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costsize.c
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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 leader 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) 1996-2021, 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/nodeAgg.h"
81 #include "executor/nodeHash.h"
82 #include "executor/nodeMemoize.h"
83 #include "miscadmin.h"
84 #include "nodes/makefuncs.h"
85 #include "nodes/nodeFuncs.h"
86 #include "optimizer/clauses.h"
87 #include "optimizer/cost.h"
88 #include "optimizer/optimizer.h"
89 #include "optimizer/pathnode.h"
90 #include "optimizer/paths.h"
91 #include "optimizer/placeholder.h"
92 #include "optimizer/plancat.h"
93 #include "optimizer/planmain.h"
94 #include "optimizer/restrictinfo.h"
95 #include "parser/parsetree.h"
96 #include "utils/lsyscache.h"
97 #include "utils/selfuncs.h"
98 #include "utils/spccache.h"
99 #include "utils/tuplesort.h"
100 
101 
102 #define LOG2(x) (log(x) / 0.693147180559945)
103 
104 /*
105  * Append and MergeAppend nodes are less expensive than some other operations
106  * which use cpu_tuple_cost; instead of adding a separate GUC, estimate the
107  * per-tuple cost as cpu_tuple_cost multiplied by this value.
108  */
109 #define APPEND_CPU_COST_MULTIPLIER 0.5
110 
111 /*
112  * Maximum value for row estimates. We cap row estimates to this to help
113  * ensure that costs based on these estimates remain within the range of what
114  * double can represent. add_path() wouldn't act sanely given infinite or NaN
115  * cost values.
116  */
117 #define MAXIMUM_ROWCOUNT 1e100
118 
126 
128 
130 
132 
133 bool enable_seqscan = true;
134 bool enable_indexscan = true;
136 bool enable_bitmapscan = true;
137 bool enable_tidscan = true;
138 bool enable_sort = true;
140 bool enable_hashagg = true;
141 bool enable_nestloop = true;
142 bool enable_material = true;
143 bool enable_memoize = true;
144 bool enable_mergejoin = true;
145 bool enable_hashjoin = true;
146 bool enable_gathermerge = true;
153 
154 typedef struct
155 {
159 
160 static List *extract_nonindex_conditions(List *qual_clauses, List *indexclauses);
162  RestrictInfo *rinfo,
163  PathKey *pathkey);
164 static void cost_rescan(PlannerInfo *root, Path *path,
165  Cost *rescan_startup_cost, Cost *rescan_total_cost);
166 static bool cost_qual_eval_walker(Node *node, cost_qual_eval_context *context);
167 static void get_restriction_qual_cost(PlannerInfo *root, RelOptInfo *baserel,
168  ParamPathInfo *param_info,
169  QualCost *qpqual_cost);
170 static bool has_indexed_join_quals(NestPath *path);
171 static double approx_tuple_count(PlannerInfo *root, JoinPath *path,
172  List *quals);
173 static double calc_joinrel_size_estimate(PlannerInfo *root,
174  RelOptInfo *joinrel,
175  RelOptInfo *outer_rel,
176  RelOptInfo *inner_rel,
177  double outer_rows,
178  double inner_rows,
179  SpecialJoinInfo *sjinfo,
180  List *restrictlist);
182  Relids outer_relids,
183  Relids inner_relids,
184  SpecialJoinInfo *sjinfo,
185  List **restrictlist);
186 static Cost append_nonpartial_cost(List *subpaths, int numpaths,
187  int parallel_workers);
188 static void set_rel_width(PlannerInfo *root, RelOptInfo *rel);
189 static double relation_byte_size(double tuples, int width);
190 static double page_size(double tuples, int width);
191 static double get_parallel_divisor(Path *path);
192 
193 
194 /*
195  * clamp_row_est
196  * Force a row-count estimate to a sane value.
197  */
198 double
199 clamp_row_est(double nrows)
200 {
201  /*
202  * Avoid infinite and NaN row estimates. Costs derived from such values
203  * are going to be useless. Also force the estimate to be at least one
204  * row, to make explain output look better and to avoid possible
205  * divide-by-zero when interpolating costs. Make it an integer, too.
206  */
207  if (nrows > MAXIMUM_ROWCOUNT || isnan(nrows))
208  nrows = MAXIMUM_ROWCOUNT;
209  else if (nrows <= 1.0)
210  nrows = 1.0;
211  else
212  nrows = rint(nrows);
213 
214  return nrows;
215 }
216 
217 
218 /*
219  * cost_seqscan
220  * Determines and returns the cost of scanning a relation sequentially.
221  *
222  * 'baserel' is the relation to be scanned
223  * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
224  */
225 void
227  RelOptInfo *baserel, ParamPathInfo *param_info)
228 {
229  Cost startup_cost = 0;
230  Cost cpu_run_cost;
231  Cost disk_run_cost;
232  double spc_seq_page_cost;
233  QualCost qpqual_cost;
234  Cost cpu_per_tuple;
235 
236  /* Should only be applied to base relations */
237  Assert(baserel->relid > 0);
238  Assert(baserel->rtekind == RTE_RELATION);
239 
240  /* Mark the path with the correct row estimate */
241  if (param_info)
242  path->rows = param_info->ppi_rows;
243  else
244  path->rows = baserel->rows;
245 
246  if (!enable_seqscan)
247  startup_cost += disable_cost;
248 
249  /* fetch estimated page cost for tablespace containing table */
251  NULL,
252  &spc_seq_page_cost);
253 
254  /*
255  * disk costs
256  */
257  disk_run_cost = spc_seq_page_cost * baserel->pages;
258 
259  /* CPU costs */
260  get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
261 
262  startup_cost += qpqual_cost.startup;
263  cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
264  cpu_run_cost = cpu_per_tuple * baserel->tuples;
265  /* tlist eval costs are paid per output row, not per tuple scanned */
266  startup_cost += path->pathtarget->cost.startup;
267  cpu_run_cost += path->pathtarget->cost.per_tuple * path->rows;
268 
269  /* Adjust costing for parallelism, if used. */
270  if (path->parallel_workers > 0)
271  {
272  double parallel_divisor = get_parallel_divisor(path);
273 
274  /* The CPU cost is divided among all the workers. */
275  cpu_run_cost /= parallel_divisor;
276 
277  /*
278  * It may be possible to amortize some of the I/O cost, but probably
279  * not very much, because most operating systems already do aggressive
280  * prefetching. For now, we assume that the disk run cost can't be
281  * amortized at all.
282  */
283 
284  /*
285  * In the case of a parallel plan, the row count needs to represent
286  * the number of tuples processed per worker.
287  */
288  path->rows = clamp_row_est(path->rows / parallel_divisor);
289  }
290 
291  path->startup_cost = startup_cost;
292  path->total_cost = startup_cost + cpu_run_cost + disk_run_cost;
293 }
294 
295 /*
296  * cost_samplescan
297  * Determines and returns the cost of scanning a relation using sampling.
298  *
299  * 'baserel' is the relation to be scanned
300  * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
301  */
302 void
304  RelOptInfo *baserel, ParamPathInfo *param_info)
305 {
306  Cost startup_cost = 0;
307  Cost run_cost = 0;
308  RangeTblEntry *rte;
309  TableSampleClause *tsc;
310  TsmRoutine *tsm;
311  double spc_seq_page_cost,
312  spc_random_page_cost,
313  spc_page_cost;
314  QualCost qpqual_cost;
315  Cost cpu_per_tuple;
316 
317  /* Should only be applied to base relations with tablesample clauses */
318  Assert(baserel->relid > 0);
319  rte = planner_rt_fetch(baserel->relid, root);
320  Assert(rte->rtekind == RTE_RELATION);
321  tsc = rte->tablesample;
322  Assert(tsc != NULL);
323  tsm = GetTsmRoutine(tsc->tsmhandler);
324 
325  /* Mark the path with the correct row estimate */
326  if (param_info)
327  path->rows = param_info->ppi_rows;
328  else
329  path->rows = baserel->rows;
330 
331  /* fetch estimated page cost for tablespace containing table */
333  &spc_random_page_cost,
334  &spc_seq_page_cost);
335 
336  /* if NextSampleBlock is used, assume random access, else sequential */
337  spc_page_cost = (tsm->NextSampleBlock != NULL) ?
338  spc_random_page_cost : spc_seq_page_cost;
339 
340  /*
341  * disk costs (recall that baserel->pages has already been set to the
342  * number of pages the sampling method will visit)
343  */
344  run_cost += spc_page_cost * baserel->pages;
345 
346  /*
347  * CPU costs (recall that baserel->tuples has already been set to the
348  * number of tuples the sampling method will select). Note that we ignore
349  * execution cost of the TABLESAMPLE parameter expressions; they will be
350  * evaluated only once per scan, and in most usages they'll likely be
351  * simple constants anyway. We also don't charge anything for the
352  * calculations the sampling method might do internally.
353  */
354  get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
355 
356  startup_cost += qpqual_cost.startup;
357  cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
358  run_cost += cpu_per_tuple * baserel->tuples;
359  /* tlist eval costs are paid per output row, not per tuple scanned */
360  startup_cost += path->pathtarget->cost.startup;
361  run_cost += path->pathtarget->cost.per_tuple * path->rows;
362 
363  path->startup_cost = startup_cost;
364  path->total_cost = startup_cost + run_cost;
365 }
366 
367 /*
368  * cost_gather
369  * Determines and returns the cost of gather path.
370  *
371  * 'rel' is the relation to be operated upon
372  * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
373  * 'rows' may be used to point to a row estimate; if non-NULL, it overrides
374  * both 'rel' and 'param_info'. This is useful when the path doesn't exactly
375  * correspond to any particular RelOptInfo.
376  */
377 void
379  RelOptInfo *rel, ParamPathInfo *param_info,
380  double *rows)
381 {
382  Cost startup_cost = 0;
383  Cost run_cost = 0;
384 
385  /* Mark the path with the correct row estimate */
386  if (rows)
387  path->path.rows = *rows;
388  else if (param_info)
389  path->path.rows = param_info->ppi_rows;
390  else
391  path->path.rows = rel->rows;
392 
393  startup_cost = path->subpath->startup_cost;
394 
395  run_cost = path->subpath->total_cost - path->subpath->startup_cost;
396 
397  /* Parallel setup and communication cost. */
398  startup_cost += parallel_setup_cost;
399  run_cost += parallel_tuple_cost * path->path.rows;
400 
401  path->path.startup_cost = startup_cost;
402  path->path.total_cost = (startup_cost + run_cost);
403 }
404 
405 /*
406  * cost_gather_merge
407  * Determines and returns the cost of gather merge path.
408  *
409  * GatherMerge merges several pre-sorted input streams, using a heap that at
410  * any given instant holds the next tuple from each stream. If there are N
411  * streams, we need about N*log2(N) tuple comparisons to construct the heap at
412  * startup, and then for each output tuple, about log2(N) comparisons to
413  * replace the top heap entry with the next tuple from the same stream.
414  */
415 void
417  RelOptInfo *rel, ParamPathInfo *param_info,
418  Cost input_startup_cost, Cost input_total_cost,
419  double *rows)
420 {
421  Cost startup_cost = 0;
422  Cost run_cost = 0;
423  Cost comparison_cost;
424  double N;
425  double logN;
426 
427  /* Mark the path with the correct row estimate */
428  if (rows)
429  path->path.rows = *rows;
430  else if (param_info)
431  path->path.rows = param_info->ppi_rows;
432  else
433  path->path.rows = rel->rows;
434 
435  if (!enable_gathermerge)
436  startup_cost += disable_cost;
437 
438  /*
439  * Add one to the number of workers to account for the leader. This might
440  * be overgenerous since the leader will do less work than other workers
441  * in typical cases, but we'll go with it for now.
442  */
443  Assert(path->num_workers > 0);
444  N = (double) path->num_workers + 1;
445  logN = LOG2(N);
446 
447  /* Assumed cost per tuple comparison */
448  comparison_cost = 2.0 * cpu_operator_cost;
449 
450  /* Heap creation cost */
451  startup_cost += comparison_cost * N * logN;
452 
453  /* Per-tuple heap maintenance cost */
454  run_cost += path->path.rows * comparison_cost * logN;
455 
456  /* small cost for heap management, like cost_merge_append */
457  run_cost += cpu_operator_cost * path->path.rows;
458 
459  /*
460  * Parallel setup and communication cost. Since Gather Merge, unlike
461  * Gather, requires us to block until a tuple is available from every
462  * worker, we bump the IPC cost up a little bit as compared with Gather.
463  * For lack of a better idea, charge an extra 5%.
464  */
465  startup_cost += parallel_setup_cost;
466  run_cost += parallel_tuple_cost * path->path.rows * 1.05;
467 
468  path->path.startup_cost = startup_cost + input_startup_cost;
469  path->path.total_cost = (startup_cost + run_cost + input_total_cost);
470 }
471 
472 /*
473  * cost_index
474  * Determines and returns the cost of scanning a relation using an index.
475  *
476  * 'path' describes the indexscan under consideration, and is complete
477  * except for the fields to be set by this routine
478  * 'loop_count' is the number of repetitions of the indexscan to factor into
479  * estimates of caching behavior
480  *
481  * In addition to rows, startup_cost and total_cost, cost_index() sets the
482  * path's indextotalcost and indexselectivity fields. These values will be
483  * needed if the IndexPath is used in a BitmapIndexScan.
484  *
485  * NOTE: path->indexquals must contain only clauses usable as index
486  * restrictions. Any additional quals evaluated as qpquals may reduce the
487  * number of returned tuples, but they won't reduce the number of tuples
488  * we have to fetch from the table, so they don't reduce the scan cost.
489  */
490 void
491 cost_index(IndexPath *path, PlannerInfo *root, double loop_count,
492  bool partial_path)
493 {
494  IndexOptInfo *index = path->indexinfo;
495  RelOptInfo *baserel = index->rel;
496  bool indexonly = (path->path.pathtype == T_IndexOnlyScan);
497  amcostestimate_function amcostestimate;
498  List *qpquals;
499  Cost startup_cost = 0;
500  Cost run_cost = 0;
501  Cost cpu_run_cost = 0;
502  Cost indexStartupCost;
503  Cost indexTotalCost;
504  Selectivity indexSelectivity;
505  double indexCorrelation,
506  csquared;
507  double spc_seq_page_cost,
508  spc_random_page_cost;
509  Cost min_IO_cost,
510  max_IO_cost;
511  QualCost qpqual_cost;
512  Cost cpu_per_tuple;
513  double tuples_fetched;
514  double pages_fetched;
515  double rand_heap_pages;
516  double index_pages;
517 
518  /* Should only be applied to base relations */
519  Assert(IsA(baserel, RelOptInfo) &&
520  IsA(index, IndexOptInfo));
521  Assert(baserel->relid > 0);
522  Assert(baserel->rtekind == RTE_RELATION);
523 
524  /*
525  * Mark the path with the correct row estimate, and identify which quals
526  * will need to be enforced as qpquals. We need not check any quals that
527  * are implied by the index's predicate, so we can use indrestrictinfo not
528  * baserestrictinfo as the list of relevant restriction clauses for the
529  * rel.
530  */
531  if (path->path.param_info)
532  {
533  path->path.rows = path->path.param_info->ppi_rows;
534  /* qpquals come from the rel's restriction clauses and ppi_clauses */
536  path->indexclauses),
538  path->indexclauses));
539  }
540  else
541  {
542  path->path.rows = baserel->rows;
543  /* qpquals come from just the rel's restriction clauses */
545  path->indexclauses);
546  }
547 
548  if (!enable_indexscan)
549  startup_cost += disable_cost;
550  /* we don't need to check enable_indexonlyscan; indxpath.c does that */
551 
552  /*
553  * Call index-access-method-specific code to estimate the processing cost
554  * for scanning the index, as well as the selectivity of the index (ie,
555  * the fraction of main-table tuples we will have to retrieve) and its
556  * correlation to the main-table tuple order. We need a cast here because
557  * pathnodes.h uses a weak function type to avoid including amapi.h.
558  */
559  amcostestimate = (amcostestimate_function) index->amcostestimate;
560  amcostestimate(root, path, loop_count,
561  &indexStartupCost, &indexTotalCost,
562  &indexSelectivity, &indexCorrelation,
563  &index_pages);
564 
565  /*
566  * Save amcostestimate's results for possible use in bitmap scan planning.
567  * We don't bother to save indexStartupCost or indexCorrelation, because a
568  * bitmap scan doesn't care about either.
569  */
570  path->indextotalcost = indexTotalCost;
571  path->indexselectivity = indexSelectivity;
572 
573  /* all costs for touching index itself included here */
574  startup_cost += indexStartupCost;
575  run_cost += indexTotalCost - indexStartupCost;
576 
577  /* estimate number of main-table tuples fetched */
578  tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);
579 
580  /* fetch estimated page costs for tablespace containing table */
582  &spc_random_page_cost,
583  &spc_seq_page_cost);
584 
585  /*----------
586  * Estimate number of main-table pages fetched, and compute I/O cost.
587  *
588  * When the index ordering is uncorrelated with the table ordering,
589  * we use an approximation proposed by Mackert and Lohman (see
590  * index_pages_fetched() for details) to compute the number of pages
591  * fetched, and then charge spc_random_page_cost per page fetched.
592  *
593  * When the index ordering is exactly correlated with the table ordering
594  * (just after a CLUSTER, for example), the number of pages fetched should
595  * be exactly selectivity * table_size. What's more, all but the first
596  * will be sequential fetches, not the random fetches that occur in the
597  * uncorrelated case. So if the number of pages is more than 1, we
598  * ought to charge
599  * spc_random_page_cost + (pages_fetched - 1) * spc_seq_page_cost
600  * For partially-correlated indexes, we ought to charge somewhere between
601  * these two estimates. We currently interpolate linearly between the
602  * estimates based on the correlation squared (XXX is that appropriate?).
603  *
604  * If it's an index-only scan, then we will not need to fetch any heap
605  * pages for which the visibility map shows all tuples are visible.
606  * Hence, reduce the estimated number of heap fetches accordingly.
607  * We use the measured fraction of the entire heap that is all-visible,
608  * which might not be particularly relevant to the subset of the heap
609  * that this query will fetch; but it's not clear how to do better.
610  *----------
611  */
612  if (loop_count > 1)
613  {
614  /*
615  * For repeated indexscans, the appropriate estimate for the
616  * uncorrelated case is to scale up the number of tuples fetched in
617  * the Mackert and Lohman formula by the number of scans, so that we
618  * estimate the number of pages fetched by all the scans; then
619  * pro-rate the costs for one scan. In this case we assume all the
620  * fetches are random accesses.
621  */
622  pages_fetched = index_pages_fetched(tuples_fetched * loop_count,
623  baserel->pages,
624  (double) index->pages,
625  root);
626 
627  if (indexonly)
628  pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
629 
630  rand_heap_pages = pages_fetched;
631 
632  max_IO_cost = (pages_fetched * spc_random_page_cost) / loop_count;
633 
634  /*
635  * In the perfectly correlated case, the number of pages touched by
636  * each scan is selectivity * table_size, and we can use the Mackert
637  * and Lohman formula at the page level to estimate how much work is
638  * saved by caching across scans. We still assume all the fetches are
639  * random, though, which is an overestimate that's hard to correct for
640  * without double-counting the cache effects. (But in most cases
641  * where such a plan is actually interesting, only one page would get
642  * fetched per scan anyway, so it shouldn't matter much.)
643  */
644  pages_fetched = ceil(indexSelectivity * (double) baserel->pages);
645 
646  pages_fetched = index_pages_fetched(pages_fetched * loop_count,
647  baserel->pages,
648  (double) index->pages,
649  root);
650 
651  if (indexonly)
652  pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
653 
654  min_IO_cost = (pages_fetched * spc_random_page_cost) / loop_count;
655  }
656  else
657  {
658  /*
659  * Normal case: apply the Mackert and Lohman formula, and then
660  * interpolate between that and the correlation-derived result.
661  */
662  pages_fetched = index_pages_fetched(tuples_fetched,
663  baserel->pages,
664  (double) index->pages,
665  root);
666 
667  if (indexonly)
668  pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
669 
670  rand_heap_pages = pages_fetched;
671 
672  /* max_IO_cost is for the perfectly uncorrelated case (csquared=0) */
673  max_IO_cost = pages_fetched * spc_random_page_cost;
674 
675  /* min_IO_cost is for the perfectly correlated case (csquared=1) */
676  pages_fetched = ceil(indexSelectivity * (double) baserel->pages);
677 
678  if (indexonly)
679  pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
680 
681  if (pages_fetched > 0)
682  {
683  min_IO_cost = spc_random_page_cost;
684  if (pages_fetched > 1)
685  min_IO_cost += (pages_fetched - 1) * spc_seq_page_cost;
686  }
687  else
688  min_IO_cost = 0;
689  }
690 
691  if (partial_path)
692  {
693  /*
694  * For index only scans compute workers based on number of index pages
695  * fetched; the number of heap pages we fetch might be so small as to
696  * effectively rule out parallelism, which we don't want to do.
697  */
698  if (indexonly)
699  rand_heap_pages = -1;
700 
701  /*
702  * Estimate the number of parallel workers required to scan index. Use
703  * the number of heap pages computed considering heap fetches won't be
704  * sequential as for parallel scans the pages are accessed in random
705  * order.
706  */
708  rand_heap_pages,
709  index_pages,
711 
712  /*
713  * Fall out if workers can't be assigned for parallel scan, because in
714  * such a case this path will be rejected. So there is no benefit in
715  * doing extra computation.
716  */
717  if (path->path.parallel_workers <= 0)
718  return;
719 
720  path->path.parallel_aware = true;
721  }
722 
723  /*
724  * Now interpolate based on estimated index order correlation to get total
725  * disk I/O cost for main table accesses.
726  */
727  csquared = indexCorrelation * indexCorrelation;
728 
729  run_cost += max_IO_cost + csquared * (min_IO_cost - max_IO_cost);
730 
731  /*
732  * Estimate CPU costs per tuple.
733  *
734  * What we want here is cpu_tuple_cost plus the evaluation costs of any
735  * qual clauses that we have to evaluate as qpquals.
736  */
737  cost_qual_eval(&qpqual_cost, qpquals, root);
738 
739  startup_cost += qpqual_cost.startup;
740  cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
741 
742  cpu_run_cost += cpu_per_tuple * tuples_fetched;
743 
744  /* tlist eval costs are paid per output row, not per tuple scanned */
745  startup_cost += path->path.pathtarget->cost.startup;
746  cpu_run_cost += path->path.pathtarget->cost.per_tuple * path->path.rows;
747 
748  /* Adjust costing for parallelism, if used. */
749  if (path->path.parallel_workers > 0)
750  {
751  double parallel_divisor = get_parallel_divisor(&path->path);
752 
753  path->path.rows = clamp_row_est(path->path.rows / parallel_divisor);
754 
755  /* The CPU cost is divided among all the workers. */
756  cpu_run_cost /= parallel_divisor;
757  }
758 
759  run_cost += cpu_run_cost;
760 
761  path->path.startup_cost = startup_cost;
762  path->path.total_cost = startup_cost + run_cost;
763 }
764 
765 /*
766  * extract_nonindex_conditions
767  *
768  * Given a list of quals to be enforced in an indexscan, extract the ones that
769  * will have to be applied as qpquals (ie, the index machinery won't handle
770  * them). Here we detect only whether a qual clause is directly redundant
771  * with some indexclause. If the index path is chosen for use, createplan.c
772  * will try a bit harder to get rid of redundant qual conditions; specifically
773  * it will see if quals can be proven to be implied by the indexquals. But
774  * it does not seem worth the cycles to try to factor that in at this stage,
775  * since we're only trying to estimate qual eval costs. Otherwise this must
776  * match the logic in create_indexscan_plan().
777  *
778  * qual_clauses, and the result, are lists of RestrictInfos.
779  * indexclauses is a list of IndexClauses.
780  */
781 static List *
782 extract_nonindex_conditions(List *qual_clauses, List *indexclauses)
783 {
784  List *result = NIL;
785  ListCell *lc;
786 
787  foreach(lc, qual_clauses)
788  {
789  RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc);
790 
791  if (rinfo->pseudoconstant)
792  continue; /* we may drop pseudoconstants here */
793  if (is_redundant_with_indexclauses(rinfo, indexclauses))
794  continue; /* dup or derived from same EquivalenceClass */
795  /* ... skip the predicate proof attempt createplan.c will try ... */
796  result = lappend(result, rinfo);
797  }
798  return result;
799 }
800 
801 /*
802  * index_pages_fetched
803  * Estimate the number of pages actually fetched after accounting for
804  * cache effects.
805  *
806  * We use an approximation proposed by Mackert and Lohman, "Index Scans
807  * Using a Finite LRU Buffer: A Validated I/O Model", ACM Transactions
808  * on Database Systems, Vol. 14, No. 3, September 1989, Pages 401-424.
809  * The Mackert and Lohman approximation is that the number of pages
810  * fetched is
811  * PF =
812  * min(2TNs/(2T+Ns), T) when T <= b
813  * 2TNs/(2T+Ns) when T > b and Ns <= 2Tb/(2T-b)
814  * b + (Ns - 2Tb/(2T-b))*(T-b)/T when T > b and Ns > 2Tb/(2T-b)
815  * where
816  * T = # pages in table
817  * N = # tuples in table
818  * s = selectivity = fraction of table to be scanned
819  * b = # buffer pages available (we include kernel space here)
820  *
821  * We assume that effective_cache_size is the total number of buffer pages
822  * available for the whole query, and pro-rate that space across all the
823  * tables in the query and the index currently under consideration. (This
824  * ignores space needed for other indexes used by the query, but since we
825  * don't know which indexes will get used, we can't estimate that very well;
826  * and in any case counting all the tables may well be an overestimate, since
827  * depending on the join plan not all the tables may be scanned concurrently.)
828  *
829  * The product Ns is the number of tuples fetched; we pass in that
830  * product rather than calculating it here. "pages" is the number of pages
831  * in the object under consideration (either an index or a table).
832  * "index_pages" is the amount to add to the total table space, which was
833  * computed for us by make_one_rel.
834  *
835  * Caller is expected to have ensured that tuples_fetched is greater than zero
836  * and rounded to integer (see clamp_row_est). The result will likewise be
837  * greater than zero and integral.
838  */
839 double
840 index_pages_fetched(double tuples_fetched, BlockNumber pages,
841  double index_pages, PlannerInfo *root)
842 {
843  double pages_fetched;
844  double total_pages;
845  double T,
846  b;
847 
848  /* T is # pages in table, but don't allow it to be zero */
849  T = (pages > 1) ? (double) pages : 1.0;
850 
851  /* Compute number of pages assumed to be competing for cache space */
852  total_pages = root->total_table_pages + index_pages;
853  total_pages = Max(total_pages, 1.0);
854  Assert(T <= total_pages);
855 
856  /* b is pro-rated share of effective_cache_size */
857  b = (double) effective_cache_size * T / total_pages;
858 
859  /* force it positive and integral */
860  if (b <= 1.0)
861  b = 1.0;
862  else
863  b = ceil(b);
864 
865  /* This part is the Mackert and Lohman formula */
866  if (T <= b)
867  {
868  pages_fetched =
869  (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
870  if (pages_fetched >= T)
871  pages_fetched = T;
872  else
873  pages_fetched = ceil(pages_fetched);
874  }
875  else
876  {
877  double lim;
878 
879  lim = (2.0 * T * b) / (2.0 * T - b);
880  if (tuples_fetched <= lim)
881  {
882  pages_fetched =
883  (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
884  }
885  else
886  {
887  pages_fetched =
888  b + (tuples_fetched - lim) * (T - b) / T;
889  }
890  pages_fetched = ceil(pages_fetched);
891  }
892  return pages_fetched;
893 }
894 
895 /*
896  * get_indexpath_pages
897  * Determine the total size of the indexes used in a bitmap index path.
