
4541 : /* For safety, make sure result is not more than the base estimate */
4542 1756 : if (nrows > rel->rows)
4543 40 : nrows = rel->rows;
4544 1756 : return nrows;
4545 : }
4546 :
4547 : /*
4548 : * calc_joinrel_size_estimate
4549 : * Workhorse for set_joinrel_size_estimates and
4550 : * get_parameterized_joinrel_size.
4551 : *
4552 : * outer_rel/inner_rel are the relations being joined, but they should be
4553 : * assumed to have sizes outer_rows/inner_rows; those numbers might be less
4554 : * than what rel->rows says, when we are considering parameterized paths.
4555 : */
4556 : static double
4557 96214 : calc_joinrel_size_estimate(PlannerInfo *root,
4558 : RelOptInfo *joinrel,
4559 : RelOptInfo *outer_rel,
4560 : RelOptInfo *inner_rel,
4561 : double outer_rows,
4562 : double inner_rows,
4563 : SpecialJoinInfo *sjinfo,
4564 : List *restrictlist_in)
4565 : {
4566 : /* This apparently-useless variable dodges a compiler bug in VS2013: */
4567 96214 : List *restrictlist = restrictlist_in;
4568 96214 : JoinType jointype = sjinfo->jointype;
4569 : Selectivity fkselec;
4570 : Selectivity jselec;
4571 : Selectivity pselec;
4572 : double nrows;
4573 :
4574 : /*
4575 : * Compute joinclause selectivity. Note that we are only considering
4576 : * clauses that become restriction clauses at this join level; we are not
4577 : * double-counting them because they were not considered in estimating the
4578 : * sizes of the component rels.
4579 : *
4580 : * First, see whether any of the joinclauses can be matched to known FK
4581 : * constraints. If so, drop those clauses from the restrictlist, and
4582 : * instead estimate their selectivity using FK semantics. (We do this
4583 : * without regard to whether said clauses are local or "pushed down".
4584 : * Probably, an FK-matching clause could never be seen as pushed down at
4585 : * an outer join, since it would be strict and hence would be grounds for
4586 : * join strength reduction.) fkselec gets the net selectivity for
4587 : * FK-matching clauses, or 1.0 if there are none.
4588 : */
4589 96214 : fkselec = get_foreign_key_join_selectivity(root,
4590 : outer_rel->relids,
4591 : inner_rel->relids,
4592 : sjinfo,
4593 : &restrictlist);
4594 :
4595 : /*
4596 : * For an outer join, we have to distinguish the selectivity of the join's
4597 : * own clauses (JOIN/ON conditions) from any clauses that were "pushed
4598 : * down". For inner joins we just count them all as joinclauses.
4599 : */
4600 96214 : if (IS_OUTER_JOIN(jointype))
4601 : {
4602 52348 : List *joinquals = NIL;
4603 52348 : List *pushedquals = NIL;
4604 : ListCell *l;
4605 :
4606 : /* Grovel through the clauses to separate into two lists */
4607 116248 : foreach(l, restrictlist)
4608 : {
4609 63900 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, l);
4610 :
4611 63900 : if (RINFO_IS_PUSHED_DOWN(rinfo, joinrel->relids))
4612 1334 : pushedquals = lappend(pushedquals, rinfo);
4613 : else
4614 62566 : joinquals = lappend(joinquals, rinfo);
4615 : }
4616 :
4617 : /* Get the separate selectivities */
4618 52348 : jselec = clauselist_selectivity(root,
4619 : joinquals,
4620 : 0,
4621 : jointype,
4622 : sjinfo);
4623 52348 : pselec = clauselist_selectivity(root,
4624 : pushedquals,
4625 : 0,
4626 : jointype,
4627 : sjinfo);
4628 :
4629 : /* Avoid leaking a lot of ListCells */
4630 52348 : list_free(joinquals);
4631 52348 : list_free(pushedquals);
4632 : }
4633 : else
4634 : {
4635 43866 : jselec = clauselist_selectivity(root,
4636 : restrictlist,
4637 : 0,
4638 : jointype,
4639 : sjinfo);
4640 43866 : pselec = 0.0; /* not used, keep compiler quiet */
4641 : }
4642 :
4643 : /*
4644 : * Basically, we multiply size of Cartesian product by selectivity.
4645 : *
4646 : * If we are doing an outer join, take that into account: the joinqual
4647 : * selectivity has to be clamped using the knowledge that the output must
4648 : * be at least as large as the non-nullable input. However, any
4649 : * pushed-down quals are applied after the outer join, so their
4650 : * selectivity applies fully.
4651 : *
4652 : * For JOIN_SEMI and JOIN_ANTI, the selectivity is defined as the fraction
4653 : * of LHS rows that have matches, and we apply that straightforwardly.
4654 : */
4655 96214 : switch (jointype)
4656 : {
4657 : case JOIN_INNER:
4658 41992 : nrows = outer_rows * inner_rows * fkselec * jselec;
4659 : /* pselec not used */
4660 41992 : break;
4661 : case JOIN_LEFT:
4662 42164 : nrows = outer_rows * inner_rows * fkselec * jselec;
4663 42164 : if (nrows < outer_rows)
4664 25706 : nrows = outer_rows;
4665 42164 : nrows *= pselec;
4666 42164 : break;
4667 : case JOIN_FULL:
4668 630 : nrows = outer_rows * inner_rows * fkselec * jselec;
4669 630 : if (nrows < outer_rows)
4670 348 : nrows = outer_rows;
4671 630 : if (nrows < inner_rows)
4672 30 : nrows = inner_rows;
4673 630 : nrows *= pselec;
4674 630 : break;
4675 : case JOIN_SEMI:
4676 1874 : nrows = outer_rows * fkselec * jselec;
4677 : /* pselec not used */
4678 1874 : break;
4679 : case JOIN_ANTI:
4680 9554 : nrows = outer_rows * (1.0 - fkselec * jselec);
4681 9554 : nrows *= pselec;
4682 9554 : break;
4683 : default:
4684 : /* other values not expected here */
4685 0 : elog(ERROR, "unrecognized join type: %d", (int) jointype);
4686 : nrows = 0; /* keep compiler quiet */
4687 : break;
4688 : }
4689 :
4690 96214 : return clamp_row_est(nrows);
4691 : }
4692 :
4693 : /*
4694 : * get_foreign_key_join_selectivity
4695 : * Estimate join selectivity for foreign-key-related clauses.
