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) 1996-2003, 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 1.116 2003/11/29 19:51:50 pgsql 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 Selectivity estimate_hash_bucketsize(Query *root, Var *var,
107 static bool cost_qual_eval_walker(Node *node, QualCost *total);
108 static Selectivity approx_selectivity(Query *root, List *quals,
110 static void set_rel_width(Query *root, RelOptInfo *rel);
111 static double relation_byte_size(double tuples, int width);
112 static double page_size(double tuples, int width);
117 * Determines and returns the cost of scanning a relation sequentially.
120 cost_seqscan(Path *path, Query *root,
123 Cost startup_cost = 0;
127 /* Should only be applied to base relations */
128 Assert(baserel->relid > 0);
129 Assert(baserel->rtekind == RTE_RELATION);
132 startup_cost += disable_cost;
137 * The cost of reading a page sequentially is 1.0, by definition. Note
138 * that the Unix kernel will typically do some amount of read-ahead
139 * optimization, so that this cost is less than the true cost of
140 * reading a page from disk. We ignore that issue here, but must take
141 * it into account when estimating the cost of non-sequential
144 run_cost += baserel->pages; /* sequential fetches with cost 1.0 */
147 startup_cost += baserel->baserestrictcost.startup;
148 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
149 run_cost += cpu_per_tuple * baserel->tuples;
151 path->startup_cost = startup_cost;
152 path->total_cost = startup_cost + run_cost;
156 * cost_nonsequential_access
157 * Estimate the cost of accessing one page at random from a relation
158 * (or sort temp file) of the given size in pages.
160 * The simplistic model that the cost is random_page_cost is what we want
161 * to use for large relations; but for small ones that is a serious
162 * overestimate because of the effects of caching. This routine tries to
165 * Unfortunately we don't have any good way of estimating the effective cache
166 * size we are working with --- we know that Postgres itself has NBuffers
167 * internal buffers, but the size of the kernel's disk cache is uncertain,
168 * and how much of it we get to use is even less certain. We punt the problem
169 * for now by assuming we are given an effective_cache_size parameter.
171 * Given a guesstimated cache size, we estimate the actual I/O cost per page
172 * with the entirely ad-hoc equations:
173 * if relpages >= effective_cache_size:
174 * random_page_cost * (1 - (effective_cache_size/relpages)/2)
175 * if relpages < effective_cache_size:
176 * 1 + (random_page_cost/2-1) * (relpages/effective_cache_size) ** 2
177 * These give the right asymptotic behavior (=> 1.0 as relpages becomes
178 * small, => random_page_cost as it becomes large) and meet in the middle
179 * with the estimate that the cache is about 50% effective for a relation
180 * of the same size as effective_cache_size. (XXX this is probably all
181 * wrong, but I haven't been able to find any theory about how effective
182 * a disk cache should be presumed to be.)
185 cost_nonsequential_access(double relpages)
189 /* don't crash on bad input data */
190 if (relpages <= 0.0 || effective_cache_size <= 0.0)
191 return random_page_cost;
193 relsize = relpages / effective_cache_size;
196 return random_page_cost * (1.0 - 0.5 / relsize);
198 return 1.0 + (random_page_cost * 0.5 - 1.0) * relsize * relsize;
203 * Determines and returns the cost of scanning a relation using an index.
205 * NOTE: an indexscan plan node can actually represent several passes,
206 * but here we consider the cost of just one pass.
208 * 'root' is the query root
209 * 'baserel' is the base relation the index is for
210 * 'index' is the index to be used
211 * 'indexQuals' is the list of applicable qual clauses (implicit AND semantics)
212 * 'is_injoin' is T if we are considering using the index scan as the inside
213 * of a nestloop join (hence, some of the indexQuals are join clauses)
215 * NOTE: 'indexQuals' must contain only clauses usable as index restrictions.
216 * Any additional quals evaluated as qpquals may reduce the number of returned
217 * tuples, but they won't reduce the number of tuples we have to fetch from
218 * the table, so they don't reduce the scan cost.
221 cost_index(Path *path, Query *root,
227 Cost startup_cost = 0;
229 Cost indexStartupCost;
231 Selectivity indexSelectivity;
232 double indexCorrelation,
237 double tuples_fetched;
238 double pages_fetched;
242 /* Should only be applied to base relations */
243 Assert(IsA(baserel, RelOptInfo) &&
244 IsA(index, IndexOptInfo));
245 Assert(baserel->relid > 0);
246 Assert(baserel->rtekind == RTE_RELATION);
248 if (!enable_indexscan)
249 startup_cost += disable_cost;
252 * Call index-access-method-specific code to estimate the processing
253 * cost for scanning the index, as well as the selectivity of the
254 * index (ie, the fraction of main-table tuples we will have to
255 * retrieve) and its correlation to the main-table tuple order.
257 OidFunctionCall8(index->amcostestimate,
258 PointerGetDatum(root),
259 PointerGetDatum(baserel),
260 PointerGetDatum(index),
261 PointerGetDatum(indexQuals),
262 PointerGetDatum(&indexStartupCost),
263 PointerGetDatum(&indexTotalCost),
264 PointerGetDatum(&indexSelectivity),
265 PointerGetDatum(&indexCorrelation));
267 /* all costs for touching index itself included here */
268 startup_cost += indexStartupCost;
269 run_cost += indexTotalCost - indexStartupCost;
272 * Estimate number of main-table tuples and pages fetched.
274 * When the index ordering is uncorrelated with the table ordering,
275 * we use an approximation proposed by Mackert and Lohman, "Index Scans
276 * Using a Finite LRU Buffer: A Validated I/O Model", ACM Transactions
277 * on Database Systems, Vol. 14, No. 3, September 1989, Pages 401-424.
278 * The Mackert and Lohman approximation is that the number of pages
281 * min(2TNs/(2T+Ns), T) when T <= b
282 * 2TNs/(2T+Ns) when T > b and Ns <= 2Tb/(2T-b)
283 * b + (Ns - 2Tb/(2T-b))*(T-b)/T when T > b and Ns > 2Tb/(2T-b)
285 * T = # pages in table
286 * N = # tuples in table
287 * s = selectivity = fraction of table to be scanned
288 * b = # buffer pages available (we include kernel space here)
290 * When the index ordering is exactly correlated with the table ordering
291 * (just after a CLUSTER, for example), the number of pages fetched should
292 * be just sT. What's more, these will be sequential fetches, not the
293 * random fetches that occur in the uncorrelated case. So, depending on
294 * the extent of correlation, we should estimate the actual I/O cost
295 * somewhere between s * T * 1.0 and PF * random_cost. We currently
296 * interpolate linearly between these two endpoints based on the
297 * correlation squared (XXX is that appropriate?).
299 * In any case the number of tuples fetched is Ns.