898  *
899  * Note: if the same index is used more than once in a bitmap tree, we will
900  * count it multiple times, which perhaps is the wrong thing ... but it's
901  * not completely clear, and detecting duplicates is difficult, so ignore it
902  * for now.
903  */
904 static double
906 {
907  double result = 0;
908  ListCell *l;
909 
910  if (IsA(bitmapqual, BitmapAndPath))
911  {
912  BitmapAndPath *apath = (BitmapAndPath *) bitmapqual;
913 
914  foreach(l, apath->bitmapquals)
915  {
916  result += get_indexpath_pages((Path *) lfirst(l));
917  }
918  }
919  else if (IsA(bitmapqual, BitmapOrPath))
920  {
921  BitmapOrPath *opath = (BitmapOrPath *) bitmapqual;
922 
923  foreach(l, opath->bitmapquals)
924  {
925  result += get_indexpath_pages((Path *) lfirst(l));
926  }
927  }
928  else if (IsA(bitmapqual, IndexPath))
929  {
930  IndexPath *ipath = (IndexPath *) bitmapqual;
931 
932  result = (double) ipath->indexinfo->pages;
933  }
934  else
935  elog(ERROR, "unrecognized node type: %d", nodeTag(bitmapqual));
936 
937  return result;
938 }
939 
940 /*
941  * cost_bitmap_heap_scan
942  * Determines and returns the cost of scanning a relation using a bitmap
943  * index-then-heap plan.
944  *
945  * 'baserel' is the relation to be scanned
946  * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
947  * 'bitmapqual' is a tree of IndexPaths, BitmapAndPaths, and BitmapOrPaths
948  * 'loop_count' is the number of repetitions of the indexscan to factor into
949  * estimates of caching behavior
950  *
951  * Note: the component IndexPaths in bitmapqual should have been costed
952  * using the same loop_count.
953  */
954 void
956  ParamPathInfo *param_info,
957  Path *bitmapqual, double loop_count)
958 {
959  Cost startup_cost = 0;
960  Cost run_cost = 0;
961  Cost indexTotalCost;
962  QualCost qpqual_cost;
963  Cost cpu_per_tuple;
964  Cost cost_per_page;
965  Cost cpu_run_cost;
966  double tuples_fetched;
967  double pages_fetched;
968  double spc_seq_page_cost,
969  spc_random_page_cost;
970  double T;
971 
972  /* Should only be applied to base relations */
973  Assert(IsA(baserel, RelOptInfo));
974  Assert(baserel->relid > 0);
975  Assert(baserel->rtekind == RTE_RELATION);
976 
977  /* Mark the path with the correct row estimate */
978  if (param_info)
979  path->rows = param_info->ppi_rows;
980  else
981  path->rows = baserel->rows;
982 
983  if (!enable_bitmapscan)
984  startup_cost += disable_cost;
985 
986  pages_fetched = compute_bitmap_pages(root, baserel, bitmapqual,
987  loop_count, &indexTotalCost,
988  &tuples_fetched);
989 
990  startup_cost += indexTotalCost;
991  T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
992 
993  /* Fetch estimated page costs for tablespace containing table. */
995  &spc_random_page_cost,
996  &spc_seq_page_cost);
997 
998  /*
999  * For small numbers of pages we should charge spc_random_page_cost
1000  * apiece, while if nearly all the table's pages are being read, it's more
1001  * appropriate to charge spc_seq_page_cost apiece. The effect is
1002  * nonlinear, too. For lack of a better idea, interpolate like this to
1003  * determine the cost per page.
1004  */
1005  if (pages_fetched >= 2.0)
1006  cost_per_page = spc_random_page_cost -
1007  (spc_random_page_cost - spc_seq_page_cost)
1008  * sqrt(pages_fetched / T);
1009  else
1010  cost_per_page = spc_random_page_cost;
1011 
1012  run_cost += pages_fetched * cost_per_page;
1013 
1014  /*
1015  * Estimate CPU costs per tuple.
1016  *
1017  * Often the indexquals don't need to be rechecked at each tuple ... but
1018  * not always, especially not if there are enough tuples involved that the
1019  * bitmaps become lossy. For the moment, just assume they will be
1020  * rechecked always. This means we charge the full freight for all the
1021  * scan clauses.
1022  */
1023  get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1024 
1025  startup_cost += qpqual_cost.startup;
1026  cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1027  cpu_run_cost = cpu_per_tuple * tuples_fetched;
1028 
1029  /* Adjust costing for parallelism, if used. */
1030  if (path->parallel_workers > 0)
1031  {
1032  double parallel_divisor = get_parallel_divisor(path);
1033 
1034  /* The CPU cost is divided among all the workers. */
1035  cpu_run_cost /= parallel_divisor;
1036 
1037  path->rows = clamp_row_est(path->rows / parallel_divisor);
1038  }
1039 
1040 
1041  run_cost += cpu_run_cost;
1042 
1043  /* tlist eval costs are paid per output row, not per tuple scanned */
1044  startup_cost += path->pathtarget->cost.startup;
1045  run_cost += path->pathtarget->cost.per_tuple * path->rows;
1046 
1047  path->startup_cost = startup_cost;
1048  path->total_cost = startup_cost + run_cost;
1049 }
1050 
1051 /*
1052  * cost_bitmap_tree_node
1053  * Extract cost and selectivity from a bitmap tree node (index/and/or)
1054  */
1055 void
1057 {
1058  if (IsA(path, IndexPath))
1059  {
1060  *cost = ((IndexPath *) path)->indextotalcost;
1061  *selec = ((IndexPath *) path)->indexselectivity;
1062 
1063  /*
1064  * Charge a small amount per retrieved tuple to reflect the costs of
1065  * manipulating the bitmap. This is mostly to make sure that a bitmap
1066  * scan doesn't look to be the same cost as an indexscan to retrieve a
1067  * single tuple.
1068  */
1069  *cost += 0.1 * cpu_operator_cost * path->rows;
1070  }
1071  else if (IsA(path, BitmapAndPath))
1072  {
1073  *cost = path->total_cost;
1074  *selec = ((BitmapAndPath *) path)->bitmapselectivity;
1075  }
1076  else if (IsA(path, BitmapOrPath))
1077  {
1078  *cost = path->total_cost;
1079  *selec = ((BitmapOrPath *) path)->bitmapselectivity;
1080  }
1081  else
1082  {
1083  elog(ERROR, "unrecognized node type: %d", nodeTag(path));
1084  *cost = *selec = 0; /* keep compiler quiet */
1085  }
1086 }
1087 
1088 /*
1089  * cost_bitmap_and_node
1090  * Estimate the cost of a BitmapAnd node
1091  *
1092  * Note that this considers only the costs of index scanning and bitmap
1093  * creation, not the eventual heap access. In that sense the object isn't
1094  * truly a Path, but it has enough path-like properties (costs in particular)
1095  * to warrant treating it as one. We don't bother to set the path rows field,
1096  * however.
1097  */
1098 void
1100 {
1101  Cost totalCost;
1102  Selectivity selec;
1103  ListCell *l;
1104 
1105  /*
1106  * We estimate AND selectivity on the assumption that the inputs are
1107  * independent. This is probably often wrong, but we don't have the info
1108  * to do better.
1109  *
1110  * The runtime cost of the BitmapAnd itself is estimated at 100x
1111  * cpu_operator_cost for each tbm_intersect needed. Probably too small,
1112  * definitely too simplistic?
1113  */
1114  totalCost = 0.0;
1115  selec = 1.0;
1116  foreach(l, path->bitmapquals)
1117  {
1118  Path *subpath = (Path *) lfirst(l);
1119  Cost subCost;
1120  Selectivity subselec;
1121 
1122  cost_bitmap_tree_node(subpath, &subCost, &subselec);
1123 
1124  selec *= subselec;
1125 
1126  totalCost += subCost;
1127  if (l != list_head(path->bitmapquals))
1128  totalCost += 100.0 * cpu_operator_cost;
1129  }
1130  path->bitmapselectivity = selec;
1131  path->path.rows = 0; /* per above, not used */
1132  path->path.startup_cost = totalCost;
1133  path->path.total_cost = totalCost;
1134 }
1135 
1136 /*
1137  * cost_bitmap_or_node
1138  * Estimate the cost of a BitmapOr node
1139  *
1140  * See comments for cost_bitmap_and_node.
1141  */
1142 void
1144 {
1145  Cost totalCost;
1146  Selectivity selec;
1147  ListCell *l;
1148 
1149  /*
1150  * We estimate OR selectivity on the assumption that the inputs are
1151  * non-overlapping, since that's often the case in "x IN (list)" type
1152  * situations. Of course, we clamp to 1.0 at the end.
1153  *
1154  * The runtime cost of the BitmapOr itself is estimated at 100x
1155  * cpu_operator_cost for each tbm_union needed. Probably too small,
1156  * definitely too simplistic? We are aware that the tbm_unions are
1157  * optimized out when the inputs are BitmapIndexScans.
1158  */
1159  totalCost = 0.0;
1160  selec = 0.0;
1161  foreach(l, path->bitmapquals)
1162  {
1163  Path *subpath = (Path *) lfirst(l);
1164  Cost subCost;
1165  Selectivity subselec;
1166 
1167  cost_bitmap_tree_node(subpath, &subCost, &subselec);
1168 
1169  selec += subselec;
1170 
1171  totalCost += subCost;
1172  if (l != list_head(path->bitmapquals) &&
1173  !IsA(subpath, IndexPath))
1174  totalCost += 100.0 * cpu_operator_cost;
1175  }
1176  path->bitmapselectivity = Min(selec, 1.0);
1177  path->path.rows = 0; /* per above, not used */
1178  path->path.startup_cost = totalCost;
1179  path->path.total_cost = totalCost;
1180 }
1181 
1182 /*
1183  * cost_tidscan
1184  * Determines and returns the cost of scanning a relation using TIDs.
1185  *
1186  * 'baserel' is the relation to be scanned
1187  * 'tidquals' is the list of TID-checkable quals
1188  * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1189  */
1190 void
1192  RelOptInfo *baserel, List *tidquals, ParamPathInfo *param_info)
1193 {
1194  Cost startup_cost = 0;
1195  Cost run_cost = 0;
1196  bool isCurrentOf = false;
1197  QualCost qpqual_cost;
1198  Cost cpu_per_tuple;
1199  QualCost tid_qual_cost;
1200  int ntuples;
1201  ListCell *l;
1202  double spc_random_page_cost;
1203 
1204  /* Should only be applied to base relations */
1205  Assert(baserel->relid > 0);
1206  Assert(baserel->rtekind == RTE_RELATION);
1207 
1208  /* Mark the path with the correct row estimate */
1209  if (param_info)
1210  path->rows = param_info->ppi_rows;
1211  else
1212  path->rows = baserel->rows;
1213 
1214  /* Count how many tuples we expect to retrieve */
1215  ntuples = 0;
1216  foreach(l, tidquals)
1217  {
1218  RestrictInfo *rinfo = lfirst_node(RestrictInfo, l);
1219  Expr *qual = rinfo->clause;
1220 
1221  if (IsA(qual, ScalarArrayOpExpr))
1222  {
1223  /* Each element of the array yields 1 tuple */
1224  ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) qual;
1225  Node *arraynode = (Node *) lsecond(saop->args);
1226 
1227  ntuples += estimate_array_length(arraynode);
1228  }
1229  else if (IsA(qual, CurrentOfExpr))
1230  {
1231  /* CURRENT OF yields 1 tuple */
1232  isCurrentOf = true;
1233  ntuples++;
1234  }
1235  else
1236  {
1237  /* It's just CTID = something, count 1 tuple */
1238  ntuples++;
1239  }
1240  }
1241 
1242  /*
1243  * We must force TID scan for WHERE CURRENT OF, because only nodeTidscan.c
1244  * understands how to do it correctly. Therefore, honor enable_tidscan
1245  * only when CURRENT OF isn't present. Also note that cost_qual_eval
1246  * counts a CurrentOfExpr as having startup cost disable_cost, which we
1247  * subtract off here; that's to prevent other plan types such as seqscan
1248  * from winning.
1249  */
1250  if (isCurrentOf)
1251  {
1253  startup_cost -= disable_cost;
1254  }
1255  else if (!enable_tidscan)
1256  startup_cost += disable_cost;
1257 
1258  /*
1259  * The TID qual expressions will be computed once, any other baserestrict
1260  * quals once per retrieved tuple.
1261  */
1262  cost_qual_eval(&tid_qual_cost, tidquals, root);
1263 
1264  /* fetch estimated page cost for tablespace containing table */
1266  &spc_random_page_cost,
1267  NULL);
1268 
1269  /* disk costs --- assume each tuple on a different page */
1270  run_cost += spc_random_page_cost * ntuples;
1271 
1272  /* Add scanning CPU costs */
1273  get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1274 
1275  /* XXX currently we assume TID quals are a subset of qpquals */
1276  startup_cost += qpqual_cost.startup + tid_qual_cost.per_tuple;
1277  cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple -
1278  tid_qual_cost.per_tuple;
1279  run_cost += cpu_per_tuple * ntuples;
1280 
1281  /* tlist eval costs are paid per output row, not per tuple scanned */
1282  startup_cost += path->pathtarget->cost.startup;
1283  run_cost += path->pathtarget->cost.per_tuple * path->rows;
1284 
1285  path->startup_cost = startup_cost;
1286  path->total_cost = startup_cost + run_cost;
1287 }
1288 
1289 /*
1290  * cost_tidrangescan
1291  * Determines and sets the costs of scanning a relation using a range of
1292  * TIDs for 'path'
1293  *
1294  * 'baserel' is the relation to be scanned
1295  * 'tidrangequals' is the list of TID-checkable range quals
1296  * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1297  */
1298 void
1300  RelOptInfo *baserel, List *tidrangequals,
1301  ParamPathInfo *param_info)
1302 {
1303  Selectivity selectivity;
1304  double pages;
1305  Cost startup_cost = 0;
1306  Cost run_cost = 0;
1307  QualCost qpqual_cost;
1308  Cost cpu_per_tuple;
1309  QualCost tid_qual_cost;
1310  double ntuples;
1311  double nseqpages;
1312  double spc_random_page_cost;
1313  double spc_seq_page_cost;
1314 
1315  /* Should only be applied to base relations */
1316  Assert(baserel->relid > 0);
1317  Assert(baserel->rtekind == RTE_RELATION);
1318 
1319  /* Mark the path with the correct row estimate */
1320  if (param_info)
1321  path->rows = param_info->ppi_rows;
1322  else
1323  path->rows = baserel->rows;
1324 
1325  /* Count how many tuples and pages we expect to scan */
1326  selectivity = clauselist_selectivity(root, tidrangequals, baserel->relid,
1327  JOIN_INNER, NULL);
1328  pages = ceil(selectivity * baserel->pages);
1329 
1330  if (pages <= 0.0)
1331  pages = 1.0;
1332 
1333  /*
1334  * The first page in a range requires a random seek, but each subsequent
1335  * page is just a normal sequential page read. NOTE: it's desirable for
1336  * TID Range Scans to cost more than the equivalent Sequential Scans,
1337  * because Seq Scans have some performance advantages such as scan
1338  * synchronization and parallelizability, and we'd prefer one of them to
1339  * be picked unless a TID Range Scan really is better.
1340  */
1341  ntuples = selectivity * baserel->tuples;
1342  nseqpages = pages - 1.0;
1343 
1344  if (!enable_tidscan)
1345  startup_cost += disable_cost;
1346 
1347  /*
1348  * The TID qual expressions will be computed once, any other baserestrict
1349  * quals once per retrieved tuple.
1350  */
1351  cost_qual_eval(&tid_qual_cost, tidrangequals, root);
1352 
1353  /* fetch estimated page cost for tablespace containing table */
1355  &spc_random_page_cost,
1356  &spc_seq_page_cost);
1357 
1358  /* disk costs; 1 random page and the remainder as seq pages */
1359  run_cost += spc_random_page_cost + spc_seq_page_cost * nseqpages;
1360 
1361  /* Add scanning CPU costs */
1362  get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1363 
1364  /*
1365  * XXX currently we assume TID quals are a subset of qpquals at this
1366  * point; they will be removed (if possible) when we create the plan, so
1367  * we subtract their cost from the total qpqual cost. (If the TID quals
1368  * can't be removed, this is a mistake and we're going to underestimate
1369  * the CPU cost a bit.)
1370  */
1371  startup_cost += qpqual_cost.startup + tid_qual_cost.per_tuple;
1372  cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple -
1373  tid_qual_cost.per_tuple;
1374  run_cost += cpu_per_tuple * ntuples;
1375 
1376  /* tlist eval costs are paid per output row, not per tuple scanned */
1377  startup_cost += path->pathtarget->cost.startup;
1378  run_cost += path->pathtarget->cost.per_tuple * path->rows;
1379 
1380  path->startup_cost = startup_cost;
1381  path->total_cost = startup_cost + run_cost;
1382 }
1383 
1384 /*
1385  * cost_subqueryscan
1386  * Determines and returns the cost of scanning a subquery RTE.
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
1393  RelOptInfo *baserel, ParamPathInfo *param_info)
1394 {
1395  Cost startup_cost;
1396  Cost run_cost;
1397  QualCost qpqual_cost;
1398  Cost cpu_per_tuple;
1399 
1400  /* Should only be applied to base relations that are subqueries */
1401  Assert(baserel->relid > 0);
1402  Assert(baserel->rtekind == RTE_SUBQUERY);
1403 
1404  /* Mark the path with the correct row estimate */
1405  if (param_info)
1406  path->path.rows = param_info->ppi_rows;
1407  else
1408  path->path.rows = baserel->rows;
1409 
1410  /*
1411  * Cost of path is cost of evaluating the subplan, plus cost of evaluating
1412  * any restriction clauses and tlist that will be attached to the
1413  * SubqueryScan node, plus cpu_tuple_cost to account for selection and
1414  * projection overhead.
1415  */
1416  path->path.startup_cost = path->subpath->startup_cost;
1417  path->path.total_cost = path->subpath->total_cost;
1418 
1419  get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1420 
1421  startup_cost = qpqual_cost.startup;
1422  cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1423  run_cost = cpu_per_tuple * baserel->tuples;
1424 
1425  /* tlist eval costs are paid per output row, not per tuple scanned */
1426  startup_cost += path->path.pathtarget->cost.startup;
1427  run_cost += path->path.pathtarget->cost.per_tuple * path->path.rows;
1428 
1429  path->path.startup_cost += startup_cost;
1430  path->path.total_cost += startup_cost + run_cost;
1431 }
1432 
1433 /*
1434  * cost_functionscan
1435  * Determines and returns the cost of scanning a function RTE.
1436  *
1437  * 'baserel' is the relation to be scanned
1438  * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1439  */
1440 void
1442  RelOptInfo *baserel, ParamPathInfo *param_info)
1443 {
1444  Cost startup_cost = 0;
1445  Cost run_cost = 0;
1446  QualCost qpqual_cost;
1447  Cost cpu_per_tuple;
1448  RangeTblEntry *rte;
1449  QualCost exprcost;
1450 
1451  /* Should only be applied to base relations that are functions */
1452  Assert(baserel->relid > 0);
1453  rte = planner_rt_fetch(baserel->relid, root);
1454  Assert(rte->rtekind == RTE_FUNCTION);
1455 
1456  /* Mark the path with the correct row estimate */
1457  if (param_info)
1458  path->rows = param_info->ppi_rows;
1459  else
1460  path->rows = baserel->rows;
1461 
1462  /*
1463  * Estimate costs of executing the function expression(s).
1464  *
1465  * Currently, nodeFunctionscan.c always executes the functions to
1466  * completion before returning any rows, and caches the results in a
1467  * tuplestore. So the function eval cost is all startup cost, and per-row
1468  * costs are minimal.
1469  *
1470  * XXX in principle we ought to charge tuplestore spill costs if the
1471  * number of rows is large. However, given how phony our rowcount
1472  * estimates for functions tend to be, there's not a lot of point in that
1473  * refinement right now.
1474  */
1475  cost_qual_eval_node(&exprcost, (Node *) rte->functions, root);
1476 
1477  startup_cost += exprcost.startup + exprcost.per_tuple;
1478 
1479  /* Add scanning CPU costs */
1480  get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1481 
1482  startup_cost += qpqual_cost.startup;
1483  cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1484  run_cost += cpu_per_tuple * baserel->tuples;
1485 
1486  /* tlist eval costs are paid per output row, not per tuple scanned */
1487  startup_cost += path->pathtarget->cost.startup;
1488  run_cost += path->pathtarget->cost.per_tuple * path->rows;
1489 
1490  path->startup_cost = startup_cost;
1491  path->total_cost = startup_cost + run_cost;
1492 }
1493 
1494 /*
1495  * cost_tablefuncscan
1496  * Determines and returns the cost of scanning a table function.
1497  *
1498  * 'baserel' is the relation to be scanned
1499  * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1500  */
1501 void
1503  RelOptInfo *baserel, ParamPathInfo *param_info)
1504 {
1505  Cost startup_cost = 0;
1506  Cost run_cost = 0;
1507  QualCost qpqual_cost;
1508  Cost cpu_per_tuple;
1509  RangeTblEntry *rte;
1510  QualCost exprcost;
1511 
1512  /* Should only be applied to base relations that are functions */
1513  Assert(baserel->relid > 0);
1514  rte = planner_rt_fetch(baserel->relid, root);
1515  Assert(rte->rtekind == RTE_TABLEFUNC);
1516 
1517  /* Mark the path with the correct row estimate */
1518  if (param_info)
1519  path->rows = param_info->ppi_rows;
1520  else
1521  path->rows = baserel->rows;
1522 
1523  /*
1524  * Estimate costs of executing the table func expression(s).
1525  *
1526  * XXX in principle we ought to charge tuplestore spill costs if the
1527  * number of rows is large. However, given how phony our rowcount
1528  * estimates for tablefuncs tend to be, there's not a lot of point in that
1529  * refinement right now.
1530  */
1531  cost_qual_eval_node(&exprcost, (Node *) rte->tablefunc, root);
1532 
1533  startup_cost += exprcost.startup + exprcost.per_tuple;
1534 
1535  /* Add scanning CPU costs */
1536  get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1537 
1538  startup_cost += qpqual_cost.startup;
1539  cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1540  run_cost += cpu_per_tuple * baserel->tuples;
1541 
1542  /* tlist eval costs are paid per output row, not per tuple scanned */
1543  startup_cost += path->pathtarget->cost.startup;
1544  run_cost += path->pathtarget->cost.per_tuple * path->rows;
1545 
1546  path->startup_cost = startup_cost;
1547  path->total_cost = startup_cost + run_cost;
1548 }
1549 
1550 /*
1551  * cost_valuesscan
1552  * Determines and returns the cost of scanning a VALUES RTE.
1553  *
1554  * 'baserel' is the relation to be scanned
1555  * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1556  */
1557 void
1559  RelOptInfo *baserel, ParamPathInfo *param_info)
1560 {
1561  Cost startup_cost = 0;
1562  Cost run_cost = 0;
1563  QualCost qpqual_cost;
1564  Cost cpu_per_tuple;
1565 
1566  /* Should only be applied to base relations that are values lists */
1567  Assert(baserel->relid > 0);
1568  Assert(baserel->rtekind == RTE_VALUES);
1569 
1570  /* Mark the path with the correct row estimate */
1571  if (param_info)
1572  path->rows = param_info->ppi_rows;
1573  else
1574  path->rows = baserel->rows;
1575 
1576  /*
1577  * For now, estimate list evaluation cost at one operator eval per list
1578  * (probably pretty bogus, but is it worth being smarter?)
1579  */
1580  cpu_per_tuple = cpu_operator_cost;
1581 
1582  /* Add scanning CPU costs */
1583  get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1584 
1585  startup_cost += qpqual_cost.startup;
1586  cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
1587  run_cost += cpu_per_tuple * baserel->tuples;
1588 
1589  /* tlist eval costs are paid per output row, not per tuple scanned */
1590  startup_cost += path->pathtarget->cost.startup;
1591  run_cost += path->pathtarget->cost.per_tuple * path->rows;
1592 
1593  path->startup_cost = startup_cost;
1594  path->total_cost = startup_cost + run_cost;
1595 }
1596 
1597 /*
1598  * cost_ctescan
1599  * Determines and returns the cost of scanning a CTE RTE.
1600  *
1601  * Note: this is used for both self-reference and regular CTEs; the
1602  * possible cost differences are below the threshold of what we could
1603  * estimate accurately anyway. Note that the costs of evaluating the
1604  * referenced CTE query are added into the final plan as initplan costs,
1605  * and should NOT be counted here.
1606  */
1607 void
1609  RelOptInfo *baserel, ParamPathInfo *param_info)
1610 {
1611  Cost startup_cost = 0;
1612  Cost run_cost = 0;
1613  QualCost qpqual_cost;
1614  Cost cpu_per_tuple;
1615 
1616  /* Should only be applied to base relations that are CTEs */
1617  Assert(baserel->relid > 0);
1618  Assert(baserel->rtekind == RTE_CTE);
1619 
1620  /* Mark the path with the correct row estimate */
1621  if (param_info)
1622  path->rows = param_info->ppi_rows;
1623  else
1624  path->rows = baserel->rows;
1625 
1626  /* Charge one CPU tuple cost per row for tuplestore manipulation */
1627  cpu_per_tuple = cpu_tuple_cost;
1628 
1629  /* Add scanning CPU costs */
1630  get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1631 
1632  startup_cost += qpqual_cost.startup;
1633  cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
1634  run_cost += cpu_per_tuple * baserel->tuples;
1635 
1636  /* tlist eval costs are paid per output row, not per tuple scanned */
1637  startup_cost += path->pathtarget->cost.startup;
1638  run_cost += path->pathtarget->cost.per_tuple * path->rows;
1639 
1640  path->startup_cost = startup_cost;
1641  path->total_cost = startup_cost + run_cost;
1642 }
1643 
1644 /*
1645  * cost_namedtuplestorescan
1646  * Determines and returns the cost of scanning a named tuplestore.
1647  */
1648 void
1650  RelOptInfo *baserel, ParamPathInfo *param_info)
1651 {
1652  Cost startup_cost = 0;
1653  Cost run_cost = 0;
1654  QualCost qpqual_cost;
1655  Cost cpu_per_tuple;
1656 
1657  /* Should only be applied to base relations that are Tuplestores */
1658  Assert(baserel->relid > 0);
1659  Assert(baserel->rtekind == RTE_NAMEDTUPLESTORE);
1660 
1661  /* Mark the path with the correct row estimate */
1662  if (param_info)
1663  path->rows = param_info->ppi_rows;
1664  else
1665  path->rows = baserel->rows;
1666 
1667  /* Charge one CPU tuple cost per row for tuplestore manipulation */
1668  cpu_per_tuple = cpu_tuple_cost;
1669 
1670  /* Add scanning CPU costs */
1671  get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1672 
1673  startup_cost += qpqual_cost.startup;
1674  cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
1675  run_cost += cpu_per_tuple * baserel->tuples;
1676 
1677  path->startup_cost = startup_cost;
1678  path->total_cost = startup_cost + run_cost;
1679 }
1680 
1681 /*
1682  * cost_resultscan
1683  * Determines and returns the cost of scanning an RTE_RESULT relation.