4696 : *
4697 : * Remove any clauses that can be matched to FK constraints from *restrictlist,
4698 : * and return a substitute estimate of their selectivity. 1.0 is returned
4699 : * when there are no such clauses.
4700 : *
4701 : * The reason for treating such clauses specially is that we can get better
4702 : * estimates this way than by relying on clauselist_selectivity(), especially
4703 : * for multi-column FKs where that function's assumption that the clauses are
4704 : * independent falls down badly. But even with single-column FKs, we may be
4705 : * able to get a better answer when the pg_statistic stats are missing or out
4706 : * of date.
4707 : */
4708 : static Selectivity
4709 96214 : get_foreign_key_join_selectivity(PlannerInfo *root,
4710 : Relids outer_relids,
4711 : Relids inner_relids,
4712 : SpecialJoinInfo *sjinfo,
4713 : List **restrictlist)
4714 : {
4715 96214 : Selectivity fkselec = 1.0;
4716 96214 : JoinType jointype = sjinfo->jointype;
4717 96214 : List *worklist = *restrictlist;
4718 : ListCell *lc;
4719 :
4720 : /* Consider each FK constraint that is known to match the query */
4721 97430 : foreach(lc, root->fkey_list)
4722 : {
4723 1216 : ForeignKeyOptInfo *fkinfo = (ForeignKeyOptInfo *) lfirst(lc);
4724 : bool ref_is_outer;
4725 : List *removedlist;
4726 : ListCell *cell;
4727 : ListCell *prev;
4728 : ListCell *next;
4729 :
4730 : /*
4731 : * This FK is not relevant unless it connects a baserel on one side of
4732 : * this join to a baserel on the other side.
4733 : */
4734 2236 : if (bms_is_member(fkinfo->con_relid, outer_relids) &&
4735 1020 : bms_is_member(fkinfo->ref_relid, inner_relids))
4736 900 : ref_is_outer = false;
4737 508 : else if (bms_is_member(fkinfo->ref_relid, outer_relids) &&
4738 192 : bms_is_member(fkinfo->con_relid, inner_relids))
4739 60 : ref_is_outer = true;
4740 : else
4741 256 : continue;
4742 :
4743 : /*
4744 : * If we're dealing with a semi/anti join, and the FK's referenced
4745 : * relation is on the outside, then knowledge of the FK doesn't help
4746 : * us figure out what we need to know (which is the fraction of outer
4747 : * rows that have matches). On the other hand, if the referenced rel
4748 : * is on the inside, then all outer rows must have matches in the
4749 : * referenced table (ignoring nulls). But any restriction or join
4750 : * clauses that filter that table will reduce the fraction of matches.
4751 : * We can account for restriction clauses, but it's too hard to guess
4752 : * how many table rows would get through a join that's inside the RHS.
4753 : * Hence, if either case applies, punt and ignore the FK.
4754 : */
4755 960 : if ((jointype == JOIN_SEMI || jointype == JOIN_ANTI) &&
4756 676 : (ref_is_outer || bms_membership(inner_relids) != BMS_SINGLETON))
4757 0 : continue;
4758 :
4759 : /*
4760 : * Modify the restrictlist by removing clauses that match the FK (and
4761 : * putting them into removedlist instead). It seems unsafe to modify
4762 : * the originally-passed List structure, so we make a shallow copy the
4763 : * first time through.
4764 : */
4765 960 : if (worklist == *restrictlist)
4766 806 : worklist = list_copy(worklist);
4767 :
4768 960 : removedlist = NIL;
4769 960 : prev = NULL;
4770 1952 : for (cell = list_head(worklist); cell; cell = next)
4771 : {
4772 992 : RestrictInfo *rinfo = (RestrictInfo *) lfirst(cell);
4773 992 : bool remove_it = false;
4774 : int i;
4775 :
4776 992 : next = lnext(cell);
4777 : /* Drop this clause if it matches any column of the FK */
4778 1198 : for (i = 0; i < fkinfo->nkeys; i++)
4779 : {
4780 1182 : if (rinfo->parent_ec)
4781 : {
4782 : /*
4783 : * EC-derived clauses can only match by EC. It is okay to
4784 : * consider any clause derived from the same EC as
4785 : * matching the FK: even if equivclass.c chose to generate
4786 : * a clause equating some other pair of Vars, it could
4787 : * have generated one equating the FK's Vars. So for
4788 : * purposes of estimation, we can act as though it did so.
4789 : *
4790 : * Note: checking parent_ec is a bit of a cheat because
4791 : * there are EC-derived clauses that don't have parent_ec
4792 : * set; but such clauses must compare expressions that
4793 : * aren't just Vars, so they cannot match the FK anyway.