303 tuples_fetched = indexSelectivity * baserel->tuples;
304 /* Don't believe estimates less than 1... */
305 if (tuples_fetched < 1.0)
306 tuples_fetched = 1.0;
308 /* This part is the Mackert and Lohman formula */
310 T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
311 b = (effective_cache_size > 1) ? effective_cache_size : 1.0;
316 (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
317 if (pages_fetched > T)
324 lim = (2.0 * T * b) / (2.0 * T - b);
325 if (tuples_fetched <= lim)
328 (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
333 b + (tuples_fetched - lim) * (T - b) / T;
338 * min_IO_cost corresponds to the perfectly correlated case
339 * (csquared=1), max_IO_cost to the perfectly uncorrelated case
340 * (csquared=0). Note that we just charge random_page_cost per page
341 * in the uncorrelated case, rather than using
342 * cost_nonsequential_access, since we've already accounted for
343 * caching effects by using the Mackert model.
345 min_IO_cost = ceil(indexSelectivity * T);
346 max_IO_cost = pages_fetched * random_page_cost;
349 * Now interpolate based on estimated index order correlation to get
350 * total disk I/O cost for main table accesses.
352 csquared = indexCorrelation * indexCorrelation;
354 run_cost += max_IO_cost + csquared * (min_IO_cost - max_IO_cost);
357 * Estimate CPU costs per tuple.
359 * Normally the indexquals will be removed from the list of restriction
360 * clauses that we have to evaluate as qpquals, so we should subtract
361 * their costs from baserestrictcost. But if we are doing a join then
362 * some of the indexquals are join clauses and shouldn't be
363 * subtracted. Rather than work out exactly how much to subtract, we
364 * don't subtract anything.
366 * XXX For a lossy index, not all the quals will be removed and so we
367 * really shouldn't subtract their costs; but detecting that seems
368 * more expensive than it's worth.
370 startup_cost += baserel->baserestrictcost.startup;
371 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
375 QualCost index_qual_cost;
377 cost_qual_eval(&index_qual_cost, indexQuals);
378 cpu_per_tuple -= index_qual_cost.per_tuple;
381 run_cost += cpu_per_tuple * tuples_fetched;
383 path->startup_cost = startup_cost;
384 path->total_cost = startup_cost + run_cost;
389 * Determines and returns the cost of scanning a relation using TIDs.
392 cost_tidscan(Path *path, Query *root,
393 RelOptInfo *baserel, List *tideval)
395 Cost startup_cost = 0;
398 int ntuples = length(tideval);
400 /* Should only be applied to base relations */
401 Assert(baserel->relid > 0);
402 Assert(baserel->rtekind == RTE_RELATION);
405 startup_cost += disable_cost;
407 /* disk costs --- assume each tuple on a different page */
408 run_cost += random_page_cost * ntuples;
411 startup_cost += baserel->baserestrictcost.startup;
412 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
413 run_cost += cpu_per_tuple * ntuples;
415 path->startup_cost = startup_cost;
416 path->total_cost = startup_cost + run_cost;
421 * Determines and returns the cost of scanning a subquery RTE.
424 cost_subqueryscan(Path *path, RelOptInfo *baserel)
430 /* Should only be applied to base relations that are subqueries */
431 Assert(baserel->relid > 0);
432 Assert(baserel->rtekind == RTE_SUBQUERY);
435 * Cost of path is cost of evaluating the subplan, plus cost of
436 * evaluating any restriction clauses that will be attached to the
437 * SubqueryScan node, plus cpu_tuple_cost to account for selection and
438 * projection overhead.
440 path->startup_cost = baserel->subplan->startup_cost;
441 path->total_cost = baserel->subplan->total_cost;
443 startup_cost = baserel->baserestrictcost.startup;
444 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
445 run_cost = cpu_per_tuple * baserel->tuples;
447 path->startup_cost += startup_cost;
448 path->total_cost += startup_cost + run_cost;
453 * Determines and returns the cost of scanning a function RTE.
456 cost_functionscan(Path *path, Query *root, RelOptInfo *baserel)
458 Cost startup_cost = 0;
462 /* Should only be applied to base relations that are functions */
463 Assert(baserel->relid > 0);
464 Assert(baserel->rtekind == RTE_FUNCTION);
467 * For now, estimate function's cost at one operator eval per function
468 * call. Someday we should revive the function cost estimate columns
471 cpu_per_tuple = cpu_operator_cost;
473 /* Add scanning CPU costs */
474 startup_cost += baserel->baserestrictcost.startup;
475 cpu_per_tuple += cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
476 run_cost += cpu_per_tuple * baserel->tuples;
478 path->startup_cost = startup_cost;
479 path->total_cost = startup_cost + run_cost;
484 * Determines and returns the cost of sorting a relation, including
485 * the cost of reading the input data.
487 * If the total volume of data to sort is less than SortMem, we will do
488 * an in-memory sort, which requires no I/O and about t*log2(t) tuple
489 * comparisons for t tuples.
491 * If the total volume exceeds SortMem, we switch to a tape-style merge
492 * algorithm. There will still be about t*log2(t) tuple comparisons in
493 * total, but we will also need to write and read each tuple once per
494 * merge pass. We expect about ceil(log6(r)) merge passes where r is the
495 * number of initial runs formed (log6 because tuplesort.c uses six-tape
496 * merging). Since the average initial run should be about twice SortMem,
498 * disk traffic = 2 * relsize * ceil(log6(p / (2*SortMem)))
499 * cpu = comparison_cost * t * log2(t)
501 * The disk traffic is assumed to be half sequential and half random
502 * accesses (XXX can't we refine that guess?)
504 * We charge two operator evals per tuple comparison, which should be in
505 * the right ballpark in most cases.
507 * 'pathkeys' is a list of sort keys
508 * 'input_cost' is the total cost for reading the input data
509 * 'tuples' is the number of tuples in the relation
510 * 'width' is the average tuple width in bytes
512 * NOTE: some callers currently pass NIL for pathkeys because they
513 * can't conveniently supply the sort keys. Since this routine doesn't
514 * currently do anything with pathkeys anyway, that doesn't matter...
515 * but if it ever does, it should react gracefully to lack of key data.
516 * (Actually, the thing we'd most likely be interested in is just the number
517 * of sort keys, which all callers *could* supply.)
520 cost_sort(Path *path, Query *root,
521 List *pathkeys, Cost input_cost, double tuples, int width)
523 Cost startup_cost = input_cost;
525 double nbytes = relation_byte_size(tuples, width);
526 long sortmembytes = SortMem * 1024L;
529 startup_cost += disable_cost;
532 * We want to be sure the cost of a sort is never estimated as zero,
533 * even if passed-in tuple count is zero. Besides, mustn't do
542 * Assume about two operator evals per tuple comparison and N log2 N
545 startup_cost += 2.0 * cpu_operator_cost * tuples * LOG2(tuples);
548 if (nbytes > sortmembytes)
550 double npages = ceil(nbytes / BLCKSZ);
551 double nruns = nbytes / (sortmembytes * 2);
552 double log_runs = ceil(LOG6(nruns));
553 double npageaccesses;
557 npageaccesses = 2.0 * npages * log_runs;
558 /* Assume half are sequential (cost 1), half are not */
559 startup_cost += npageaccesses *
560 (1.0 + cost_nonsequential_access(npages)) * 0.5;
564 * Also charge a small amount (arbitrarily set equal to operator cost)
565 * per extracted tuple.
567 run_cost += cpu_operator_cost * tuples;
569 path->startup_cost = startup_cost;
570 path->total_cost = startup_cost + run_cost;
575 * Determines and returns the cost of materializing a relation, including
576 * the cost of reading the input data.