1684  */
1685 void
1687  RelOptInfo *baserel, ParamPathInfo *param_info)
1688 {
1689  Cost startup_cost = 0;
1690  Cost run_cost = 0;
1691  QualCost qpqual_cost;
1692  Cost cpu_per_tuple;
1693 
1694  /* Should only be applied to RTE_RESULT base relations */
1695  Assert(baserel->relid > 0);
1696  Assert(baserel->rtekind == RTE_RESULT);
1697 
1698  /* Mark the path with the correct row estimate */
1699  if (param_info)
1700  path->rows = param_info->ppi_rows;
1701  else
1702  path->rows = baserel->rows;
1703 
1704  /* We charge qual cost plus cpu_tuple_cost */
1705  get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1706 
1707  startup_cost += qpqual_cost.startup;
1708  cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1709  run_cost += cpu_per_tuple * baserel->tuples;
1710 
1711  path->startup_cost = startup_cost;
1712  path->total_cost = startup_cost + run_cost;
1713 }
1714 
1715 /*
1716  * cost_recursive_union
1717  * Determines and returns the cost of performing a recursive union,
1718  * and also the estimated output size.
1719  *
1720  * We are given Paths for the nonrecursive and recursive terms.
1721  */
1722 void
1723 cost_recursive_union(Path *runion, Path *nrterm, Path *rterm)
1724 {
1725  Cost startup_cost;
1726  Cost total_cost;
1727  double total_rows;
1728 
1729  /* We probably have decent estimates for the non-recursive term */
1730  startup_cost = nrterm->startup_cost;
1731  total_cost = nrterm->total_cost;
1732  total_rows = nrterm->rows;
1733 
1734  /*
1735  * We arbitrarily assume that about 10 recursive iterations will be
1736  * needed, and that we've managed to get a good fix on the cost and output
1737  * size of each one of them. These are mighty shaky assumptions but it's
1738  * hard to see how to do better.
1739  */
1740  total_cost += 10 * rterm->total_cost;
1741  total_rows += 10 * rterm->rows;
1742 
1743  /*
1744  * Also charge cpu_tuple_cost per row to account for the costs of
1745  * manipulating the tuplestores. (We don't worry about possible
1746  * spill-to-disk costs.)
1747  */
1748  total_cost += cpu_tuple_cost * total_rows;
1749 
1750  runion->startup_cost = startup_cost;
1751  runion->total_cost = total_cost;
1752  runion->rows = total_rows;
1753  runion->pathtarget->width = Max(nrterm->pathtarget->width,
1754  rterm->pathtarget->width);
1755 }
1756 
1757 /*
1758  * cost_tuplesort
1759  * Determines and returns the cost of sorting a relation using tuplesort,
1760  * not including the cost of reading the input data.
1761  *
1762  * If the total volume of data to sort is less than sort_mem, we will do
1763  * an in-memory sort, which requires no I/O and about t*log2(t) tuple
1764  * comparisons for t tuples.
1765  *
1766  * If the total volume exceeds sort_mem, we switch to a tape-style merge
1767  * algorithm. There will still be about t*log2(t) tuple comparisons in
1768  * total, but we will also need to write and read each tuple once per
1769  * merge pass. We expect about ceil(logM(r)) merge passes where r is the
1770  * number of initial runs formed and M is the merge order used by tuplesort.c.
1771  * Since the average initial run should be about sort_mem, we have
1772  * disk traffic = 2 * relsize * ceil(logM(p / sort_mem))
1773  * cpu = comparison_cost * t * log2(t)
1774  *
1775  * If the sort is bounded (i.e., only the first k result tuples are needed)
1776  * and k tuples can fit into sort_mem, we use a heap method that keeps only
1777  * k tuples in the heap; this will require about t*log2(k) tuple comparisons.
1778  *
1779  * The disk traffic is assumed to be 3/4ths sequential and 1/4th random
1780  * accesses (XXX can't we refine that guess?)
1781  *
1782  * By default, we charge two operator evals per tuple comparison, which should
1783  * be in the right ballpark in most cases. The caller can tweak this by
1784  * specifying nonzero comparison_cost; typically that's used for any extra
1785  * work that has to be done to prepare the inputs to the comparison operators.
1786  *
1787  * 'tuples' is the number of tuples in the relation
1788  * 'width' is the average tuple width in bytes
1789  * 'comparison_cost' is the extra cost per comparison, if any
1790  * 'sort_mem' is the number of kilobytes of work memory allowed for the sort
1791  * 'limit_tuples' is the bound on the number of output tuples; -1 if no bound
1792  */
1793 static void
1794 cost_tuplesort(Cost *startup_cost, Cost *run_cost,
1795  double tuples, int width,
1796  Cost comparison_cost, int sort_mem,
1797  double limit_tuples)
1798 {
1799  double input_bytes = relation_byte_size(tuples, width);
1800  double output_bytes;
1801  double output_tuples;
1802  long sort_mem_bytes = sort_mem * 1024L;
1803 
1804  /*
1805  * We want to be sure the cost of a sort is never estimated as zero, even
1806  * if passed-in tuple count is zero. Besides, mustn't do log(0)...
1807  */
1808  if (tuples < 2.0)
1809  tuples = 2.0;
1810 
1811  /* Include the default cost-per-comparison */
1812  comparison_cost += 2.0 * cpu_operator_cost;
1813 
1814  /* Do we have a useful LIMIT? */
1815  if (limit_tuples > 0 && limit_tuples < tuples)
1816  {
1817  output_tuples = limit_tuples;
1818  output_bytes = relation_byte_size(output_tuples, width);
1819  }
1820  else
1821  {
1822  output_tuples = tuples;
1823  output_bytes = input_bytes;
1824  }
1825 
1826  if (output_bytes > sort_mem_bytes)
1827  {
1828  /*
1829  * We'll have to use a disk-based sort of all the tuples
1830  */
1831  double npages = ceil(input_bytes / BLCKSZ);
1832  double nruns = input_bytes / sort_mem_bytes;
1833  double mergeorder = tuplesort_merge_order(sort_mem_bytes);
1834  double log_runs;
1835  double npageaccesses;
1836 
1837  /*
1838  * CPU costs
1839  *
1840  * Assume about N log2 N comparisons
1841  */
1842  *startup_cost = comparison_cost * tuples * LOG2(tuples);
1843 
1844  /* Disk costs */
1845 
1846  /* Compute logM(r) as log(r) / log(M) */
1847  if (nruns > mergeorder)
1848  log_runs = ceil(log(nruns) / log(mergeorder));
1849  else
1850  log_runs = 1.0;
1851  npageaccesses = 2.0 * npages * log_runs;
1852  /* Assume 3/4ths of accesses are sequential, 1/4th are not */
1853  *startup_cost += npageaccesses *
1854  (seq_page_cost * 0.75 + random_page_cost * 0.25);
1855  }
1856  else if (tuples > 2 * output_tuples || input_bytes > sort_mem_bytes)
1857  {
1858  /*
1859  * We'll use a bounded heap-sort keeping just K tuples in memory, for
1860  * a total number of tuple comparisons of N log2 K; but the constant
1861  * factor is a bit higher than for quicksort. Tweak it so that the
1862  * cost curve is continuous at the crossover point.
1863  */
1864  *startup_cost = comparison_cost * tuples * LOG2(2.0 * output_tuples);
1865  }
1866  else
1867  {
1868  /* We'll use plain quicksort on all the input tuples */
1869  *startup_cost = comparison_cost * tuples * LOG2(tuples);
1870  }
1871 
1872  /*
1873  * Also charge a small amount (arbitrarily set equal to operator cost) per
1874  * extracted tuple. We don't charge cpu_tuple_cost because a Sort node
1875  * doesn't do qual-checking or projection, so it has less overhead than
1876  * most plan nodes. Note it's correct to use tuples not output_tuples
1877  * here --- the upper LIMIT will pro-rate the run cost so we'd be double
1878  * counting the LIMIT otherwise.
1879  */
1880  *run_cost = cpu_operator_cost * tuples;
1881 }
1882 
1883 /*
1884  * cost_incremental_sort
1885  * Determines and returns the cost of sorting a relation incrementally, when
1886  * the input path is presorted by a prefix of the pathkeys.
1887  *
1888  * 'presorted_keys' is the number of leading pathkeys by which the input path
1889  * is sorted.
1890  *
1891  * We estimate the number of groups into which the relation is divided by the
1892  * leading pathkeys, and then calculate the cost of sorting a single group
1893  * with tuplesort using cost_tuplesort().
1894  */
1895 void
1897  PlannerInfo *root, List *pathkeys, int presorted_keys,
1898  Cost input_startup_cost, Cost input_total_cost,
1899  double input_tuples, int width, Cost comparison_cost, int sort_mem,
1900  double limit_tuples)
1901 {
1902  Cost startup_cost = 0,
1903  run_cost = 0,
1904  input_run_cost = input_total_cost - input_startup_cost;
1905  double group_tuples,
1906  input_groups;
1907  Cost group_startup_cost,
1908  group_run_cost,
1909  group_input_run_cost;
1910  List *presortedExprs = NIL;
1911  ListCell *l;
1912  int i = 0;
1913  bool unknown_varno = false;
1914 
1915  Assert(presorted_keys != 0);
1916 
1917  /*
1918  * We want to be sure the cost of a sort is never estimated as zero, even
1919  * if passed-in tuple count is zero. Besides, mustn't do log(0)...
1920  */
1921  if (input_tuples < 2.0)
1922  input_tuples = 2.0;
1923 
1924  /* Default estimate of number of groups, capped to one group per row. */
1925  input_groups = Min(input_tuples, DEFAULT_NUM_DISTINCT);
1926 
1927  /*
1928  * Extract presorted keys as list of expressions.
1929  *
1930  * We need to be careful about Vars containing "varno 0" which might have
1931  * been introduced by generate_append_tlist, which would confuse
1932  * estimate_num_groups (in fact it'd fail for such expressions). See
1933  * recurse_set_operations which has to deal with the same issue.
1934  *
1935  * Unlike recurse_set_operations we can't access the original target list
1936  * here, and even if we could it's not very clear how useful would that be
1937  * for a set operation combining multiple tables. So we simply detect if
1938  * there are any expressions with "varno 0" and use the default
1939  * DEFAULT_NUM_DISTINCT in that case.
1940  *
1941  * We might also use either 1.0 (a single group) or input_tuples (each row
1942  * being a separate group), pretty much the worst and best case for
1943  * incremental sort. But those are extreme cases and using something in
1944  * between seems reasonable. Furthermore, generate_append_tlist is used
1945  * for set operations, which are likely to produce mostly unique output
1946  * anyway - from that standpoint the DEFAULT_NUM_DISTINCT is defensive
1947  * while maintaining lower startup cost.
1948  */
1949  foreach(l, pathkeys)
1950  {
1951  PathKey *key = (PathKey *) lfirst(l);
1952  EquivalenceMember *member = (EquivalenceMember *)
1953  linitial(key->pk_eclass->ec_members);
1954 
1955  /*
1956  * Check if the expression contains Var with "varno 0" so that we
1957  * don't call estimate_num_groups in that case.
1958  */
1959  if (bms_is_member(0, pull_varnos(root, (Node *) member->em_expr)))
1960  {
1961  unknown_varno = true;
1962  break;
1963  }
1964 
1965  /* expression not containing any Vars with "varno 0" */
1966  presortedExprs = lappend(presortedExprs, member->em_expr);
1967 
1968  i++;
1969  if (i >= presorted_keys)
1970  break;
1971  }
1972 
1973  /* Estimate number of groups with equal presorted keys. */
1974  if (!unknown_varno)
1975  input_groups = estimate_num_groups(root, presortedExprs, input_tuples,
1976  NULL, NULL);
1977 
1978  group_tuples = input_tuples / input_groups;
1979  group_input_run_cost = input_run_cost / input_groups;
1980 
1981  /*
1982  * Estimate average cost of sorting of one group where presorted keys are
1983  * equal. Incremental sort is sensitive to distribution of tuples to the
1984  * groups, where we're relying on quite rough assumptions. Thus, we're
1985  * pessimistic about incremental sort performance and increase its average
1986  * group size by half.
1987  */
1988  cost_tuplesort(&group_startup_cost, &group_run_cost,
1989  1.5 * group_tuples, width, comparison_cost, sort_mem,
1990  limit_tuples);
1991 
1992  /*
1993  * Startup cost of incremental sort is the startup cost of its first group
1994  * plus the cost of its input.
1995  */
1996  startup_cost += group_startup_cost
1997  + input_startup_cost + group_input_run_cost;
1998 
1999  /*
2000  * After we started producing tuples from the first group, the cost of
2001  * producing all the tuples is given by the cost to finish processing this
2002  * group, plus the total cost to process the remaining groups, plus the
2003  * remaining cost of input.
2004  */
2005  run_cost += group_run_cost
2006  + (group_run_cost + group_startup_cost) * (input_groups - 1)
2007  + group_input_run_cost * (input_groups - 1);
2008 
2009  /*
2010  * Incremental sort adds some overhead by itself. Firstly, it has to
2011  * detect the sort groups. This is roughly equal to one extra copy and
2012  * comparison per tuple. Secondly, it has to reset the tuplesort context
2013  * for every group.
2014  */
2015  run_cost += (cpu_tuple_cost + comparison_cost) * input_tuples;
2016  run_cost += 2.0 * cpu_tuple_cost * input_groups;
2017 
2018  path->rows = input_tuples;
2019  path->startup_cost = startup_cost;
2020  path->total_cost = startup_cost + run_cost;
2021 }
2022 
2023 /*
2024  * cost_sort
2025  * Determines and returns the cost of sorting a relation, including
2026  * the cost of reading the input data.
2027  *
2028  * NOTE: some callers currently pass NIL for pathkeys because they
2029  * can't conveniently supply the sort keys. Since this routine doesn't
2030  * currently do anything with pathkeys anyway, that doesn't matter...
2031  * but if it ever does, it should react gracefully to lack of key data.
2032  * (Actually, the thing we'd most likely be interested in is just the number
2033  * of sort keys, which all callers *could* supply.)
2034  */
2035 void
2037  List *pathkeys, Cost input_cost, double tuples, int width,
2038  Cost comparison_cost, int sort_mem,
2039  double limit_tuples)
2040 
2041 {
2042  Cost startup_cost;
2043  Cost run_cost;
2044 
2045  cost_tuplesort(&startup_cost, &run_cost,
2046  tuples, width,
2047  comparison_cost, sort_mem,
2048  limit_tuples);
2049 
2050  if (!enable_sort)
2051  startup_cost += disable_cost;
2052 
2053  startup_cost += input_cost;
2054 
2055  path->rows = tuples;
2056  path->startup_cost = startup_cost;
2057  path->total_cost = startup_cost + run_cost;
2058 }
2059 
2060 /*
2061  * append_nonpartial_cost
2062  * Estimate the cost of the non-partial paths in a Parallel Append.
2063  * The non-partial paths are assumed to be the first "numpaths" paths
2064  * from the subpaths list, and to be in order of decreasing cost.
2065  */
2066 static Cost
2067 append_nonpartial_cost(List *subpaths, int numpaths, int parallel_workers)
2068 {
2069  Cost *costarr;
2070  int arrlen;
2071  ListCell *l;
2072  ListCell *cell;
2073  int i;
2074  int path_index;
2075  int min_index;
2076  int max_index;
2077 
2078  if (numpaths == 0)
2079  return 0;
2080 
2081  /*
2082  * Array length is number of workers or number of relevant paths,
2083  * whichever is less.
2084  */
2085  arrlen = Min(parallel_workers, numpaths);
2086  costarr = (Cost *) palloc(sizeof(Cost) * arrlen);
2087 
2088  /* The first few paths will each be claimed by a different worker. */
2089  path_index = 0;
2090  foreach(cell, subpaths)
2091  {
2092  Path *subpath = (Path *) lfirst(cell);
2093 
2094  if (path_index == arrlen)
2095  break;
2096  costarr[path_index++] = subpath->total_cost;
2097  }
2098 
2099  /*
2100  * Since subpaths are sorted by decreasing cost, the last one will have
2101  * the minimum cost.
2102  */
2103  min_index = arrlen - 1;
2104 
2105  /*
2106  * For each of the remaining subpaths, add its cost to the array element
2107  * with minimum cost.
2108  */
2109  for_each_cell(l, subpaths, cell)
2110  {
2111  Path *subpath = (Path *) lfirst(l);
2112  int i;
2113 
2114  /* Consider only the non-partial paths */
2115  if (path_index++ == numpaths)
2116  break;
2117 
2118  costarr[min_index] += subpath->total_cost;
2119 
2120  /* Update the new min cost array index */
2121  for (min_index = i = 0; i < arrlen; i++)
2122  {
2123  if (costarr[i] < costarr[min_index])
2124  min_index = i;
2125  }
2126  }
2127 
2128  /* Return the highest cost from the array */
2129  for (max_index = i = 0; i < arrlen; i++)
2130  {
2131  if (costarr[i] > costarr[max_index])
2132  max_index = i;
2133  }
2134 
2135  return costarr[max_index];
2136 }
2137 
2138 /*
2139  * cost_append
2140  * Determines and returns the cost of an Append node.
2141  */
2142 void
2144 {
2145  ListCell *l;
2146 
2147  apath->path.startup_cost = 0;
2148  apath->path.total_cost = 0;
2149  apath->path.rows = 0;
2150 
2151  if (apath->subpaths == NIL)
2152  return;
2153 
2154  if (!apath->path.parallel_aware)
2155  {
2156  List *pathkeys = apath->path.pathkeys;
2157 
2158  if (pathkeys == NIL)
2159  {
2160  Path *subpath = (Path *) linitial(apath->subpaths);
2161 
2162  /*
2163  * For an unordered, non-parallel-aware Append we take the startup
2164  * cost as the startup cost of the first subpath.
2165  */
2166  apath->path.startup_cost = subpath->startup_cost;
2167 
2168  /* Compute rows and costs as sums of subplan rows and costs. */
2169  foreach(l, apath->subpaths)
2170  {
2171  Path *subpath = (Path *) lfirst(l);
2172 
2173  apath->path.rows += subpath->rows;
2174  apath->path.total_cost += subpath->total_cost;
2175  }
2176  }
2177  else
2178  {
2179  /*
2180  * For an ordered, non-parallel-aware Append we take the startup
2181  * cost as the sum of the subpath startup costs. This ensures
2182  * that we don't underestimate the startup cost when a query's
2183  * LIMIT is such that several of the children have to be run to
2184  * satisfy it. This might be overkill --- another plausible hack
2185  * would be to take the Append's startup cost as the maximum of
2186  * the child startup costs. But we don't want to risk believing
2187  * that an ORDER BY LIMIT query can be satisfied at small cost
2188  * when the first child has small startup cost but later ones
2189  * don't. (If we had the ability to deal with nonlinear cost
2190  * interpolation for partial retrievals, we would not need to be
2191  * so conservative about this.)
2192  *
2193  * This case is also different from the above in that we have to
2194  * account for possibly injecting sorts into subpaths that aren't
2195  * natively ordered.
2196  */
2197  foreach(l, apath->subpaths)
2198  {
2199  Path *subpath = (Path *) lfirst(l);
2200  Path sort_path; /* dummy for result of cost_sort */
2201 
2202  if (!pathkeys_contained_in(pathkeys, subpath->pathkeys))
2203  {
2204  /*
2205  * We'll need to insert a Sort node, so include costs for
2206  * that. We can use the parent's LIMIT if any, since we
2207  * certainly won't pull more than that many tuples from
2208  * any child.
2209  */
2210  cost_sort(&sort_path,
2211  NULL, /* doesn't currently need root */
2212  pathkeys,
2213  subpath->total_cost,
2214  subpath->rows,
2215  subpath->pathtarget->width,
2216  0.0,
2217  work_mem,
2218  apath->limit_tuples);
2219  subpath = &sort_path;
2220  }
2221 
2222  apath->path.rows += subpath->rows;
2223  apath->path.startup_cost += subpath->startup_cost;
2224  apath->path.total_cost += subpath->total_cost;
2225  }
2226  }
2227  }
2228  else /* parallel-aware */
2229  {
2230  int i = 0;
2231  double parallel_divisor = get_parallel_divisor(&apath->path);
2232 
2233  /* Parallel-aware Append never produces ordered output. */
2234  Assert(apath->path.pathkeys == NIL);
2235 
2236  /* Calculate startup cost. */
2237  foreach(l, apath->subpaths)
2238  {
2239  Path *subpath = (Path *) lfirst(l);
2240 
2241  /*
2242  * Append will start returning tuples when the child node having
2243  * lowest startup cost is done setting up. We consider only the
2244  * first few subplans that immediately get a worker assigned.
2245  */
2246  if (i == 0)
2247  apath->path.startup_cost = subpath->startup_cost;
2248  else if (i < apath->path.parallel_workers)
2249  apath->path.startup_cost = Min(apath->path.startup_cost,
2250  subpath->startup_cost);
2251 
2252  /*
2253  * Apply parallel divisor to subpaths. Scale the number of rows
2254  * for each partial subpath based on the ratio of the parallel
2255  * divisor originally used for the subpath to the one we adopted.
2256  * Also add the cost of partial paths to the total cost, but
2257  * ignore non-partial paths for now.
2258  */
2259  if (i < apath->first_partial_path)
2260  apath->path.rows += subpath->rows / parallel_divisor;
2261  else
2262  {
2263  double subpath_parallel_divisor;
2264 
2265  subpath_parallel_divisor = get_parallel_divisor(subpath);
2266  apath->path.rows += subpath->rows * (subpath_parallel_divisor /
2267  parallel_divisor);
2268  apath->path.total_cost += subpath->total_cost;
2269  }
2270 
2271  apath->path.rows = clamp_row_est(apath->path.rows);
2272 
2273  i++;
2274  }
2275 
2276  /* Add cost for non-partial subpaths. */
2277  apath->path.total_cost +=
2279  apath->first_partial_path,
2280  apath->path.parallel_workers);
2281  }
2282 
2283  /*
2284  * Although Append does not do any selection or projection, it's not free;
2285  * add a small per-tuple overhead.
2286  */
2287  apath->path.total_cost +=
2289 }
2290 
2291 /*
2292  * cost_merge_append
2293  * Determines and returns the cost of a MergeAppend node.
2294  *
2295  * MergeAppend merges several pre-sorted input streams, using a heap that
2296  * at any given instant holds the next tuple from each stream. If there
2297  * are N streams, we need about N*log2(N) tuple comparisons to construct
2298  * the heap at startup, and then for each output tuple, about log2(N)
2299  * comparisons to replace the top entry.
2300  *
2301  * (The effective value of N will drop once some of the input streams are
2302  * exhausted, but it seems unlikely to be worth trying to account for that.)
2303  *
2304  * The heap is never spilled to disk, since we assume N is not very large.
2305  * So this is much simpler than cost_sort.
2306  *
2307  * As in cost_sort, we charge two operator evals per tuple comparison.
2308  *
2309  * 'pathkeys' is a list of sort keys
2310  * 'n_streams' is the number of input streams
2311  * 'input_startup_cost' is the sum of the input streams' startup costs
2312  * 'input_total_cost' is the sum of the input streams' total costs
2313  * 'tuples' is the number of tuples in all the streams
2314  */
2315 void
2317  List *pathkeys, int n_streams,
2318  Cost input_startup_cost, Cost input_total_cost,
2319  double tuples)
2320 {
2321  Cost startup_cost = 0;
2322  Cost run_cost = 0;
2323  Cost comparison_cost;
2324  double N;
2325  double logN;
2326 
2327  /*
2328  * Avoid log(0)...
2329  */
2330  N = (n_streams < 2) ? 2.0 : (double) n_streams;
2331  logN = LOG2(N);
2332 
2333  /* Assumed cost per tuple comparison */
2334  comparison_cost = 2.0 * cpu_operator_cost;
2335 
2336  /* Heap creation cost */
2337  startup_cost += comparison_cost * N * logN;
2338 
2339  /* Per-tuple heap maintenance cost */
2340  run_cost += tuples * comparison_cost * logN;
2341 
2342  /*
2343  * Although MergeAppend does not do any selection or projection, it's not
2344  * free; add a small per-tuple overhead.
2345  */
2346  run_cost += cpu_tuple_cost * APPEND_CPU_COST_MULTIPLIER * tuples;
2347 
2348  path->startup_cost = startup_cost + input_startup_cost;
2349  path->total_cost = startup_cost + run_cost + input_total_cost;
2350 }
2351 
2352 /*
2353  * cost_material
2354  * Determines and returns the cost of materializing a relation, including
2355  * the cost of reading the input data.
2356  *
2357  * If the total volume of data to materialize exceeds work_mem, we will need
2358  * to write it to disk, so the cost is much higher in that case.
2359  *
2360  * Note that here we are estimating the costs for the first scan of the
2361  * relation, so the materialization is all overhead --- any savings will
2362  * occur only on rescan, which is estimated in cost_rescan.
2363  */
2364 void
2366  Cost input_startup_cost, Cost input_total_cost,
2367  double tuples, int width)
2368 {
2369  Cost startup_cost = input_startup_cost;
2370  Cost run_cost = input_total_cost - input_startup_cost;
2371  double nbytes = relation_byte_size(tuples, width);
2372  long work_mem_bytes = work_mem * 1024L;
2373 
2374  path->rows = tuples;
2375 
2376  /*
2377  * Whether spilling or not, charge 2x cpu_operator_cost per tuple to
2378  * reflect bookkeeping overhead. (This rate must be more than what
2379  * cost_rescan charges for materialize, ie, cpu_operator_cost per tuple;
2380  * if it is exactly the same then there will be a cost tie between
2381  * nestloop with A outer, materialized B inner and nestloop with B outer,
2382  * materialized A inner. The extra cost ensures we'll prefer
2383  * materializing the smaller rel.) Note that this is normally a good deal
2384  * less than cpu_tuple_cost; which is OK because a Material plan node
2385  * doesn't do qual-checking or projection, so it's got less overhead than
2386  * most plan nodes.
2387  */
2388  run_cost += 2 * cpu_operator_cost * tuples;
2389 
2390  /*
2391  * If we will spill to disk, charge at the rate of seq_page_cost per page.
2392  * This cost is assumed to be evenly spread through the plan run phase,
2393  * which isn't exactly accurate but our cost model doesn't allow for
2394  * nonuniform costs within the run phase.
2395  */
2396  if (nbytes > work_mem_bytes)
2397  {
2398  double npages = ceil(nbytes / BLCKSZ);
2399 
2400  run_cost += seq_page_cost * npages;
2401  }
2402 
2403  path->startup_cost = startup_cost;
2404  path->total_cost = startup_cost + run_cost;
2405 }
2406 
2407 /*
2408  * cost_memoize_rescan
2409  * Determines the estimated cost of rescanning a Memoize node.
