1 /*-------------------------------------------------------------------------
4 * Routines to compute (and set) relation sizes and path costs
6 * Path costs are measured in units of disk accesses: one sequential page
7 * fetch has cost 1. All else is scaled relative to a page fetch, using
8 * the scaling parameters
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 process a typical WHERE operator
15 * We also use a rough estimate "effective_cache_size" of the number of
16 * disk pages in Postgres + OS-level disk cache. (We can't simply use
17 * NBuffers for this purpose because that would ignore the effects of
18 * the kernel's disk cache.)
20 * Obviously, taking constants for these values is an oversimplification,
21 * but it's tough enough to get any useful estimates even at this level of
22 * detail. Note that all of these parameters are user-settable, in case
23 * the default values are drastically off for a particular platform.
25 * We compute two separate costs for each path:
26 * total_cost: total estimated cost to fetch all tuples
27 * startup_cost: cost that is expended before first tuple is fetched
28 * In some scenarios, such as when there is a LIMIT or we are implementing
29 * an EXISTS(...) sub-select, it is not necessary to fetch all tuples of the
30 * path's result. A caller can estimate the cost of fetching a partial
31 * result by interpolating between startup_cost and total_cost. In detail:
32 * actual_cost = startup_cost +
33 * (total_cost - startup_cost) * tuples_to_fetch / path->parent->rows;
34 * Note that a base relation's rows count (and, by extension, plan_rows for
35 * plan nodes below the LIMIT node) are set without regard to any LIMIT, so
36 * that this equation works properly. (Also, these routines guarantee not to
37 * set the rows count to zero, so there will be no zero divide.) The LIMIT is
38 * applied as a top-level plan node.
40 * For largely historical reasons, most of the routines in this module use
41 * the passed result Path only to store their startup_cost and total_cost
42 * results into. All the input data they need is passed as separate
43 * parameters, even though much of it could be extracted from the Path.
44 * An exception is made for the cost_XXXjoin() routines, which expect all
45 * the non-cost fields of the passed XXXPath to be filled in.
48 * Portions Copyright (c) 1996-2005, PostgreSQL Global Development Group
49 * Portions Copyright (c) 1994, Regents of the University of California
52 * $PostgreSQL: pgsql/src/backend/optimizer/path/costsize.c,v 1.141 2005/04/04 01:43:12 tgl Exp $
54 *-------------------------------------------------------------------------
61 #include "catalog/pg_statistic.h"
62 #include "executor/nodeHash.h"
63 #include "miscadmin.h"
64 #include "optimizer/clauses.h"
65 #include "optimizer/cost.h"
66 #include "optimizer/pathnode.h"
67 #include "optimizer/plancat.h"
68 #include "parser/parsetree.h"
69 #include "utils/selfuncs.h"
70 #include "utils/lsyscache.h"
71 #include "utils/syscache.h"
74 #define LOG2(x) (log(x) / 0.693147180559945)
75 #define LOG6(x) (log(x) / 1.79175946922805)
78 * Some Paths return less than the nominal number of rows of their parent
79 * relations; join nodes need to do this to get the correct input count:
81 #define PATH_ROWS(path) \
82 (IsA(path, UniquePath) ? \
83 ((UniquePath *) (path))->rows : \
87 double effective_cache_size = DEFAULT_EFFECTIVE_CACHE_SIZE;
88 double random_page_cost = DEFAULT_RANDOM_PAGE_COST;
89 double cpu_tuple_cost = DEFAULT_CPU_TUPLE_COST;
90 double cpu_index_tuple_cost = DEFAULT_CPU_INDEX_TUPLE_COST;
91 double cpu_operator_cost = DEFAULT_CPU_OPERATOR_COST;
93 Cost disable_cost = 100000000.0;
95 bool enable_seqscan = true;
96 bool enable_indexscan = true;
97 bool enable_tidscan = true;
98 bool enable_sort = true;
99 bool enable_hashagg = true;
100 bool enable_nestloop = true;
101 bool enable_mergejoin = true;
102 bool enable_hashjoin = true;
105 static bool cost_qual_eval_walker(Node *node, QualCost *total);
106 static Selectivity approx_selectivity(Query *root, List *quals,
108 static Selectivity join_in_selectivity(JoinPath *path, Query *root);
109 static void set_rel_width(Query *root, RelOptInfo *rel);
110 static double relation_byte_size(double tuples, int width);
111 static double page_size(double tuples, int width);
116 * Force a row-count estimate to a sane value.
119 clamp_row_est(double nrows)
122 * Force estimate to be at least one row, to make explain output look
123 * better and to avoid possible divide-by-zero when interpolating
124 * costs. Make it an integer, too.
137 * Determines and returns the cost of scanning a relation sequentially.
140 cost_seqscan(Path *path, Query *root,
143 Cost startup_cost = 0;
147 /* Should only be applied to base relations */
148 Assert(baserel->relid > 0);
149 Assert(baserel->rtekind == RTE_RELATION);
152 startup_cost += disable_cost;
157 * The cost of reading a page sequentially is 1.0, by definition. Note
158 * that the Unix kernel will typically do some amount of read-ahead
159 * optimization, so that this cost is less than the true cost of
160 * reading a page from disk. We ignore that issue here, but must take
161 * it into account when estimating the cost of non-sequential
164 run_cost += baserel->pages; /* sequential fetches with cost 1.0 */
167 startup_cost += baserel->baserestrictcost.startup;
168 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
169 run_cost += cpu_per_tuple * baserel->tuples;
171 path->startup_cost = startup_cost;
172 path->total_cost = startup_cost + run_cost;
176 * cost_nonsequential_access
177 * Estimate the cost of accessing one page at random from a relation
178 * (or sort temp file) of the given size in pages.
180 * The simplistic model that the cost is random_page_cost is what we want
181 * to use for large relations; but for small ones that is a serious
182 * overestimate because of the effects of caching. This routine tries to
185 * Unfortunately we don't have any good way of estimating the effective cache
186 * size we are working with --- we know that Postgres itself has NBuffers
187 * internal buffers, but the size of the kernel's disk cache is uncertain,
188 * and how much of it we get to use is even less certain. We punt the problem
189 * for now by assuming we are given an effective_cache_size parameter.
191 * Given a guesstimated cache size, we estimate the actual I/O cost per page
192 * with the entirely ad-hoc equations (writing relsize for
193 * relpages/effective_cache_size):
195 * random_page_cost - (random_page_cost-1)/2 * (1/relsize)
197 * 1 + ((random_page_cost-1)/2) * relsize ** 2
198 * These give the right asymptotic behavior (=> 1.0 as relpages becomes
199 * small, => random_page_cost as it becomes large) and meet in the middle
200 * with the estimate that the cache is about 50% effective for a relation
201 * of the same size as effective_cache_size. (XXX this is probably all
202 * wrong, but I haven't been able to find any theory about how effective
203 * a disk cache should be presumed to be.)