4794 : */
4795 176 : if (fkinfo->eclass[i] == rinfo->parent_ec)
4796 : {
4797 176 : remove_it = true;
4798 176 : break;
4799 : }
4800 : }
4801 : else
4802 : {
4803 : /*
4804 : * Otherwise, see if rinfo was previously matched to FK as
4805 : * a "loose" clause.
4806 : */
4807 1006 : if (list_member_ptr(fkinfo->rinfos[i], rinfo))
4808 : {
4809 800 : remove_it = true;
4810 800 : break;
4811 : }
4812 : }
4813 : }
4814 992 : if (remove_it)
4815 : {
4816 976 : worklist = list_delete_cell(worklist, cell, prev);
4817 976 : removedlist = lappend(removedlist, rinfo);
4818 : }
4819 : else
4820 16 : prev = cell;
4821 : }
4822 :
4823 : /*
4824 : * If we failed to remove all the matching clauses we expected to
4825 : * find, chicken out and ignore this FK; applying its selectivity
4826 : * might result in double-counting. Put any clauses we did manage to
4827 : * remove back into the worklist.
4828 : *
4829 : * Since the matching clauses are known not outerjoin-delayed, they
4830 : * should certainly have appeared in the initial joinclause list. If
4831 : * we didn't find them, they must have been matched to, and removed
4832 : * by, some other FK in a previous iteration of this loop. (A likely
4833 : * case is that two FKs are matched to the same EC; there will be only
4834 : * one EC-derived clause in the initial list, so the first FK will
4835 : * consume it.) Applying both FKs' selectivity independently risks
4836 : * underestimating the join size; in particular, this would undo one
4837 : * of the main things that ECs were invented for, namely to avoid
4838 : * double-counting the selectivity of redundant equality conditions.
4839 : * Later we might think of a reasonable way to combine the estimates,
4840 : * but for now, just punt, since this is a fairly uncommon situation.
4841 : */
4842 1920 : if (list_length(removedlist) !=
4843 960 : (fkinfo->nmatched_ec + fkinfo->nmatched_ri))
4844 : {
4845 162 : worklist = list_concat(worklist, removedlist);
4846 162 : continue;
4847 : }
4848 :
4849 : /*
4850 : * Finally we get to the payoff: estimate selectivity using the
4851 : * knowledge that each referencing row will match exactly one row in
4852 : * the referenced table.
4853 : *
4854 : * XXX that's not true in the presence of nulls in the referencing
4855 : * column(s), so in principle we should derate the estimate for those.
4856 : * However (1) if there are any strict restriction clauses for the
4857 : * referencing column(s) elsewhere in the query, derating here would
4858 : * be double-counting the null fraction, and (2) it's not very clear
4859 : * how to combine null fractions for multiple referencing columns. So
4860 : * we do nothing for now about correcting for nulls.
4861 : *
4862 : * XXX another point here is that if either side of an FK constraint
4863 : * is an inheritance parent, we estimate as though the constraint
4864 : * covers all its children as well. This is not an unreasonable
4865 : * assumption for a referencing table, ie the user probably applied
4866 : * identical constraints to all child tables (though perhaps we ought
4867 : * to check that). But it's not possible to have done that for a
4868 : * referenced table. Fortunately, precisely because that doesn't
4869 : * work, it is uncommon in practice to have an FK referencing a parent
4870 : * table. So, at least for now, disregard inheritance here.
4871 : */
4872 798 : if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
4873 522 : {
4874 : /*
4875 : * For JOIN_SEMI and JOIN_ANTI, we only get here when the FK's
4876 : * referenced table is exactly the inside of the join. The join
4877 : * selectivity is defined as the fraction of LHS rows that have
4878 : * matches. The FK implies that every LHS row has a match *in the
4879 : * referenced table*; but any restriction clauses on it will
4880 : * reduce the number of matches. Hence we take the join
4881 : * selectivity as equal to the selectivity of the table's
4882 : * restriction clauses, which is rows / tuples; but we must guard
4883 : * against tuples == 0.
4884 : */
4885 522 : RelOptInfo *ref_rel = find_base_rel(root, fkinfo->ref_relid);
4886 522 : double ref_tuples = Max(ref_rel->tuples, 1.0);
4887 :
4888 522 : fkselec *= ref_rel->rows / ref_tuples;
4889 : }
4890 : else
4891 : {
4892 : /*
4893 : * Otherwise, selectivity is exactly 1/referenced-table-size; but
4894 : * guard against tuples == 0. Note we should use the raw table
4895 : * tuple count, not any estimate of its filtered or joined size.
4896 : */
4897 276 : RelOptInfo *ref_rel = find_base_rel(root, fkinfo->ref_relid);
4898 276 : double ref_tuples = Max(ref_rel->tuples, 1.0);
4899 :
4900 276 : fkselec *= 1.0 / ref_tuples;
4901 : }
4902 : }
4903 :
4904 96214 : *restrictlist = worklist;
4905 96214 : return fkselec;
4906 : }
4907 :
4908 : /*
4909 : * set_subquery_size_estimates
4910 : * Set the size estimates for a base relation that is a subquery.
4911 : *
4912 : * The rel's targetlist and restrictinfo list must have been constructed
4913 : * already, and the Paths for the subquery must have been completed.
4914 : * We look at the subquery's PlannerInfo to extract data.
4915 : *
4916 : * We set the same fields as set_baserel_size_estimates.
4917 : */
4918 : void
4919 7682 : set_subquery_size_estimates(PlannerInfo *root, RelOptInfo *rel)
4920 : {
4921 7682 : PlannerInfo *subroot = rel->subroot;
4922 : RelOptInfo *sub_final_rel;
4923 : ListCell *lc;
4924 :
4925 : /* Should only be applied to base relations that are subqueries */
4926 : Assert(rel->relid > 0);
4927 : Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_SUBQUERY);
4928 :
4929 : /*
4930 : * Copy raw number of output rows from subquery. All of its paths should
4931 : * have the same output rowcount, so just look at cheapest-total.