2410  *
2411  * In order to estimate this, we must gain knowledge of how often we expect to
2412  * be called and how many distinct sets of parameters we are likely to be
2413  * called with. If we expect a good cache hit ratio, then we can set our
2414  * costs to account for that hit ratio, plus a little bit of cost for the
2415  * caching itself. Caching will not work out well if we expect to be called
2416  * with too many distinct parameter values. The worst-case here is that we
2417  * never see any parameter value twice, in which case we'd never get a cache
2418  * hit and caching would be a complete waste of effort.
2419  */
2420 static void
2422  Cost *rescan_startup_cost, Cost *rescan_total_cost)
2423 {
2424  EstimationInfo estinfo;
2425  Cost input_startup_cost = mpath->subpath->startup_cost;
2426  Cost input_total_cost = mpath->subpath->total_cost;
2427  double tuples = mpath->subpath->rows;
2428  double calls = mpath->calls;
2429  int width = mpath->subpath->pathtarget->width;
2430 
2431  double hash_mem_bytes;
2432  double est_entry_bytes;
2433  double est_cache_entries;
2434  double ndistinct;
2435  double evict_ratio;
2436  double hit_ratio;
2437  Cost startup_cost;
2438  Cost total_cost;
2439 
2440  /* available cache space */
2441  hash_mem_bytes = get_hash_memory_limit();
2442 
2443  /*
2444  * Set the number of bytes each cache entry should consume in the cache.
2445  * To provide us with better estimations on how many cache entries we can
2446  * store at once, we make a call to the executor here to ask it what
2447  * memory overheads there are for a single cache entry.
2448  *
2449  * XXX we also store the cache key, but that's not accounted for here.
2450  */
2451  est_entry_bytes = relation_byte_size(tuples, width) +
2453 
2454  /* estimate on the upper limit of cache entries we can hold at once */
2455  est_cache_entries = floor(hash_mem_bytes / est_entry_bytes);
2456 
2457  /* estimate on the distinct number of parameter values */
2458  ndistinct = estimate_num_groups(root, mpath->param_exprs, calls, NULL,
2459  &estinfo);
2460 
2461  /*
2462  * When the estimation fell back on using a default value, it's a bit too
2463  * risky to assume that it's ok to use a Memoize node. The use of a
2464  * default could cause us to use a Memoize node when it's really
2465  * inappropriate to do so. If we see that this has been done, then we'll
2466  * assume that every call will have unique parameters, which will almost
2467  * certainly mean a MemoizePath will never survive add_path().
2468  */
2469  if ((estinfo.flags & SELFLAG_USED_DEFAULT) != 0)
2470  ndistinct = calls;
2471 
2472  /*
2473  * Since we've already estimated the maximum number of entries we can
2474  * store at once and know the estimated number of distinct values we'll be
2475  * called with, we'll take this opportunity to set the path's est_entries.
2476  * This will ultimately determine the hash table size that the executor
2477  * will use. If we leave this at zero, the executor will just choose the
2478  * size itself. Really this is not the right place to do this, but it's
2479  * convenient since everything is already calculated.
2480  */
2481  mpath->est_entries = Min(Min(ndistinct, est_cache_entries),
2482  PG_UINT32_MAX);
2483 
2484  /*
2485  * When the number of distinct parameter values is above the amount we can
2486  * store in the cache, then we'll have to evict some entries from the
2487  * cache. This is not free. Here we estimate how often we'll incur the
2488  * cost of that eviction.
2489  */
2490  evict_ratio = 1.0 - Min(est_cache_entries, ndistinct) / ndistinct;
2491 
2492  /*
2493  * In order to estimate how costly a single scan will be, we need to
2494  * attempt to estimate what the cache hit ratio will be. To do that we
2495  * must look at how many scans are estimated in total for this node and
2496  * how many of those scans we expect to get a cache hit.
2497  */
2498  hit_ratio = 1.0 / ndistinct * Min(est_cache_entries, ndistinct) -
2499  (ndistinct / calls);
2500 
2501  /* Ensure we don't go negative */
2502  hit_ratio = Max(hit_ratio, 0.0);
2503 
2504  /*
2505  * Set the total_cost accounting for the expected cache hit ratio. We
2506  * also add on a cpu_operator_cost to account for a cache lookup. This
2507  * will happen regardless of whether it's a cache hit or not.
2508  */
2509  total_cost = input_total_cost * (1.0 - hit_ratio) + cpu_operator_cost;
2510 
2511  /* Now adjust the total cost to account for cache evictions */
2512 
2513  /* Charge a cpu_tuple_cost for evicting the actual cache entry */
2514  total_cost += cpu_tuple_cost * evict_ratio;
2515 
2516  /*
2517  * Charge a 10th of cpu_operator_cost to evict every tuple in that entry.
2518  * The per-tuple eviction is really just a pfree, so charging a whole
2519  * cpu_operator_cost seems a little excessive.
2520  */
2521  total_cost += cpu_operator_cost / 10.0 * evict_ratio * tuples;
2522 
2523  /*
2524  * Now adjust for storing things in the cache, since that's not free
2525  * either. Everything must go in the cache. We don't proportion this
2526  * over any ratio, just apply it once for the scan. We charge a
2527  * cpu_tuple_cost for the creation of the cache entry and also a
2528  * cpu_operator_cost for each tuple we expect to cache.
2529  */
2530  total_cost += cpu_tuple_cost + cpu_operator_cost * tuples;
2531 
2532  /*
2533  * Getting the first row must be also be proportioned according to the
2534  * expected cache hit ratio.
2535  */
2536  startup_cost = input_startup_cost * (1.0 - hit_ratio);
2537 
2538  /*
2539  * Additionally we charge a cpu_tuple_cost to account for cache lookups,
2540  * which we'll do regardless of whether it was a cache hit or not.
2541  */
2542  startup_cost += cpu_tuple_cost;
2543 
2544  *rescan_startup_cost = startup_cost;
2545  *rescan_total_cost = total_cost;
2546 }
2547 
2548 /*
2549  * cost_agg
2550  * Determines and returns the cost of performing an Agg plan node,
2551  * including the cost of its input.
2552  *
2553  * aggcosts can be NULL when there are no actual aggregate functions (i.e.,
2554  * we are using a hashed Agg node just to do grouping).
2555  *
2556  * Note: when aggstrategy == AGG_SORTED, caller must ensure that input costs
2557  * are for appropriately-sorted input.
2558  */
2559 void
2561  AggStrategy aggstrategy, const AggClauseCosts *aggcosts,
2562  int numGroupCols, double numGroups,
2563  List *quals,
2564  Cost input_startup_cost, Cost input_total_cost,
2565  double input_tuples, double input_width)
2566 {
2567  double output_tuples;
2568  Cost startup_cost;
2569  Cost total_cost;
2570  AggClauseCosts dummy_aggcosts;
2571 
2572  /* Use all-zero per-aggregate costs if NULL is passed */
2573  if (aggcosts == NULL)
2574  {
2575  Assert(aggstrategy == AGG_HASHED);
2576  MemSet(&dummy_aggcosts, 0, sizeof(AggClauseCosts));
2577  aggcosts = &dummy_aggcosts;
2578  }
2579 
2580  /*
2581  * The transCost.per_tuple component of aggcosts should be charged once
2582  * per input tuple, corresponding to the costs of evaluating the aggregate
2583  * transfns and their input expressions. The finalCost.per_tuple component
2584  * is charged once per output tuple, corresponding to the costs of
2585  * evaluating the finalfns. Startup costs are of course charged but once.
2586  *
2587  * If we are grouping, we charge an additional cpu_operator_cost per
2588  * grouping column per input tuple for grouping comparisons.
2589  *
2590  * We will produce a single output tuple if not grouping, and a tuple per
2591  * group otherwise. We charge cpu_tuple_cost for each output tuple.
2592  *
2593  * Note: in this cost model, AGG_SORTED and AGG_HASHED have exactly the
2594  * same total CPU cost, but AGG_SORTED has lower startup cost. If the
2595  * input path is already sorted appropriately, AGG_SORTED should be
2596  * preferred (since it has no risk of memory overflow). This will happen
2597  * as long as the computed total costs are indeed exactly equal --- but if
2598  * there's roundoff error we might do the wrong thing. So be sure that
2599  * the computations below form the same intermediate values in the same
2600  * order.
2601  */
2602  if (aggstrategy == AGG_PLAIN)
2603  {
2604  startup_cost = input_total_cost;
2605  startup_cost += aggcosts->transCost.startup;
2606  startup_cost += aggcosts->transCost.per_tuple * input_tuples;
2607  startup_cost += aggcosts->finalCost.startup;
2608  startup_cost += aggcosts->finalCost.per_tuple;
2609  /* we aren't grouping */
2610  total_cost = startup_cost + cpu_tuple_cost;
2611  output_tuples = 1;
2612  }
2613  else if (aggstrategy == AGG_SORTED || aggstrategy == AGG_MIXED)
2614  {
2615  /* Here we are able to deliver output on-the-fly */
2616  startup_cost = input_startup_cost;
2617  total_cost = input_total_cost;
2618  if (aggstrategy == AGG_MIXED && !enable_hashagg)
2619  {
2620  startup_cost += disable_cost;
2621  total_cost += disable_cost;
2622  }
2623  /* calcs phrased this way to match HASHED case, see note above */
2624  total_cost += aggcosts->transCost.startup;
2625  total_cost += aggcosts->transCost.per_tuple * input_tuples;
2626  total_cost += (cpu_operator_cost * numGroupCols) * input_tuples;
2627  total_cost += aggcosts->finalCost.startup;
2628  total_cost += aggcosts->finalCost.per_tuple * numGroups;
2629  total_cost += cpu_tuple_cost * numGroups;
2630  output_tuples = numGroups;
2631  }
2632  else
2633  {
2634  /* must be AGG_HASHED */
2635  startup_cost = input_total_cost;
2636  if (!enable_hashagg)
2637  startup_cost += disable_cost;
2638  startup_cost += aggcosts->transCost.startup;
2639  startup_cost += aggcosts->transCost.per_tuple * input_tuples;
2640  /* cost of computing hash value */
2641  startup_cost += (cpu_operator_cost * numGroupCols) * input_tuples;
2642  startup_cost += aggcosts->finalCost.startup;
2643 
2644  total_cost = startup_cost;
2645  total_cost += aggcosts->finalCost.per_tuple * numGroups;
2646  /* cost of retrieving from hash table */
2647  total_cost += cpu_tuple_cost * numGroups;
2648  output_tuples = numGroups;
2649  }
2650 
2651  /*
2652  * Add the disk costs of hash aggregation that spills to disk.
2653  *
2654  * Groups that go into the hash table stay in memory until finalized, so
2655  * spilling and reprocessing tuples doesn't incur additional invocations
2656  * of transCost or finalCost. Furthermore, the computed hash value is
2657  * stored with the spilled tuples, so we don't incur extra invocations of
2658  * the hash function.
2659  *
2660  * Hash Agg begins returning tuples after the first batch is complete.
2661  * Accrue writes (spilled tuples) to startup_cost and to total_cost;
2662  * accrue reads only to total_cost.
2663  */
2664  if (aggstrategy == AGG_HASHED || aggstrategy == AGG_MIXED)
2665  {
2666  double pages;
2667  double pages_written = 0.0;
2668  double pages_read = 0.0;
2669  double spill_cost;
2670  double hashentrysize;
2671  double nbatches;
2672  Size mem_limit;
2673  uint64 ngroups_limit;
2674  int num_partitions;
2675  int depth;
2676 
2677  /*
2678  * Estimate number of batches based on the computed limits. If less
2679  * than or equal to one, all groups are expected to fit in memory;
2680  * otherwise we expect to spill.
2681  */
2682  hashentrysize = hash_agg_entry_size(list_length(root->aggtransinfos),
2683  input_width,
2684  aggcosts->transitionSpace);
2685  hash_agg_set_limits(hashentrysize, numGroups, 0, &mem_limit,
2686  &ngroups_limit, &num_partitions);
2687 
2688  nbatches = Max((numGroups * hashentrysize) / mem_limit,
2689  numGroups / ngroups_limit);
2690 
2691  nbatches = Max(ceil(nbatches), 1.0);
2692  num_partitions = Max(num_partitions, 2);
2693 
2694  /*
2695  * The number of partitions can change at different levels of
2696  * recursion; but for the purposes of this calculation assume it stays
2697  * constant.
2698  */
2699  depth = ceil(log(nbatches) / log(num_partitions));
2700 
2701  /*
2702  * Estimate number of pages read and written. For each level of
2703  * recursion, a tuple must be written and then later read.
2704  */
2705  pages = relation_byte_size(input_tuples, input_width) / BLCKSZ;
2706  pages_written = pages_read = pages * depth;
2707 
2708  /*
2709  * HashAgg has somewhat worse IO behavior than Sort on typical
2710  * hardware/OS combinations. Account for this with a generic penalty.
2711  */
2712  pages_read *= 2.0;
2713  pages_written *= 2.0;
2714 
2715  startup_cost += pages_written * random_page_cost;
2716  total_cost += pages_written * random_page_cost;
2717  total_cost += pages_read * seq_page_cost;
2718 
2719  /* account for CPU cost of spilling a tuple and reading it back */
2720  spill_cost = depth * input_tuples * 2.0 * cpu_tuple_cost;
2721  startup_cost += spill_cost;
2722  total_cost += spill_cost;
2723  }
2724 
2725  /*
2726  * If there are quals (HAVING quals), account for their cost and
2727  * selectivity.
2728  */
2729  if (quals)
2730  {
2731  QualCost qual_cost;
2732 
2733  cost_qual_eval(&qual_cost, quals, root);
2734  startup_cost += qual_cost.startup;
2735  total_cost += qual_cost.startup + output_tuples * qual_cost.per_tuple;
2736 
2737  output_tuples = clamp_row_est(output_tuples *
2739  quals,
2740  0,
2741  JOIN_INNER,
2742  NULL));
2743  }
2744 
2745  path->rows = output_tuples;
2746  path->startup_cost = startup_cost;
2747  path->total_cost = total_cost;
2748 }
2749 
2750 /*
2751  * cost_windowagg
2752  * Determines and returns the cost of performing a WindowAgg plan node,
2753  * including the cost of its input.
2754  *
2755  * Input is assumed already properly sorted.
2756  */
2757 void
2759  List *windowFuncs, int numPartCols, int numOrderCols,
2760  Cost input_startup_cost, Cost input_total_cost,
2761  double input_tuples)
2762 {
2763  Cost startup_cost;
2764  Cost total_cost;
2765  ListCell *lc;
2766 
2767  startup_cost = input_startup_cost;
2768  total_cost = input_total_cost;
2769 
2770  /*
2771  * Window functions are assumed to cost their stated execution cost, plus
2772  * the cost of evaluating their input expressions, per tuple. Since they
2773  * may in fact evaluate their inputs at multiple rows during each cycle,
2774  * this could be a drastic underestimate; but without a way to know how
2775  * many rows the window function will fetch, it's hard to do better. In
2776  * any case, it's a good estimate for all the built-in window functions,
2777  * so we'll just do this for now.
2778  */
2779  foreach(lc, windowFuncs)
2780  {
2781  WindowFunc *wfunc = lfirst_node(WindowFunc, lc);
2782  Cost wfunccost;
2783  QualCost argcosts;
2784 
2785  argcosts.startup = argcosts.per_tuple = 0;
2786  add_function_cost(root, wfunc->winfnoid, (Node *) wfunc,
2787  &argcosts);
2788  startup_cost += argcosts.startup;
2789  wfunccost = argcosts.per_tuple;
2790 
2791  /* also add the input expressions' cost to per-input-row costs */
2792  cost_qual_eval_node(&argcosts, (Node *) wfunc->args, root);
2793  startup_cost += argcosts.startup;
2794  wfunccost += argcosts.per_tuple;
2795 
2796  /*
2797  * Add the filter's cost to per-input-row costs. XXX We should reduce
2798  * input expression costs according to filter selectivity.
2799  */
2800  cost_qual_eval_node(&argcosts, (Node *) wfunc->aggfilter, root);
2801  startup_cost += argcosts.startup;
2802  wfunccost += argcosts.per_tuple;
2803 
2804  total_cost += wfunccost * input_tuples;
2805  }
2806 
2807  /*
2808  * We also charge cpu_operator_cost per grouping column per tuple for
2809  * grouping comparisons, plus cpu_tuple_cost per tuple for general
2810  * overhead.
2811  *
2812  * XXX this neglects costs of spooling the data to disk when it overflows
2813  * work_mem. Sooner or later that should get accounted for.
2814  */
2815  total_cost += cpu_operator_cost * (numPartCols + numOrderCols) * input_tuples;
2816  total_cost += cpu_tuple_cost * input_tuples;
2817 
2818  path->rows = input_tuples;
2819  path->startup_cost = startup_cost;
2820  path->total_cost = total_cost;
2821 }
2822 
2823 /*
2824  * cost_group
2825  * Determines and returns the cost of performing a Group plan node,
2826  * including the cost of its input.
2827  *
2828  * Note: caller must ensure that input costs are for appropriately-sorted
2829  * input.
2830  */
2831 void
2833  int numGroupCols, double numGroups,
2834  List *quals,
2835  Cost input_startup_cost, Cost input_total_cost,
2836  double input_tuples)
2837 {
2838  double output_tuples;
2839  Cost startup_cost;
2840  Cost total_cost;
2841 
2842  output_tuples = numGroups;
2843  startup_cost = input_startup_cost;
2844  total_cost = input_total_cost;
2845 
2846  /*
2847  * Charge one cpu_operator_cost per comparison per input tuple. We assume
2848  * all columns get compared at most of the tuples.
2849  */
2850  total_cost += cpu_operator_cost * input_tuples * numGroupCols;
2851 
2852  /*
2853  * If there are quals (HAVING quals), account for their cost and
2854  * selectivity.
2855  */
2856  if (quals)
2857  {
2858  QualCost qual_cost;
2859 
2860  cost_qual_eval(&qual_cost, quals, root);
2861  startup_cost += qual_cost.startup;
2862  total_cost += qual_cost.startup + output_tuples * qual_cost.per_tuple;
2863 
2864  output_tuples = clamp_row_est(output_tuples *
2866  quals,
2867  0,
2868  JOIN_INNER,
2869  NULL));
2870  }
2871 
2872  path->rows = output_tuples;
2873  path->startup_cost = startup_cost;
2874  path->total_cost = total_cost;
2875 }
2876 
2877 /*
2878  * initial_cost_nestloop
2879  * Preliminary estimate of the cost of a nestloop join path.
2880  *
2881  * This must quickly produce lower-bound estimates of the path's startup and
2882  * total costs. If we are unable to eliminate the proposed path from
2883  * consideration using the lower bounds, final_cost_nestloop will be called
2884  * to obtain the final estimates.
2885  *
2886  * The exact division of labor between this function and final_cost_nestloop
2887  * is private to them, and represents a tradeoff between speed of the initial
2888  * estimate and getting a tight lower bound. We choose to not examine the
2889  * join quals here, since that's by far the most expensive part of the
2890  * calculations. The end result is that CPU-cost considerations must be
2891  * left for the second phase; and for SEMI/ANTI joins, we must also postpone
2892  * incorporation of the inner path's run cost.
2893  *
2894  * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
2895  * other data to be used by final_cost_nestloop
2896  * 'jointype' is the type of join to be performed
2897  * 'outer_path' is the outer input to the join
2898  * 'inner_path' is the inner input to the join
2899  * 'extra' contains miscellaneous information about the join
2900  */
2901 void
2903  JoinType jointype,
2904  Path *outer_path, Path *inner_path,
2905  JoinPathExtraData *extra)
2906 {
2907  Cost startup_cost = 0;
2908  Cost run_cost = 0;
2909  double outer_path_rows = outer_path->rows;
2910  Cost inner_rescan_start_cost;
2911  Cost inner_rescan_total_cost;
2912  Cost inner_run_cost;
2913  Cost inner_rescan_run_cost;
2914 
2915  /* estimate costs to rescan the inner relation */
2916  cost_rescan(root, inner_path,
2917  &inner_rescan_start_cost,
2918  &inner_rescan_total_cost);
2919 
2920  /* cost of source data */
2921 
2922  /*
2923  * NOTE: clearly, we must pay both outer and inner paths' startup_cost
2924  * before we can start returning tuples, so the join's startup cost is
2925  * their sum. We'll also pay the inner path's rescan startup cost
2926  * multiple times.
2927  */
2928  startup_cost += outer_path->startup_cost + inner_path->startup_cost;
2929  run_cost += outer_path->total_cost - outer_path->startup_cost;
2930  if (outer_path_rows > 1)
2931  run_cost += (outer_path_rows - 1) * inner_rescan_start_cost;
2932 
2933  inner_run_cost = inner_path->total_cost - inner_path->startup_cost;
2934  inner_rescan_run_cost = inner_rescan_total_cost - inner_rescan_start_cost;
2935 
2936  if (jointype == JOIN_SEMI || jointype == JOIN_ANTI ||
2937  extra->inner_unique)
2938  {
2939  /*
2940  * With a SEMI or ANTI join, or if the innerrel is known unique, the
2941  * executor will stop after the first match.
2942  *
2943  * Getting decent estimates requires inspection of the join quals,
2944  * which we choose to postpone to final_cost_nestloop.
2945  */
2946 
2947  /* Save private data for final_cost_nestloop */
2948  workspace->inner_run_cost = inner_run_cost;
2949  workspace->inner_rescan_run_cost = inner_rescan_run_cost;
2950  }
2951  else
2952  {
2953  /* Normal case; we'll scan whole input rel for each outer row */
2954  run_cost += inner_run_cost;
2955  if (outer_path_rows > 1)
2956  run_cost += (outer_path_rows - 1) * inner_rescan_run_cost;
2957  }
2958 
2959  /* CPU costs left for later */
2960 
2961  /* Public result fields */
2962  workspace->startup_cost = startup_cost;
2963  workspace->total_cost = startup_cost + run_cost;
2964  /* Save private data for final_cost_nestloop */
2965  workspace->run_cost = run_cost;
2966 }
2967 
2968 /*
2969  * final_cost_nestloop
2970  * Final estimate of the cost and result size of a nestloop join path.
2971  *
2972  * 'path' is already filled in except for the rows and cost fields
2973  * 'workspace' is the result from initial_cost_nestloop
2974  * 'extra' contains miscellaneous information about the join
2975  */
2976 void
2978  JoinCostWorkspace *workspace,
2979  JoinPathExtraData *extra)
2980 {
2981  Path *outer_path = path->jpath.outerjoinpath;
2982  Path *inner_path = path->jpath.innerjoinpath;
2983  double outer_path_rows = outer_path->rows;
2984  double inner_path_rows = inner_path->rows;
2985  Cost startup_cost = workspace->startup_cost;
2986  Cost run_cost = workspace->run_cost;
2987  Cost cpu_per_tuple;
2988  QualCost restrict_qual_cost;
2989  double ntuples;
2990 
2991  /* Protect some assumptions below that rowcounts aren't zero */
2992  if (outer_path_rows <= 0)
2993  outer_path_rows = 1;
2994  if (inner_path_rows <= 0)
2995  inner_path_rows = 1;
2996  /* Mark the path with the correct row estimate */
2997  if (path->jpath.path.param_info)
2998  path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
2999  else
3000  path->jpath.path.rows = path->jpath.path.parent->rows;
3001 
3002  /* For partial paths, scale row estimate. */
3003  if (path->jpath.path.parallel_workers > 0)
3004  {
3005  double parallel_divisor = get_parallel_divisor(&path->jpath.path);
3006 
3007  path->jpath.path.rows =
3008  clamp_row_est(path->jpath.path.rows / parallel_divisor);
3009  }
3010 
3011  /*
3012  * We could include disable_cost in the preliminary estimate, but that
3013  * would amount to optimizing for the case where the join method is
3014  * disabled, which doesn't seem like the way to bet.
3015  */
3016  if (!enable_nestloop)
3017  startup_cost += disable_cost;
3018 
3019  /* cost of inner-relation source data (we already dealt with outer rel) */
3020 
3021  if (path->jpath.jointype == JOIN_SEMI || path->jpath.jointype == JOIN_ANTI ||
3022  extra->inner_unique)
3023  {
3024  /*
3025  * With a SEMI or ANTI join, or if the innerrel is known unique, the
3026  * executor will stop after the first match.
3027  */
3028  Cost inner_run_cost = workspace->inner_run_cost;
3029  Cost inner_rescan_run_cost = workspace->inner_rescan_run_cost;
3030  double outer_matched_rows;
3031  double outer_unmatched_rows;
3032  Selectivity inner_scan_frac;
3033 
3034  /*
3035  * For an outer-rel row that has at least one match, we can expect the
3036  * inner scan to stop after a fraction 1/(match_count+1) of the inner
3037  * rows, if the matches are evenly distributed. Since they probably
3038  * aren't quite evenly distributed, we apply a fuzz factor of 2.0 to
3039  * that fraction. (If we used a larger fuzz factor, we'd have to
3040  * clamp inner_scan_frac to at most 1.0; but since match_count is at
3041  * least 1, no such clamp is needed now.)
3042  */
3043  outer_matched_rows = rint(outer_path_rows * extra->semifactors.outer_match_frac);
3044  outer_unmatched_rows = outer_path_rows - outer_matched_rows;
3045  inner_scan_frac = 2.0 / (extra->semifactors.match_count + 1.0);
3046 
3047  /*
3048  * Compute number of tuples processed (not number emitted!). First,
3049  * account for successfully-matched outer rows.
3050  */
3051  ntuples = outer_matched_rows * inner_path_rows * inner_scan_frac;
3052 
3053  /*
3054  * Now we need to estimate the actual costs of scanning the inner
3055  * relation, which may be quite a bit less than N times inner_run_cost
3056  * due to early scan stops. We consider two cases. If the inner path
3057  * is an indexscan using all the joinquals as indexquals, then an
3058  * unmatched outer row results in an indexscan returning no rows,
3059  * which is probably quite cheap. Otherwise, the executor will have
3060  * to scan the whole inner rel for an unmatched row; not so cheap.
3061  */
3062  if (has_indexed_join_quals(path))
3063  {
3064  /*
3065  * Successfully-matched outer rows will only require scanning
3066  * inner_scan_frac of the inner relation. In this case, we don't
3067  * need to charge the full inner_run_cost even when that's more
3068  * than inner_rescan_run_cost, because we can assume that none of
3069  * the inner scans ever scan the whole inner relation. So it's
3070  * okay to assume that all the inner scan executions can be
3071  * fractions of the full cost, even if materialization is reducing
3072  * the rescan cost. At this writing, it's impossible to get here
3073  * for a materialized inner scan, so inner_run_cost and
3074  * inner_rescan_run_cost will be the same anyway; but just in
3075  * case, use inner_run_cost for the first matched tuple and
3076  * inner_rescan_run_cost for additional ones.
3077  */
3078  run_cost += inner_run_cost * inner_scan_frac;
3079  if (outer_matched_rows > 1)
3080  run_cost += (outer_matched_rows - 1) * inner_rescan_run_cost * inner_scan_frac;
3081 
3082  /*
3083  * Add the cost of inner-scan executions for unmatched outer rows.
3084  * We estimate this as the same cost as returning the first tuple
3085  * of a nonempty scan. We consider that these are all rescans,
3086  * since we used inner_run_cost once already.