206 cost_nonsequential_access(double relpages)
210 /* don't crash on bad input data */
211 if (relpages <= 0.0 || effective_cache_size <= 0.0)
212 return random_page_cost;
214 relsize = relpages / effective_cache_size;
217 return random_page_cost - (random_page_cost - 1.0) * 0.5 / relsize;
219 return 1.0 + (random_page_cost - 1.0) * 0.5 * relsize * relsize;
224 * Determines and returns the cost of scanning a relation using an index.
226 * NOTE: an indexscan plan node can actually represent several passes,
227 * but here we consider the cost of just one pass.
229 * 'root' is the query root
230 * 'index' is the index to be used
231 * 'indexQuals' is the list of applicable qual clauses (implicit AND semantics)
232 * 'is_injoin' is T if we are considering using the index scan as the inside
233 * of a nestloop join (hence, some of the indexQuals are join clauses)
235 * NOTE: 'indexQuals' must contain only clauses usable as index restrictions.
236 * Any additional quals evaluated as qpquals may reduce the number of returned
237 * tuples, but they won't reduce the number of tuples we have to fetch from
238 * the table, so they don't reduce the scan cost.
240 * NOTE: as of 8.0, indexQuals is a list of RestrictInfo nodes, where formerly
241 * it was a list of bare clause expressions.
244 cost_index(Path *path, Query *root,
249 RelOptInfo *baserel = index->rel;
250 Cost startup_cost = 0;
252 Cost indexStartupCost;
254 Selectivity indexSelectivity;
255 double indexCorrelation,
260 double tuples_fetched;
261 double pages_fetched;
265 /* Should only be applied to base relations */
266 Assert(IsA(baserel, RelOptInfo) &&
267 IsA(index, IndexOptInfo));
268 Assert(baserel->relid > 0);
269 Assert(baserel->rtekind == RTE_RELATION);
271 if (!enable_indexscan)
272 startup_cost += disable_cost;
275 * Call index-access-method-specific code to estimate the processing
276 * cost for scanning the index, as well as the selectivity of the
277 * index (ie, the fraction of main-table tuples we will have to
278 * retrieve) and its correlation to the main-table tuple order.
280 OidFunctionCall7(index->amcostestimate,
281 PointerGetDatum(root),
282 PointerGetDatum(index),
283 PointerGetDatum(indexQuals),
284 PointerGetDatum(&indexStartupCost),
285 PointerGetDatum(&indexTotalCost),
286 PointerGetDatum(&indexSelectivity),
287 PointerGetDatum(&indexCorrelation));
289 /* all costs for touching index itself included here */
290 startup_cost += indexStartupCost;
291 run_cost += indexTotalCost - indexStartupCost;
294 * Estimate number of main-table tuples and pages fetched.
296 * When the index ordering is uncorrelated with the table ordering,
297 * we use an approximation proposed by Mackert and Lohman, "Index Scans
298 * Using a Finite LRU Buffer: A Validated I/O Model", ACM Transactions
299 * on Database Systems, Vol. 14, No. 3, September 1989, Pages 401-424.
300 * The Mackert and Lohman approximation is that the number of pages
303 * min(2TNs/(2T+Ns), T) when T <= b
304 * 2TNs/(2T+Ns) when T > b and Ns <= 2Tb/(2T-b)
305 * b + (Ns - 2Tb/(2T-b))*(T-b)/T when T > b and Ns > 2Tb/(2T-b)
307 * T = # pages in table
308 * N = # tuples in table
309 * s = selectivity = fraction of table to be scanned
310 * b = # buffer pages available (we include kernel space here)
312 * When the index ordering is exactly correlated with the table ordering
313 * (just after a CLUSTER, for example), the number of pages fetched should
314 * be just sT. What's more, these will be sequential fetches, not the
315 * random fetches that occur in the uncorrelated case. So, depending on
316 * the extent of correlation, we should estimate the actual I/O cost
317 * somewhere between s * T * 1.0 and PF * random_cost. We currently
318 * interpolate linearly between these two endpoints based on the
319 * correlation squared (XXX is that appropriate?).
321 * In any case the number of tuples fetched is Ns.
325 tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);
327 /* This part is the Mackert and Lohman formula */
329 T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
330 b = (effective_cache_size > 1) ? effective_cache_size : 1.0;
335 (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
336 if (pages_fetched > T)
343 lim = (2.0 * T * b) / (2.0 * T - b);
344 if (tuples_fetched <= lim)
347 (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
352 b + (tuples_fetched - lim) * (T - b) / T;
357 * min_IO_cost corresponds to the perfectly correlated case
358 * (csquared=1), max_IO_cost to the perfectly uncorrelated case
359 * (csquared=0). Note that we just charge random_page_cost per page
360 * in the uncorrelated case, rather than using
361 * cost_nonsequential_access, since we've already accounted for
362 * caching effects by using the Mackert model.
364 min_IO_cost = ceil(indexSelectivity * T);
365 max_IO_cost = pages_fetched * random_page_cost;
368 * Now interpolate based on estimated index order correlation to get
369 * total disk I/O cost for main table accesses.
371 csquared = indexCorrelation * indexCorrelation;
373 run_cost += max_IO_cost + csquared * (min_IO_cost - max_IO_cost);
376 * Estimate CPU costs per tuple.
378 * Normally the indexquals will be removed from the list of restriction
379 * clauses that we have to evaluate as qpquals, so we should subtract
380 * their costs from baserestrictcost. But if we are doing a join then
381 * some of the indexquals are join clauses and shouldn't be
382 * subtracted. Rather than work out exactly how much to subtract, we
383 * don't subtract anything.
385 startup_cost += baserel->baserestrictcost.startup;
386 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
390 QualCost index_qual_cost;
392 cost_qual_eval(&index_qual_cost, indexQuals);
393 /* any startup cost still has to be paid ... */
394 cpu_per_tuple -= index_qual_cost.per_tuple;
397 run_cost += cpu_per_tuple * tuples_fetched;
399 path->startup_cost = startup_cost;
400 path->total_cost = startup_cost + run_cost;
405 * Determines and returns the cost of scanning a relation using TIDs.
408 cost_tidscan(Path *path, Query *root,
409 RelOptInfo *baserel, List *tideval)
411 Cost startup_cost = 0;
414 int ntuples = list_length(tideval);
416 /* Should only be applied to base relations */
417 Assert(baserel->relid > 0);
418 Assert(baserel->rtekind == RTE_RELATION);
421 startup_cost += disable_cost;
423 /* disk costs --- assume each tuple on a different page */
424 run_cost += random_page_cost * ntuples;
427 startup_cost += baserel->baserestrictcost.startup;
428 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
429 run_cost += cpu_per_tuple * ntuples;
431 path->startup_cost = startup_cost;
432 path->total_cost = startup_cost + run_cost;
437 * Determines and returns the cost of scanning a subquery RTE.
440 cost_subqueryscan(Path *path, RelOptInfo *baserel)
446 /* Should only be applied to base relations that are subqueries */
447 Assert(baserel->relid > 0);
448 Assert(baserel->rtekind == RTE_SUBQUERY);
451 * Cost of path is cost of evaluating the subplan, plus cost of
452 * evaluating any restriction clauses that will be attached to the
453 * SubqueryScan node, plus cpu_tuple_cost to account for selection and
454 * projection overhead.