4932 : */
4933 7682 : sub_final_rel = fetch_upper_rel(subroot, UPPERREL_FINAL, NULL);
4934 7682 : rel->tuples = sub_final_rel->cheapest_total_path->rows;
4935 :
4936 : /*
4937 : * Compute per-output-column width estimates by examining the subquery's
4938 : * targetlist. For any output that is a plain Var, get the width estimate
4939 : * that was made while planning the subquery. Otherwise, we leave it to
4940 : * set_rel_width to fill in a datatype-based default estimate.
4941 : */
4942 24722 : foreach(lc, subroot->parse->targetList)
4943 : {
4944 17040 : TargetEntry *te = lfirst_node(TargetEntry, lc);
4945 17040 : Node *texpr = (Node *) te->expr;
4946 17040 : int32 item_width = 0;
4947 :
4948 : /* junk columns aren't visible to upper query */
4949 17040 : if (te->resjunk)
4950 920 : continue;
4951 :
4952 : /*
4953 : * The subquery could be an expansion of a view that's had columns
4954 : * added to it since the current query was parsed, so that there are
4955 : * non-junk tlist columns in it that don't correspond to any column
4956 : * visible at our query level. Ignore such columns.
4957 : */
4958 16120 : if (te->resno < rel->min_attr || te->resno > rel->max_attr)
4959 0 : continue;
4960 :
4961 : /*
4962 : * XXX This currently doesn't work for subqueries containing set
4963 : * operations, because the Vars in their tlists are bogus references
4964 : * to the first leaf subquery, which wouldn't give the right answer
4965 : * even if we could still get to its PlannerInfo.
4966 : *
4967 : * Also, the subquery could be an appendrel for which all branches are
4968 : * known empty due to constraint exclusion, in which case
4969 : * set_append_rel_pathlist will have left the attr_widths set to zero.
4970 : *
4971 : * In either case, we just leave the width estimate zero until
4972 : * set_rel_width fixes it.
4973 : */
4974 22810 : if (IsA(texpr, Var) &&
4975 6690 : subroot->parse->setOperations == NULL)
4976 : {
4977 6188 : Var *var = (Var *) texpr;
4978 6188 : RelOptInfo *subrel = find_base_rel(subroot, var->varno);
4979 :
4980 6188 : item_width = subrel->attr_widths[var->varattno - subrel->min_attr];
4981 : }
4982 16120 : rel->attr_widths[te->resno - rel->min_attr] = item_width;
4983 : }
4984 :
4985 : /* Now estimate number of output rows, etc */
4986 7682 : set_baserel_size_estimates(root, rel);
4987 7682 : }
4988 :
4989 : /*
4990 : * set_function_size_estimates
4991 : * Set the size estimates for a base relation that is a function call.
4992 : *
4993 : * The rel's targetlist and restrictinfo list must have been constructed
4994 : * already.
4995 : *
4996 : * We set the same fields as set_baserel_size_estimates.
4997 : */
4998 : void
4999 28720 : set_function_size_estimates(PlannerInfo *root, RelOptInfo *rel)
5000 : {
5001 : RangeTblEntry *rte;
5002 : ListCell *lc;
5003 :
5004 : /* Should only be applied to base relations that are functions */
5005 : Assert(rel->relid > 0);
5006 28720 : rte = planner_rt_fetch(rel->relid, root);
5007 : Assert(rte->rtekind == RTE_FUNCTION);
5008 :
5009 : /*
5010 : * Estimate number of rows the functions will return. The rowcount of the
5011 : * node is that of the largest function result.
5012 : */
5013 28720 : rel->tuples = 0;
5014 57632 : foreach(lc, rte->functions)
5015 : {
5016 28912 : RangeTblFunction *rtfunc = (RangeTblFunction *) lfirst(lc);
5017 28912 : double ntup = expression_returns_set_rows(root, rtfunc->funcexpr);
5018 :
5019 28912 : if (ntup > rel->tuples)
5020 28736 : rel->tuples = ntup;
5021 : }
5022 :
5023 : /* Now estimate number of output rows, etc */
5024 28720 : set_baserel_size_estimates(root, rel);
5025 28720 : }
5026 :
5027 : /*
5028 : * set_function_size_estimates
5029 : * Set the size estimates for a base relation that is a function call.
5030 : *
5031 : * The rel's targetlist and restrictinfo list must have been constructed
5032 : * already.
5033 : *
5034 : * We set the same fields as set_tablefunc_size_estimates.
5035 : */
5036 : void
5037 140 : set_tablefunc_size_estimates(PlannerInfo *root, RelOptInfo *rel)
5038 : {
5039 : /* Should only be applied to base relations that are functions */
5040 : Assert(rel->relid > 0);
5041 : Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_TABLEFUNC);
5042 :
5043 140 : rel->tuples = 100;
5044 :
5045 : /* Now estimate number of output rows, etc */
5046 140 : set_baserel_size_estimates(root, rel);
5047 140 : }
5048 :
5049 : /*
5050 : * set_values_size_estimates
5051 : * Set the size estimates for a base relation that is a values list.
5052 : *
5053 : * The rel's targetlist and restrictinfo list must have been constructed
5054 : * already.
5055 : *
5056 : * We set the same fields as set_baserel_size_estimates.