3087  */
3088  run_cost += outer_unmatched_rows *
3089  inner_rescan_run_cost / inner_path_rows;
3090 
3091  /*
3092  * We won't be evaluating any quals at all for unmatched rows, so
3093  * don't add them to ntuples.
3094  */
3095  }
3096  else
3097  {
3098  /*
3099  * Here, a complicating factor is that rescans may be cheaper than
3100  * first scans. If we never scan all the way to the end of the
3101  * inner rel, it might be (depending on the plan type) that we'd
3102  * never pay the whole inner first-scan run cost. However it is
3103  * difficult to estimate whether that will happen (and it could
3104  * not happen if there are any unmatched outer rows!), so be
3105  * conservative and always charge the whole first-scan cost once.
3106  * We consider this charge to correspond to the first unmatched
3107  * outer row, unless there isn't one in our estimate, in which
3108  * case blame it on the first matched row.
3109  */
3110 
3111  /* First, count all unmatched join tuples as being processed */
3112  ntuples += outer_unmatched_rows * inner_path_rows;
3113 
3114  /* Now add the forced full scan, and decrement appropriate count */
3115  run_cost += inner_run_cost;
3116  if (outer_unmatched_rows >= 1)
3117  outer_unmatched_rows -= 1;
3118  else
3119  outer_matched_rows -= 1;
3120 
3121  /* Add inner run cost for additional outer tuples having matches */
3122  if (outer_matched_rows > 0)
3123  run_cost += outer_matched_rows * inner_rescan_run_cost * inner_scan_frac;
3124 
3125  /* Add inner run cost for additional unmatched outer tuples */
3126  if (outer_unmatched_rows > 0)
3127  run_cost += outer_unmatched_rows * inner_rescan_run_cost;
3128  }
3129  }
3130  else
3131  {
3132  /* Normal-case source costs were included in preliminary estimate */
3133 
3134  /* Compute number of tuples processed (not number emitted!) */
3135  ntuples = outer_path_rows * inner_path_rows;
3136  }
3137 
3138  /* CPU costs */
3139  cost_qual_eval(&restrict_qual_cost, path->jpath.joinrestrictinfo, root);
3140  startup_cost += restrict_qual_cost.startup;
3141  cpu_per_tuple = cpu_tuple_cost + restrict_qual_cost.per_tuple;
3142  run_cost += cpu_per_tuple * ntuples;
3143 
3144  /* tlist eval costs are paid per output row, not per tuple scanned */
3145  startup_cost += path->jpath.path.pathtarget->cost.startup;
3146  run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows;
3147 
3148  path->jpath.path.startup_cost = startup_cost;
3149  path->jpath.path.total_cost = startup_cost + run_cost;
3150 }
3151 
3152 /*
3153  * initial_cost_mergejoin
3154  * Preliminary estimate of the cost of a mergejoin path.
3155  *
3156  * This must quickly produce lower-bound estimates of the path's startup and
3157  * total costs. If we are unable to eliminate the proposed path from
3158  * consideration using the lower bounds, final_cost_mergejoin will be called
3159  * to obtain the final estimates.
3160  *
3161  * The exact division of labor between this function and final_cost_mergejoin
3162  * is private to them, and represents a tradeoff between speed of the initial
3163  * estimate and getting a tight lower bound. We choose to not examine the
3164  * join quals here, except for obtaining the scan selectivity estimate which
3165  * is really essential (but fortunately, use of caching keeps the cost of
3166  * getting that down to something reasonable).
3167  * We also assume that cost_sort is cheap enough to use here.
3168  *
3169  * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
3170  * other data to be used by final_cost_mergejoin
3171  * 'jointype' is the type of join to be performed
3172  * 'mergeclauses' is the list of joinclauses to be used as merge clauses
3173  * 'outer_path' is the outer input to the join
3174  * 'inner_path' is the inner input to the join
3175  * 'outersortkeys' is the list of sort keys for the outer path
3176  * 'innersortkeys' is the list of sort keys for the inner path
3177  * 'extra' contains miscellaneous information about the join
3178  *
3179  * Note: outersortkeys and innersortkeys should be NIL if no explicit
3180  * sort is needed because the respective source path is already ordered.
3181  */
3182 void
3184  JoinType jointype,
3185  List *mergeclauses,
3186  Path *outer_path, Path *inner_path,
3187  List *outersortkeys, List *innersortkeys,
3188  JoinPathExtraData *extra)
3189 {
3190  Cost startup_cost = 0;
3191  Cost run_cost = 0;
3192  double outer_path_rows = outer_path->rows;
3193  double inner_path_rows = inner_path->rows;
3194  Cost inner_run_cost;
3195  double outer_rows,
3196  inner_rows,
3197  outer_skip_rows,
3198  inner_skip_rows;
3199  Selectivity outerstartsel,
3200  outerendsel,
3201  innerstartsel,
3202  innerendsel;
3203  Path sort_path; /* dummy for result of cost_sort */
3204 
3205  /* Protect some assumptions below that rowcounts aren't zero */
3206  if (outer_path_rows <= 0)
3207  outer_path_rows = 1;
3208  if (inner_path_rows <= 0)
3209  inner_path_rows = 1;
3210 
3211  /*
3212  * A merge join will stop as soon as it exhausts either input stream
3213  * (unless it's an outer join, in which case the outer side has to be
3214  * scanned all the way anyway). Estimate fraction of the left and right
3215  * inputs that will actually need to be scanned. Likewise, we can
3216  * estimate the number of rows that will be skipped before the first join
3217  * pair is found, which should be factored into startup cost. We use only
3218  * the first (most significant) merge clause for this purpose. Since
3219  * mergejoinscansel() is a fairly expensive computation, we cache the
3220  * results in the merge clause RestrictInfo.
3221  */
3222  if (mergeclauses && jointype != JOIN_FULL)
3223  {
3224  RestrictInfo *firstclause = (RestrictInfo *) linitial(mergeclauses);
3225  List *opathkeys;
3226  List *ipathkeys;
3227  PathKey *opathkey;
3228  PathKey *ipathkey;
3229  MergeScanSelCache *cache;
3230 
3231  /* Get the input pathkeys to determine the sort-order details */
3232  opathkeys = outersortkeys ? outersortkeys : outer_path->pathkeys;
3233  ipathkeys = innersortkeys ? innersortkeys : inner_path->pathkeys;
3234  Assert(opathkeys);
3235  Assert(ipathkeys);
3236  opathkey = (PathKey *) linitial(opathkeys);
3237  ipathkey = (PathKey *) linitial(ipathkeys);
3238  /* debugging check */
3239  if (opathkey->pk_opfamily != ipathkey->pk_opfamily ||
3240  opathkey->pk_eclass->ec_collation != ipathkey->pk_eclass->ec_collation ||
3241  opathkey->pk_strategy != ipathkey->pk_strategy ||
3242  opathkey->pk_nulls_first != ipathkey->pk_nulls_first)
3243  elog(ERROR, "left and right pathkeys do not match in mergejoin");
3244 
3245  /* Get the selectivity with caching */
3246  cache = cached_scansel(root, firstclause, opathkey);
3247 
3248  if (bms_is_subset(firstclause->left_relids,
3249  outer_path->parent->relids))
3250  {
3251  /* left side of clause is outer */
3252  outerstartsel = cache->leftstartsel;
3253  outerendsel = cache->leftendsel;
3254  innerstartsel = cache->rightstartsel;
3255  innerendsel = cache->rightendsel;
3256  }
3257  else
3258  {
3259  /* left side of clause is inner */
3260  outerstartsel = cache->rightstartsel;
3261  outerendsel = cache->rightendsel;
3262  innerstartsel = cache->leftstartsel;
3263  innerendsel = cache->leftendsel;
3264  }
3265  if (jointype == JOIN_LEFT ||
3266  jointype == JOIN_ANTI)
3267  {
3268  outerstartsel = 0.0;
3269  outerendsel = 1.0;
3270  }
3271  else if (jointype == JOIN_RIGHT)
3272  {
3273  innerstartsel = 0.0;
3274  innerendsel = 1.0;
3275  }
3276  }
3277  else
3278  {
3279  /* cope with clauseless or full mergejoin */
3280  outerstartsel = innerstartsel = 0.0;
3281  outerendsel = innerendsel = 1.0;
3282  }
3283 
3284  /*
3285  * Convert selectivities to row counts. We force outer_rows and
3286  * inner_rows to be at least 1, but the skip_rows estimates can be zero.
3287  */
3288  outer_skip_rows = rint(outer_path_rows * outerstartsel);
3289  inner_skip_rows = rint(inner_path_rows * innerstartsel);
3290  outer_rows = clamp_row_est(outer_path_rows * outerendsel);
3291  inner_rows = clamp_row_est(inner_path_rows * innerendsel);
3292 
3293  Assert(outer_skip_rows <= outer_rows);
3294  Assert(inner_skip_rows <= inner_rows);
3295 
3296  /*
3297  * Readjust scan selectivities to account for above rounding. This is
3298  * normally an insignificant effect, but when there are only a few rows in
3299  * the inputs, failing to do this makes for a large percentage error.
3300  */
3301  outerstartsel = outer_skip_rows / outer_path_rows;
3302  innerstartsel = inner_skip_rows / inner_path_rows;
3303  outerendsel = outer_rows / outer_path_rows;
3304  innerendsel = inner_rows / inner_path_rows;
3305 
3306  Assert(outerstartsel <= outerendsel);
3307  Assert(innerstartsel <= innerendsel);
3308 
3309  /* cost of source data */
3310 
3311  if (outersortkeys) /* do we need to sort outer? */
3312  {
3313  cost_sort(&sort_path,
3314  root,
3315  outersortkeys,
3316  outer_path->total_cost,
3317  outer_path_rows,
3318  outer_path->pathtarget->width,
3319  0.0,
3320  work_mem,
3321  -1.0);
3322  startup_cost += sort_path.startup_cost;
3323  startup_cost += (sort_path.total_cost - sort_path.startup_cost)
3324  * outerstartsel;
3325  run_cost += (sort_path.total_cost - sort_path.startup_cost)
3326  * (outerendsel - outerstartsel);
3327  }
3328  else
3329  {
3330  startup_cost += outer_path->startup_cost;
3331  startup_cost += (outer_path->total_cost - outer_path->startup_cost)
3332  * outerstartsel;
3333  run_cost += (outer_path->total_cost - outer_path->startup_cost)
3334  * (outerendsel - outerstartsel);
3335  }
3336 
3337  if (innersortkeys) /* do we need to sort inner? */
3338  {
3339  cost_sort(&sort_path,
3340  root,
3341  innersortkeys,
3342  inner_path->total_cost,
3343  inner_path_rows,
3344  inner_path->pathtarget->width,
3345  0.0,
3346  work_mem,
3347  -1.0);
3348  startup_cost += sort_path.startup_cost;
3349  startup_cost += (sort_path.total_cost - sort_path.startup_cost)
3350  * innerstartsel;
3351  inner_run_cost = (sort_path.total_cost - sort_path.startup_cost)
3352  * (innerendsel - innerstartsel);
3353  }
3354  else
3355  {
3356  startup_cost += inner_path->startup_cost;
3357  startup_cost += (inner_path->total_cost - inner_path->startup_cost)
3358  * innerstartsel;
3359  inner_run_cost = (inner_path->total_cost - inner_path->startup_cost)
3360  * (innerendsel - innerstartsel);
3361  }
3362 
3363  /*
3364  * We can't yet determine whether rescanning occurs, or whether
3365  * materialization of the inner input should be done. The minimum
3366  * possible inner input cost, regardless of rescan and materialization
3367  * considerations, is inner_run_cost. We include that in
3368  * workspace->total_cost, but not yet in run_cost.
3369  */
3370 
3371  /* CPU costs left for later */
3372 
3373  /* Public result fields */
3374  workspace->startup_cost = startup_cost;
3375  workspace->total_cost = startup_cost + run_cost + inner_run_cost;
3376  /* Save private data for final_cost_mergejoin */
3377  workspace->run_cost = run_cost;
3378  workspace->inner_run_cost = inner_run_cost;
3379  workspace->outer_rows = outer_rows;
3380  workspace->inner_rows = inner_rows;
3381  workspace->outer_skip_rows = outer_skip_rows;
3382  workspace->inner_skip_rows = inner_skip_rows;
3383 }
3384 
3385 /*
3386  * final_cost_mergejoin
3387  * Final estimate of the cost and result size of a mergejoin path.
3388  *
3389  * Unlike other costsize functions, this routine makes two actual decisions:
3390  * whether the executor will need to do mark/restore, and whether we should
3391  * materialize the inner path. It would be logically cleaner to build
3392  * separate paths testing these alternatives, but that would require repeating
3393  * most of the cost calculations, which are not all that cheap. Since the
3394  * choice will not affect output pathkeys or startup cost, only total cost,
3395  * there is no possibility of wanting to keep more than one path. So it seems
3396  * best to make the decisions here and record them in the path's
3397  * skip_mark_restore and materialize_inner fields.
3398  *
3399  * Mark/restore overhead is usually required, but can be skipped if we know
3400  * that the executor need find only one match per outer tuple, and that the
3401  * mergeclauses are sufficient to identify a match.
3402  *
3403  * We materialize the inner path if we need mark/restore and either the inner
3404  * path can't support mark/restore, or it's cheaper to use an interposed
3405  * Material node to handle mark/restore.
3406  *
3407  * 'path' is already filled in except for the rows and cost fields and
3408  * skip_mark_restore and materialize_inner
3409  * 'workspace' is the result from initial_cost_mergejoin
3410  * 'extra' contains miscellaneous information about the join
3411  */
3412 void
3414  JoinCostWorkspace *workspace,
3415  JoinPathExtraData *extra)
3416 {
3417  Path *outer_path = path->jpath.outerjoinpath;
3418  Path *inner_path = path->jpath.innerjoinpath;
3419  double inner_path_rows = inner_path->rows;
3420  List *mergeclauses = path->path_mergeclauses;
3421  List *innersortkeys = path->innersortkeys;
3422  Cost startup_cost = workspace->startup_cost;
3423  Cost run_cost = workspace->run_cost;
3424  Cost inner_run_cost = workspace->inner_run_cost;
3425  double outer_rows = workspace->outer_rows;
3426  double inner_rows = workspace->inner_rows;
3427  double outer_skip_rows = workspace->outer_skip_rows;
3428  double inner_skip_rows = workspace->inner_skip_rows;
3429  Cost cpu_per_tuple,
3430  bare_inner_cost,
3431  mat_inner_cost;
3432  QualCost merge_qual_cost;
3433  QualCost qp_qual_cost;
3434  double mergejointuples,
3435  rescannedtuples;
3436  double rescanratio;
3437 
3438  /* Protect some assumptions below that rowcounts aren't zero */
3439  if (inner_path_rows <= 0)
3440  inner_path_rows = 1;
3441 
3442  /* Mark the path with the correct row estimate */
3443  if (path->jpath.path.param_info)
3444  path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
3445  else
3446  path->jpath.path.rows = path->jpath.path.parent->rows;
3447 
3448  /* For partial paths, scale row estimate. */
3449  if (path->jpath.path.parallel_workers > 0)
3450  {
3451  double parallel_divisor = get_parallel_divisor(&path->jpath.path);
3452 
3453  path->jpath.path.rows =
3454  clamp_row_est(path->jpath.path.rows / parallel_divisor);
3455  }
3456 
3457  /*
3458  * We could include disable_cost in the preliminary estimate, but that
3459  * would amount to optimizing for the case where the join method is
3460  * disabled, which doesn't seem like the way to bet.
3461  */
3462  if (!enable_mergejoin)
3463  startup_cost += disable_cost;
3464 
3465  /*
3466  * Compute cost of the mergequals and qpquals (other restriction clauses)
3467  * separately.
3468  */
3469  cost_qual_eval(&merge_qual_cost, mergeclauses, root);
3470  cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo, root);
3471  qp_qual_cost.startup -= merge_qual_cost.startup;
3472  qp_qual_cost.per_tuple -= merge_qual_cost.per_tuple;
3473 
3474  /*
3475  * With a SEMI or ANTI join, or if the innerrel is known unique, the
3476  * executor will stop scanning for matches after the first match. When
3477  * all the joinclauses are merge clauses, this means we don't ever need to
3478  * back up the merge, and so we can skip mark/restore overhead.
3479  */
3480  if ((path->jpath.jointype == JOIN_SEMI ||
3481  path->jpath.jointype == JOIN_ANTI ||
3482  extra->inner_unique) &&
3485  path->skip_mark_restore = true;
3486  else
3487  path->skip_mark_restore = false;
3488 
3489  /*
3490  * Get approx # tuples passing the mergequals. We use approx_tuple_count
3491  * here because we need an estimate done with JOIN_INNER semantics.
3492  */
3493  mergejointuples = approx_tuple_count(root, &path->jpath, mergeclauses);
3494 
3495  /*
3496  * When there are equal merge keys in the outer relation, the mergejoin
3497  * must rescan any matching tuples in the inner relation. This means
3498  * re-fetching inner tuples; we have to estimate how often that happens.
3499  *
3500  * For regular inner and outer joins, the number of re-fetches can be
3501  * estimated approximately as size of merge join output minus size of
3502  * inner relation. Assume that the distinct key values are 1, 2, ..., and
3503  * denote the number of values of each key in the outer relation as m1,
3504  * m2, ...; in the inner relation, n1, n2, ... Then we have
3505  *
3506  * size of join = m1 * n1 + m2 * n2 + ...
3507  *
3508  * number of rescanned tuples = (m1 - 1) * n1 + (m2 - 1) * n2 + ... = m1 *
3509  * n1 + m2 * n2 + ... - (n1 + n2 + ...) = size of join - size of inner
3510  * relation
3511  *
3512  * This equation works correctly for outer tuples having no inner match
3513  * (nk = 0), but not for inner tuples having no outer match (mk = 0); we
3514  * are effectively subtracting those from the number of rescanned tuples,
3515  * when we should not. Can we do better without expensive selectivity
3516  * computations?
3517  *
3518  * The whole issue is moot if we are working from a unique-ified outer
3519  * input, or if we know we don't need to mark/restore at all.
3520  */
3521  if (IsA(outer_path, UniquePath) || path->skip_mark_restore)
3522  rescannedtuples = 0;
3523  else
3524  {
3525  rescannedtuples = mergejointuples - inner_path_rows;
3526  /* Must clamp because of possible underestimate */
3527  if (rescannedtuples < 0)
3528  rescannedtuples = 0;
3529  }
3530 
3531  /*
3532  * We'll inflate various costs this much to account for rescanning. Note
3533  * that this is to be multiplied by something involving inner_rows, or
3534  * another number related to the portion of the inner rel we'll scan.
3535  */
3536  rescanratio = 1.0 + (rescannedtuples / inner_rows);
3537 
3538  /*
3539  * Decide whether we want to materialize the inner input to shield it from
3540  * mark/restore and performing re-fetches. Our cost model for regular
3541  * re-fetches is that a re-fetch costs the same as an original fetch,
3542  * which is probably an overestimate; but on the other hand we ignore the
3543  * bookkeeping costs of mark/restore. Not clear if it's worth developing
3544  * a more refined model. So we just need to inflate the inner run cost by
3545  * rescanratio.
3546  */
3547  bare_inner_cost = inner_run_cost * rescanratio;
3548 
3549  /*
3550  * When we interpose a Material node the re-fetch cost is assumed to be
3551  * just cpu_operator_cost per tuple, independently of the underlying
3552  * plan's cost; and we charge an extra cpu_operator_cost per original
3553  * fetch as well. Note that we're assuming the materialize node will
3554  * never spill to disk, since it only has to remember tuples back to the
3555  * last mark. (If there are a huge number of duplicates, our other cost
3556  * factors will make the path so expensive that it probably won't get
3557  * chosen anyway.) So we don't use cost_rescan here.
3558  *
3559  * Note: keep this estimate in sync with create_mergejoin_plan's labeling
3560  * of the generated Material node.
3561  */
3562  mat_inner_cost = inner_run_cost +
3563  cpu_operator_cost * inner_rows * rescanratio;
3564 
3565  /*
3566  * If we don't need mark/restore at all, we don't need materialization.
3567  */
3568  if (path->skip_mark_restore)
3569  path->materialize_inner = false;
3570 
3571  /*
3572  * Prefer materializing if it looks cheaper, unless the user has asked to
3573  * suppress materialization.
3574  */
3575  else if (enable_material && mat_inner_cost < bare_inner_cost)
3576  path->materialize_inner = true;
3577 
3578  /*
3579  * Even if materializing doesn't look cheaper, we *must* do it if the
3580  * inner path is to be used directly (without sorting) and it doesn't
3581  * support mark/restore.
3582  *
3583  * Since the inner side must be ordered, and only Sorts and IndexScans can
3584  * create order to begin with, and they both support mark/restore, you
3585  * might think there's no problem --- but you'd be wrong. Nestloop and
3586  * merge joins can *preserve* the order of their inputs, so they can be
3587  * selected as the input of a mergejoin, and they don't support
3588  * mark/restore at present.
3589  *
3590  * We don't test the value of enable_material here, because
3591  * materialization is required for correctness in this case, and turning
3592  * it off does not entitle us to deliver an invalid plan.
3593  */
3594  else if (innersortkeys == NIL &&
3595  !ExecSupportsMarkRestore(inner_path))
3596  path->materialize_inner = true;
3597 
3598  /*
3599  * Also, force materializing if the inner path is to be sorted and the
3600  * sort is expected to spill to disk. This is because the final merge
3601  * pass can be done on-the-fly if it doesn't have to support mark/restore.
3602  * We don't try to adjust the cost estimates for this consideration,
3603  * though.
3604  *
3605  * Since materialization is a performance optimization in this case,
3606  * rather than necessary for correctness, we skip it if enable_material is
3607  * off.
3608  */
3609  else if (enable_material && innersortkeys != NIL &&
3610  relation_byte_size(inner_path_rows,
3611  inner_path->pathtarget->width) >
3612  (work_mem * 1024L))
3613  path->materialize_inner = true;
3614  else
3615  path->materialize_inner = false;
3616 
3617  /* Charge the right incremental cost for the chosen case */
3618  if (path->materialize_inner)
3619  run_cost += mat_inner_cost;
3620  else
3621  run_cost += bare_inner_cost;
3622 
3623  /* CPU costs */
3624 
3625  /*
3626  * The number of tuple comparisons needed is approximately number of outer
3627  * rows plus number of inner rows plus number of rescanned tuples (can we
3628  * refine this?). At each one, we need to evaluate the mergejoin quals.
3629  */
3630  startup_cost += merge_qual_cost.startup;
3631  startup_cost += merge_qual_cost.per_tuple *
3632  (outer_skip_rows + inner_skip_rows * rescanratio);
3633  run_cost += merge_qual_cost.per_tuple *
3634  ((outer_rows - outer_skip_rows) +
3635  (inner_rows - inner_skip_rows) * rescanratio);
3636 
3637  /*
3638  * For each tuple that gets through the mergejoin proper, we charge
3639  * cpu_tuple_cost plus the cost of evaluating additional restriction
3640  * clauses that are to be applied at the join. (This is pessimistic since
3641  * not all of the quals may get evaluated at each tuple.)
3642  *
3643  * Note: we could adjust for SEMI/ANTI joins skipping some qual
3644  * evaluations here, but it's probably not worth the trouble.
3645  */
3646  startup_cost += qp_qual_cost.startup;
3647  cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
3648  run_cost += cpu_per_tuple * mergejointuples;
3649 
3650  /* tlist eval costs are paid per output row, not per tuple scanned */
3651  startup_cost += path->jpath.path.pathtarget->cost.startup;
3652  run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows;
3653 
3654  path->jpath.path.startup_cost = startup_cost;
3655  path->jpath.path.total_cost = startup_cost + run_cost;
3656 }
3657 
3658 /*
3659  * run mergejoinscansel() with caching
3660  */
3661 static MergeScanSelCache *
3663 {
3664  MergeScanSelCache *cache;
3665  ListCell *lc;
3666  Selectivity leftstartsel,
3667  leftendsel,
3668  rightstartsel,
3669  rightendsel;
3670  MemoryContext oldcontext;
3671 
3672  /* Do we have this result already? */
3673  foreach(lc, rinfo->scansel_cache)
3674  {
3675  cache = (MergeScanSelCache *) lfirst(lc);
3676  if (cache->opfamily == pathkey->pk_opfamily &&
3677  cache->collation == pathkey->pk_eclass->ec_collation &&
3678  cache->strategy == pathkey->pk_strategy &&
3679  cache->nulls_first == pathkey->pk_nulls_first)
3680  return cache;
3681  }
3682 
3683  /* Nope, do the computation */
3684  mergejoinscansel(root,
3685  (Node *) rinfo->clause,
3686  pathkey->pk_opfamily,
3687  pathkey->pk_strategy,
3688  pathkey->pk_nulls_first,
3689  &leftstartsel,
3690  &leftendsel,
3691  &rightstartsel,
3692  &rightendsel);
3693 
3694  /* Cache the result in suitably long-lived workspace */
3695  oldcontext = MemoryContextSwitchTo(root->planner_cxt);
3696 
3697  cache = (MergeScanSelCache *) palloc(sizeof(MergeScanSelCache));
3698  cache->opfamily = pathkey->pk_opfamily;
3699  cache->collation = pathkey->pk_eclass->ec_collation;
3700  cache->strategy = pathkey->pk_strategy;
3701  cache->nulls_first = pathkey->pk_nulls_first;
3702  cache->leftstartsel = leftstartsel;
3703  cache->leftendsel = leftendsel;
3704  cache->rightstartsel = rightstartsel;
3705  cache->rightendsel = rightendsel;
3706 
3707  rinfo->scansel_cache = lappend(rinfo->scansel_cache, cache);
3708 
3709  MemoryContextSwitchTo(oldcontext);
3710 
3711  return cache;
3712 }
3713 
3714 /*
3715  * initial_cost_hashjoin
3716  * Preliminary estimate of the cost of a hashjoin path.
3717  *
3718  * This must quickly produce lower-bound estimates of the path's startup and
3719  * total costs. If we are unable to eliminate the proposed path from
3720  * consideration using the lower bounds, final_cost_hashjoin will be called
3721  * to obtain the final estimates.
3722  *
3723  * The exact division of labor between this function and final_cost_hashjoin
3724  * is private to them, and represents a tradeoff between speed of the initial
3725  * estimate and getting a tight lower bound. We choose to not examine the
3726  * join quals here (other than by counting the number of hash clauses),
3727  * so we can't do much with CPU costs. We do assume that
3728  * ExecChooseHashTableSize is cheap enough to use here.
3729  *
3730  * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
3731  * other data to be used by final_cost_hashjoin
3732  * 'jointype' is the type of join to be performed
3733  * 'hashclauses' is the list of joinclauses to be used as hash clauses
3734  * 'outer_path' is the outer input to the join
3735  * 'inner_path' is the inner input to the join
3736  * 'extra' contains miscellaneous information about the join
3737  * 'parallel_hash' indicates that inner_path is partial and that a shared
3738  * hash table will be built in parallel
3739  */
3740 void
3742  JoinType jointype,
3743  List *hashclauses,
3744  Path *outer_path, Path *inner_path,
3745  JoinPathExtraData *extra,
3746  bool parallel_hash)
3747 {
3748  Cost startup_cost = 0;
3749  Cost run_cost = 0;
3750  double outer_path_rows = outer_path->rows;
3751  double inner_path_rows = inner_path->rows;
3752  double inner_path_rows_total = inner_path_rows;
3753  int num_hashclauses = list_length(hashclauses);
3754  int numbuckets;
3755  int numbatches;
3756  int num_skew_mcvs;
3757  size_t space_allowed; /* unused */
3758 
3759  /* cost of source data */
3760  startup_cost += outer_path->startup_cost;
3761  run_cost += outer_path->total_cost - outer_path->startup_cost;
3762  startup_cost += inner_path->total_cost;
3763 
3764  /*
3765  * Cost of computing hash function: must do it once per input tuple. We
3766  * charge one cpu_operator_cost for each column's hash function. Also,
3767  * tack on one cpu_tuple_cost per inner row, to model the costs of
3768  * inserting the row into the hashtable.