456 path->startup_cost = baserel->subplan->startup_cost;
457 path->total_cost = baserel->subplan->total_cost;
459 startup_cost = baserel->baserestrictcost.startup;
460 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
461 run_cost = cpu_per_tuple * baserel->tuples;
463 path->startup_cost += startup_cost;
464 path->total_cost += startup_cost + run_cost;
469 * Determines and returns the cost of scanning a function RTE.
472 cost_functionscan(Path *path, Query *root, RelOptInfo *baserel)
474 Cost startup_cost = 0;
478 /* Should only be applied to base relations that are functions */
479 Assert(baserel->relid > 0);
480 Assert(baserel->rtekind == RTE_FUNCTION);
483 * For now, estimate function's cost at one operator eval per function
484 * call. Someday we should revive the function cost estimate columns
487 cpu_per_tuple = cpu_operator_cost;
489 /* Add scanning CPU costs */
490 startup_cost += baserel->baserestrictcost.startup;
491 cpu_per_tuple += cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
492 run_cost += cpu_per_tuple * baserel->tuples;
494 path->startup_cost = startup_cost;
495 path->total_cost = startup_cost + run_cost;
500 * Determines and returns the cost of sorting a relation, including
501 * the cost of reading the input data.
503 * If the total volume of data to sort is less than work_mem, we will do
504 * an in-memory sort, which requires no I/O and about t*log2(t) tuple
505 * comparisons for t tuples.
507 * If the total volume exceeds work_mem, we switch to a tape-style merge
508 * algorithm. There will still be about t*log2(t) tuple comparisons in
509 * total, but we will also need to write and read each tuple once per
510 * merge pass. We expect about ceil(log6(r)) merge passes where r is the
511 * number of initial runs formed (log6 because tuplesort.c uses six-tape
512 * merging). Since the average initial run should be about twice work_mem,
514 * disk traffic = 2 * relsize * ceil(log6(p / (2*work_mem)))
515 * cpu = comparison_cost * t * log2(t)
517 * The disk traffic is assumed to be half sequential and half random
518 * accesses (XXX can't we refine that guess?)
520 * We charge two operator evals per tuple comparison, which should be in
521 * the right ballpark in most cases.
523 * 'pathkeys' is a list of sort keys
524 * 'input_cost' is the total cost for reading the input data
525 * 'tuples' is the number of tuples in the relation
526 * 'width' is the average tuple width in bytes
528 * NOTE: some callers currently pass NIL for pathkeys because they
529 * can't conveniently supply the sort keys. Since this routine doesn't
530 * currently do anything with pathkeys anyway, that doesn't matter...
531 * but if it ever does, it should react gracefully to lack of key data.
532 * (Actually, the thing we'd most likely be interested in is just the number
533 * of sort keys, which all callers *could* supply.)
536 cost_sort(Path *path, Query *root,
537 List *pathkeys, Cost input_cost, double tuples, int width)
539 Cost startup_cost = input_cost;
541 double nbytes = relation_byte_size(tuples, width);
542 long work_mem_bytes = work_mem * 1024L;
545 startup_cost += disable_cost;
548 * We want to be sure the cost of a sort is never estimated as zero,
549 * even if passed-in tuple count is zero. Besides, mustn't do
558 * Assume about two operator evals per tuple comparison and N log2 N
561 startup_cost += 2.0 * cpu_operator_cost * tuples * LOG2(tuples);
564 if (nbytes > work_mem_bytes)
566 double npages = ceil(nbytes / BLCKSZ);
567 double nruns = (nbytes / work_mem_bytes) * 0.5;
568 double log_runs = ceil(LOG6(nruns));
569 double npageaccesses;
573 npageaccesses = 2.0 * npages * log_runs;
574 /* Assume half are sequential (cost 1), half are not */
575 startup_cost += npageaccesses *
576 (1.0 + cost_nonsequential_access(npages)) * 0.5;
580 * Also charge a small amount (arbitrarily set equal to operator cost)
581 * per extracted tuple.
583 run_cost += cpu_operator_cost * tuples;
585 path->startup_cost = startup_cost;
586 path->total_cost = startup_cost + run_cost;
591 * Determines and returns the cost of materializing a relation, including
592 * the cost of reading the input data.
594 * If the total volume of data to materialize exceeds work_mem, we will need
595 * to write it to disk, so the cost is much higher in that case.
598 cost_material(Path *path,
599 Cost input_cost, double tuples, int width)
601 Cost startup_cost = input_cost;
603 double nbytes = relation_byte_size(tuples, width);
604 long work_mem_bytes = work_mem * 1024L;
607 if (nbytes > work_mem_bytes)
609 double npages = ceil(nbytes / BLCKSZ);
611 /* We'll write during startup and read during retrieval */
612 startup_cost += npages;
617 * Charge a very small amount per inserted tuple, to reflect bookkeeping
618 * costs. We use cpu_tuple_cost/10 for this. This is needed to break
619 * the tie that would otherwise exist between nestloop with A outer,
620 * materialized B inner and nestloop with B outer, materialized A inner.
621 * The extra cost ensures we'll prefer materializing the smaller rel.
623 startup_cost += cpu_tuple_cost * 0.1 * tuples;
626 * Also charge a small amount per extracted tuple. We use
627 * cpu_tuple_cost so that it doesn't appear worthwhile to materialize
630 run_cost += cpu_tuple_cost * tuples;
632 path->startup_cost = startup_cost;
633 path->total_cost = startup_cost + run_cost;
638 * Determines and returns the cost of performing an Agg plan node,
639 * including the cost of its input.
641 * Note: when aggstrategy == AGG_SORTED, caller must ensure that input costs
642 * are for appropriately-sorted input.
645 cost_agg(Path *path, Query *root,
646 AggStrategy aggstrategy, int numAggs,
647 int numGroupCols, double numGroups,
648 Cost input_startup_cost, Cost input_total_cost,
655 * We charge one cpu_operator_cost per aggregate function per input
656 * tuple, and another one per output tuple (corresponding to transfn
657 * and finalfn calls respectively). If we are grouping, we charge an
658 * additional cpu_operator_cost per grouping column per input tuple
659 * for grouping comparisons.
661 * We will produce a single output tuple if not grouping, and a tuple per
664 * Note: in this cost model, AGG_SORTED and AGG_HASHED have exactly the
665 * same total CPU cost, but AGG_SORTED has lower startup cost. If the
666 * input path is already sorted appropriately, AGG_SORTED should be
667 * preferred (since it has no risk of memory overflow). This will
668 * happen as long as the computed total costs are indeed exactly equal
669 * --- but if there's roundoff error we might do the wrong thing. So
670 * be sure that the computations below form the same intermediate
671 * values in the same order.