5057 : */
5058 : void
5059 3770 : set_values_size_estimates(PlannerInfo *root, RelOptInfo *rel)
5060 : {
5061 : RangeTblEntry *rte;
5062 :
5063 : /* Should only be applied to base relations that are values lists */
5064 : Assert(rel->relid > 0);
5065 3770 : rte = planner_rt_fetch(rel->relid, root);
5066 : Assert(rte->rtekind == RTE_VALUES);
5067 :
5068 : /*
5069 : * Estimate number of rows the values list will return. We know this
5070 : * precisely based on the list length (well, barring set-returning
5071 : * functions in list items, but that's a refinement not catered for
5072 : * anywhere else either).
5073 : */
5074 3770 : rel->tuples = list_length(rte->values_lists);
5075 :
5076 : /* Now estimate number of output rows, etc */
5077 3770 : set_baserel_size_estimates(root, rel);
5078 3770 : }
5079 :
5080 : /*
5081 : * set_cte_size_estimates
5082 : * Set the size estimates for a base relation that is a CTE reference.
5083 : *
5084 : * The rel's targetlist and restrictinfo list must have been constructed
5085 : * already, and we need an estimate of the number of rows returned by the CTE
5086 : * (if a regular CTE) or the non-recursive term (if a self-reference).
5087 : *
5088 : * We set the same fields as set_baserel_size_estimates.
5089 : */
5090 : void
5091 1220 : set_cte_size_estimates(PlannerInfo *root, RelOptInfo *rel, double cte_rows)
5092 : {
5093 : RangeTblEntry *rte;
5094 :
5095 : /* Should only be applied to base relations that are CTE references */
5096 : Assert(rel->relid > 0);
5097 1220 : rte = planner_rt_fetch(rel->relid, root);
5098 : Assert(rte->rtekind == RTE_CTE);
5099 :
5100 1220 : if (rte->self_reference)
5101 : {
5102 : /*
5103 : * In a self-reference, arbitrarily assume the average worktable size
5104 : * is about 10 times the nonrecursive term's size.
5105 : */
5106 326 : rel->tuples = 10 * cte_rows;
5107 : }
5108 : else
5109 : {
5110 : /* Otherwise just believe the CTE's rowcount estimate */
5111 894 : rel->tuples = cte_rows;
5112 : }
5113 :
5114 : /* Now estimate number of output rows, etc */
5115 1220 : set_baserel_size_estimates(root, rel);
5116 1220 : }
5117 :
5118 : /*
5119 : * set_namedtuplestore_size_estimates
5120 : * Set the size estimates for a base relation that is a tuplestore reference.
5121 : *
5122 : * The rel's targetlist and restrictinfo list must have been constructed
5123 : * already.
5124 : *
5125 : * We set the same fields as set_baserel_size_estimates.
5126 : */
5127 : void
5128 256 : set_namedtuplestore_size_estimates(PlannerInfo *root, RelOptInfo *rel)
5129 : {
5130 : RangeTblEntry *rte;
5131 :
5132 : /* Should only be applied to base relations that are tuplestore references */
5133 : Assert(rel->relid > 0);
5134 256 : rte = planner_rt_fetch(rel->relid, root);
5135 : Assert(rte->rtekind == RTE_NAMEDTUPLESTORE);
5136 :
5137 : /*
5138 : * Use the estimate provided by the code which is generating the named
5139 : * tuplestore. In some cases, the actual number might be available; in
5140 : * others the same plan will be re-used, so a "typical" value might be
5141 : * estimated and used.
5142 : */
5143 256 : rel->tuples = rte->enrtuples;
5144 256 : if (rel->tuples < 0)
5145 0 : rel->tuples = 1000;
5146 :
5147 : /* Now estimate number of output rows, etc */
5148 256 : set_baserel_size_estimates(root, rel);
5149 256 : }
5150 :
5151 : /*
5152 : * set_result_size_estimates
5153 : * Set the size estimates for an RTE_RESULT base relation
5154 : *
5155 : * The rel's targetlist and restrictinfo list must have been constructed
5156 : * already.
5157 : *
5158 : * We set the same fields as set_baserel_size_estimates.
5159 : */
5160 : void
5161 566 : set_result_size_estimates(PlannerInfo *root, RelOptInfo *rel)
5162 : {
5163 : /* Should only be applied to RTE_RESULT base relations */
5164 : Assert(rel->relid > 0);
5165 : Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_RESULT);
5166 :
5167 : /* RTE_RESULT always generates a single row, natively */
5168 566 : rel->tuples = 1;
5169 :
5170 : /* Now estimate number of output rows, etc */
5171 566 : set_baserel_size_estimates(root, rel);
5172 566 : }
5173 :
5174 : /*
5175 : * set_foreign_size_estimates
5176 : * Set the size estimates for a base relation that is a foreign table.
5177 : *
5178 : * There is not a whole lot that we can do here; the foreign-data wrapper
5179 : * is responsible for producing useful estimates. We can do a decent job
5180 : * of estimating baserestrictcost, so we set that, and we also set up width
5181 : * using what will be purely datatype-driven estimates from the targetlist.
5182 : * There is no way to do anything sane with the rows value, so we just put
5183 : * a default estimate and hope that the wrapper can improve on it. The
5184 : * wrapper's GetForeignRelSize function will be called momentarily.
5185 : *
5186 : * The rel's targetlist and restrictinfo list must have been constructed
5187 : * already.