578 * If the total volume of data to materialize exceeds SortMem, we will need
579 * to write it to disk, so the cost is much higher in that case.
582 cost_material(Path *path,
583 Cost input_cost, double tuples, int width)
585 Cost startup_cost = input_cost;
587 double nbytes = relation_byte_size(tuples, width);
588 long sortmembytes = SortMem * 1024L;
591 if (nbytes > sortmembytes)
593 double npages = ceil(nbytes / BLCKSZ);
595 /* We'll write during startup and read during retrieval */
596 startup_cost += npages;
601 * Also charge a small amount per extracted tuple. We use
602 * cpu_tuple_cost so that it doesn't appear worthwhile to materialize
605 run_cost += cpu_tuple_cost * tuples;
607 path->startup_cost = startup_cost;
608 path->total_cost = startup_cost + run_cost;
613 * Determines and returns the cost of performing an Agg plan node,
614 * including the cost of its input.
616 * Note: when aggstrategy == AGG_SORTED, caller must ensure that input costs
617 * are for appropriately-sorted input.
620 cost_agg(Path *path, Query *root,
621 AggStrategy aggstrategy, int numAggs,
622 int numGroupCols, double numGroups,
623 Cost input_startup_cost, Cost input_total_cost,
630 * We charge one cpu_operator_cost per aggregate function per input
631 * tuple, and another one per output tuple (corresponding to transfn
632 * and finalfn calls respectively). If we are grouping, we charge an
633 * additional cpu_operator_cost per grouping column per input tuple
634 * for grouping comparisons.
636 * We will produce a single output tuple if not grouping, and a tuple per
639 * Note: in this cost model, AGG_SORTED and AGG_HASHED have exactly the
640 * same total CPU cost, but AGG_SORTED has lower startup cost. If the
641 * input path is already sorted appropriately, AGG_SORTED should be
642 * preferred (since it has no risk of memory overflow). This will
643 * happen as long as the computed total costs are indeed exactly equal
644 * --- but if there's roundoff error we might do the wrong thing. So
645 * be sure that the computations below form the same intermediate
646 * values in the same order.
648 if (aggstrategy == AGG_PLAIN)
650 startup_cost = input_total_cost;
651 startup_cost += cpu_operator_cost * (input_tuples + 1) * numAggs;
652 /* we aren't grouping */
653 total_cost = startup_cost;
655 else if (aggstrategy == AGG_SORTED)
657 /* Here we are able to deliver output on-the-fly */
658 startup_cost = input_startup_cost;
659 total_cost = input_total_cost;
660 /* calcs phrased this way to match HASHED case, see note above */
661 total_cost += cpu_operator_cost * input_tuples * numGroupCols;
662 total_cost += cpu_operator_cost * input_tuples * numAggs;
663 total_cost += cpu_operator_cost * numGroups * numAggs;
667 /* must be AGG_HASHED */
668 startup_cost = input_total_cost;
669 startup_cost += cpu_operator_cost * input_tuples * numGroupCols;
670 startup_cost += cpu_operator_cost * input_tuples * numAggs;
671 total_cost = startup_cost;
672 total_cost += cpu_operator_cost * numGroups * numAggs;
675 path->startup_cost = startup_cost;
676 path->total_cost = total_cost;
681 * Determines and returns the cost of performing a Group plan node,
682 * including the cost of its input.
684 * Note: caller must ensure that input costs are for appropriately-sorted
688 cost_group(Path *path, Query *root,
689 int numGroupCols, double numGroups,
690 Cost input_startup_cost, Cost input_total_cost,
696 startup_cost = input_startup_cost;
697 total_cost = input_total_cost;
700 * Charge one cpu_operator_cost per comparison per input tuple. We
701 * assume all columns get compared at most of the tuples.
703 total_cost += cpu_operator_cost * input_tuples * numGroupCols;
705 path->startup_cost = startup_cost;
706 path->total_cost = total_cost;
711 * Determines and returns the cost of joining two relations using the
712 * nested loop algorithm.
714 * 'path' is already filled in except for the cost fields
717 cost_nestloop(NestPath *path, Query *root)
719 Path *outer_path = path->outerjoinpath;
720 Path *inner_path = path->innerjoinpath;
721 List *restrictlist = path->joinrestrictinfo;
722 Cost startup_cost = 0;
725 QualCost restrict_qual_cost;
726 double outer_path_rows = PATH_ROWS(outer_path);
727 double inner_path_rows = PATH_ROWS(inner_path);
729 Selectivity joininfactor;
731 if (!enable_nestloop)
732 startup_cost += disable_cost;
735 * If we're doing JOIN_IN then we will stop scanning inner tuples for
736 * an outer tuple as soon as we have one match. Account for the
737 * effects of this by scaling down the cost estimates in proportion to
738 * the expected output size. (This assumes that all the quals
739 * attached to the join are IN quals, which should be true.)
741 * Note: it's probably bogus to use the normal selectivity calculation
742 * here when either the outer or inner path is a UniquePath.
744 if (path->jointype == JOIN_IN)
746 Selectivity qual_selec = approx_selectivity(root, restrictlist,
750 qptuples = ceil(qual_selec * outer_path_rows * inner_path_rows);
751 if (qptuples > path->path.parent->rows)
752 joininfactor = path->path.parent->rows / qptuples;
759 /* cost of source data */
762 * NOTE: clearly, we must pay both outer and inner paths' startup_cost
763 * before we can start returning tuples, so the join's startup cost is
764 * their sum. What's not so clear is whether the inner path's
765 * startup_cost must be paid again on each rescan of the inner path.
766 * This is not true if the inner path is materialized or is a
767 * hashjoin, but probably is true otherwise.
769 startup_cost += outer_path->startup_cost + inner_path->startup_cost;
770 run_cost += outer_path->total_cost - outer_path->startup_cost;
771 if (IsA(inner_path, MaterialPath) ||
772 IsA(inner_path, HashPath))
774 /* charge only run cost for each iteration of inner path */
779 * charge startup cost for each iteration of inner path, except we
780 * already charged the first startup_cost in our own startup
782 run_cost += (outer_path_rows - 1) * inner_path->startup_cost;
784 run_cost += outer_path_rows *
785 (inner_path->total_cost - inner_path->startup_cost) * joininfactor;
788 * Compute number of tuples processed (not number emitted!). If inner
789 * path is an indexscan, be sure to use its estimated output row
790 * count, which may be lower than the restriction-clause-only row
791 * count of its parent. (We don't include this case in the PATH_ROWS
792 * macro because it applies *only* to a nestloop's inner relation.)
793 * Note: it is correct to use the unadjusted inner_path_rows in the
794 * above calculation for joininfactor, since otherwise we'd be
795 * double-counting the selectivity of the join clause being used for
798 if (IsA(inner_path, IndexPath))
799 inner_path_rows = ((IndexPath *) inner_path)->rows;
801 ntuples = inner_path_rows * outer_path_rows;
804 cost_qual_eval(&restrict_qual_cost, restrictlist);
805 startup_cost += restrict_qual_cost.startup;
806 cpu_per_tuple = cpu_tuple_cost + restrict_qual_cost.per_tuple;
807 run_cost += cpu_per_tuple * ntuples;
809 path->path.startup_cost = startup_cost;
810 path->path.total_cost = startup_cost + run_cost;
815 * Determines and returns the cost of joining two relations using the
816 * merge join algorithm.