673 if (aggstrategy == AGG_PLAIN)
675 startup_cost = input_total_cost;
676 startup_cost += cpu_operator_cost * (input_tuples + 1) * numAggs;
677 /* we aren't grouping */
678 total_cost = startup_cost;
680 else if (aggstrategy == AGG_SORTED)
682 /* Here we are able to deliver output on-the-fly */
683 startup_cost = input_startup_cost;
684 total_cost = input_total_cost;
685 /* calcs phrased this way to match HASHED case, see note above */
686 total_cost += cpu_operator_cost * input_tuples * numGroupCols;
687 total_cost += cpu_operator_cost * input_tuples * numAggs;
688 total_cost += cpu_operator_cost * numGroups * numAggs;
692 /* must be AGG_HASHED */
693 startup_cost = input_total_cost;
694 startup_cost += cpu_operator_cost * input_tuples * numGroupCols;
695 startup_cost += cpu_operator_cost * input_tuples * numAggs;
696 total_cost = startup_cost;
697 total_cost += cpu_operator_cost * numGroups * numAggs;
700 path->startup_cost = startup_cost;
701 path->total_cost = total_cost;
706 * Determines and returns the cost of performing a Group plan node,
707 * including the cost of its input.
709 * Note: caller must ensure that input costs are for appropriately-sorted
713 cost_group(Path *path, Query *root,
714 int numGroupCols, double numGroups,
715 Cost input_startup_cost, Cost input_total_cost,
721 startup_cost = input_startup_cost;
722 total_cost = input_total_cost;
725 * Charge one cpu_operator_cost per comparison per input tuple. We
726 * assume all columns get compared at most of the tuples.
728 total_cost += cpu_operator_cost * input_tuples * numGroupCols;
730 path->startup_cost = startup_cost;
731 path->total_cost = total_cost;
736 * Determines and returns the cost of joining two relations using the
737 * nested loop algorithm.
739 * 'path' is already filled in except for the cost fields
742 cost_nestloop(NestPath *path, Query *root)
744 Path *outer_path = path->outerjoinpath;
745 Path *inner_path = path->innerjoinpath;
746 Cost startup_cost = 0;
749 QualCost restrict_qual_cost;
750 double outer_path_rows = PATH_ROWS(outer_path);
751 double inner_path_rows = PATH_ROWS(inner_path);
753 Selectivity joininfactor;
756 * If inner path is an indexscan, be sure to use its estimated output
757 * row count, which may be lower than the restriction-clause-only row
758 * count of its parent. (We don't include this case in the PATH_ROWS
759 * macro because it applies *only* to a nestloop's inner relation.)
761 if (IsA(inner_path, IndexPath))
762 inner_path_rows = ((IndexPath *) inner_path)->rows;
764 if (!enable_nestloop)
765 startup_cost += disable_cost;
768 * If we're doing JOIN_IN then we will stop scanning inner tuples for
769 * an outer tuple as soon as we have one match. Account for the
770 * effects of this by scaling down the cost estimates in proportion to
771 * the JOIN_IN selectivity. (This assumes that all the quals attached
772 * to the join are IN quals, which should be true.)
774 joininfactor = join_in_selectivity(path, root);
776 /* cost of source data */
779 * NOTE: clearly, we must pay both outer and inner paths' startup_cost
780 * before we can start returning tuples, so the join's startup cost is
781 * their sum. What's not so clear is whether the inner path's
782 * startup_cost must be paid again on each rescan of the inner path.
783 * This is not true if the inner path is materialized or is a
784 * hashjoin, but probably is true otherwise.
786 startup_cost += outer_path->startup_cost + inner_path->startup_cost;
787 run_cost += outer_path->total_cost - outer_path->startup_cost;
788 if (IsA(inner_path, MaterialPath) ||
789 IsA(inner_path, HashPath))
791 /* charge only run cost for each iteration of inner path */
796 * charge startup cost for each iteration of inner path, except we
797 * already charged the first startup_cost in our own startup
799 run_cost += (outer_path_rows - 1) * inner_path->startup_cost;
801 run_cost += outer_path_rows *
802 (inner_path->total_cost - inner_path->startup_cost) * joininfactor;
805 * Compute number of tuples processed (not number emitted!)
807 ntuples = outer_path_rows * inner_path_rows * joininfactor;
810 cost_qual_eval(&restrict_qual_cost, path->joinrestrictinfo);
811 startup_cost += restrict_qual_cost.startup;
812 cpu_per_tuple = cpu_tuple_cost + restrict_qual_cost.per_tuple;
813 run_cost += cpu_per_tuple * ntuples;
815 path->path.startup_cost = startup_cost;
816 path->path.total_cost = startup_cost + run_cost;
821 * Determines and returns the cost of joining two relations using the
822 * merge join algorithm.
824 * 'path' is already filled in except for the cost fields
826 * Notes: path's mergeclauses should be a subset of the joinrestrictinfo list;
827 * outersortkeys and innersortkeys are lists of the keys to be used
828 * to sort the outer and inner relations, or NIL if no explicit
829 * sort is needed because the source path is already ordered.
832 cost_mergejoin(MergePath *path, Query *root)
834 Path *outer_path = path->jpath.outerjoinpath;
835 Path *inner_path = path->jpath.innerjoinpath;
836 List *mergeclauses = path->path_mergeclauses;
837 List *outersortkeys = path->outersortkeys;
838 List *innersortkeys = path->innersortkeys;
839 Cost startup_cost = 0;
842 Selectivity merge_selec;
843 QualCost merge_qual_cost;
844 QualCost qp_qual_cost;
845 RestrictInfo *firstclause;
846 double outer_path_rows = PATH_ROWS(outer_path);
847 double inner_path_rows = PATH_ROWS(inner_path);
850 double mergejointuples,
853 Selectivity outerscansel,
855 Selectivity joininfactor;
856 Path sort_path; /* dummy for result of cost_sort */
858 if (!enable_mergejoin)
859 startup_cost += disable_cost;
862 * Compute cost and selectivity of the mergequals and qpquals (other
863 * restriction clauses) separately. We use approx_selectivity here
864 * for speed --- in most cases, any errors won't affect the result
867 * Note: it's probably bogus to use the normal selectivity calculation
868 * here when either the outer or inner path is a UniquePath.
870 merge_selec = approx_selectivity(root, mergeclauses,
871 path->jpath.jointype);
872 cost_qual_eval(&merge_qual_cost, mergeclauses);
873 cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo);
874 qp_qual_cost.startup -= merge_qual_cost.startup;
875 qp_qual_cost.per_tuple -= merge_qual_cost.per_tuple;
877 /* approx # tuples passing the merge quals */
878 mergejointuples = clamp_row_est(merge_selec * outer_path_rows * inner_path_rows);
881 * When there are equal merge keys in the outer relation, the
882 * mergejoin must rescan any matching tuples in the inner relation.
883 * This means re-fetching inner tuples. Our cost model for this is
884 * that a re-fetch costs the same as an original fetch, which is
885 * probably an overestimate; but on the other hand we ignore the
886 * bookkeeping costs of mark/restore. Not clear if it's worth
887 * developing a more refined model.
889 * The number of re-fetches can be estimated approximately as size of
890 * merge join output minus size of inner relation. Assume that the
891 * distinct key values are 1, 2, ..., and denote the number of values
892 * of each key in the outer relation as m1, m2, ...; in the inner
893 * relation, n1, n2, ... Then we have
895 * size of join = m1 * n1 + m2 * n2 + ...