5188 : */
5189 : void
5190 1786 : set_foreign_size_estimates(PlannerInfo *root, RelOptInfo *rel)
5191 : {
5192 : /* Should only be applied to base relations */
5193 : Assert(rel->relid > 0);
5194 :
5195 1786 : rel->rows = 1000; /* entirely bogus default estimate */
5196 :
5197 1786 : cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo, root);
5198 :
5199 1786 : set_rel_width(root, rel);
5200 1786 : }
5201 :
5202 :
5203 : /*
5204 : * set_rel_width
5205 : * Set the estimated output width of a base relation.
5206 : *
5207 : * The estimated output width is the sum of the per-attribute width estimates
5208 : * for the actually-referenced columns, plus any PHVs or other expressions
5209 : * that have to be calculated at this relation. This is the amount of data
5210 : * we'd need to pass upwards in case of a sort, hash, etc.
5211 : *
5212 : * This function also sets reltarget->cost, so it's a bit misnamed now.
5213 : *
5214 : * NB: this works best on plain relations because it prefers to look at
5215 : * real Vars. For subqueries, set_subquery_size_estimates will already have
5216 : * copied up whatever per-column estimates were made within the subquery,
5217 : * and for other types of rels there isn't much we can do anyway. We fall
5218 : * back on (fairly stupid) datatype-based width estimates if we can't get
5219 : * any better number.
5220 : *
5221 : * The per-attribute width estimates are cached for possible re-use while
5222 : * building join relations or post-scan/join pathtargets.
5223 : */
5224 : static void
5225 262042 : set_rel_width(PlannerInfo *root, RelOptInfo *rel)
5226 : {
5227 262042 : Oid reloid = planner_rt_fetch(rel->relid, root)->relid;
5228 262042 : int32 tuple_width = 0;
5229 262042 : bool have_wholerow_var = false;
5230 : ListCell *lc;
5231 :
5232 : /* Vars are assumed to have cost zero, but other exprs do not */
5233 262042 : rel->reltarget->cost.startup = 0;
5234 262042 : rel->reltarget->cost.per_tuple = 0;
5235 :
5236 1097832 : foreach(lc, rel->reltarget->exprs)
5237 : {
5238 835790 : Node *node = (Node *) lfirst(lc);
5239 :
5240 : /*
5241 : * Ordinarily, a Var in a rel's targetlist must belong to that rel;
5242 : * but there are corner cases involving LATERAL references where that
5243 : * isn't so. If the Var has the wrong varno, fall through to the
5244 : * generic case (it doesn't seem worth the trouble to be any smarter).
5245 : */
5246 1667852 : if (IsA(node, Var) &&
5247 832062 : ((Var *) node)->varno == rel->relid)
5248 178186 : {
5249 832022 : Var *var = (Var *) node;
5250 : int ndx;
5251 : int32 item_width;
5252 :
5253 : Assert(var->varattno >= rel->min_attr);
5254 : Assert(var->varattno <= rel->max_attr);
5255 :
5256 832022 : ndx = var->varattno - rel->min_attr;
5257 :
5258 : /*
5259 : * If it's a whole-row Var, we'll deal with it below after we have
5260 : * already cached as many attr widths as possible.
5261 : */
5262 832022 : if (var->varattno == 0)
5263 : {
5264 1710 : have_wholerow_var = true;
5265 1710 : continue;
5266 : }
5267 :
5268 : /*
5269 : * The width may have been cached already (especially if it's a
5270 : * subquery), so don't duplicate effort.
5271 : */
5272 830312 : if (rel->attr_widths[ndx] > 0)
5273 : {
5274 150174 : tuple_width += rel->attr_widths[ndx];
5275 150174 : continue;
5276 : }
5277 :
5278 : /* Try to get column width from statistics */
5279 680138 : if (reloid != InvalidOid && var->varattno > 0)
5280 : {
5281 566230 : item_width = get_attavgwidth(reloid, var->varattno);
5282 566230 : if (item_width > 0)
5283 : {
5284 501952 : rel->attr_widths[ndx] = item_width;
5285 501952 : tuple_width += item_width;
5286 501952 : continue;
5287 : }
5288 : }
5289 :
5290 : /*
5291 : * Not a plain relation, or can't find statistics for it. Estimate
5292 : * using just the type info.
5293 : */
5294 178186 : item_width = get_typavgwidth(var->vartype, var->vartypmod);
5295 : Assert(item_width > 0);
5296 178186 : rel->attr_widths[ndx] = item_width;
5297 178186 : tuple_width += item_width;
5298 : }
5299 3768 : else if (IsA(node, PlaceHolderVar))
5300 : {
5301 : /*
5302 : * We will need to evaluate the PHV's contained expression while
5303 : * scanning this rel, so be sure to include it in reltarget->cost.
5304 : */
5305 392 : PlaceHolderVar *phv = (PlaceHolderVar *) node;
5306 392 : PlaceHolderInfo *phinfo = find_placeholder_info(root, phv, false);
5307 : QualCost cost;
5308 :
5309 392 : tuple_width += phinfo->ph_width;
5310 392 : cost_qual_eval_node(&cost, (Node *) phv->phexpr, root);
5311 392 : rel->reltarget->cost.startup += cost.startup;
5312 392 : rel->reltarget->cost.per_tuple += cost.per_tuple;
5313 : }
5314 : else
5315 : {
5316 : /*
5317 : * We could be looking at an expression pulled up from a subquery,
5318 : * or a ROW() representing a whole-row child Var, etc. Do what we
5319 : * can using the expression type information.