818 * 'path' is already filled in except for the cost fields
820 * Notes: path's mergeclauses should be a subset of the joinrestrictinfo list;
821 * outersortkeys and innersortkeys are lists of the keys to be used
822 * to sort the outer and inner relations, or NIL if no explicit
823 * sort is needed because the source path is already ordered.
826 cost_mergejoin(MergePath *path, Query *root)
828 Path *outer_path = path->jpath.outerjoinpath;
829 Path *inner_path = path->jpath.innerjoinpath;
830 List *restrictlist = path->jpath.joinrestrictinfo;
831 List *mergeclauses = path->path_mergeclauses;
832 List *outersortkeys = path->outersortkeys;
833 List *innersortkeys = path->innersortkeys;
834 Cost startup_cost = 0;
837 Selectivity merge_selec;
838 Selectivity qp_selec;
839 QualCost merge_qual_cost;
840 QualCost qp_qual_cost;
841 RestrictInfo *firstclause;
843 double outer_path_rows = PATH_ROWS(outer_path);
844 double inner_path_rows = PATH_ROWS(inner_path);
847 double mergejointuples,
851 Selectivity outerscansel,
853 Selectivity joininfactor;
854 Path sort_path; /* dummy for result of cost_sort */
856 if (!enable_mergejoin)
857 startup_cost += disable_cost;
860 * Compute cost and selectivity of the mergequals and qpquals (other
861 * restriction clauses) separately. We use approx_selectivity here
862 * for speed --- in most cases, any errors won't affect the result
865 * Note: it's probably bogus to use the normal selectivity calculation
866 * here when either the outer or inner path is a UniquePath.
868 merge_selec = approx_selectivity(root, mergeclauses,
869 path->jpath.jointype);
870 cost_qual_eval(&merge_qual_cost, mergeclauses);
871 qpquals = set_ptrDifference(restrictlist, mergeclauses);
872 qp_selec = approx_selectivity(root, qpquals,
873 path->jpath.jointype);
874 cost_qual_eval(&qp_qual_cost, qpquals);
877 /* approx # tuples passing the merge quals */
878 mergejointuples = ceil(merge_selec * outer_path_rows * inner_path_rows);
879 /* approx # tuples passing qpquals as well */
880 qptuples = ceil(mergejointuples * qp_selec);
883 * When there are equal merge keys in the outer relation, the
884 * mergejoin must rescan any matching tuples in the inner relation.
885 * This means re-fetching inner tuples. Our cost model for this is
886 * that a re-fetch costs the same as an original fetch, which is
887 * probably an overestimate; but on the other hand we ignore the
888 * bookkeeping costs of mark/restore. Not clear if it's worth
889 * developing a more refined model.
891 * The number of re-fetches can be estimated approximately as size of
892 * merge join output minus size of inner relation. Assume that the
893 * distinct key values are 1, 2, ..., and denote the number of values
894 * of each key in the outer relation as m1, m2, ...; in the inner
895 * relation, n1, n2, ... Then we have
897 * size of join = m1 * n1 + m2 * n2 + ...
899 * number of rescanned tuples = (m1 - 1) * n1 + (m2 - 1) * n2 + ... = m1 *
900 * n1 + m2 * n2 + ... - (n1 + n2 + ...) = size of join - size of inner
903 * This equation works correctly for outer tuples having no inner match
904 * (nk = 0), but not for inner tuples having no outer match (mk = 0);
905 * we are effectively subtracting those from the number of rescanned
906 * tuples, when we should not. Can we do better without expensive
907 * selectivity computations?
909 if (IsA(outer_path, UniquePath))
913 rescannedtuples = mergejointuples - inner_path_rows;
914 /* Must clamp because of possible underestimate */
915 if (rescannedtuples < 0)
918 /* We'll inflate inner run cost this much to account for rescanning */
919 rescanratio = 1.0 + (rescannedtuples / inner_path_rows);
922 * A merge join will stop as soon as it exhausts either input stream.
923 * Estimate fraction of the left and right inputs that will actually
924 * need to be scanned. We use only the first (most significant) merge
925 * clause for this purpose.
927 * Since this calculation is somewhat expensive, and will be the same for
928 * all mergejoin paths associated with the merge clause, we cache the
929 * results in the RestrictInfo node.
931 firstclause = (RestrictInfo *) lfirst(mergeclauses);
932 if (firstclause->left_mergescansel < 0) /* not computed yet? */
933 mergejoinscansel(root, (Node *) firstclause->clause,
934 &firstclause->left_mergescansel,
935 &firstclause->right_mergescansel);
937 if (bms_is_subset(firstclause->left_relids, outer_path->parent->relids))
939 /* left side of clause is outer */
940 outerscansel = firstclause->left_mergescansel;
941 innerscansel = firstclause->right_mergescansel;
945 /* left side of clause is inner */
946 outerscansel = firstclause->right_mergescansel;
947 innerscansel = firstclause->left_mergescansel;
950 /* convert selectivity to row count; must scan at least one row */
952 outer_rows = ceil(outer_path_rows * outerscansel);
955 inner_rows = ceil(inner_path_rows * innerscansel);
960 * Readjust scan selectivities to account for above rounding. This is
961 * normally an insignificant effect, but when there are only a few
962 * rows in the inputs, failing to do this makes for a large percentage
965 outerscansel = outer_rows / outer_path_rows;
966 innerscansel = inner_rows / inner_path_rows;
968 /* cost of source data */
970 if (outersortkeys) /* do we need to sort outer? */
972 cost_sort(&sort_path,
975 outer_path->total_cost,
977 outer_path->parent->width);
978 startup_cost += sort_path.startup_cost;
979 run_cost += (sort_path.total_cost - sort_path.startup_cost)
984 startup_cost += outer_path->startup_cost;
985 run_cost += (outer_path->total_cost - outer_path->startup_cost)
989 if (innersortkeys) /* do we need to sort inner? */
991 cost_sort(&sort_path,
994 inner_path->total_cost,
996 inner_path->parent->width);
997 startup_cost += sort_path.startup_cost;
998 run_cost += (sort_path.total_cost - sort_path.startup_cost)
999 * innerscansel * rescanratio;
1003 startup_cost += inner_path->startup_cost;
1004 run_cost += (inner_path->total_cost - inner_path->startup_cost)
1005 * innerscansel * rescanratio;
1011 * If we're doing JOIN_IN then we will stop outputting inner tuples
1012 * for an outer tuple as soon as we have one match. Account for the
1013 * effects of this by scaling down the cost estimates in proportion to
1014 * the expected output size. (This assumes that all the quals
1015 * attached to the join are IN quals, which should be true.)
1017 if (path->jpath.jointype == JOIN_IN &&
1018 qptuples > path->jpath.path.parent->rows)
1019 joininfactor = path->jpath.path.parent->rows / qptuples;
1024 * The number of tuple comparisons needed is approximately number of
1025 * outer rows plus number of inner rows plus number of rescanned
1026 * tuples (can we refine this?). At each one, we need to evaluate the
1027 * mergejoin quals. NOTE: JOIN_IN mode does not save any work here,
1028 * so do NOT include joininfactor.