897 * number of rescanned tuples = (m1 - 1) * n1 + (m2 - 1) * n2 + ... = m1 *
898 * n1 + m2 * n2 + ... - (n1 + n2 + ...) = size of join - size of inner
901 * This equation works correctly for outer tuples having no inner match
902 * (nk = 0), but not for inner tuples having no outer match (mk = 0);
903 * we are effectively subtracting those from the number of rescanned
904 * tuples, when we should not. Can we do better without expensive
905 * selectivity computations?
907 if (IsA(outer_path, UniquePath))
911 rescannedtuples = mergejointuples - inner_path_rows;
912 /* Must clamp because of possible underestimate */
913 if (rescannedtuples < 0)
916 /* We'll inflate inner run cost this much to account for rescanning */
917 rescanratio = 1.0 + (rescannedtuples / inner_path_rows);
920 * A merge join will stop as soon as it exhausts either input stream
921 * (unless it's an outer join, in which case the outer side has to be
922 * scanned all the way anyway). Estimate fraction of the left and right
923 * inputs that will actually need to be scanned. We use only the first
924 * (most significant) merge clause for this purpose.
926 * Since this calculation is somewhat expensive, and will be the same for
927 * all mergejoin paths associated with the merge clause, we cache the
928 * results in the RestrictInfo node.
930 if (mergeclauses && path->jpath.jointype != JOIN_FULL)
932 firstclause = (RestrictInfo *) linitial(mergeclauses);
933 if (firstclause->left_mergescansel < 0) /* not computed yet? */
934 mergejoinscansel(root, (Node *) firstclause->clause,
935 &firstclause->left_mergescansel,
936 &firstclause->right_mergescansel);
938 if (bms_is_subset(firstclause->left_relids, outer_path->parent->relids))
940 /* left side of clause is outer */
941 outerscansel = firstclause->left_mergescansel;
942 innerscansel = firstclause->right_mergescansel;
946 /* left side of clause is inner */
947 outerscansel = firstclause->right_mergescansel;
948 innerscansel = firstclause->left_mergescansel;
950 if (path->jpath.jointype == JOIN_LEFT)
952 else if (path->jpath.jointype == JOIN_RIGHT)
957 /* cope with clauseless or full mergejoin */
958 outerscansel = innerscansel = 1.0;
961 /* convert selectivity to row count; must scan at least one row */
962 outer_rows = clamp_row_est(outer_path_rows * outerscansel);
963 inner_rows = clamp_row_est(inner_path_rows * innerscansel);
966 * Readjust scan selectivities to account for above rounding. This is
967 * normally an insignificant effect, but when there are only a few
968 * rows in the inputs, failing to do this makes for a large percentage
971 outerscansel = outer_rows / outer_path_rows;
972 innerscansel = inner_rows / inner_path_rows;
974 /* cost of source data */
976 if (outersortkeys) /* do we need to sort outer? */
978 cost_sort(&sort_path,
981 outer_path->total_cost,
983 outer_path->parent->width);
984 startup_cost += sort_path.startup_cost;
985 run_cost += (sort_path.total_cost - sort_path.startup_cost)
990 startup_cost += outer_path->startup_cost;
991 run_cost += (outer_path->total_cost - outer_path->startup_cost)
995 if (innersortkeys) /* do we need to sort inner? */
997 cost_sort(&sort_path,
1000 inner_path->total_cost,
1002 inner_path->parent->width);
1003 startup_cost += sort_path.startup_cost;
1004 run_cost += (sort_path.total_cost - sort_path.startup_cost)
1005 * innerscansel * rescanratio;
1009 startup_cost += inner_path->startup_cost;
1010 run_cost += (inner_path->total_cost - inner_path->startup_cost)
1011 * innerscansel * rescanratio;
1017 * If we're doing JOIN_IN then we will stop outputting inner tuples
1018 * for an outer tuple as soon as we have one match. Account for the
1019 * effects of this by scaling down the cost estimates in proportion to
1020 * the expected output size. (This assumes that all the quals
1021 * attached to the join are IN quals, which should be true.)
1023 joininfactor = join_in_selectivity(&path->jpath, root);
1026 * The number of tuple comparisons needed is approximately number of
1027 * outer rows plus number of inner rows plus number of rescanned
1028 * tuples (can we refine this?). At each one, we need to evaluate the
1029 * mergejoin quals. NOTE: JOIN_IN mode does not save any work here,
1030 * so do NOT include joininfactor.
1032 startup_cost += merge_qual_cost.startup;
1033 run_cost += merge_qual_cost.per_tuple *
1034 (outer_rows + inner_rows * rescanratio);
1037 * For each tuple that gets through the mergejoin proper, we charge
1038 * cpu_tuple_cost plus the cost of evaluating additional restriction
1039 * clauses that are to be applied at the join. (This is pessimistic
1040 * since not all of the quals may get evaluated at each tuple.) This
1041 * work is skipped in JOIN_IN mode, so apply the factor.
1043 startup_cost += qp_qual_cost.startup;
1044 cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
1045 run_cost += cpu_per_tuple * mergejointuples * joininfactor;
1047 path->jpath.path.startup_cost = startup_cost;
1048 path->jpath.path.total_cost = startup_cost + run_cost;
1053 * Determines and returns the cost of joining two relations using the
1054 * hash join algorithm.
1056 * 'path' is already filled in except for the cost fields
1058 * Note: path's hashclauses should be a subset of the joinrestrictinfo list
1061 cost_hashjoin(HashPath *path, Query *root)
1063 Path *outer_path = path->jpath.outerjoinpath;
1064 Path *inner_path = path->jpath.innerjoinpath;
1065 List *hashclauses = path->path_hashclauses;
1066 Cost startup_cost = 0;
1069 Selectivity hash_selec;
1070 QualCost hash_qual_cost;
1071 QualCost qp_qual_cost;
1072 double hashjointuples;
1073 double outer_path_rows = PATH_ROWS(outer_path);
1074 double inner_path_rows = PATH_ROWS(inner_path);
1075 double outerbytes = relation_byte_size(outer_path_rows,
1076 outer_path->parent->width);
1077 double innerbytes = relation_byte_size(inner_path_rows,
1078 inner_path->parent->width);
1079 int num_hashclauses = list_length(hashclauses);
1082 double virtualbuckets;
1083 Selectivity innerbucketsize;
1084 Selectivity joininfactor;
1087 if (!enable_hashjoin)
1088 startup_cost += disable_cost;
1091 * Compute cost and selectivity of the hashquals and qpquals (other
1092 * restriction clauses) separately. We use approx_selectivity here
1093 * for speed --- in most cases, any errors won't affect the result
1096 * Note: it's probably bogus to use the normal selectivity calculation
1097 * here when either the outer or inner path is a UniquePath.