5320 : */
5321 : int32 item_width;
5322 : QualCost cost;
5323 :
5324 3376 : item_width = get_typavgwidth(exprType(node), exprTypmod(node));
5325 : Assert(item_width > 0);
5326 3376 : tuple_width += item_width;
5327 : /* Not entirely clear if we need to account for cost, but do so */
5328 3376 : cost_qual_eval_node(&cost, node, root);
5329 3376 : rel->reltarget->cost.startup += cost.startup;
5330 3376 : rel->reltarget->cost.per_tuple += cost.per_tuple;
5331 : }
5332 : }
5333 :
5334 : /*
5335 : * If we have a whole-row reference, estimate its width as the sum of
5336 : * per-column widths plus heap tuple header overhead.
5337 : */
5338 262042 : if (have_wholerow_var)
5339 : {
5340 1710 : int32 wholerow_width = MAXALIGN(SizeofHeapTupleHeader);
5341 :
5342 1710 : if (reloid != InvalidOid)
5343 : {
5344 : /* Real relation, so estimate true tuple width */
5345 1312 : wholerow_width += get_relation_data_width(reloid,
5346 1312 : rel->attr_widths - rel->min_attr);
5347 : }
5348 : else
5349 : {
5350 : /* Do what we can with info for a phony rel */
5351 : AttrNumber i;
5352 :
5353 1048 : for (i = 1; i <= rel->max_attr; i++)
5354 650 : wholerow_width += rel->attr_widths[i - rel->min_attr];
5355 : }
5356 :
5357 1710 : rel->attr_widths[0 - rel->min_attr] = wholerow_width;
5358 :
5359 : /*
5360 : * Include the whole-row Var as part of the output tuple. Yes, that
5361 : * really is what happens at runtime.
5362 : */
5363 1710 : tuple_width += wholerow_width;
5364 : }
5365 :
5366 : Assert(tuple_width >= 0);
5367 262042 : rel->reltarget->width = tuple_width;
5368 262042 : }
5369 :
5370 : /*
5371 : * set_pathtarget_cost_width
5372 : * Set the estimated eval cost and output width of a PathTarget tlist.
5373 : *
5374 : * As a notational convenience, returns the same PathTarget pointer passed in.
5375 : *
5376 : * Most, though not quite all, uses of this function occur after we've run
5377 : * set_rel_width() for base relations; so we can usually obtain cached width
5378 : * estimates for Vars. If we can't, fall back on datatype-based width
5379 : * estimates. Present early-planning uses of PathTargets don't need accurate
5380 : * widths badly enough to justify going to the catalogs for better data.
5381 : */
5382 : PathTarget *
5383 330728 : set_pathtarget_cost_width(PlannerInfo *root, PathTarget *target)
5384 : {
5385 330728 : int32 tuple_width = 0;
5386 : ListCell *lc;
5387 :
5388 : /* Vars are assumed to have cost zero, but other exprs do not */
5389 330728 : target->cost.startup = 0;
5390 330728 : target->cost.per_tuple = 0;
5391 :
5392 1383792 : foreach(lc, target->exprs)
5393 : {
5394 1053064 : Node *node = (Node *) lfirst(lc);
5395 :
5396 1053064 : if (IsA(node, Var))
5397 : {
5398 576948 : Var *var = (Var *) node;
5399 : int32 item_width;
5400 :
5401 : /* We should not see any upper-level Vars here */
5402 : Assert(var->varlevelsup == 0);
5403 :
5404 : /* Try to get data from RelOptInfo cache */
5405 576948 : if (var->varno < root->simple_rel_array_size)
5406 : {
5407 576892 : RelOptInfo *rel = root->simple_rel_array[var->varno];
5408 :
5409 1151300 : if (rel != NULL &&
5410 1148816 : var->varattno >= rel->min_attr &&
5411 574408 : var->varattno <= rel->max_attr)
5412 : {
5413 574408 : int ndx = var->varattno - rel->min_attr;
5414 :
5415 574408 : if (rel->attr_widths[ndx] > 0)
5416 : {
5417 571574 : tuple_width += rel->attr_widths[ndx];
5418 571574 : continue;
5419 : }
5420 : }
5421 : }
5422 :
5423 : /*
5424 : * No cached data available, so estimate using just the type info.
5425 : */
5426 5374 : item_width = get_typavgwidth(var->vartype, var->vartypmod);
5427 : Assert(item_width > 0);
5428 5374 : tuple_width += item_width;
5429 : }
5430 : else
5431 : {
5432 : /*
5433 : * Handle general expressions using type info.
5434 : */
5435 : int32 item_width;
5436 : QualCost cost;
5437 :
5438 476116 : item_width = get_typavgwidth(exprType(node), exprTypmod(node));
5439 : Assert(item_width > 0);
5440 476116 : tuple_width += item_width;
5441 :
5442 : /* Account for cost, too */
5443 476116 : cost_qual_eval_node(&cost, node, root);
5444 476116 : target->cost.startup += cost.startup;
5445 476116 : target->cost.per_tuple += cost.per_tuple;
5446 : }
5447 : }
5448 :
5449 : Assert(tuple_width >= 0);
5450 330728 : target->width = tuple_width;
5451 :
5452 330728 : return target;
5453 : }
5454 :
5455 : /*
5456 : * relation_byte_size
5457 : * Estimate the storage space in bytes for a given number of tuples
5458 : * of a given width (size in bytes).
5459 : */
5460 : static double
5461 1439044 : relation_byte_size(double tuples, int width)
5462 : {
5463 1439044 : return tuples * (MAXALIGN(width) + MAXALIGN(SizeofHeapTupleHeader));
5464 : }
5465 :
5466 : /*
5467 : * page_size
5468 : * Returns an estimate of the number of pages covered by a given
5469 : * number of tuples of a given width (size in bytes).