1030 startup_cost += merge_qual_cost.startup;
1031 run_cost += merge_qual_cost.per_tuple *
1032 (outer_rows + inner_rows * rescanratio);
1035 * For each tuple that gets through the mergejoin proper, we charge
1036 * cpu_tuple_cost plus the cost of evaluating additional restriction
1037 * clauses that are to be applied at the join. (This is pessimistic
1038 * since not all of the quals may get evaluated at each tuple.) This
1039 * work is skipped in JOIN_IN mode, so apply the factor.
1041 startup_cost += qp_qual_cost.startup;
1042 cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
1043 run_cost += cpu_per_tuple * mergejointuples * joininfactor;
1045 path->jpath.path.startup_cost = startup_cost;
1046 path->jpath.path.total_cost = startup_cost + run_cost;
1051 * Determines and returns the cost of joining two relations using the
1052 * hash join algorithm.
1054 * 'path' is already filled in except for the cost fields
1056 * Note: path's hashclauses should be a subset of the joinrestrictinfo list
1059 cost_hashjoin(HashPath *path, Query *root)
1061 Path *outer_path = path->jpath.outerjoinpath;
1062 Path *inner_path = path->jpath.innerjoinpath;
1063 List *restrictlist = path->jpath.joinrestrictinfo;
1064 List *hashclauses = path->path_hashclauses;
1065 Cost startup_cost = 0;
1068 Selectivity hash_selec;
1069 Selectivity qp_selec;
1070 QualCost hash_qual_cost;
1071 QualCost qp_qual_cost;
1072 double hashjointuples;
1074 double outer_path_rows = PATH_ROWS(outer_path);
1075 double inner_path_rows = PATH_ROWS(inner_path);
1076 double outerbytes = relation_byte_size(outer_path_rows,
1077 outer_path->parent->width);
1078 double innerbytes = relation_byte_size(inner_path_rows,
1079 inner_path->parent->width);
1080 int num_hashclauses = length(hashclauses);
1082 int physicalbuckets;
1084 Selectivity innerbucketsize;
1085 Selectivity joininfactor;
1089 if (!enable_hashjoin)
1090 startup_cost += disable_cost;
1093 * Compute cost and selectivity of the hashquals and qpquals (other
1094 * restriction clauses) separately. We use approx_selectivity here
1095 * for speed --- in most cases, any errors won't affect the result
1098 * Note: it's probably bogus to use the normal selectivity calculation
1099 * here when either the outer or inner path is a UniquePath.
1101 hash_selec = approx_selectivity(root, hashclauses,
1102 path->jpath.jointype);
1103 cost_qual_eval(&hash_qual_cost, hashclauses);
1104 qpquals = set_ptrDifference(restrictlist, hashclauses);
1105 qp_selec = approx_selectivity(root, qpquals,
1106 path->jpath.jointype);
1107 cost_qual_eval(&qp_qual_cost, qpquals);
1110 /* approx # tuples passing the hash quals */
1111 hashjointuples = ceil(hash_selec * outer_path_rows * inner_path_rows);
1112 /* approx # tuples passing qpquals as well */
1113 qptuples = ceil(hashjointuples * qp_selec);
1115 /* cost of source data */
1116 startup_cost += outer_path->startup_cost;
1117 run_cost += outer_path->total_cost - outer_path->startup_cost;
1118 startup_cost += inner_path->total_cost;
1121 * Cost of computing hash function: must do it once per input tuple.
1122 * We charge one cpu_operator_cost for each column's hash function.
1124 * XXX when a hashclause is more complex than a single operator, we
1125 * really should charge the extra eval costs of the left or right
1126 * side, as appropriate, here. This seems more work than it's worth
1129 startup_cost += cpu_operator_cost * num_hashclauses * inner_path_rows;
1130 run_cost += cpu_operator_cost * num_hashclauses * outer_path_rows;
1132 /* Get hash table size that executor would use for inner relation */
1133 ExecChooseHashTableSize(inner_path_rows,
1134 inner_path->parent->width,
1140 * Determine bucketsize fraction for inner relation. We use the
1141 * smallest bucketsize estimated for any individual hashclause; this
1142 * is undoubtedly conservative.
1144 * BUT: if inner relation has been unique-ified, we can assume it's good
1145 * for hashing. This is important both because it's the right answer,
1146 * and because we avoid contaminating the cache with a value that's
1147 * wrong for non-unique-ified paths.
1149 if (IsA(inner_path, UniquePath))
1150 innerbucketsize = 1.0 / virtualbuckets;
1153 innerbucketsize = 1.0;
1154 foreach(hcl, hashclauses)
1156 RestrictInfo *restrictinfo = (RestrictInfo *) lfirst(hcl);
1157 Selectivity thisbucketsize;
1159 Assert(IsA(restrictinfo, RestrictInfo));
1162 * First we have to figure out which side of the hashjoin
1163 * clause is the inner side.
1165 * Since we tend to visit the same clauses over and over when
1166 * planning a large query, we cache the bucketsize estimate in
1167 * the RestrictInfo node to avoid repeated lookups of
1170 if (bms_is_subset(restrictinfo->right_relids,
1171 inner_path->parent->relids))
1173 /* righthand side is inner */
1174 thisbucketsize = restrictinfo->right_bucketsize;
1175 if (thisbucketsize < 0)
1177 /* not cached yet */
1179 estimate_hash_bucketsize(root,
1180 (Var *) get_rightop(restrictinfo->clause),
1182 restrictinfo->right_bucketsize = thisbucketsize;
1187 Assert(bms_is_subset(restrictinfo->left_relids,
1188 inner_path->parent->relids));
1189 /* lefthand side is inner */
1190 thisbucketsize = restrictinfo->left_bucketsize;
1191 if (thisbucketsize < 0)
1193 /* not cached yet */
1195 estimate_hash_bucketsize(root,
1196 (Var *) get_leftop(restrictinfo->clause),
1198 restrictinfo->left_bucketsize = thisbucketsize;
1202 if (innerbucketsize > thisbucketsize)
1203 innerbucketsize = thisbucketsize;
1208 * if inner relation is too big then we will need to "batch" the join,
1209 * which implies writing and reading most of the tuples to disk an
1210 * extra time. Charge one cost unit per page of I/O (correct since it
1211 * should be nice and sequential...). Writing the inner rel counts as
1212 * startup cost, all the rest as run cost.
1216 double outerpages = page_size(outer_path_rows,
1217 outer_path->parent->width);
1218 double innerpages = page_size(inner_path_rows,
1219 inner_path->parent->width);
1221 startup_cost += innerpages;
1222 run_cost += innerpages + 2 * outerpages;
1228 * If we're doing JOIN_IN then we will stop comparing inner tuples to
1229 * an outer tuple as soon as we have one match. Account for the
1230 * effects of this by scaling down the cost estimates in proportion to
1231 * the expected output size. (This assumes that all the quals
1232 * attached to the join are IN quals, which should be true.)