1099 hash_selec = approx_selectivity(root, hashclauses,
1100 path->jpath.jointype);
1101 cost_qual_eval(&hash_qual_cost, hashclauses);
1102 cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo);
1103 qp_qual_cost.startup -= hash_qual_cost.startup;
1104 qp_qual_cost.per_tuple -= hash_qual_cost.per_tuple;
1106 /* approx # tuples passing the hash quals */
1107 hashjointuples = clamp_row_est(hash_selec * outer_path_rows * inner_path_rows);
1109 /* cost of source data */
1110 startup_cost += outer_path->startup_cost;
1111 run_cost += outer_path->total_cost - outer_path->startup_cost;
1112 startup_cost += inner_path->total_cost;
1115 * Cost of computing hash function: must do it once per input tuple.
1116 * We charge one cpu_operator_cost for each column's hash function.
1118 * XXX when a hashclause is more complex than a single operator, we
1119 * really should charge the extra eval costs of the left or right
1120 * side, as appropriate, here. This seems more work than it's worth
1123 startup_cost += cpu_operator_cost * num_hashclauses * inner_path_rows;
1124 run_cost += cpu_operator_cost * num_hashclauses * outer_path_rows;
1126 /* Get hash table size that executor would use for inner relation */
1127 ExecChooseHashTableSize(inner_path_rows,
1128 inner_path->parent->width,
1131 virtualbuckets = (double) numbuckets * (double) numbatches;
1134 * Determine bucketsize fraction for inner relation. We use the
1135 * smallest bucketsize estimated for any individual hashclause; this
1136 * is undoubtedly conservative.
1138 * BUT: if inner relation has been unique-ified, we can assume it's good
1139 * for hashing. This is important both because it's the right answer,
1140 * and because we avoid contaminating the cache with a value that's
1141 * wrong for non-unique-ified paths.
1143 if (IsA(inner_path, UniquePath))
1144 innerbucketsize = 1.0 / virtualbuckets;
1147 innerbucketsize = 1.0;
1148 foreach(hcl, hashclauses)
1150 RestrictInfo *restrictinfo = (RestrictInfo *) lfirst(hcl);
1151 Selectivity thisbucketsize;
1153 Assert(IsA(restrictinfo, RestrictInfo));
1156 * First we have to figure out which side of the hashjoin
1157 * clause is the inner side.
1159 * Since we tend to visit the same clauses over and over when
1160 * planning a large query, we cache the bucketsize estimate in
1161 * the RestrictInfo node to avoid repeated lookups of
1164 if (bms_is_subset(restrictinfo->right_relids,
1165 inner_path->parent->relids))
1167 /* righthand side is inner */
1168 thisbucketsize = restrictinfo->right_bucketsize;
1169 if (thisbucketsize < 0)
1171 /* not cached yet */
1173 estimate_hash_bucketsize(root,
1174 get_rightop(restrictinfo->clause),
1176 restrictinfo->right_bucketsize = thisbucketsize;
1181 Assert(bms_is_subset(restrictinfo->left_relids,
1182 inner_path->parent->relids));
1183 /* lefthand side is inner */
1184 thisbucketsize = restrictinfo->left_bucketsize;
1185 if (thisbucketsize < 0)
1187 /* not cached yet */
1189 estimate_hash_bucketsize(root,
1190 get_leftop(restrictinfo->clause),
1192 restrictinfo->left_bucketsize = thisbucketsize;
1196 if (innerbucketsize > thisbucketsize)
1197 innerbucketsize = thisbucketsize;
1202 * If inner relation is too big then we will need to "batch" the join,
1203 * which implies writing and reading most of the tuples to disk an
1204 * extra time. Charge one cost unit per page of I/O (correct since it
1205 * should be nice and sequential...). Writing the inner rel counts as
1206 * startup cost, all the rest as run cost.
1210 double outerpages = page_size(outer_path_rows,
1211 outer_path->parent->width);
1212 double innerpages = page_size(inner_path_rows,
1213 inner_path->parent->width);
1215 startup_cost += innerpages;
1216 run_cost += innerpages + 2 * outerpages;
1222 * If we're doing JOIN_IN then we will stop comparing inner tuples to
1223 * an outer tuple as soon as we have one match. Account for the
1224 * effects of this by scaling down the cost estimates in proportion to
1225 * the expected output size. (This assumes that all the quals
1226 * attached to the join are IN quals, which should be true.)
1228 joininfactor = join_in_selectivity(&path->jpath, root);
1231 * The number of tuple comparisons needed is the number of outer
1232 * tuples times the typical number of tuples in a hash bucket, which
1233 * is the inner relation size times its bucketsize fraction. At each
1234 * one, we need to evaluate the hashjoin quals. (Note: charging the
1235 * full qual eval cost at each tuple is pessimistic, since we don't
1236 * evaluate the quals unless the hash values match exactly.)
1238 startup_cost += hash_qual_cost.startup;
1239 run_cost += hash_qual_cost.per_tuple *
1240 outer_path_rows * clamp_row_est(inner_path_rows * innerbucketsize) *
1244 * For each tuple that gets through the hashjoin proper, we charge
1245 * cpu_tuple_cost plus the cost of evaluating additional restriction
1246 * clauses that are to be applied at the join. (This is pessimistic
1247 * since not all of the quals may get evaluated at each tuple.)
1249 startup_cost += qp_qual_cost.startup;
1250 cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
1251 run_cost += cpu_per_tuple * hashjointuples * joininfactor;
1254 * Bias against putting larger relation on inside. We don't want an
1255 * absolute prohibition, though, since larger relation might have
1256 * better bucketsize --- and we can't trust the size estimates
1257 * unreservedly, anyway. Instead, inflate the run cost by the square
1258 * root of the size ratio. (Why square root? No real good reason,
1259 * but it seems reasonable...)
1261 * Note: before 7.4 we implemented this by inflating startup cost; but if
1262 * there's a disable_cost component in the input paths' startup cost,
1263 * that unfairly penalizes the hash. Probably it'd be better to keep
1264 * track of disable penalty separately from cost.
1266 if (innerbytes > outerbytes && outerbytes > 0)
1267 run_cost *= sqrt(innerbytes / outerbytes);
1269 path->jpath.path.startup_cost = startup_cost;
1270 path->jpath.path.total_cost = startup_cost + run_cost;
1276 * Estimate the CPU costs of evaluating a WHERE clause.
1277 * The input can be either an implicitly-ANDed list of boolean
1278 * expressions, or a list of RestrictInfo nodes.
1279 * The result includes both a one-time (startup) component,
1280 * and a per-evaluation component.
1283 cost_qual_eval(QualCost *cost, List *quals)
1288 cost->per_tuple = 0;
1290 /* We don't charge any cost for the implicit ANDing at top level ... */
1294 Node *qual = (Node *) lfirst(l);
1297 * RestrictInfo nodes contain an eval_cost field reserved for this
1298 * routine's use, so that it's not necessary to evaluate the qual
1299 * clause's cost more than once. If the clause's cost hasn't been
1300 * computed yet, the field's startup value will contain -1.