3769  *
3770  * XXX when a hashclause is more complex than a single operator, we really
3771  * should charge the extra eval costs of the left or right side, as
3772  * appropriate, here. This seems more work than it's worth at the moment.
3773  */
3774  startup_cost += (cpu_operator_cost * num_hashclauses + cpu_tuple_cost)
3775  * inner_path_rows;
3776  run_cost += cpu_operator_cost * num_hashclauses * outer_path_rows;
3777 
3778  /*
3779  * If this is a parallel hash build, then the value we have for
3780  * inner_rows_total currently refers only to the rows returned by each
3781  * participant. For shared hash table size estimation, we need the total
3782  * number, so we need to undo the division.
3783  */
3784  if (parallel_hash)
3785  inner_path_rows_total *= get_parallel_divisor(inner_path);
3786 
3787  /*
3788  * Get hash table size that executor would use for inner relation.
3789  *
3790  * XXX for the moment, always assume that skew optimization will be
3791  * performed. As long as SKEW_HASH_MEM_PERCENT is small, it's not worth
3792  * trying to determine that for sure.
3793  *
3794  * XXX at some point it might be interesting to try to account for skew
3795  * optimization in the cost estimate, but for now, we don't.
3796  */
3797  ExecChooseHashTableSize(inner_path_rows_total,
3798  inner_path->pathtarget->width,
3799  true, /* useskew */
3800  parallel_hash, /* try_combined_hash_mem */
3801  outer_path->parallel_workers,
3802  &space_allowed,
3803  &numbuckets,
3804  &numbatches,
3805  &num_skew_mcvs);
3806 
3807  /*
3808  * If inner relation is too big then we will need to "batch" the join,
3809  * which implies writing and reading most of the tuples to disk an extra
3810  * time. Charge seq_page_cost per page, since the I/O should be nice and
3811  * sequential. Writing the inner rel counts as startup cost, all the rest
3812  * as run cost.
3813  */
3814  if (numbatches > 1)
3815  {
3816  double outerpages = page_size(outer_path_rows,
3817  outer_path->pathtarget->width);
3818  double innerpages = page_size(inner_path_rows,
3819  inner_path->pathtarget->width);
3820 
3821  startup_cost += seq_page_cost * innerpages;
3822  run_cost += seq_page_cost * (innerpages + 2 * outerpages);
3823  }
3824 
3825  /* CPU costs left for later */
3826 
3827  /* Public result fields */
3828  workspace->startup_cost = startup_cost;
3829  workspace->total_cost = startup_cost + run_cost;
3830  /* Save private data for final_cost_hashjoin */
3831  workspace->run_cost = run_cost;
3832  workspace->numbuckets = numbuckets;
3833  workspace->numbatches = numbatches;
3834  workspace->inner_rows_total = inner_path_rows_total;
3835 }
3836 
3837 /*
3838  * final_cost_hashjoin
3839  * Final estimate of the cost and result size of a hashjoin path.
3840  *
3841  * Note: the numbatches estimate is also saved into 'path' for use later
3842  *
3843  * 'path' is already filled in except for the rows and cost fields and
3844  * num_batches
3845  * 'workspace' is the result from initial_cost_hashjoin
3846  * 'extra' contains miscellaneous information about the join
3847  */
3848 void
3850  JoinCostWorkspace *workspace,
3851  JoinPathExtraData *extra)
3852 {
3853  Path *outer_path = path->jpath.outerjoinpath;
3854  Path *inner_path = path->jpath.innerjoinpath;
3855  double outer_path_rows = outer_path->rows;
3856  double inner_path_rows = inner_path->rows;
3857  double inner_path_rows_total = workspace->inner_rows_total;
3858  List *hashclauses = path->path_hashclauses;
3859  Cost startup_cost = workspace->startup_cost;
3860  Cost run_cost = workspace->run_cost;
3861  int numbuckets = workspace->numbuckets;
3862  int numbatches = workspace->numbatches;
3863  Cost cpu_per_tuple;
3864  QualCost hash_qual_cost;
3865  QualCost qp_qual_cost;
3866  double hashjointuples;
3867  double virtualbuckets;
3868  Selectivity innerbucketsize;
3869  Selectivity innermcvfreq;
3870  ListCell *hcl;
3871 
3872  /* Mark the path with the correct row estimate */
3873  if (path->jpath.path.param_info)
3874  path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
3875  else
3876  path->jpath.path.rows = path->jpath.path.parent->rows;
3877 
3878  /* For partial paths, scale row estimate. */
3879  if (path->jpath.path.parallel_workers > 0)
3880  {
3881  double parallel_divisor = get_parallel_divisor(&path->jpath.path);
3882 
3883  path->jpath.path.rows =
3884  clamp_row_est(path->jpath.path.rows / parallel_divisor);
3885  }
3886 
3887  /*
3888  * We could include disable_cost in the preliminary estimate, but that
3889  * would amount to optimizing for the case where the join method is
3890  * disabled, which doesn't seem like the way to bet.
3891  */
3892  if (!enable_hashjoin)
3893  startup_cost += disable_cost;
3894 
3895  /* mark the path with estimated # of batches */
3896  path->num_batches = numbatches;
3897 
3898  /* store the total number of tuples (sum of partial row estimates) */
3899  path->inner_rows_total = inner_path_rows_total;
3900 
3901  /* and compute the number of "virtual" buckets in the whole join */
3902  virtualbuckets = (double) numbuckets * (double) numbatches;
3903 
3904  /*
3905  * Determine bucketsize fraction and MCV frequency for the inner relation.
3906  * We use the smallest bucketsize or MCV frequency estimated for any
3907  * individual hashclause; this is undoubtedly conservative.
3908  *
3909  * BUT: if inner relation has been unique-ified, we can assume it's good
3910  * for hashing. This is important both because it's the right answer, and
3911  * because we avoid contaminating the cache with a value that's wrong for
3912  * non-unique-ified paths.
3913  */
3914  if (IsA(inner_path, UniquePath))
3915  {
3916  innerbucketsize = 1.0 / virtualbuckets;
3917  innermcvfreq = 0.0;
3918  }
3919  else
3920  {
3921  innerbucketsize = 1.0;
3922  innermcvfreq = 1.0;
3923  foreach(hcl, hashclauses)
3924  {
3925  RestrictInfo *restrictinfo = lfirst_node(RestrictInfo, hcl);
3926  Selectivity thisbucketsize;
3927  Selectivity thismcvfreq;
3928 
3929  /*
3930  * First we have to figure out which side of the hashjoin clause
3931  * is the inner side.
3932  *
3933  * Since we tend to visit the same clauses over and over when
3934  * planning a large query, we cache the bucket stats estimates in
3935  * the RestrictInfo node to avoid repeated lookups of statistics.
3936  */
3937  if (bms_is_subset(restrictinfo->right_relids,
3938  inner_path->parent->relids))
3939  {
3940  /* righthand side is inner */
3941  thisbucketsize = restrictinfo->right_bucketsize;
3942  if (thisbucketsize < 0)
3943  {
3944  /* not cached yet */
3946  get_rightop(restrictinfo->clause),
3947  virtualbuckets,
3948  &restrictinfo->right_mcvfreq,
3949  &restrictinfo->right_bucketsize);
3950  thisbucketsize = restrictinfo->right_bucketsize;
3951  }
3952  thismcvfreq = restrictinfo->right_mcvfreq;
3953  }
3954  else
3955  {
3956  Assert(bms_is_subset(restrictinfo->left_relids,
3957  inner_path->parent->relids));
3958  /* lefthand side is inner */
3959  thisbucketsize = restrictinfo->left_bucketsize;
3960  if (thisbucketsize < 0)
3961  {
3962  /* not cached yet */
3964  get_leftop(restrictinfo->clause),
3965  virtualbuckets,
3966  &restrictinfo->left_mcvfreq,
3967  &restrictinfo->left_bucketsize);
3968  thisbucketsize = restrictinfo->left_bucketsize;
3969  }
3970  thismcvfreq = restrictinfo->left_mcvfreq;
3971  }
3972 
3973  if (innerbucketsize > thisbucketsize)
3974  innerbucketsize = thisbucketsize;
3975  if (innermcvfreq > thismcvfreq)
3976  innermcvfreq = thismcvfreq;
3977  }
3978  }
3979 
3980  /*
3981  * If the bucket holding the inner MCV would exceed hash_mem, we don't
3982  * want to hash unless there is really no other alternative, so apply
3983  * disable_cost. (The executor normally copes with excessive memory usage
3984  * by splitting batches, but obviously it cannot separate equal values
3985  * that way, so it will be unable to drive the batch size below hash_mem
3986  * when this is true.)
3987  */
3988  if (relation_byte_size(clamp_row_est(inner_path_rows * innermcvfreq),
3989  inner_path->pathtarget->width) > get_hash_memory_limit())
3990  startup_cost += disable_cost;
3991 
3992  /*
3993  * Compute cost of the hashquals and qpquals (other restriction clauses)
3994  * separately.
3995  */
3996  cost_qual_eval(&hash_qual_cost, hashclauses, root);
3997  cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo, root);
3998  qp_qual_cost.startup -= hash_qual_cost.startup;
3999  qp_qual_cost.per_tuple -= hash_qual_cost.per_tuple;
4000 
4001  /* CPU costs */
4002 
4003  if (path->jpath.jointype == JOIN_SEMI ||
4004  path->jpath.jointype == JOIN_ANTI ||
4005  extra->inner_unique)
4006  {
4007  double outer_matched_rows;
4008  Selectivity inner_scan_frac;
4009 
4010  /*
4011  * With a SEMI or ANTI join, or if the innerrel is known unique, the
4012  * executor will stop after the first match.
4013  *
4014  * For an outer-rel row that has at least one match, we can expect the
4015  * bucket scan to stop after a fraction 1/(match_count+1) of the
4016  * bucket's rows, if the matches are evenly distributed. Since they
4017  * probably aren't quite evenly distributed, we apply a fuzz factor of
4018  * 2.0 to that fraction. (If we used a larger fuzz factor, we'd have
4019  * to clamp inner_scan_frac to at most 1.0; but since match_count is
4020  * at least 1, no such clamp is needed now.)
4021  */
4022  outer_matched_rows = rint(outer_path_rows * extra->semifactors.outer_match_frac);
4023  inner_scan_frac = 2.0 / (extra->semifactors.match_count + 1.0);
4024 
4025  startup_cost += hash_qual_cost.startup;
4026  run_cost += hash_qual_cost.per_tuple * outer_matched_rows *
4027  clamp_row_est(inner_path_rows * innerbucketsize * inner_scan_frac) * 0.5;
4028 
4029  /*
4030  * For unmatched outer-rel rows, the picture is quite a lot different.
4031  * In the first place, there is no reason to assume that these rows
4032  * preferentially hit heavily-populated buckets; instead assume they
4033  * are uncorrelated with the inner distribution and so they see an
4034  * average bucket size of inner_path_rows / virtualbuckets. In the
4035  * second place, it seems likely that they will have few if any exact
4036  * hash-code matches and so very few of the tuples in the bucket will
4037  * actually require eval of the hash quals. We don't have any good
4038  * way to estimate how many will, but for the moment assume that the
4039  * effective cost per bucket entry is one-tenth what it is for
4040  * matchable tuples.
4041  */
4042  run_cost += hash_qual_cost.per_tuple *
4043  (outer_path_rows - outer_matched_rows) *
4044  clamp_row_est(inner_path_rows / virtualbuckets) * 0.05;
4045 
4046  /* Get # of tuples that will pass the basic join */
4047  if (path->jpath.jointype == JOIN_ANTI)
4048  hashjointuples = outer_path_rows - outer_matched_rows;
4049  else
4050  hashjointuples = outer_matched_rows;
4051  }
4052  else
4053  {
4054  /*
4055  * The number of tuple comparisons needed is the number of outer
4056  * tuples times the typical number of tuples in a hash bucket, which
4057  * is the inner relation size times its bucketsize fraction. At each
4058  * one, we need to evaluate the hashjoin quals. But actually,
4059  * charging the full qual eval cost at each tuple is pessimistic,
4060  * since we don't evaluate the quals unless the hash values match
4061  * exactly. For lack of a better idea, halve the cost estimate to
4062  * allow for that.
4063  */
4064  startup_cost += hash_qual_cost.startup;
4065  run_cost += hash_qual_cost.per_tuple * outer_path_rows *
4066  clamp_row_est(inner_path_rows * innerbucketsize) * 0.5;
4067 
4068  /*
4069  * Get approx # tuples passing the hashquals. We use
4070  * approx_tuple_count here because we need an estimate done with
4071  * JOIN_INNER semantics.
4072  */
4073  hashjointuples = approx_tuple_count(root, &path->jpath, hashclauses);
4074  }
4075 
4076  /*
4077  * For each tuple that gets through the hashjoin proper, we charge
4078  * cpu_tuple_cost plus the cost of evaluating additional restriction
4079  * clauses that are to be applied at the join. (This is pessimistic since
4080  * not all of the quals may get evaluated at each tuple.)
4081  */
4082  startup_cost += qp_qual_cost.startup;
4083  cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
4084  run_cost += cpu_per_tuple * hashjointuples;
4085 
4086  /* tlist eval costs are paid per output row, not per tuple scanned */
4087  startup_cost += path->jpath.path.pathtarget->cost.startup;
4088  run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows;
4089 
4090  path->jpath.path.startup_cost = startup_cost;
4091  path->jpath.path.total_cost = startup_cost + run_cost;
4092 }
4093 
4094 
4095 /*
4096  * cost_subplan
4097  * Figure the costs for a SubPlan (or initplan).
4098  *
4099  * Note: we could dig the subplan's Plan out of the root list, but in practice
4100  * all callers have it handy already, so we make them pass it.
4101  */
4102 void
4103 cost_subplan(PlannerInfo *root, SubPlan *subplan, Plan *plan)
4104 {
4105  QualCost sp_cost;
4106 
4107  /* Figure any cost for evaluating the testexpr */
4108  cost_qual_eval(&sp_cost,
4109  make_ands_implicit((Expr *) subplan->testexpr),
4110  root);
4111 
4112  if (subplan->useHashTable)
4113  {
4114  /*
4115  * If we are using a hash table for the subquery outputs, then the
4116  * cost of evaluating the query is a one-time cost. We charge one
4117  * cpu_operator_cost per tuple for the work of loading the hashtable,
4118  * too.
4119  */
4120  sp_cost.startup += plan->total_cost +
4121  cpu_operator_cost * plan->plan_rows;
4122 
4123  /*
4124  * The per-tuple costs include the cost of evaluating the lefthand
4125  * expressions, plus the cost of probing the hashtable. We already
4126  * accounted for the lefthand expressions as part of the testexpr, and
4127  * will also have counted one cpu_operator_cost for each comparison
4128  * operator. That is probably too low for the probing cost, but it's
4129  * hard to make a better estimate, so live with it for now.
4130  */
4131  }
4132  else
4133  {
4134  /*
4135  * Otherwise we will be rescanning the subplan output on each
4136  * evaluation. We need to estimate how much of the output we will
4137  * actually need to scan. NOTE: this logic should agree with the
4138  * tuple_fraction estimates used by make_subplan() in
4139  * plan/subselect.c.
4140  */
4141  Cost plan_run_cost = plan->total_cost - plan->startup_cost;
4142 
4143  if (subplan->subLinkType == EXISTS_SUBLINK)
4144  {
4145  /* we only need to fetch 1 tuple; clamp to avoid zero divide */
4146  sp_cost.per_tuple += plan_run_cost / clamp_row_est(plan->plan_rows);
4147  }
4148  else if (subplan->subLinkType == ALL_SUBLINK ||
4149  subplan->subLinkType == ANY_SUBLINK)
4150  {
4151  /* assume we need 50% of the tuples */
4152  sp_cost.per_tuple += 0.50 * plan_run_cost;
4153  /* also charge a cpu_operator_cost per row examined */
4154  sp_cost.per_tuple += 0.50 * plan->plan_rows * cpu_operator_cost;
4155  }
4156  else
4157  {
4158  /* assume we need all tuples */
4159  sp_cost.per_tuple += plan_run_cost;
4160  }
4161 
4162  /*
4163  * Also account for subplan's startup cost. If the subplan is
4164  * uncorrelated or undirect correlated, AND its topmost node is one
4165  * that materializes its output, assume that we'll only need to pay
4166  * its startup cost once; otherwise assume we pay the startup cost
4167  * every time.
4168  */
4169  if (subplan->parParam == NIL &&
4171  sp_cost.startup += plan->startup_cost;
4172  else
4173  sp_cost.per_tuple += plan->startup_cost;
4174  }
4175 
4176  subplan->startup_cost = sp_cost.startup;
4177  subplan->per_call_cost = sp_cost.per_tuple;
4178 }
4179 
4180 
4181 /*
4182  * cost_rescan
4183  * Given a finished Path, estimate the costs of rescanning it after
4184  * having done so the first time. For some Path types a rescan is
4185  * cheaper than an original scan (if no parameters change), and this
4186  * function embodies knowledge about that. The default is to return
4187  * the same costs stored in the Path. (Note that the cost estimates
4188  * actually stored in Paths are always for first scans.)
4189  *
4190  * This function is not currently intended to model effects such as rescans
4191  * being cheaper due to disk block caching; what we are concerned with is
4192  * plan types wherein the executor caches results explicitly, or doesn't
4193  * redo startup calculations, etc.
4194  */
4195 static void
4197  Cost *rescan_startup_cost, /* output parameters */
4198  Cost *rescan_total_cost)
4199 {
4200  switch (path->pathtype)
4201  {
4202  case T_FunctionScan:
4203 
4204  /*
4205  * Currently, nodeFunctionscan.c always executes the function to
4206  * completion before returning any rows, and caches the results in
4207  * a tuplestore. So the function eval cost is all startup cost
4208  * and isn't paid over again on rescans. However, all run costs
4209  * will be paid over again.
4210  */
4211  *rescan_startup_cost = 0;
4212  *rescan_total_cost = path->total_cost - path->startup_cost;
4213  break;
4214  case T_HashJoin:
4215 
4216  /*
4217  * If it's a single-batch join, we don't need to rebuild the hash
4218  * table during a rescan.
4219  */
4220  if (((HashPath *) path)->num_batches == 1)
4221  {
4222  /* Startup cost is exactly the cost of hash table building */
4223  *rescan_startup_cost = 0;
4224  *rescan_total_cost = path->total_cost - path->startup_cost;
4225  }
4226  else
4227  {
4228  /* Otherwise, no special treatment */
4229  *rescan_startup_cost = path->startup_cost;
4230  *rescan_total_cost = path->total_cost;
4231  }
4232  break;
4233  case T_CteScan:
4234  case T_WorkTableScan:
4235  {
4236  /*
4237  * These plan types materialize their final result in a
4238  * tuplestore or tuplesort object. So the rescan cost is only
4239  * cpu_tuple_cost per tuple, unless the result is large enough
4240  * to spill to disk.
4241  */
4242  Cost run_cost = cpu_tuple_cost * path->rows;
4243  double nbytes = relation_byte_size(path->rows,
4244  path->pathtarget->width);
4245  long work_mem_bytes = work_mem * 1024L;
4246 
4247  if (nbytes > work_mem_bytes)
4248  {
4249  /* It will spill, so account for re-read cost */
4250  double npages = ceil(nbytes / BLCKSZ);
4251 
4252  run_cost += seq_page_cost * npages;
4253  }
4254  *rescan_startup_cost = 0;
4255  *rescan_total_cost = run_cost;
4256  }
4257  break;
4258  case T_Material:
4259  case T_Sort:
4260  {
4261  /*
4262  * These plan types not only materialize their results, but do
4263  * not implement qual filtering or projection. So they are
4264  * even cheaper to rescan than the ones above. We charge only
4265  * cpu_operator_cost per tuple. (Note: keep that in sync with
4266  * the run_cost charge in cost_sort, and also see comments in
4267  * cost_material before you change it.)
4268  */
4269  Cost run_cost = cpu_operator_cost * path->rows;
4270  double nbytes = relation_byte_size(path->rows,
4271  path->pathtarget->width);
4272  long work_mem_bytes = work_mem * 1024L;
4273 
4274  if (nbytes > work_mem_bytes)
4275  {
4276  /* It will spill, so account for re-read cost */
4277  double npages = ceil(nbytes / BLCKSZ);
4278 
4279  run_cost += seq_page_cost * npages;
4280  }
4281  *rescan_startup_cost = 0;
4282  *rescan_total_cost = run_cost;
4283  }
4284  break;
4285  case T_Memoize:
4286  /* All the hard work is done by cost_memoize_rescan */
4287  cost_memoize_rescan(root, (MemoizePath *) path,
4288  rescan_startup_cost, rescan_total_cost);
4289  break;
4290  default:
4291  *rescan_startup_cost = path->startup_cost;
4292  *rescan_total_cost = path->total_cost;
4293  break;
4294  }
4295 }
4296 
4297 
4298 /*
4299  * cost_qual_eval
4300  * Estimate the CPU costs of evaluating a WHERE clause.
4301  * The input can be either an implicitly-ANDed list of boolean
4302  * expressions, or a list of RestrictInfo nodes. (The latter is
4303  * preferred since it allows caching of the results.)
4304  * The result includes both a one-time (startup) component,
4305  * and a per-evaluation component.
4306  */
4307 void
4309 {
4310  cost_qual_eval_context context;
4311  ListCell *l;
4312 
4313  context.root = root;
4314  context.total.startup = 0;
4315  context.total.per_tuple = 0;
4316 
4317  /* We don't charge any cost for the implicit ANDing at top level ... */
4318 
4319  foreach(l, quals)
4320  {
4321  Node *qual = (Node *) lfirst(l);
4322 
4323  cost_qual_eval_walker(qual, &context);
4324  }
4325 
4326  *cost = context.total;
4327 }
4328 
4329 /*
4330  * cost_qual_eval_node
4331  * As above, for a single RestrictInfo or expression.
4332  */
4333 void
4335 {
4336  cost_qual_eval_context context;
4337 
4338  context.root = root;
4339  context.total.startup = 0;
4340  context.total.per_tuple = 0;
4341 
4342  cost_qual_eval_walker(qual, &context);
4343 
4344  *cost = context.total;
4345 }
4346 
4347 static bool
4349 {
4350  if (node == NULL)
4351  return false;
4352 
4353  /*
4354  * RestrictInfo nodes contain an eval_cost field reserved for this
4355  * routine's use, so that it's not necessary to evaluate the qual clause's
4356  * cost more than once. If the clause's cost hasn't been computed yet,
4357  * the field's startup value will contain -1.
4358  */
4359  if (IsA(node, RestrictInfo))
4360  {
4361  RestrictInfo *rinfo = (RestrictInfo *) node;
4362 
4363  if (rinfo->eval_cost.startup < 0)
4364  {
4365  cost_qual_eval_context locContext;
4366 
4367  locContext.root = context->root;
4368  locContext.total.startup = 0;
4369  locContext.total.per_tuple = 0;
4370 
4371  /*
4372  * For an OR clause, recurse into the marked-up tree so that we
4373  * set the eval_cost for contained RestrictInfos too.
4374  */
4375  if (rinfo->orclause)
4376  cost_qual_eval_walker((Node *) rinfo->orclause, &locContext);
4377  else
4378  cost_qual_eval_walker((Node *) rinfo->clause, &locContext);
4379 
4380  /*
4381  * If the RestrictInfo is marked pseudoconstant, it will be tested
4382  * only once, so treat its cost as all startup cost.
4383  */
4384  if (rinfo->pseudoconstant)
4385  {
4386  /* count one execution during startup */
4387  locContext.total.startup += locContext.total.per_tuple;
4388  locContext.total.per_tuple = 0;
4389  }
4390  rinfo->eval_cost = locContext.total;
4391  }
4392  context->total.startup += rinfo->eval_cost.startup;
4393  context->total.per_tuple += rinfo->eval_cost.per_tuple;
4394  /* do NOT recurse into children */
4395  return false;
4396  }
4397 
4398  /*
4399  * For each operator or function node in the given tree, we charge the
4400  * estimated execution cost given by pg_proc.procost (remember to multiply
4401  * this by cpu_operator_cost).
4402  *
4403  * Vars and Consts are charged zero, and so are boolean operators (AND,
4404  * OR, NOT). Simplistic, but a lot better than no model at all.
4405  *
4406  * Should we try to account for the possibility of short-circuit
4407  * evaluation of AND/OR? Probably *not*, because that would make the
4408  * results depend on the clause ordering, and we are not in any position
4409  * to expect that the current ordering of the clauses is the one that's
4410  * going to end up being used. The above per-RestrictInfo caching would
4411  * not mix well with trying to re-order clauses anyway.
4412  *
4413  * Another issue that is entirely ignored here is that if a set-returning
4414  * function is below top level in the tree, the functions/operators above
4415  * it will need to be evaluated multiple times. In practical use, such
4416  * cases arise so seldom as to not be worth the added complexity needed;
4417  * moreover, since our rowcount estimates for functions tend to be pretty
4418  * phony, the results would also be pretty phony.
4419  */
4420  if (IsA(node, FuncExpr))
4421  {
4422  add_function_cost(context->root, ((FuncExpr *) node)->funcid, node,
4423  &context->total);
4424  }
4425  else if (IsA(node, OpExpr) ||
4426  IsA(node, DistinctExpr) ||
4427  IsA(node, NullIfExpr))
4428  {
4429  /* rely on struct equivalence to treat these all alike */
4430  set_opfuncid((OpExpr *) node);
4431  add_function_cost(context->root, ((OpExpr *) node)->opfuncid, node,
4432  &context->total);
4433  }
4434  else if (IsA(node, ScalarArrayOpExpr))
4435  {
4436  ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) node;
4437  Node *arraynode = (Node *) lsecond(saop->args);
4438  QualCost sacosts;
4439  QualCost hcosts;
4440  int estarraylen = estimate_array_length(arraynode);
4441 
4442  set_sa_opfuncid(saop);
4443  sacosts.startup = sacosts.per_tuple = 0;
4444  add_function_cost(context->root, saop->opfuncid, NULL,
4445  &sacosts);
4446 
4447  if (OidIsValid(saop->hashfuncid))
4448  {
4449  /* Handle costs for hashed ScalarArrayOpExpr */
4450  hcosts.startup = hcosts.per_tuple = 0;
4451 
4452  add_function_cost(context->root, saop->hashfuncid, NULL, &hcosts);
4453  context->total.startup += sacosts.startup + hcosts.startup;
4454 
4455  /* Estimate the cost of building the hashtable. */
4456  context->total.startup += estarraylen * hcosts.per_tuple;
4457 
4458  /*
4459  * XXX should we charge a little bit for sacosts.per_tuple when
4460  * building the table, or is it ok to assume there will be zero
4461  * hash collision?