1234 if (path->jpath.jointype == JOIN_IN &&
1235 qptuples > path->jpath.path.parent->rows)
1236 joininfactor = path->jpath.path.parent->rows / qptuples;
1241 * The number of tuple comparisons needed is the number of outer
1242 * tuples times the typical number of tuples in a hash bucket, which
1243 * is the inner relation size times its bucketsize fraction. At each
1244 * one, we need to evaluate the hashjoin quals.
1246 startup_cost += hash_qual_cost.startup;
1247 run_cost += hash_qual_cost.per_tuple *
1248 outer_path_rows * ceil(inner_path_rows * innerbucketsize) *
1252 * For each tuple that gets through the hashjoin proper, we charge
1253 * cpu_tuple_cost plus the cost of evaluating additional restriction
1254 * clauses that are to be applied at the join. (This is pessimistic
1255 * since not all of the quals may get evaluated at each tuple.)
1257 startup_cost += qp_qual_cost.startup;
1258 cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
1259 run_cost += cpu_per_tuple * hashjointuples * joininfactor;
1262 * Bias against putting larger relation on inside. We don't want an
1263 * absolute prohibition, though, since larger relation might have
1264 * better bucketsize --- and we can't trust the size estimates
1265 * unreservedly, anyway. Instead, inflate the run cost by the square
1266 * root of the size ratio. (Why square root? No real good reason,
1267 * but it seems reasonable...)
1269 * Note: before 7.4 we implemented this by inflating startup cost; but if
1270 * there's a disable_cost component in the input paths' startup cost,
1271 * that unfairly penalizes the hash. Probably it'd be better to keep
1272 * track of disable penalty separately from cost.
1274 if (innerbytes > outerbytes && outerbytes > 0)
1275 run_cost *= sqrt(innerbytes / outerbytes);
1277 path->jpath.path.startup_cost = startup_cost;
1278 path->jpath.path.total_cost = startup_cost + run_cost;
1282 * Estimate hash bucketsize fraction (ie, number of entries in a bucket
1283 * divided by total tuples in relation) if the specified Var is used
1286 * XXX This is really pretty bogus since we're effectively assuming that the
1287 * distribution of hash keys will be the same after applying restriction
1288 * clauses as it was in the underlying relation. However, we are not nearly
1289 * smart enough to figure out how the restrict clauses might change the
1290 * distribution, so this will have to do for now.
1292 * We are passed the number of buckets the executor will use for the given
1293 * input relation. If the data were perfectly distributed, with the same
1294 * number of tuples going into each available bucket, then the bucketsize
1295 * fraction would be 1/nbuckets. But this happy state of affairs will occur
1296 * only if (a) there are at least nbuckets distinct data values, and (b)
1297 * we have a not-too-skewed data distribution. Otherwise the buckets will
1298 * be nonuniformly occupied. If the other relation in the join has a key
1299 * distribution similar to this one's, then the most-loaded buckets are
1300 * exactly those that will be probed most often. Therefore, the "average"
1301 * bucket size for costing purposes should really be taken as something close
1302 * to the "worst case" bucket size. We try to estimate this by adjusting the
1303 * fraction if there are too few distinct data values, and then scaling up
1304 * by the ratio of the most common value's frequency to the average frequency.
1306 * If no statistics are available, use a default estimate of 0.1. This will
1307 * discourage use of a hash rather strongly if the inner relation is large,
1308 * which is what we want. We do not want to hash unless we know that the
1309 * inner rel is well-dispersed (or the alternatives seem much worse).
1312 estimate_hash_bucketsize(Query *root, Var *var, int nbuckets)
1317 Form_pg_statistic stats;
1326 * Lookup info about var's relation and attribute; if none available,
1327 * return default estimate.
1329 if (var == NULL || !IsA(var, Var))
1332 relid = getrelid(var->varno, root->rtable);
1333 if (relid == InvalidOid)
1336 rel = find_base_rel(root, var->varno);
1338 if (rel->tuples <= 0.0 || rel->rows <= 0.0)
1339 return 0.1; /* ensure we can divide below */
1341 tuple = SearchSysCache(STATRELATT,
1342 ObjectIdGetDatum(relid),
1343 Int16GetDatum(var->varattno),
1345 if (!HeapTupleIsValid(tuple))
1348 * If the attribute is known unique because of an index,
1349 * we can treat it as well-distributed.
1351 if (has_unique_index(rel, var->varattno))
1352 return 1.0 / (double) nbuckets;
1355 * Perhaps the Var is a system attribute; if so, it will have no
1356 * entry in pg_statistic, but we may be able to guess something
1357 * about its distribution anyway.
1359 switch (var->varattno)
1361 case ObjectIdAttributeNumber:
1362 case SelfItemPointerAttributeNumber:
1363 /* these are unique, so buckets should be well-distributed */
1364 return 1.0 / (double) nbuckets;
1365 case TableOidAttributeNumber:
1366 /* hashing this is a terrible idea... */
1371 stats = (Form_pg_statistic) GETSTRUCT(tuple);
1374 * Obtain number of distinct data values in raw relation.
1376 ndistinct = stats->stadistinct;
1377 if (ndistinct < 0.0)
1378 ndistinct = -ndistinct * rel->tuples;
1380 if (ndistinct <= 0.0) /* ensure we can divide */
1382 ReleaseSysCache(tuple);
1386 /* Also compute avg freq of all distinct data values in raw relation */
1387 avgfreq = (1.0 - stats->stanullfrac) / ndistinct;
1390 * Adjust ndistinct to account for restriction clauses. Observe we
1391 * are assuming that the data distribution is affected uniformly by
1392 * the restriction clauses!
1394 * XXX Possibly better way, but much more expensive: multiply by
1395 * selectivity of rel's restriction clauses that mention the target
1398 ndistinct *= rel->rows / rel->tuples;
1401 * Initial estimate of bucketsize fraction is 1/nbuckets as long as
1402 * the number of buckets is less than the expected number of distinct
1403 * values; otherwise it is 1/ndistinct.
1405 if (ndistinct > (double) nbuckets)
1406 estfract = 1.0 / (double) nbuckets;
1408 estfract = 1.0 / ndistinct;
1411 * Look up the frequency of the most common value, if available.
1415 if (get_attstatsslot(tuple, var->vartype, var->vartypmod,
1416 STATISTIC_KIND_MCV, InvalidOid,
1417 NULL, NULL, &numbers, &nnumbers))
1420 * The first MCV stat is for the most common value.
1423 mcvfreq = numbers[0];
1424 free_attstatsslot(var->vartype, NULL, 0,
1429 * Adjust estimated bucketsize upward to account for skewed
1432 if (avgfreq > 0.0 && mcvfreq > avgfreq)
1433 estfract *= mcvfreq / avgfreq;
1436 * Clamp bucketsize to sane range (the above adjustment could easily
1437 * produce an out-of-range result). We set the lower bound a little
1438 * above zero, since zero isn't a very sane result.
1440 if (estfract < 1.0e-6)
1442 else if (estfract > 1.0)
1445 ReleaseSysCache(tuple);
1447 return (Selectivity) estfract;
1453 * Estimate the CPU costs of evaluating a WHERE clause.
1454 * The input can be either an implicitly-ANDed list of boolean
1455 * expressions, or a list of RestrictInfo nodes.
1456 * The result includes both a one-time (startup) component,
1457 * and a per-evaluation component.