1302 if (qual && IsA(qual, RestrictInfo))
1304 RestrictInfo *restrictinfo = (RestrictInfo *) qual;
1306 if (restrictinfo->eval_cost.startup < 0)
1308 restrictinfo->eval_cost.startup = 0;
1309 restrictinfo->eval_cost.per_tuple = 0;
1310 cost_qual_eval_walker((Node *) restrictinfo->clause,
1311 &restrictinfo->eval_cost);
1313 cost->startup += restrictinfo->eval_cost.startup;
1314 cost->per_tuple += restrictinfo->eval_cost.per_tuple;
1318 /* If it's a bare expression, must always do it the hard way */
1319 cost_qual_eval_walker(qual, cost);
1325 cost_qual_eval_walker(Node *node, QualCost *total)
1331 * Our basic strategy is to charge one cpu_operator_cost for each
1332 * operator or function node in the given tree. Vars and Consts are
1333 * charged zero, and so are boolean operators (AND, OR, NOT).
1334 * Simplistic, but a lot better than no model at all.
1336 * Should we try to account for the possibility of short-circuit
1337 * evaluation of AND/OR?
1339 if (IsA(node, FuncExpr) ||
1340 IsA(node, OpExpr) ||
1341 IsA(node, DistinctExpr) ||
1342 IsA(node, NullIfExpr))
1343 total->per_tuple += cpu_operator_cost;
1344 else if (IsA(node, ScalarArrayOpExpr))
1346 /* should charge more than 1 op cost, but how many? */
1347 total->per_tuple += cpu_operator_cost * 10;
1349 else if (IsA(node, SubLink))
1351 /* This routine should not be applied to un-planned expressions */
1352 elog(ERROR, "cannot handle unplanned sub-select");
1354 else if (IsA(node, SubPlan))
1357 * A subplan node in an expression typically indicates that the
1358 * subplan will be executed on each evaluation, so charge
1359 * accordingly. (Sub-selects that can be executed as InitPlans
1360 * have already been removed from the expression.)
1362 * An exception occurs when we have decided we can implement the
1363 * subplan by hashing.
1366 SubPlan *subplan = (SubPlan *) node;
1367 Plan *plan = subplan->plan;
1369 if (subplan->useHashTable)
1372 * If we are using a hash table for the subquery outputs, then
1373 * the cost of evaluating the query is a one-time cost. We
1374 * charge one cpu_operator_cost per tuple for the work of
1375 * loading the hashtable, too.
1377 total->startup += plan->total_cost +
1378 cpu_operator_cost * plan->plan_rows;
1381 * The per-tuple costs include the cost of evaluating the
1382 * lefthand expressions, plus the cost of probing the
1383 * hashtable. Recursion into the exprs list will handle the
1384 * lefthand expressions properly, and will count one
1385 * cpu_operator_cost for each comparison operator. That is
1386 * probably too low for the probing cost, but it's hard to
1387 * make a better estimate, so live with it for now.
1393 * Otherwise we will be rescanning the subplan output on each
1394 * evaluation. We need to estimate how much of the output we
1395 * will actually need to scan. NOTE: this logic should agree
1396 * with the estimates used by make_subplan() in
1399 Cost plan_run_cost = plan->total_cost - plan->startup_cost;
1401 if (subplan->subLinkType == EXISTS_SUBLINK)
1403 /* we only need to fetch 1 tuple */
1404 total->per_tuple += plan_run_cost / plan->plan_rows;
1406 else if (subplan->subLinkType == ALL_SUBLINK ||
1407 subplan->subLinkType == ANY_SUBLINK)
1409 /* assume we need 50% of the tuples */
1410 total->per_tuple += 0.50 * plan_run_cost;
1411 /* also charge a cpu_operator_cost per row examined */
1412 total->per_tuple += 0.50 * plan->plan_rows * cpu_operator_cost;
1416 /* assume we need all tuples */
1417 total->per_tuple += plan_run_cost;
1421 * Also account for subplan's startup cost. If the subplan is
1422 * uncorrelated or undirect correlated, AND its topmost node
1423 * is a Sort or Material node, assume that we'll only need to
1424 * pay its startup cost once; otherwise assume we pay the
1425 * startup cost every time.
1427 if (subplan->parParam == NIL &&
1429 IsA(plan, Material)))
1430 total->startup += plan->startup_cost;
1432 total->per_tuple += plan->startup_cost;
1436 return expression_tree_walker(node, cost_qual_eval_walker,
1442 * approx_selectivity
1443 * Quick-and-dirty estimation of clause selectivities.
1444 * The input can be either an implicitly-ANDed list of boolean
1445 * expressions, or a list of RestrictInfo nodes (typically the latter).
1447 * This is quick-and-dirty because we bypass clauselist_selectivity, and
1448 * simply multiply the independent clause selectivities together. Now
1449 * clauselist_selectivity often can't do any better than that anyhow, but
1450 * for some situations (such as range constraints) it is smarter. However,
1451 * we can't effectively cache the results of clauselist_selectivity, whereas
1452 * the individual clause selectivities can be and are cached.
1454 * Since we are only using the results to estimate how many potential
1455 * output tuples are generated and passed through qpqual checking, it
1456 * seems OK to live with the approximation.
1459 approx_selectivity(Query *root, List *quals, JoinType jointype)
1461 Selectivity total = 1.0;
1466 Node *qual = (Node *) lfirst(l);
1468 /* Note that clause_selectivity will be able to cache its result */
1469 total *= clause_selectivity(root, qual, 0, jointype);
1476 * set_baserel_size_estimates
1477 * Set the size estimates for the given base relation.
1479 * The rel's targetlist and restrictinfo list must have been constructed
1482 * We set the following fields of the rel node:
1483 * rows: the estimated number of output tuples (after applying
1484 * restriction clauses).
1485 * width: the estimated average output tuple width in bytes.
1486 * baserestrictcost: estimated cost of evaluating baserestrictinfo clauses.
1489 set_baserel_size_estimates(Query *root, RelOptInfo *rel)
1493 /* Should only be applied to base relations */
1494 Assert(rel->relid > 0);
1496 nrows = rel->tuples *
1497 clauselist_selectivity(root,
1498 rel->baserestrictinfo,
1502 rel->rows = clamp_row_est(nrows);
1504 cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo);
1506 set_rel_width(root, rel);
1510 * set_joinrel_size_estimates
1511 * Set the size estimates for the given join relation.
1513 * The rel's targetlist must have been constructed already, and a
1514 * restriction clause list that matches the given component rels must
1517 * Since there is more than one way to make a joinrel for more than two
1518 * base relations, the results we get here could depend on which component
1519 * rel pair is provided. In theory we should get the same answers no matter
1520 * which pair is provided; in practice, since the selectivity estimation
1521 * routines don't handle all cases equally well, we might not. But there's
1522 * not much to be done about it. (Would it make sense to repeat the
1523 * calculations for each pair of input rels that's encountered, and somehow
1524 * average the results? Probably way more trouble than it's worth.)
1526 * It's important that the results for symmetric JoinTypes be symmetric,
1527 * eg, (rel1, rel2, JOIN_LEFT) should produce the same result as (rel2,
1528 * rel1, JOIN_RIGHT). Also, JOIN_IN should produce the same result as
1529 * JOIN_UNIQUE_INNER, likewise JOIN_REVERSE_IN == JOIN_UNIQUE_OUTER.