4462  */
4463 
4464  /*
4465  * Charge for hashtable lookups. Charge a single hash and a
4466  * single comparison.
4467  */
4468  context->total.per_tuple += hcosts.per_tuple + sacosts.per_tuple;
4469  }
4470  else
4471  {
4472  /*
4473  * Estimate that the operator will be applied to about half of the
4474  * array elements before the answer is determined.
4475  */
4476  context->total.startup += sacosts.startup;
4477  context->total.per_tuple += sacosts.per_tuple *
4478  estimate_array_length(arraynode) * 0.5;
4479  }
4480  }
4481  else if (IsA(node, Aggref) ||
4482  IsA(node, WindowFunc))
4483  {
4484  /*
4485  * Aggref and WindowFunc nodes are (and should be) treated like Vars,
4486  * ie, zero execution cost in the current model, because they behave
4487  * essentially like Vars at execution. We disregard the costs of
4488  * their input expressions for the same reason. The actual execution
4489  * costs of the aggregate/window functions and their arguments have to
4490  * be factored into plan-node-specific costing of the Agg or WindowAgg
4491  * plan node.
4492  */
4493  return false; /* don't recurse into children */
4494  }
4495  else if (IsA(node, CoerceViaIO))
4496  {
4497  CoerceViaIO *iocoerce = (CoerceViaIO *) node;
4498  Oid iofunc;
4499  Oid typioparam;
4500  bool typisvarlena;
4501 
4502  /* check the result type's input function */
4503  getTypeInputInfo(iocoerce->resulttype,
4504  &iofunc, &typioparam);
4505  add_function_cost(context->root, iofunc, NULL,
4506  &context->total);
4507  /* check the input type's output function */
4508  getTypeOutputInfo(exprType((Node *) iocoerce->arg),
4509  &iofunc, &typisvarlena);
4510  add_function_cost(context->root, iofunc, NULL,
4511  &context->total);
4512  }
4513  else if (IsA(node, ArrayCoerceExpr))
4514  {
4515  ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node;
4516  QualCost perelemcost;
4517 
4518  cost_qual_eval_node(&perelemcost, (Node *) acoerce->elemexpr,
4519  context->root);
4520  context->total.startup += perelemcost.startup;
4521  if (perelemcost.per_tuple > 0)
4522  context->total.per_tuple += perelemcost.per_tuple *
4523  estimate_array_length((Node *) acoerce->arg);
4524  }
4525  else if (IsA(node, RowCompareExpr))
4526  {
4527  /* Conservatively assume we will check all the columns */
4528  RowCompareExpr *rcexpr = (RowCompareExpr *) node;
4529  ListCell *lc;
4530 
4531  foreach(lc, rcexpr->opnos)
4532  {
4533  Oid opid = lfirst_oid(lc);
4534 
4535  add_function_cost(context->root, get_opcode(opid), NULL,
4536  &context->total);
4537  }
4538  }
4539  else if (IsA(node, MinMaxExpr) ||
4540  IsA(node, SQLValueFunction) ||
4541  IsA(node, XmlExpr) ||
4542  IsA(node, CoerceToDomain) ||
4543  IsA(node, NextValueExpr))
4544  {
4545  /* Treat all these as having cost 1 */
4546  context->total.per_tuple += cpu_operator_cost;
4547  }
4548  else if (IsA(node, CurrentOfExpr))
4549  {
4550  /* Report high cost to prevent selection of anything but TID scan */
4551  context->total.startup += disable_cost;
4552  }
4553  else if (IsA(node, SubLink))
4554  {
4555  /* This routine should not be applied to un-planned expressions */
4556  elog(ERROR, "cannot handle unplanned sub-select");
4557  }
4558  else if (IsA(node, SubPlan))
4559  {
4560  /*
4561  * A subplan node in an expression typically indicates that the
4562  * subplan will be executed on each evaluation, so charge accordingly.
4563  * (Sub-selects that can be executed as InitPlans have already been
4564  * removed from the expression.)
4565  */
4566  SubPlan *subplan = (SubPlan *) node;
4567 
4568  context->total.startup += subplan->startup_cost;
4569  context->total.per_tuple += subplan->per_call_cost;
4570 
4571  /*
4572  * We don't want to recurse into the testexpr, because it was already
4573  * counted in the SubPlan node's costs. So we're done.
4574  */
4575  return false;
4576  }
4577  else if (IsA(node, AlternativeSubPlan))
4578  {
4579  /*
4580  * Arbitrarily use the first alternative plan for costing. (We should
4581  * certainly only include one alternative, and we don't yet have
4582  * enough information to know which one the executor is most likely to
4583  * use.)
4584  */
4585  AlternativeSubPlan *asplan = (AlternativeSubPlan *) node;
4586 
4587  return cost_qual_eval_walker((Node *) linitial(asplan->subplans),
4588  context);
4589  }
4590  else if (IsA(node, PlaceHolderVar))
4591  {
4592  /*
4593  * A PlaceHolderVar should be given cost zero when considering general
4594  * expression evaluation costs. The expense of doing the contained
4595  * expression is charged as part of the tlist eval costs of the scan
4596  * or join where the PHV is first computed (see set_rel_width and
4597  * add_placeholders_to_joinrel). If we charged it again here, we'd be
4598  * double-counting the cost for each level of plan that the PHV
4599  * bubbles up through. Hence, return without recursing into the
4600  * phexpr.
4601  */
4602  return false;
4603  }
4604 
4605  /* recurse into children */
4607  (void *) context);
4608 }
4609 
4610 /*
4611  * get_restriction_qual_cost
4612  * Compute evaluation costs of a baserel's restriction quals, plus any
4613  * movable join quals that have been pushed down to the scan.
4614  * Results are returned into *qpqual_cost.
4615  *
4616  * This is a convenience subroutine that works for seqscans and other cases
4617  * where all the given quals will be evaluated the hard way. It's not useful
4618  * for cost_index(), for example, where the index machinery takes care of
4619  * some of the quals. We assume baserestrictcost was previously set by
4620  * set_baserel_size_estimates().
4621  */
4622 static void
4624  ParamPathInfo *param_info,
4625  QualCost *qpqual_cost)
4626 {
4627  if (param_info)
4628  {
4629  /* Include costs of pushed-down clauses */
4630  cost_qual_eval(qpqual_cost, param_info->ppi_clauses, root);
4631 
4632  qpqual_cost->startup += baserel->baserestrictcost.startup;
4633  qpqual_cost->per_tuple += baserel->baserestrictcost.per_tuple;
4634  }
4635  else
4636  *qpqual_cost = baserel->baserestrictcost;
4637 }
4638 
4639 
4640 /*
4641  * compute_semi_anti_join_factors
4642  * Estimate how much of the inner input a SEMI, ANTI, or inner_unique join
4643  * can be expected to scan.
4644  *
4645  * In a hash or nestloop SEMI/ANTI join, the executor will stop scanning
4646  * inner rows as soon as it finds a match to the current outer row.
4647  * The same happens if we have detected the inner rel is unique.
4648  * We should therefore adjust some of the cost components for this effect.
4649  * This function computes some estimates needed for these adjustments.
4650  * These estimates will be the same regardless of the particular paths used
4651  * for the outer and inner relation, so we compute these once and then pass
4652  * them to all the join cost estimation functions.
4653  *
4654  * Input parameters:
4655  * joinrel: join relation under consideration
4656  * outerrel: outer relation under consideration
4657  * innerrel: inner relation under consideration
4658  * jointype: if not JOIN_SEMI or JOIN_ANTI, we assume it's inner_unique
4659  * sjinfo: SpecialJoinInfo relevant to this join
4660  * restrictlist: join quals
4661  * Output parameters:
4662  * *semifactors is filled in (see pathnodes.h for field definitions)
4663  */
4664 void
4666  RelOptInfo *joinrel,
4667  RelOptInfo *outerrel,
4668  RelOptInfo *innerrel,
4669  JoinType jointype,
4670  SpecialJoinInfo *sjinfo,
4671  List *restrictlist,
4672  SemiAntiJoinFactors *semifactors)
4673 {
4674  Selectivity jselec;
4675  Selectivity nselec;
4676  Selectivity avgmatch;
4677  SpecialJoinInfo norm_sjinfo;
4678  List *joinquals;
4679  ListCell *l;
4680 
4681  /*
4682  * In an ANTI join, we must ignore clauses that are "pushed down", since
4683  * those won't affect the match logic. In a SEMI join, we do not
4684  * distinguish joinquals from "pushed down" quals, so just use the whole
4685  * restrictinfo list. For other outer join types, we should consider only
4686  * non-pushed-down quals, so that this devolves to an IS_OUTER_JOIN check.
4687  */
4688  if (IS_OUTER_JOIN(jointype))
4689  {
4690  joinquals = NIL;
4691  foreach(l, restrictlist)
4692  {
4693  RestrictInfo *rinfo = lfirst_node(RestrictInfo, l);
4694 
4695  if (!RINFO_IS_PUSHED_DOWN(rinfo, joinrel->relids))
4696  joinquals = lappend(joinquals, rinfo);
4697  }
4698  }
4699  else
4700  joinquals = restrictlist;
4701 
4702  /*
4703  * Get the JOIN_SEMI or JOIN_ANTI selectivity of the join clauses.
4704  */
4705  jselec = clauselist_selectivity(root,
4706  joinquals,
4707  0,
4708  (jointype == JOIN_ANTI) ? JOIN_ANTI : JOIN_SEMI,
4709  sjinfo);
4710 
4711  /*
4712  * Also get the normal inner-join selectivity of the join clauses.
4713  */
4714  norm_sjinfo.type = T_SpecialJoinInfo;
4715  norm_sjinfo.min_lefthand = outerrel->relids;
4716  norm_sjinfo.min_righthand = innerrel->relids;
4717  norm_sjinfo.syn_lefthand = outerrel->relids;
4718  norm_sjinfo.syn_righthand = innerrel->relids;
4719  norm_sjinfo.jointype = JOIN_INNER;
4720  /* we don't bother trying to make the remaining fields valid */
4721  norm_sjinfo.lhs_strict = false;
4722  norm_sjinfo.delay_upper_joins = false;
4723  norm_sjinfo.semi_can_btree = false;
4724  norm_sjinfo.semi_can_hash = false;
4725  norm_sjinfo.semi_operators = NIL;
4726  norm_sjinfo.semi_rhs_exprs = NIL;
4727 
4728  nselec = clauselist_selectivity(root,
4729  joinquals,
4730  0,
4731  JOIN_INNER,
4732  &norm_sjinfo);
4733 
4734  /* Avoid leaking a lot of ListCells */
4735  if (IS_OUTER_JOIN(jointype))
4736  list_free(joinquals);
4737 
4738  /*
4739  * jselec can be interpreted as the fraction of outer-rel rows that have
4740  * any matches (this is true for both SEMI and ANTI cases). And nselec is
4741  * the fraction of the Cartesian product that matches. So, the average
4742  * number of matches for each outer-rel row that has at least one match is
4743  * nselec * inner_rows / jselec.
4744  *
4745  * Note: it is correct to use the inner rel's "rows" count here, even
4746  * though we might later be considering a parameterized inner path with
4747  * fewer rows. This is because we have included all the join clauses in
4748  * the selectivity estimate.
4749  */
4750  if (jselec > 0) /* protect against zero divide */
4751  {
4752  avgmatch = nselec * innerrel->rows / jselec;
4753  /* Clamp to sane range */
4754  avgmatch = Max(1.0, avgmatch);
4755  }
4756  else
4757  avgmatch = 1.0;
4758 
4759  semifactors->outer_match_frac = jselec;
4760  semifactors->match_count = avgmatch;
4761 }
4762 
4763 /*
4764  * has_indexed_join_quals
4765  * Check whether all the joinquals of a nestloop join are used as
4766  * inner index quals.
4767  *
4768  * If the inner path of a SEMI/ANTI join is an indexscan (including bitmap
4769  * indexscan) that uses all the joinquals as indexquals, we can assume that an
4770  * unmatched outer tuple is cheap to process, whereas otherwise it's probably
4771  * expensive.
4772  */
4773 static bool
4775 {
4776  JoinPath *joinpath = &path->jpath;
4777  Relids joinrelids = joinpath->path.parent->relids;
4778  Path *innerpath = joinpath->innerjoinpath;
4779  List *indexclauses;
4780  bool found_one;
4781  ListCell *lc;
4782 
4783  /* If join still has quals to evaluate, it's not fast */
4784  if (joinpath->joinrestrictinfo != NIL)
4785  return false;
4786  /* Nor if the inner path isn't parameterized at all */
4787  if (innerpath->param_info == NULL)
4788  return false;
4789 
4790  /* Find the indexclauses list for the inner scan */
4791  switch (innerpath->pathtype)
4792  {
4793  case T_IndexScan:
4794  case T_IndexOnlyScan:
4795  indexclauses = ((IndexPath *) innerpath)->indexclauses;
4796  break;
4797  case T_BitmapHeapScan:
4798  {
4799  /* Accept only a simple bitmap scan, not AND/OR cases */
4800  Path *bmqual = ((BitmapHeapPath *) innerpath)->bitmapqual;
4801 
4802  if (IsA(bmqual, IndexPath))
4803  indexclauses = ((IndexPath *) bmqual)->indexclauses;
4804  else
4805  return false;
4806  break;
4807  }
4808  default:
4809 
4810  /*
4811  * If it's not a simple indexscan, it probably doesn't run quickly
4812  * for zero rows out, even if it's a parameterized path using all
4813  * the joinquals.
4814  */
4815  return false;
4816  }
4817 
4818  /*
4819  * Examine the inner path's param clauses. Any that are from the outer
4820  * path must be found in the indexclauses list, either exactly or in an
4821  * equivalent form generated by equivclass.c. Also, we must find at least
4822  * one such clause, else it's a clauseless join which isn't fast.
4823  */
4824  found_one = false;
4825  foreach(lc, innerpath->param_info->ppi_clauses)
4826  {
4827  RestrictInfo *rinfo = (RestrictInfo *) lfirst(lc);
4828 
4829  if (join_clause_is_movable_into(rinfo,
4830  innerpath->parent->relids,
4831  joinrelids))
4832  {
4833  if (!is_redundant_with_indexclauses(rinfo, indexclauses))
4834  return false;
4835  found_one = true;
4836  }
4837  }
4838  return found_one;
4839 }
4840 
4841 
4842 /*
4843  * approx_tuple_count
4844  * Quick-and-dirty estimation of the number of join rows passing
4845  * a set of qual conditions.
4846  *
4847  * The quals can be either an implicitly-ANDed list of boolean expressions,
4848  * or a list of RestrictInfo nodes (typically the latter).
4849  *
4850  * We intentionally compute the selectivity under JOIN_INNER rules, even
4851  * if it's some type of outer join. This is appropriate because we are
4852  * trying to figure out how many tuples pass the initial merge or hash
4853  * join step.
4854  *
4855  * This is quick-and-dirty because we bypass clauselist_selectivity, and
4856  * simply multiply the independent clause selectivities together. Now
4857  * clauselist_selectivity often can't do any better than that anyhow, but
4858  * for some situations (such as range constraints) it is smarter. However,
4859  * we can't effectively cache the results of clauselist_selectivity, whereas
4860  * the individual clause selectivities can be and are cached.
4861  *
4862  * Since we are only using the results to estimate how many potential
4863  * output tuples are generated and passed through qpqual checking, it
4864  * seems OK to live with the approximation.
4865  */
4866 static double
4868 {
4869  double tuples;
4870  double outer_tuples = path->outerjoinpath->rows;
4871  double inner_tuples = path->innerjoinpath->rows;
4872  SpecialJoinInfo sjinfo;
4873  Selectivity selec = 1.0;
4874  ListCell *l;
4875 
4876  /*
4877  * Make up a SpecialJoinInfo for JOIN_INNER semantics.
4878  */
4879  sjinfo.type = T_SpecialJoinInfo;
4880  sjinfo.min_lefthand = path->outerjoinpath->parent->relids;
4881  sjinfo.min_righthand = path->innerjoinpath->parent->relids;
4882  sjinfo.syn_lefthand = path->outerjoinpath->parent->relids;
4883  sjinfo.syn_righthand = path->innerjoinpath->parent->relids;
4884  sjinfo.jointype = JOIN_INNER;
4885  /* we don't bother trying to make the remaining fields valid */
4886  sjinfo.lhs_strict = false;
4887  sjinfo.delay_upper_joins = false;
4888  sjinfo.semi_can_btree = false;
4889  sjinfo.semi_can_hash = false;
4890  sjinfo.semi_operators = NIL;
4891  sjinfo.semi_rhs_exprs = NIL;
4892 
4893  /* Get the approximate selectivity */
4894  foreach(l, quals)
4895  {
4896  Node *qual = (Node *) lfirst(l);
4897 
4898  /* Note that clause_selectivity will be able to cache its result */
4899  selec *= clause_selectivity(root, qual, 0, JOIN_INNER, &sjinfo);
4900  }
4901 
4902  /* Apply it to the input relation sizes */
4903  tuples = selec * outer_tuples * inner_tuples;
4904 
4905  return clamp_row_est(tuples);
4906 }
4907 
4908 
4909 /*
4910  * set_baserel_size_estimates
4911  * Set the size estimates for the given base relation.
4912  *
4913  * The rel's targetlist and restrictinfo list must have been constructed
4914  * already, and rel->tuples must be set.
4915  *
4916  * We set the following fields of the rel node:
4917  * rows: the estimated number of output tuples (after applying
4918  * restriction clauses).
4919  * width: the estimated average output tuple width in bytes.
4920  * baserestrictcost: estimated cost of evaluating baserestrictinfo clauses.
4921  */
4922 void
4924 {
4925  double nrows;
4926 
4927  /* Should only be applied to base relations */
4928  Assert(rel->relid > 0);
4929 
4930  nrows = rel->tuples *
4932  rel->baserestrictinfo,
4933  0,
4934  JOIN_INNER,
4935  NULL);
4936 
4937  rel->rows = clamp_row_est(nrows);
4938 
4940 
4941  set_rel_width(root, rel);
4942 }
4943 
4944 /*
4945  * get_parameterized_baserel_size
4946  * Make a size estimate for a parameterized scan of a base relation.
4947  *
4948  * 'param_clauses' lists the additional join clauses to be used.
4949  *
4950  * set_baserel_size_estimates must have been applied already.
4951  */
4952 double
4954  List *param_clauses)
4955 {
4956  List *allclauses;
4957  double nrows;
4958 
4959  /*
4960  * Estimate the number of rows returned by the parameterized scan, knowing
4961  * that it will apply all the extra join clauses as well as the rel's own
4962  * restriction clauses. Note that we force the clauses to be treated as
4963  * non-join clauses during selectivity estimation.
4964  */
4965  allclauses = list_concat_copy(param_clauses, rel->baserestrictinfo);
4966  nrows = rel->tuples *
4968  allclauses,
4969  rel->relid, /* do not use 0! */
4970  JOIN_INNER,
4971  NULL);
4972  nrows = clamp_row_est(nrows);
4973  /* For safety, make sure result is not more than the base estimate */
4974  if (nrows > rel->rows)
4975  nrows = rel->rows;
4976  return nrows;
4977 }
4978 
4979 /*
4980  * set_joinrel_size_estimates
4981  * Set the size estimates for the given join relation.
4982  *
4983  * The rel's targetlist must have been constructed already, and a
4984  * restriction clause list that matches the given component rels must
4985  * be provided.
4986  *
4987  * Since there is more than one way to make a joinrel for more than two
4988  * base relations, the results we get here could depend on which component
4989  * rel pair is provided. In theory we should get the same answers no matter
4990  * which pair is provided; in practice, since the selectivity estimation
4991  * routines don't handle all cases equally well, we might not. But there's
4992  * not much to be done about it. (Would it make sense to repeat the
4993  * calculations for each pair of input rels that's encountered, and somehow
4994  * average the results? Probably way more trouble than it's worth, and
4995  * anyway we must keep the rowcount estimate the same for all paths for the
4996  * joinrel.)
4997  *
4998  * We set only the rows field here. The reltarget field was already set by
4999  * build_joinrel_tlist, and baserestrictcost is not used for join rels.
5000  */
5001 void
5003  RelOptInfo *outer_rel,
5004  RelOptInfo *inner_rel,
5005  SpecialJoinInfo *sjinfo,
5006  List *restrictlist)
5007 {
5008  rel->rows = calc_joinrel_size_estimate(root,
5009  rel,
5010  outer_rel,
5011  inner_rel,
5012  outer_rel->rows,
5013  inner_rel->rows,
5014  sjinfo,
5015  restrictlist);
5016 }
5017 
5018 /*
5019  * get_parameterized_joinrel_size
5020  * Make a size estimate for a parameterized scan of a join relation.
5021  *
5022  * 'rel' is the joinrel under consideration.
5023  * 'outer_path', 'inner_path' are (probably also parameterized) Paths that
5024  * produce the relations being joined.
5025  * 'sjinfo' is any SpecialJoinInfo relevant to this join.
5026  * 'restrict_clauses' lists the join clauses that need to be applied at the
5027  * join node (including any movable clauses that were moved down to this join,
5028  * and not including any movable clauses that were pushed down into the
5029  * child paths).
5030  *
5031  * set_joinrel_size_estimates must have been applied already.
5032  */
5033 double
5035  Path *outer_path,
5036  Path *inner_path,
5037  SpecialJoinInfo *sjinfo,
5038  List *restrict_clauses)
5039 {
5040  double nrows;
5041 
5042  /*
5043  * Estimate the number of rows returned by the parameterized join as the
5044  * sizes of the input paths times the selectivity of the clauses that have
5045  * ended up at this join node.
5046  *
5047  * As with set_joinrel_size_estimates, the rowcount estimate could depend
5048  * on the pair of input paths provided, though ideally we'd get the same
5049  * estimate for any pair with the same parameterization.
5050  */
5051  nrows = calc_joinrel_size_estimate(root,
5052  rel,
5053  outer_path->parent,
5054  inner_path->parent,
5055  outer_path->rows,
5056  inner_path->rows,
5057  sjinfo,
5058  restrict_clauses);
5059  /* For safety, make sure result is not more than the base estimate */
5060  if (nrows > rel->rows)
5061  nrows = rel->rows;
5062  return nrows;
5063 }
5064 
5065 /*
5066  * calc_joinrel_size_estimate
5067  * Workhorse for set_joinrel_size_estimates and
5068  * get_parameterized_joinrel_size.
5069  *
5070  * outer_rel/inner_rel are the relations being joined, but they should be
5071  * assumed to have sizes outer_rows/inner_rows; those numbers might be less
5072  * than what rel->rows says, when we are considering parameterized paths.
5073  */
5074 static double
5076  RelOptInfo *joinrel,
5077  RelOptInfo *outer_rel,
5078  RelOptInfo *inner_rel,
5079  double outer_rows,
5080  double inner_rows,
5081  SpecialJoinInfo *sjinfo,
5082  List *restrictlist_in)
5083 {
5084  /* This apparently-useless variable dodges a compiler bug in VS2013: */
5085  List *restrictlist = restrictlist_in;
5086  JoinType jointype = sjinfo->jointype;
5087  Selectivity fkselec;
5088  Selectivity jselec;
5089  Selectivity pselec;
5090  double nrows;
5091 
5092  /*
5093  * Compute joinclause selectivity. Note that we are only considering
5094  * clauses that become restriction clauses at this join level; we are not
5095  * double-counting them because they were not considered in estimating the
5096  * sizes of the component rels.
5097  *
5098  * First, see whether any of the joinclauses can be matched to known FK
5099  * constraints. If so, drop those clauses from the restrictlist, and
5100  * instead estimate their selectivity using FK semantics. (We do this
5101  * without regard to whether said clauses are local or "pushed down".
5102  * Probably, an FK-matching clause could never be seen as pushed down at
5103  * an outer join, since it would be strict and hence would be grounds for
5104  * join strength reduction.) fkselec gets the net selectivity for
5105  * FK-matching clauses, or 1.0 if there are none.
5106  */
5107  fkselec = get_foreign_key_join_selectivity(root,
5108  outer_rel->relids,
5109  inner_rel->relids,
5110  sjinfo,
5111  &restrictlist);
5112 
5113  /*
5114  * For an outer join, we have to distinguish the selectivity of the join's
5115  * own clauses (JOIN/ON conditions) from any clauses that were "pushed
5116  * down". For inner joins we just count them all as joinclauses.
5117  */
5118  if (IS_OUTER_JOIN(jointype))
5119  {
5120  List *joinquals = NIL;
5121  List *pushedquals = NIL;
5122  ListCell *l;
5123 
5124  /* Grovel through the clauses to separate into two lists */
5125  foreach(l, restrictlist)
5126  {
5127  RestrictInfo *rinfo = lfirst_node(RestrictInfo, l);
5128 
5129  if (RINFO_IS_PUSHED_DOWN(rinfo, joinrel->relids))
5130  pushedquals = lappend(pushedquals, rinfo);
5131  else
5132  joinquals = lappend(joinquals, rinfo);
5133  }
5134 
5135  /* Get the separate selectivities */
5136  jselec = clauselist_selectivity(root,
5137  joinquals,
5138  0,
5139  jointype,
5140  sjinfo);
5141  pselec = clauselist_selectivity(root,
5142  pushedquals,
5143  0,
5144  jointype,
5145  sjinfo);
5146 
5147  /* Avoid leaking a lot of ListCells */
5148  list_free(joinquals);
5149  list_free(pushedquals);
5150  }
5151  else
5152  {
5153  jselec = clauselist_selectivity(root,
5154  restrictlist,
5155  0,
5156  jointype,
5157  sjinfo);
5158  pselec = 0.0; /* not used, keep compiler quiet */
5159  }
5160 
5161  /*
5162  * Basically, we multiply size of Cartesian product by selectivity.
5163  *
5164  * If we are doing an outer join, take that into account: the joinqual
5165  * selectivity has to be clamped using the knowledge that the output must
5166  * be at least as large as the non-nullable input. However, any
5167  * pushed-down quals are applied after the outer join, so their
5168  * selectivity applies fully.
5169  *
5170  * For JOIN_SEMI and JOIN_ANTI, the selectivity is defined as the fraction
5171  * of LHS rows that have matches, and we apply that straightforwardly.
5172  */
5173  switch (jointype)
5174  {
5175  case JOIN_INNER:
5176  nrows = outer_rows * inner_rows * fkselec * jselec;
5177  /* pselec not used */
5178  break;
5179  case JOIN_LEFT:
5180  nrows = outer_rows * inner_rows * fkselec * jselec;
5181  if (nrows < outer_rows)
5182  nrows = outer_rows;
5183  nrows *= pselec;
5184  break;
5185  case JOIN_FULL:
5186  nrows = outer_rows * inner_rows * fkselec * jselec;
5187  if (nrows < outer_rows)
5188  nrows = outer_rows;
5189  if (nrows < inner_rows)
5190  nrows = inner_rows;
5191  nrows *= pselec;
5192  break;
5193  case JOIN_SEMI:
5194  nrows = outer_rows * fkselec * jselec;
5195  /* pselec not used */
5196  break;
5197  case JOIN_ANTI:
5198  nrows = outer_rows * (1.0 - fkselec * jselec);
5199  nrows *= pselec;
5200  break;
5201  default:
5202  /* other values not expected here */
5203  elog(ERROR, "unrecognized join type: %d", (int) jointype);
5204  nrows = 0; /* keep compiler quiet */
5205  break;
5206  }
5207 
5208  return clamp_row_est(nrows);
5209 }
5210 
5211 /*
5212  * get_foreign_key_join_selectivity
5213  * Estimate join selectivity for foreign-key-related clauses.