1460 cost_qual_eval(QualCost *cost, List *quals)
1465 cost->per_tuple = 0;
1467 /* We don't charge any cost for the implicit ANDing at top level ... */
1471 Node *qual = (Node *) lfirst(l);
1474 * RestrictInfo nodes contain an eval_cost field reserved for this
1475 * routine's use, so that it's not necessary to evaluate the qual
1476 * clause's cost more than once. If the clause's cost hasn't been
1477 * computed yet, the field's startup value will contain -1.
1479 if (qual && IsA(qual, RestrictInfo))
1481 RestrictInfo *restrictinfo = (RestrictInfo *) qual;
1483 if (restrictinfo->eval_cost.startup < 0)
1485 restrictinfo->eval_cost.startup = 0;
1486 restrictinfo->eval_cost.per_tuple = 0;
1487 cost_qual_eval_walker((Node *) restrictinfo->clause,
1488 &restrictinfo->eval_cost);
1490 cost->startup += restrictinfo->eval_cost.startup;
1491 cost->per_tuple += restrictinfo->eval_cost.per_tuple;
1495 /* If it's a bare expression, must always do it the hard way */
1496 cost_qual_eval_walker(qual, cost);
1502 cost_qual_eval_walker(Node *node, QualCost *total)
1508 * Our basic strategy is to charge one cpu_operator_cost for each
1509 * operator or function node in the given tree. Vars and Consts are
1510 * charged zero, and so are boolean operators (AND, OR, NOT).
1511 * Simplistic, but a lot better than no model at all.
1513 * Should we try to account for the possibility of short-circuit
1514 * evaluation of AND/OR?
1516 if (IsA(node, FuncExpr) ||
1517 IsA(node, OpExpr) ||
1518 IsA(node, DistinctExpr) ||
1519 IsA(node, NullIfExpr))
1520 total->per_tuple += cpu_operator_cost;
1521 else if (IsA(node, ScalarArrayOpExpr))
1523 /* should charge more than 1 op cost, but how many? */
1524 total->per_tuple += cpu_operator_cost * 10;
1526 else if (IsA(node, SubLink))
1528 /* This routine should not be applied to un-planned expressions */
1529 elog(ERROR, "cannot handle unplanned sub-select");
1531 else if (IsA(node, SubPlan))
1534 * A subplan node in an expression typically indicates that the
1535 * subplan will be executed on each evaluation, so charge
1536 * accordingly. (Sub-selects that can be executed as InitPlans
1537 * have already been removed from the expression.)
1539 * An exception occurs when we have decided we can implement the
1540 * subplan by hashing.
1543 SubPlan *subplan = (SubPlan *) node;
1544 Plan *plan = subplan->plan;
1546 if (subplan->useHashTable)
1549 * If we are using a hash table for the subquery outputs, then
1550 * the cost of evaluating the query is a one-time cost. We
1551 * charge one cpu_operator_cost per tuple for the work of
1552 * loading the hashtable, too.
1554 total->startup += plan->total_cost +
1555 cpu_operator_cost * plan->plan_rows;
1558 * The per-tuple costs include the cost of evaluating the
1559 * lefthand expressions, plus the cost of probing the
1560 * hashtable. Recursion into the exprs list will handle the
1561 * lefthand expressions properly, and will count one
1562 * cpu_operator_cost for each comparison operator. That is
1563 * probably too low for the probing cost, but it's hard to
1564 * make a better estimate, so live with it for now.
1570 * Otherwise we will be rescanning the subplan output on each
1571 * evaluation. We need to estimate how much of the output we
1572 * will actually need to scan. NOTE: this logic should agree
1573 * with the estimates used by make_subplan() in
1576 Cost plan_run_cost = plan->total_cost - plan->startup_cost;
1578 if (subplan->subLinkType == EXISTS_SUBLINK)
1580 /* we only need to fetch 1 tuple */
1581 total->per_tuple += plan_run_cost / plan->plan_rows;
1583 else if (subplan->subLinkType == ALL_SUBLINK ||
1584 subplan->subLinkType == ANY_SUBLINK)
1586 /* assume we need 50% of the tuples */
1587 total->per_tuple += 0.50 * plan_run_cost;
1588 /* also charge a cpu_operator_cost per row examined */
1589 total->per_tuple += 0.50 * plan->plan_rows * cpu_operator_cost;
1593 /* assume we need all tuples */
1594 total->per_tuple += plan_run_cost;
1598 * Also account for subplan's startup cost. If the subplan is
1599 * uncorrelated or undirect correlated, AND its topmost node
1600 * is a Sort or Material node, assume that we'll only need to
1601 * pay its startup cost once; otherwise assume we pay the
1602 * startup cost every time.
1604 if (subplan->parParam == NIL &&
1606 IsA(plan, Material)))
1607 total->startup += plan->startup_cost;
1609 total->per_tuple += plan->startup_cost;
1613 return expression_tree_walker(node, cost_qual_eval_walker,
1619 * approx_selectivity
1620 * Quick-and-dirty estimation of clause selectivities.
1621 * The input can be either an implicitly-ANDed list of boolean
1622 * expressions, or a list of RestrictInfo nodes (typically the latter).
1624 * The "quick" part comes from caching the selectivity estimates so we can
1625 * avoid recomputing them later. (Since the same clauses are typically
1626 * examined over and over in different possible join trees, this makes a
1629 * The "dirty" part comes from the fact that the selectivities of multiple
1630 * clauses are estimated independently and multiplied together. Now
1631 * clauselist_selectivity often can't do any better than that anyhow, but
1632 * for some situations (such as range constraints) it is smarter.
1634 * Since we are only using the results to estimate how many potential
1635 * output tuples are generated and passed through qpqual checking, it
1636 * seems OK to live with the approximation.
1639 approx_selectivity(Query *root, List *quals, JoinType jointype)
1641 Selectivity total = 1.0;
1646 Node *qual = (Node *) lfirst(l);
1650 * RestrictInfo nodes contain a this_selec field reserved for this
1651 * routine's use, so that it's not necessary to evaluate the qual
1652 * clause's selectivity more than once. If the clause's
1653 * selectivity hasn't been computed yet, the field will contain
1656 if (qual && IsA(qual, RestrictInfo))
1658 RestrictInfo *restrictinfo = (RestrictInfo *) qual;
1660 if (restrictinfo->this_selec < 0)
1661 restrictinfo->this_selec =
1662 clause_selectivity(root,
1663 (Node *) restrictinfo->clause,
1666 selec = restrictinfo->this_selec;
1670 /* If it's a bare expression, must always do it the hard way */
1671 selec = clause_selectivity(root, qual, 0, jointype);
1680 * set_baserel_size_estimates
1681 * Set the size estimates for the given base relation.
1683 * The rel's targetlist and restrictinfo list must have been constructed
1686 * We set the following fields of the rel node:
1687 * rows: the estimated number of output tuples (after applying
1688 * restriction clauses).
1689 * width: the estimated average output tuple width in bytes.
1690 * baserestrictcost: estimated cost of evaluating baserestrictinfo clauses.