5470 : */
5471 : static double
5472 4320 : page_size(double tuples, int width)
5473 : {
5474 4320 : return ceil(relation_byte_size(tuples, width) / BLCKSZ);
5475 : }
5476 :
5477 : /*
5478 : * Estimate the fraction of the work that each worker will do given the
5479 : * number of workers budgeted for the path.
5480 : */
5481 : static double
5482 56772 : get_parallel_divisor(Path *path)
5483 : {
5484 56772 : double parallel_divisor = path->parallel_workers;
5485 :
5486 : /*
5487 : * Early experience with parallel query suggests that when there is only
5488 : * one worker, the leader often makes a very substantial contribution to
5489 : * executing the parallel portion of the plan, but as more workers are
5490 : * added, it does less and less, because it's busy reading tuples from the
5491 : * workers and doing whatever non-parallel post-processing is needed. By
5492 : * the time we reach 4 workers, the leader no longer makes a meaningful
5493 : * contribution. Thus, for now, estimate that the leader spends 30% of
5494 : * its time servicing each worker, and the remainder executing the
5495 : * parallel plan.
5496 : */
5497 56772 : if (parallel_leader_participation)
5498 : {
5499 : double leader_contribution;
5500 :
5501 56424 : leader_contribution = 1.0 - (0.3 * path->parallel_workers);
5502 56424 : if (leader_contribution > 0)
5503 55832 : parallel_divisor += leader_contribution;
5504 : }
5505 :
5506 56772 : return parallel_divisor;
5507 : }
5508 :
5509 : /*
5510 : * compute_bitmap_pages
5511 : *
5512 : * compute number of pages fetched from heap in bitmap heap scan.
5513 : */
5514 : double
5515 317402 : compute_bitmap_pages(PlannerInfo *root, RelOptInfo *baserel, Path *bitmapqual,
5516 : int loop_count, Cost *cost, double *tuple)
5517 : {
5518 : Cost indexTotalCost;
5519 : Selectivity indexSelectivity;
5520 : double T;
5521 : double pages_fetched;
5522 : double tuples_fetched;
5523 : double heap_pages;
5524 : long maxentries;
5525 :
5526 : /*
5527 : * Fetch total cost of obtaining the bitmap, as well as its total
5528 : * selectivity.
5529 : */
5530 317402 : cost_bitmap_tree_node(bitmapqual, &indexTotalCost, &indexSelectivity);
5531 :
5532 : /*
5533 : * Estimate number of main-table pages fetched.
5534 : */
5535 317402 : tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);
5536 :
5537 317402 : T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
5538 :
5539 : /*
5540 : * For a single scan, the number of heap pages that need to be fetched is
5541 : * the same as the Mackert and Lohman formula for the case T <= b (ie, no
5542 : * re-reads needed).
5543 : */
5544 317402 : pages_fetched = (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
5545 :
5546 : /*
5547 : * Calculate the number of pages fetched from the heap. Then based on
5548 : * current work_mem estimate get the estimated maxentries in the bitmap.
5549 : * (Note that we always do this calculation based on the number of pages
5550 : * that would be fetched in a single iteration, even if loop_count > 1.
5551 : * That's correct, because only that number of entries will be stored in
5552 : * the bitmap at one time.)
5553 : */
5554 317402 : heap_pages = Min(pages_fetched, baserel->pages);
5555 317402 : maxentries = tbm_calculate_entries(work_mem * 1024L);
5556 :
5557 317402 : if (loop_count > 1)
5558 : {
5559 : /*
5560 : * For repeated bitmap scans, scale up the number of tuples fetched in
5561 : * the Mackert and Lohman formula by the number of scans, so that we
5562 : * estimate the number of pages fetched by all the scans. Then
5563 : * pro-rate for one scan.
5564 : */
5565 61532 : pages_fetched = index_pages_fetched(tuples_fetched * loop_count,
5566 : baserel->pages,
5567 : get_indexpath_pages(bitmapqual),
5568 : root);
5569 61532 : pages_fetched /= loop_count;
5570 : }
5571 :
5572 317402 : if (pages_fetched >= T)
5573 24606 : pages_fetched = T;
5574 : else
5575 292796 : pages_fetched = ceil(pages_fetched);
5576 :
5577 317402 : if (maxentries < heap_pages)
5578 : {
5579 : double exact_pages;
5580 : double lossy_pages;
5581 :
5582 : /*
5583 : * Crude approximation of the number of lossy pages. Because of the
5584 : * way tbm_lossify() is coded, the number of lossy pages increases
5585 : * very sharply as soon as we run short of memory; this formula has
5586 : * that property and seems to perform adequately in testing, but it's
5587 : * possible we could do better somehow.
5588 : */
5589 12 : lossy_pages = Max(0, heap_pages - maxentries / 2);
5590 12 : exact_pages = heap_pages - lossy_pages;
5591 :
5592 : /*
5593 : * If there are lossy pages then recompute the number of tuples
5594 : * processed by the bitmap heap node. We assume here that the chance
5595 : * of a given tuple coming from an exact page is the same as the
5596 : * chance that a given page is exact. This might not be true, but
5597 : * it's not clear how we can do any better.
5598 : */
5599 12 : if (lossy_pages > 0)
5600 12 : tuples_fetched =
5601 24 : clamp_row_est(indexSelectivity *
5602 24 : (exact_pages / heap_pages) * baserel->tuples +
5603 12 : (lossy_pages / heap_pages) * baserel->tuples);
5604 : }
5605 :
5606 317402 : if (cost)
5607 244328 : *cost = indexTotalCost;
5608 317402 : if (tuple)
5609 244328 : *tuple = tuples_fetched;
5610 :
5611 317402 : return pages_fetched;
5612 : }
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