1531 * We set only the rows field here. The width field was already set by
1532 * build_joinrel_tlist, and baserestrictcost is not used for join rels.
1535 set_joinrel_size_estimates(Query *root, RelOptInfo *rel,
1536 RelOptInfo *outer_rel,
1537 RelOptInfo *inner_rel,
1546 * Compute joinclause selectivity. Note that we are only considering
1547 * clauses that become restriction clauses at this join level; we are
1548 * not double-counting them because they were not considered in
1549 * estimating the sizes of the component rels.
1551 selec = clauselist_selectivity(root,
1557 * Basically, we multiply size of Cartesian product by selectivity.
1559 * If we are doing an outer join, take that into account: the output must
1560 * be at least as large as the non-nullable input. (Is there any
1561 * chance of being even smarter?)
1563 * For JOIN_IN and variants, the Cartesian product is figured with
1564 * respect to a unique-ified input, and then we can clamp to the size
1565 * of the other input.
1570 nrows = outer_rel->rows * inner_rel->rows * selec;
1573 nrows = outer_rel->rows * inner_rel->rows * selec;
1574 if (nrows < outer_rel->rows)
1575 nrows = outer_rel->rows;
1578 nrows = outer_rel->rows * inner_rel->rows * selec;
1579 if (nrows < inner_rel->rows)
1580 nrows = inner_rel->rows;
1583 nrows = outer_rel->rows * inner_rel->rows * selec;
1584 if (nrows < outer_rel->rows)
1585 nrows = outer_rel->rows;
1586 if (nrows < inner_rel->rows)
1587 nrows = inner_rel->rows;
1590 case JOIN_UNIQUE_INNER:
1591 upath = create_unique_path(root, inner_rel,
1592 inner_rel->cheapest_total_path);
1593 nrows = outer_rel->rows * upath->rows * selec;
1594 if (nrows > outer_rel->rows)
1595 nrows = outer_rel->rows;
1597 case JOIN_REVERSE_IN:
1598 case JOIN_UNIQUE_OUTER:
1599 upath = create_unique_path(root, outer_rel,
1600 outer_rel->cheapest_total_path);
1601 nrows = upath->rows * inner_rel->rows * selec;
1602 if (nrows > inner_rel->rows)
1603 nrows = inner_rel->rows;
1606 elog(ERROR, "unrecognized join type: %d", (int) jointype);
1607 nrows = 0; /* keep compiler quiet */
1611 rel->rows = clamp_row_est(nrows);
1615 * join_in_selectivity
1616 * Determines the factor by which a JOIN_IN join's result is expected
1617 * to be smaller than an ordinary inner join.
1619 * 'path' is already filled in except for the cost fields
1622 join_in_selectivity(JoinPath *path, Query *root)
1624 RelOptInfo *innerrel;
1625 UniquePath *innerunique;
1629 /* Return 1.0 whenever it's not JOIN_IN */
1630 if (path->jointype != JOIN_IN)
1634 * Return 1.0 if the inner side is already known unique. The case
1635 * where the inner path is already a UniquePath probably cannot happen
1636 * in current usage, but check it anyway for completeness. The
1637 * interesting case is where we've determined the inner relation
1638 * itself is unique, which we can check by looking at the rows
1639 * estimate for its UniquePath.
1641 if (IsA(path->innerjoinpath, UniquePath))
1643 innerrel = path->innerjoinpath->parent;
1644 innerunique = create_unique_path(root,
1646 innerrel->cheapest_total_path);
1647 if (innerunique->rows >= innerrel->rows)
1651 * Compute same result set_joinrel_size_estimates would compute for
1652 * JOIN_INNER. Note that we use the input rels' absolute size
1653 * estimates, not PATH_ROWS() which might be less; if we used
1654 * PATH_ROWS() we'd be double-counting the effects of any join clauses
1655 * used in input scans.
1657 selec = clauselist_selectivity(root,
1658 path->joinrestrictinfo,
1661 nrows = path->outerjoinpath->parent->rows * innerrel->rows * selec;
1663 nrows = clamp_row_est(nrows);
1665 /* See if it's larger than the actual JOIN_IN size estimate */
1666 if (nrows > path->path.parent->rows)
1667 return path->path.parent->rows / nrows;
1673 * set_function_size_estimates
1674 * Set the size estimates for a base relation that is a function call.
1676 * The rel's targetlist and restrictinfo list must have been constructed
1679 * We set the same fields as set_baserel_size_estimates.
1682 set_function_size_estimates(Query *root, RelOptInfo *rel)
1684 /* Should only be applied to base relations that are functions */
1685 Assert(rel->relid > 0);
1686 Assert(rel->rtekind == RTE_FUNCTION);
1689 * Estimate number of rows the function itself will return.
1691 * XXX no idea how to do this yet; but should at least check whether
1692 * function returns set or not...
1696 /* Now estimate number of output rows, etc */
1697 set_baserel_size_estimates(root, rel);
1703 * Set the estimated output width of a base relation.
1705 * NB: this works best on plain relations because it prefers to look at
1706 * real Vars. It will fail to make use of pg_statistic info when applied
1707 * to a subquery relation, even if the subquery outputs are simple vars
1708 * that we could have gotten info for. Is it worth trying to be smarter
1711 * The per-attribute width estimates are cached for possible re-use while
1712 * building join relations.
1715 set_rel_width(Query *root, RelOptInfo *rel)
1717 int32 tuple_width = 0;
1720 foreach(tllist, rel->reltargetlist)
1722 Var *var = (Var *) lfirst(tllist);
1727 /* For now, punt on whole-row child Vars */
1730 tuple_width += 32; /* arbitrary */
1734 ndx = var->varattno - rel->min_attr;
1737 * The width probably hasn't been cached yet, but may as well
1740 if (rel->attr_widths[ndx] > 0)
1742 tuple_width += rel->attr_widths[ndx];
1746 relid = getrelid(var->varno, root->rtable);
1747 if (relid != InvalidOid)
1749 item_width = get_attavgwidth(relid, var->varattno);
1752 rel->attr_widths[ndx] = item_width;
1753 tuple_width += item_width;
1759 * Not a plain relation, or can't find statistics for it. Estimate
1760 * using just the type info.
1762 item_width = get_typavgwidth(var->vartype, var->vartypmod);
1763 Assert(item_width > 0);
1764 rel->attr_widths[ndx] = item_width;
1765 tuple_width += item_width;
1767 Assert(tuple_width >= 0);
1768 rel->width = tuple_width;
1772 * relation_byte_size
1773 * Estimate the storage space in bytes for a given number of tuples
1774 * of a given width (size in bytes).
1777 relation_byte_size(double tuples, int width)
1779 return tuples * (MAXALIGN(width) + MAXALIGN(sizeof(HeapTupleHeaderData)));
1784 * Returns an estimate of the number of pages covered by a given
1785 * number of tuples of a given width (size in bytes).
1788 page_size(double tuples, int width)
1790 return ceil(relation_byte_size(tuples, width) / BLCKSZ);