1693 set_baserel_size_estimates(Query *root, RelOptInfo *rel)
1697 /* Should only be applied to base relations */
1698 Assert(rel->relid > 0);
1700 temp = rel->tuples *
1701 restrictlist_selectivity(root,
1702 rel->baserestrictinfo,
1707 * Force estimate to be at least one row, to make explain output look
1708 * better and to avoid possible divide-by-zero when interpolating
1709 * cost. Make it an integer, too.
1718 cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo);
1720 set_rel_width(root, rel);
1724 * set_joinrel_size_estimates
1725 * Set the size estimates for the given join relation.
1727 * The rel's targetlist must have been constructed already, and a
1728 * restriction clause list that matches the given component rels must
1731 * Since there is more than one way to make a joinrel for more than two
1732 * base relations, the results we get here could depend on which component
1733 * rel pair is provided. In theory we should get the same answers no matter
1734 * which pair is provided; in practice, since the selectivity estimation
1735 * routines don't handle all cases equally well, we might not. But there's
1736 * not much to be done about it. (Would it make sense to repeat the
1737 * calculations for each pair of input rels that's encountered, and somehow
1738 * average the results? Probably way more trouble than it's worth.)
1740 * It's important that the results for symmetric JoinTypes be symmetric,
1741 * eg, (rel1, rel2, JOIN_LEFT) should produce the same result as (rel2,
1742 * rel1, JOIN_RIGHT). Also, JOIN_IN should produce the same result as
1743 * JOIN_UNIQUE_INNER, likewise JOIN_REVERSE_IN == JOIN_UNIQUE_OUTER.
1745 * We set the same relnode fields as set_baserel_size_estimates() does.
1748 set_joinrel_size_estimates(Query *root, RelOptInfo *rel,
1749 RelOptInfo *outer_rel,
1750 RelOptInfo *inner_rel,
1759 * Compute joinclause selectivity. Note that we are only considering
1760 * clauses that become restriction clauses at this join level; we are
1761 * not double-counting them because they were not considered in
1762 * estimating the sizes of the component rels.
1764 selec = restrictlist_selectivity(root,
1770 * Basically, we multiply size of Cartesian product by selectivity.
1772 * If we are doing an outer join, take that into account: the output must
1773 * be at least as large as the non-nullable input. (Is there any
1774 * chance of being even smarter?)
1776 * For JOIN_IN and variants, the Cartesian product is figured with
1777 * respect to a unique-ified input, and then we can clamp to the size
1778 * of the other input.
1783 temp = outer_rel->rows * inner_rel->rows * selec;
1786 temp = outer_rel->rows * inner_rel->rows * selec;
1787 if (temp < outer_rel->rows)
1788 temp = outer_rel->rows;
1791 temp = outer_rel->rows * inner_rel->rows * selec;
1792 if (temp < inner_rel->rows)
1793 temp = inner_rel->rows;
1796 temp = outer_rel->rows * inner_rel->rows * selec;
1797 if (temp < outer_rel->rows)
1798 temp = outer_rel->rows;
1799 if (temp < inner_rel->rows)
1800 temp = inner_rel->rows;
1803 case JOIN_UNIQUE_INNER:
1804 upath = create_unique_path(root, inner_rel,
1805 inner_rel->cheapest_total_path);
1806 temp = outer_rel->rows * upath->rows * selec;
1807 if (temp > outer_rel->rows)
1808 temp = outer_rel->rows;
1810 case JOIN_REVERSE_IN:
1811 case JOIN_UNIQUE_OUTER:
1812 upath = create_unique_path(root, outer_rel,
1813 outer_rel->cheapest_total_path);
1814 temp = upath->rows * inner_rel->rows * selec;
1815 if (temp > inner_rel->rows)
1816 temp = inner_rel->rows;
1819 elog(ERROR, "unrecognized join type: %d", (int) jointype);
1820 temp = 0; /* keep compiler quiet */
1825 * Force estimate to be at least one row, to make explain output look
1826 * better and to avoid possible divide-by-zero when interpolating
1827 * cost. Make it an integer, too.
1837 * We need not compute the output width here, because
1838 * build_joinrel_tlist already did.
1843 * set_function_size_estimates
1844 * Set the size estimates for a base relation that is a function call.
1846 * The rel's targetlist and restrictinfo list must have been constructed
1849 * We set the following fields of the rel node:
1850 * rows: the estimated number of output tuples (after applying
1851 * restriction clauses).
1852 * width: the estimated average output tuple width in bytes.
1853 * baserestrictcost: estimated cost of evaluating baserestrictinfo clauses.
1856 set_function_size_estimates(Query *root, RelOptInfo *rel)
1860 /* Should only be applied to base relations that are functions */
1861 Assert(rel->relid > 0);
1862 Assert(rel->rtekind == RTE_FUNCTION);
1865 * Estimate number of rows the function itself will return.
1867 * XXX no idea how to do this yet; but should at least check whether
1868 * function returns set or not...
1872 /* Now estimate number of output rows */
1873 temp = rel->tuples *
1874 restrictlist_selectivity(root,
1875 rel->baserestrictinfo,
1880 * Force estimate to be at least one row, to make explain output look
1881 * better and to avoid possible divide-by-zero when interpolating
1882 * cost. Make it an integer, too.
1891 cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo);
1893 set_rel_width(root, rel);
1899 * Set the estimated output width of a base relation.
1901 * NB: this works best on plain relations because it prefers to look at
1902 * real Vars. It will fail to make use of pg_statistic info when applied
1903 * to a subquery relation, even if the subquery outputs are simple vars
1904 * that we could have gotten info for. Is it worth trying to be smarter
1907 * The per-attribute width estimates are cached for possible re-use while
1908 * building join relations.
1911 set_rel_width(Query *root, RelOptInfo *rel)
1913 int32 tuple_width = 0;
1916 foreach(tllist, FastListValue(&rel->reltargetlist))
1918 Var *var = (Var *) lfirst(tllist);
1919 int ndx = var->varattno - rel->min_attr;
1923 Assert(IsA(var, Var));
1926 * The width probably hasn't been cached yet, but may as well
1929 if (rel->attr_widths[ndx] > 0)
1931 tuple_width += rel->attr_widths[ndx];
1935 relid = getrelid(var->varno, root->rtable);
1936 if (relid != InvalidOid)
1938 item_width = get_attavgwidth(relid, var->varattno);
1941 rel->attr_widths[ndx] = item_width;
1942 tuple_width += item_width;
1948 * Not a plain relation, or can't find statistics for it. Estimate
1949 * using just the type info.
1951 item_width = get_typavgwidth(var->vartype, var->vartypmod);
1952 Assert(item_width > 0);
1953 rel->attr_widths[ndx] = item_width;
1954 tuple_width += item_width;
1956 Assert(tuple_width >= 0);
1957 rel->width = tuple_width;
1961 * relation_byte_size
1962 * Estimate the storage space in bytes for a given number of tuples
1963 * of a given width (size in bytes).
1966 relation_byte_size(double tuples, int width)
1968 return tuples * (MAXALIGN(width) + MAXALIGN(sizeof(HeapTupleData)));
1973 * Returns an estimate of the number of pages covered by a given
1974 * number of tuples of a given width (size in bytes).
1977 page_size(double tuples, int width)
1979 return ceil(relation_byte_size(tuples, width) / BLCKSZ);