5214  *
5215  * Remove any clauses that can be matched to FK constraints from *restrictlist,
5216  * and return a substitute estimate of their selectivity. 1.0 is returned
5217  * when there are no such clauses.
5218  *
5219  * The reason for treating such clauses specially is that we can get better
5220  * estimates this way than by relying on clauselist_selectivity(), especially
5221  * for multi-column FKs where that function's assumption that the clauses are
5222  * independent falls down badly. But even with single-column FKs, we may be
5223  * able to get a better answer when the pg_statistic stats are missing or out
5224  * of date.
5225  */
5226 static Selectivity
5228  Relids outer_relids,
5229  Relids inner_relids,
5230  SpecialJoinInfo *sjinfo,
5231  List **restrictlist)
5232 {
5233  Selectivity fkselec = 1.0;
5234  JoinType jointype = sjinfo->jointype;
5235  List *worklist = *restrictlist;
5236  ListCell *lc;
5237 
5238  /* Consider each FK constraint that is known to match the query */
5239  foreach(lc, root->fkey_list)
5240  {
5241  ForeignKeyOptInfo *fkinfo = (ForeignKeyOptInfo *) lfirst(lc);
5242  bool ref_is_outer;
5243  List *removedlist;
5244  ListCell *cell;
5245 
5246  /*
5247  * This FK is not relevant unless it connects a baserel on one side of
5248  * this join to a baserel on the other side.
5249  */
5250  if (bms_is_member(fkinfo->con_relid, outer_relids) &&
5251  bms_is_member(fkinfo->ref_relid, inner_relids))
5252  ref_is_outer = false;
5253  else if (bms_is_member(fkinfo->ref_relid, outer_relids) &&
5254  bms_is_member(fkinfo->con_relid, inner_relids))
5255  ref_is_outer = true;
5256  else
5257  continue;
5258 
5259  /*
5260  * If we're dealing with a semi/anti join, and the FK's referenced
5261  * relation is on the outside, then knowledge of the FK doesn't help
5262  * us figure out what we need to know (which is the fraction of outer
5263  * rows that have matches). On the other hand, if the referenced rel
5264  * is on the inside, then all outer rows must have matches in the
5265  * referenced table (ignoring nulls). But any restriction or join
5266  * clauses that filter that table will reduce the fraction of matches.
5267  * We can account for restriction clauses, but it's too hard to guess
5268  * how many table rows would get through a join that's inside the RHS.
5269  * Hence, if either case applies, punt and ignore the FK.
5270  */
5271  if ((jointype == JOIN_SEMI || jointype == JOIN_ANTI) &&
5272  (ref_is_outer || bms_membership(inner_relids) != BMS_SINGLETON))
5273  continue;
5274 
5275  /*
5276  * Modify the restrictlist by removing clauses that match the FK (and
5277  * putting them into removedlist instead). It seems unsafe to modify
5278  * the originally-passed List structure, so we make a shallow copy the
5279  * first time through.
5280  */
5281  if (worklist == *restrictlist)
5282  worklist = list_copy(worklist);
5283 
5284  removedlist = NIL;
5285  foreach(cell, worklist)
5286  {
5287  RestrictInfo *rinfo = (RestrictInfo *) lfirst(cell);
5288  bool remove_it = false;
5289  int i;
5290 
5291  /* Drop this clause if it matches any column of the FK */
5292  for (i = 0; i < fkinfo->nkeys; i++)
5293  {
5294  if (rinfo->parent_ec)
5295  {
5296  /*
5297  * EC-derived clauses can only match by EC. It is okay to
5298  * consider any clause derived from the same EC as
5299  * matching the FK: even if equivclass.c chose to generate
5300  * a clause equating some other pair of Vars, it could
5301  * have generated one equating the FK's Vars. So for
5302  * purposes of estimation, we can act as though it did so.
5303  *
5304  * Note: checking parent_ec is a bit of a cheat because
5305  * there are EC-derived clauses that don't have parent_ec
5306  * set; but such clauses must compare expressions that
5307  * aren't just Vars, so they cannot match the FK anyway.
5308  */
5309  if (fkinfo->eclass[i] == rinfo->parent_ec)
5310  {
5311  remove_it = true;
5312  break;
5313  }
5314  }
5315  else
5316  {
5317  /*
5318  * Otherwise, see if rinfo was previously matched to FK as
5319  * a "loose" clause.
5320  */
5321  if (list_member_ptr(fkinfo->rinfos[i], rinfo))
5322  {
5323  remove_it = true;
5324  break;
5325  }
5326  }
5327  }
5328  if (remove_it)
5329  {
5330  worklist = foreach_delete_current(worklist, cell);
5331  removedlist = lappend(removedlist, rinfo);
5332  }
5333  }
5334 
5335  /*
5336  * If we failed to remove all the matching clauses we expected to
5337  * find, chicken out and ignore this FK; applying its selectivity
5338  * might result in double-counting. Put any clauses we did manage to
5339  * remove back into the worklist.
5340  *
5341  * Since the matching clauses are known not outerjoin-delayed, they
5342  * would normally have appeared in the initial joinclause list. If we
5343  * didn't find them, there are two possibilities:
5344  *
5345  * 1. If the FK match is based on an EC that is ec_has_const, it won't
5346  * have generated any join clauses at all. We discount such ECs while
5347  * checking to see if we have "all" the clauses. (Below, we'll adjust
5348  * the selectivity estimate for this case.)
5349  *
5350  * 2. The clauses were matched to some other FK in a previous
5351  * iteration of this loop, and thus removed from worklist. (A likely
5352  * case is that two FKs are matched to the same EC; there will be only
5353  * one EC-derived clause in the initial list, so the first FK will
5354  * consume it.) Applying both FKs' selectivity independently risks
5355  * underestimating the join size; in particular, this would undo one
5356  * of the main things that ECs were invented for, namely to avoid
5357  * double-counting the selectivity of redundant equality conditions.
5358  * Later we might think of a reasonable way to combine the estimates,
5359  * but for now, just punt, since this is a fairly uncommon situation.
5360  */
5361  if (removedlist == NIL ||
5362  list_length(removedlist) !=
5363  (fkinfo->nmatched_ec - fkinfo->nconst_ec + fkinfo->nmatched_ri))
5364  {
5365  worklist = list_concat(worklist, removedlist);
5366  continue;
5367  }
5368 
5369  /*
5370  * Finally we get to the payoff: estimate selectivity using the
5371  * knowledge that each referencing row will match exactly one row in
5372  * the referenced table.
5373  *
5374  * XXX that's not true in the presence of nulls in the referencing
5375  * column(s), so in principle we should derate the estimate for those.
5376  * However (1) if there are any strict restriction clauses for the
5377  * referencing column(s) elsewhere in the query, derating here would
5378  * be double-counting the null fraction, and (2) it's not very clear
5379  * how to combine null fractions for multiple referencing columns. So
5380  * we do nothing for now about correcting for nulls.
5381  *
5382  * XXX another point here is that if either side of an FK constraint
5383  * is an inheritance parent, we estimate as though the constraint
5384  * covers all its children as well. This is not an unreasonable
5385  * assumption for a referencing table, ie the user probably applied
5386  * identical constraints to all child tables (though perhaps we ought
5387  * to check that). But it's not possible to have done that for a
5388  * referenced table. Fortunately, precisely because that doesn't
5389  * work, it is uncommon in practice to have an FK referencing a parent
5390  * table. So, at least for now, disregard inheritance here.
5391  */
5392  if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
5393  {
5394  /*
5395  * For JOIN_SEMI and JOIN_ANTI, we only get here when the FK's
5396  * referenced table is exactly the inside of the join. The join
5397  * selectivity is defined as the fraction of LHS rows that have
5398  * matches. The FK implies that every LHS row has a match *in the
5399  * referenced table*; but any restriction clauses on it will
5400  * reduce the number of matches. Hence we take the join
5401  * selectivity as equal to the selectivity of the table's
5402  * restriction clauses, which is rows / tuples; but we must guard
5403  * against tuples == 0.
5404  */
5405  RelOptInfo *ref_rel = find_base_rel(root, fkinfo->ref_relid);
5406  double ref_tuples = Max(ref_rel->tuples, 1.0);
5407 
5408  fkselec *= ref_rel->rows / ref_tuples;
5409  }
5410  else
5411  {
5412  /*
5413  * Otherwise, selectivity is exactly 1/referenced-table-size; but
5414  * guard against tuples == 0. Note we should use the raw table
5415  * tuple count, not any estimate of its filtered or joined size.
5416  */
5417  RelOptInfo *ref_rel = find_base_rel(root, fkinfo->ref_relid);
5418  double ref_tuples = Max(ref_rel->tuples, 1.0);
5419 
5420  fkselec *= 1.0 / ref_tuples;
5421  }
5422 
5423  /*
5424  * If any of the FK columns participated in ec_has_const ECs, then
5425  * equivclass.c will have generated "var = const" restrictions for
5426  * each side of the join, thus reducing the sizes of both input
5427  * relations. Taking the fkselec at face value would amount to
5428  * double-counting the selectivity of the constant restriction for the
5429  * referencing Var. Hence, look for the restriction clause(s) that
5430  * were applied to the referencing Var(s), and divide out their
5431  * selectivity to correct for this.
5432  */
5433  if (fkinfo->nconst_ec > 0)
5434  {
5435  for (int i = 0; i < fkinfo->nkeys; i++)
5436  {
5437  EquivalenceClass *ec = fkinfo->eclass[i];
5438 
5439  if (ec && ec->ec_has_const)
5440  {
5441  EquivalenceMember *em = fkinfo->fk_eclass_member[i];
5443  em);
5444 
5445  if (rinfo)
5446  {
5447  Selectivity s0;
5448 
5449  s0 = clause_selectivity(root,
5450  (Node *) rinfo,
5451  0,
5452  jointype,
5453  sjinfo);
5454  if (s0 > 0)
5455  fkselec /= s0;
5456  }
5457  }
5458  }
5459  }
5460  }
5461 
5462  *restrictlist = worklist;
5463  CLAMP_PROBABILITY(fkselec);
5464  return fkselec;
5465 }
5466 
5467 /*
5468  * set_subquery_size_estimates
5469  * Set the size estimates for a base relation that is a subquery.
5470  *
5471  * The rel's targetlist and restrictinfo list must have been constructed
5472  * already, and the Paths for the subquery must have been completed.
5473  * We look at the subquery's PlannerInfo to extract data.
5474  *
5475  * We set the same fields as set_baserel_size_estimates.
5476  */
5477 void
5479 {
5480  PlannerInfo *subroot = rel->subroot;
5481  RelOptInfo *sub_final_rel;
5482  ListCell *lc;
5483 
5484  /* Should only be applied to base relations that are subqueries */
5485  Assert(rel->relid > 0);
5486  Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_SUBQUERY);
5487 
5488  /*
5489  * Copy raw number of output rows from subquery. All of its paths should
5490  * have the same output rowcount, so just look at cheapest-total.
5491  */
5492  sub_final_rel = fetch_upper_rel(subroot, UPPERREL_FINAL, NULL);
5493  rel->tuples = sub_final_rel->cheapest_total_path->rows;
5494 
5495  /*
5496  * Compute per-output-column width estimates by examining the subquery's
5497  * targetlist. For any output that is a plain Var, get the width estimate
5498  * that was made while planning the subquery. Otherwise, we leave it to
5499  * set_rel_width to fill in a datatype-based default estimate.
5500  */
5501  foreach(lc, subroot->parse->targetList)
5502  {
5503  TargetEntry *te = lfirst_node(TargetEntry, lc);
5504  Node *texpr = (Node *) te->expr;
5505  int32 item_width = 0;
5506 
5507  /* junk columns aren't visible to upper query */
5508  if (te->resjunk)
5509  continue;
5510 
5511  /*
5512  * The subquery could be an expansion of a view that's had columns
5513  * added to it since the current query was parsed, so that there are
5514  * non-junk tlist columns in it that don't correspond to any column
5515  * visible at our query level. Ignore such columns.
5516  */
5517  if (te->resno < rel->min_attr || te->resno > rel->max_attr)
5518  continue;
5519 
5520  /*
5521  * XXX This currently doesn't work for subqueries containing set
5522  * operations, because the Vars in their tlists are bogus references
5523  * to the first leaf subquery, which wouldn't give the right answer
5524  * even if we could still get to its PlannerInfo.
5525  *
5526  * Also, the subquery could be an appendrel for which all branches are
5527  * known empty due to constraint exclusion, in which case
5528  * set_append_rel_pathlist will have left the attr_widths set to zero.
5529  *
5530  * In either case, we just leave the width estimate zero until
5531  * set_rel_width fixes it.
5532  */
5533  if (IsA(texpr, Var) &&
5534  subroot->parse->setOperations == NULL)
5535  {
5536  Var *var = (Var *) texpr;
5537  RelOptInfo *subrel = find_base_rel(subroot, var->varno);
5538 
5539  item_width = subrel->attr_widths[var->varattno - subrel->min_attr];
5540  }
5541  rel->attr_widths[te->resno - rel->min_attr] = item_width;
5542  }
5543 
5544  /* Now estimate number of output rows, etc */
5545  set_baserel_size_estimates(root, rel);
5546 }
5547 
5548 /*
5549  * set_function_size_estimates
5550  * Set the size estimates for a base relation that is a function call.
5551  *
5552  * The rel's targetlist and restrictinfo list must have been constructed
5553  * already.
5554  *
5555  * We set the same fields as set_baserel_size_estimates.
5556  */
5557 void
5559 {
5560  RangeTblEntry *rte;
5561  ListCell *lc;
5562 
5563  /* Should only be applied to base relations that are functions */
5564  Assert(rel->relid > 0);
5565  rte = planner_rt_fetch(rel->relid, root);
5566  Assert(rte->rtekind == RTE_FUNCTION);
5567 
5568  /*
5569  * Estimate number of rows the functions will return. The rowcount of the
5570  * node is that of the largest function result.
5571  */
5572  rel->tuples = 0;
5573  foreach(lc, rte->functions)
5574  {
5575  RangeTblFunction *rtfunc = (RangeTblFunction *) lfirst(lc);
5576  double ntup = expression_returns_set_rows(root, rtfunc->funcexpr);
5577 
5578  if (ntup > rel->tuples)
5579  rel->tuples = ntup;
5580  }
5581 
5582  /* Now estimate number of output rows, etc */
5583  set_baserel_size_estimates(root, rel);
5584 }
5585 
5586 /*
5587  * set_function_size_estimates
5588  * Set the size estimates for a base relation that is a function call.
5589  *
5590  * The rel's targetlist and restrictinfo list must have been constructed
5591  * already.
5592  *
5593  * We set the same fields as set_tablefunc_size_estimates.
5594  */
5595 void
5597 {
5598  /* Should only be applied to base relations that are functions */
5599  Assert(rel->relid > 0);
5600  Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_TABLEFUNC);
5601 
5602  rel->tuples = 100;
5603 
5604  /* Now estimate number of output rows, etc */
5605  set_baserel_size_estimates(root, rel);
5606 }
5607 
5608 /*
5609  * set_values_size_estimates
5610  * Set the size estimates for a base relation that is a values list.
5611  *
5612  * The rel's targetlist and restrictinfo list must have been constructed
5613  * already.
5614  *
5615  * We set the same fields as set_baserel_size_estimates.
5616  */
5617 void
5619 {
5620  RangeTblEntry *rte;
5621 
5622  /* Should only be applied to base relations that are values lists */
5623  Assert(rel->relid > 0);
5624  rte = planner_rt_fetch(rel->relid, root);
5625  Assert(rte->rtekind == RTE_VALUES);
5626 
5627  /*
5628  * Estimate number of rows the values list will return. We know this
5629  * precisely based on the list length (well, barring set-returning
5630  * functions in list items, but that's a refinement not catered for
5631  * anywhere else either).
5632  */
5633  rel->tuples = list_length(rte->values_lists);
5634 
5635  /* Now estimate number of output rows, etc */
5636  set_baserel_size_estimates(root, rel);
5637 }
5638 
5639 /*
5640  * set_cte_size_estimates
5641  * Set the size estimates for a base relation that is a CTE reference.
5642  *
5643  * The rel's targetlist and restrictinfo list must have been constructed
5644  * already, and we need an estimate of the number of rows returned by the CTE
5645  * (if a regular CTE) or the non-recursive term (if a self-reference).
5646  *
5647  * We set the same fields as set_baserel_size_estimates.
5648  */
5649 void
5650 set_cte_size_estimates(PlannerInfo *root, RelOptInfo *rel, double cte_rows)
5651 {
5652  RangeTblEntry *rte;
5653 
5654  /* Should only be applied to base relations that are CTE references */
5655  Assert(rel->relid > 0);
5656  rte = planner_rt_fetch(rel->relid, root);
5657  Assert(rte->rtekind == RTE_CTE);
5658 
5659  if (rte->self_reference)
5660  {
5661  /*
5662  * In a self-reference, arbitrarily assume the average worktable size
5663  * is about 10 times the nonrecursive term's size.
5664  */
5665  rel->tuples = 10 * cte_rows;
5666  }
5667  else
5668  {
5669  /* Otherwise just believe the CTE's rowcount estimate */
5670  rel->tuples = cte_rows;
5671  }
5672 
5673  /* Now estimate number of output rows, etc */
5674  set_baserel_size_estimates(root, rel);
5675 }
5676 
5677 /*
5678  * set_namedtuplestore_size_estimates
5679  * Set the size estimates for a base relation that is a tuplestore reference.
5680  *
5681  * The rel's targetlist and restrictinfo list must have been constructed
5682  * already.
5683  *
5684  * We set the same fields as set_baserel_size_estimates.
5685  */
5686 void
5688 {
5689  RangeTblEntry *rte;
5690 
5691  /* Should only be applied to base relations that are tuplestore references */
5692  Assert(rel->relid > 0);
5693  rte = planner_rt_fetch(rel->relid, root);
5695 
5696  /*
5697  * Use the estimate provided by the code which is generating the named
5698  * tuplestore. In some cases, the actual number might be available; in
5699  * others the same plan will be re-used, so a "typical" value might be
5700  * estimated and used.
5701  */
5702  rel->tuples = rte->enrtuples;
5703  if (rel->tuples < 0)
5704  rel->tuples = 1000;
5705 
5706  /* Now estimate number of output rows, etc */
5707  set_baserel_size_estimates(root, rel);
5708 }
5709 
5710 /*
5711  * set_result_size_estimates
5712  * Set the size estimates for an RTE_RESULT base relation
5713  *
5714  * The rel's targetlist and restrictinfo list must have been constructed
5715  * already.
5716  *
5717  * We set the same fields as set_baserel_size_estimates.
5718  */
5719 void
5721 {
5722  /* Should only be applied to RTE_RESULT base relations */
5723  Assert(rel->relid > 0);
5724  Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_RESULT);
5725 
5726  /* RTE_RESULT always generates a single row, natively */
5727  rel->tuples = 1;
5728 
5729  /* Now estimate number of output rows, etc */
5730  set_baserel_size_estimates(root, rel);
5731 }
5732 
5733 /*
5734  * set_foreign_size_estimates
5735  * Set the size estimates for a base relation that is a foreign table.
5736  *
5737  * There is not a whole lot that we can do here; the foreign-data wrapper
5738  * is responsible for producing useful estimates. We can do a decent job
5739  * of estimating baserestrictcost, so we set that, and we also set up width
5740  * using what will be purely datatype-driven estimates from the targetlist.
5741  * There is no way to do anything sane with the rows value, so we just put
5742  * a default estimate and hope that the wrapper can improve on it. The
5743  * wrapper's GetForeignRelSize function will be called momentarily.
5744  *
5745  * The rel's targetlist and restrictinfo list must have been constructed
5746  * already.
5747  */
5748 void
5750 {
5751  /* Should only be applied to base relations */
5752  Assert(rel->relid > 0);
5753 
5754  rel->rows = 1000; /* entirely bogus default estimate */
5755 
5757 
5758  set_rel_width(root, rel);
5759 }
5760 
5761 
5762 /*
5763  * set_rel_width
5764  * Set the estimated output width of a base relation.
5765  *
5766  * The estimated output width is the sum of the per-attribute width estimates
5767  * for the actually-referenced columns, plus any PHVs or other expressions
5768  * that have to be calculated at this relation. This is the amount of data
5769  * we'd need to pass upwards in case of a sort, hash, etc.
5770  *
5771  * This function also sets reltarget->cost, so it's a bit misnamed now.
5772  *
5773  * NB: this works best on plain relations because it prefers to look at
5774  * real Vars. For subqueries, set_subquery_size_estimates will already have
5775  * copied up whatever per-column estimates were made within the subquery,
5776  * and for other types of rels there isn't much we can do anyway. We fall
5777  * back on (fairly stupid) datatype-based width estimates if we can't get
5778  * any better number.
5779  *
5780  * The per-attribute width estimates are cached for possible re-use while
5781  * building join relations or post-scan/join pathtargets.
5782  */
5783 static void
5785 {
5786  Oid reloid = planner_rt_fetch(rel->relid, root)->relid;
5787  int32 tuple_width = 0;
5788  bool have_wholerow_var = false;
5789  ListCell *lc;
5790 
5791  /* Vars are assumed to have cost zero, but other exprs do not */
5792  rel->reltarget->cost.startup = 0;
5793  rel->reltarget->cost.per_tuple = 0;
5794 
5795  foreach(lc, rel->reltarget->exprs)
5796  {
5797  Node *node = (Node *) lfirst(lc);
5798 
5799  /*
5800  * Ordinarily, a Var in a rel's targetlist must belong to that rel;
5801  * but there are corner cases involving LATERAL references where that
5802  * isn't so. If the Var has the wrong varno, fall through to the
5803  * generic case (it doesn't seem worth the trouble to be any smarter).
5804  */
5805  if (IsA(node, Var) &&
5806  ((Var *) node)->varno == rel->relid)
5807  {
5808  Var *var = (Var *) node;
5809  int ndx;
5810  int32 item_width;
5811 
5812  Assert(var->varattno >= rel->min_attr);
5813  Assert(var->varattno <= rel->max_attr);
5814 
5815  ndx = var->varattno - rel->min_attr;
5816 
5817  /*
5818  * If it's a whole-row Var, we'll deal with it below after we have
5819  * already cached as many attr widths as possible.
5820  */
5821  if (var->varattno == 0)
5822  {
5823  have_wholerow_var = true;
5824  continue;
5825  }
5826 
5827  /*
5828  * The width may have been cached already (especially if it's a
5829  * subquery), so don't duplicate effort.
5830  */
5831  if (rel->attr_widths[ndx] > 0)
5832  {
5833  tuple_width += rel->attr_widths[ndx];
5834  continue;
5835  }
5836 
5837  /* Try to get column width from statistics */
5838  if (reloid != InvalidOid && var->varattno > 0)
5839  {
5840  item_width = get_attavgwidth(reloid, var->varattno);
5841  if (item_width > 0)
5842  {
5843  rel->attr_widths[ndx] = item_width;
5844  tuple_width += item_width;
5845  continue;
5846  }
5847  }
5848 
5849  /*
5850  * Not a plain relation, or can't find statistics for it. Estimate
5851  * using just the type info.
5852  */
5853  item_width = get_typavgwidth(var->vartype, var->vartypmod);
5854  Assert(item_width > 0);
5855  rel->attr_widths[ndx] = item_width;
5856  tuple_width += item_width;
5857  }
5858  else if (IsA(node, PlaceHolderVar))
5859  {
5860  /*
5861  * We will need to evaluate the PHV's contained expression while
5862  * scanning this rel, so be sure to include it in reltarget->cost.
5863  */
5864  PlaceHolderVar *phv = (PlaceHolderVar *) node;
5865  PlaceHolderInfo *phinfo = find_placeholder_info(root, phv, false);
5866  QualCost cost;
5867 
5868  tuple_width += phinfo->ph_width;
5869  cost_qual_eval_node(&cost, (Node *) phv->phexpr, root);
5870  rel->reltarget->cost.startup += cost.startup;
5871  rel->reltarget->cost.per_tuple += cost.per_tuple;
5872  }
5873  else
5874  {
5875  /*
5876  * We could be looking at an expression pulled up from a subquery,
5877  * or a ROW() representing a whole-row child Var, etc. Do what we
5878  * can using the expression type information.
5879  */
5880  int32 item_width;
5881  QualCost cost;
5882 
5883  item_width = get_typavgwidth(exprType(node), exprTypmod(node));
5884  Assert(item_width > 0);
5885  tuple_width += item_width;
5886  /* Not entirely clear if we need to account for cost, but do so */
5887  cost_qual_eval_node(&cost, node, root);
5888  rel->reltarget->cost.startup += cost.startup;
5889  rel->reltarget->cost.per_tuple += cost.per_tuple;
5890  }
5891  }
5892 
5893  /*
5894  * If we have a whole-row reference, estimate its width as the sum of
5895  * per-column widths plus heap tuple header overhead.
5896  */
5897  if (have_wholerow_var)
5898  {
5899  int32 wholerow_width = MAXALIGN(SizeofHeapTupleHeader);
5900 
5901  if (reloid != InvalidOid)
5902  {
5903  /* Real relation, so estimate true tuple width */
5904  wholerow_width += get_relation_data_width(reloid,
5905  rel->attr_widths - rel->min_attr);
5906  }
5907  else
5908  {
5909  /* Do what we can with info for a phony rel */
5910  AttrNumber i;
5911 
5912  for (i = 1; i <= rel->max_attr; i++)
5913  wholerow_width += rel->attr_widths[i - rel->min_attr];
5914  }
5915 
5916  rel->attr_widths[0 - rel->min_attr] = wholerow_width;
5917 
5918  /*
5919  * Include the whole-row Var as part of the output tuple. Yes, that
5920  * really is what happens at runtime.
5921  */
5922  tuple_width += wholerow_width;
5923  }
5924 
5925  Assert(tuple_width >= 0);
5926  rel->reltarget->width = tuple_width;
5927 }
5928 
5929 /*
5930  * set_pathtarget_cost_width
5931  * Set the estimated eval cost and output width of a PathTarget tlist.
5932  *
5933  * As a notational convenience, returns the same PathTarget pointer passed in.
5934  *
5935  * Most, though not quite all, uses of this function occur after we've run
5936  * set_rel_width() for base relations; so we can usually obtain cached width
5937  * estimates for Vars. If we can't, fall back on datatype-based width
5938  * estimates. Present early-planning uses of PathTargets don't need accurate
5939  * widths badly enough to justify going to the catalogs for better data.
5940  */
5941 PathTarget *
5943 {
5944  int32 tuple_width = 0;
5945  ListCell *lc;
5946 
5947  /* Vars are assumed to have cost zero, but other exprs do not */
5948  target->cost.startup = 0;
5949  target->cost.per_tuple = 0;
5950 
5951  foreach(lc, target->exprs)
5952  {
5953  Node *node = (Node *) lfirst(lc);