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) 1996-2002, PostgreSQL Global Development Group
49 * Portions Copyright (c) 1994, Regents of the University of California
52 * $Header: /cvsroot/pgsql/src/backend/optimizer/path/costsize.c,v 1.112 2003/08/04 00:43:20 momjian 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 "parser/parsetree.h"
68 #include "utils/selfuncs.h"
69 #include "utils/lsyscache.h"
70 #include "utils/syscache.h"
73 #define LOG2(x) (log(x) / 0.693147180559945)
74 #define LOG6(x) (log(x) / 1.79175946922805)
77 * Some Paths return less than the nominal number of rows of their parent
78 * relations; join nodes need to do this to get the correct input count:
80 #define PATH_ROWS(path) \
81 (IsA(path, UniquePath) ? \
82 ((UniquePath *) (path))->rows : \
86 double effective_cache_size = DEFAULT_EFFECTIVE_CACHE_SIZE;
87 double random_page_cost = DEFAULT_RANDOM_PAGE_COST;
88 double cpu_tuple_cost = DEFAULT_CPU_TUPLE_COST;
89 double cpu_index_tuple_cost = DEFAULT_CPU_INDEX_TUPLE_COST;
90 double cpu_operator_cost = DEFAULT_CPU_OPERATOR_COST;
92 Cost disable_cost = 100000000.0;
94 bool enable_seqscan = true;
95 bool enable_indexscan = true;
96 bool enable_tidscan = true;
97 bool enable_sort = true;
98 bool enable_hashagg = true;
99 bool enable_nestloop = true;
100 bool enable_mergejoin = true;
101 bool enable_hashjoin = true;
104 static Selectivity estimate_hash_bucketsize(Query *root, Var *var,
106 static bool cost_qual_eval_walker(Node *node, QualCost * total);
107 static Selectivity approx_selectivity(Query *root, List *quals,
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 * Determines and returns the cost of scanning a relation sequentially.
119 cost_seqscan(Path *path, Query *root,
122 Cost startup_cost = 0;
126 /* Should only be applied to base relations */
127 Assert(baserel->relid > 0);
128 Assert(baserel->rtekind == RTE_RELATION);
131 startup_cost += disable_cost;
136 * The cost of reading a page sequentially is 1.0, by definition. Note
137 * that the Unix kernel will typically do some amount of read-ahead
138 * optimization, so that this cost is less than the true cost of
139 * reading a page from disk. We ignore that issue here, but must take
140 * it into account when estimating the cost of non-sequential
143 run_cost += baserel->pages; /* sequential fetches with cost 1.0 */
146 startup_cost += baserel->baserestrictcost.startup;
147 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
148 run_cost += cpu_per_tuple * baserel->tuples;
150 path->startup_cost = startup_cost;
151 path->total_cost = startup_cost + run_cost;
155 * cost_nonsequential_access
156 * Estimate the cost of accessing one page at random from a relation
157 * (or sort temp file) of the given size in pages.
159 * The simplistic model that the cost is random_page_cost is what we want
160 * to use for large relations; but for small ones that is a serious
161 * overestimate because of the effects of caching. This routine tries to
164 * Unfortunately we don't have any good way of estimating the effective cache
165 * size we are working with --- we know that Postgres itself has NBuffers
166 * internal buffers, but the size of the kernel's disk cache is uncertain,
167 * and how much of it we get to use is even less certain. We punt the problem
168 * for now by assuming we are given an effective_cache_size parameter.
170 * Given a guesstimated cache size, we estimate the actual I/O cost per page
171 * with the entirely ad-hoc equations:
172 * if relpages >= effective_cache_size:
173 * random_page_cost * (1 - (effective_cache_size/relpages)/2)
174 * if relpages < effective_cache_size:
175 * 1 + (random_page_cost/2-1) * (relpages/effective_cache_size) ** 2
176 * These give the right asymptotic behavior (=> 1.0 as relpages becomes
177 * small, => random_page_cost as it becomes large) and meet in the middle
178 * with the estimate that the cache is about 50% effective for a relation
179 * of the same size as effective_cache_size. (XXX this is probably all
180 * wrong, but I haven't been able to find any theory about how effective
181 * a disk cache should be presumed to be.)
184 cost_nonsequential_access(double relpages)
188 /* don't crash on bad input data */
189 if (relpages <= 0.0 || effective_cache_size <= 0.0)
190 return random_page_cost;
192 relsize = relpages / effective_cache_size;
195 return random_page_cost * (1.0 - 0.5 / relsize);
197 return 1.0 + (random_page_cost * 0.5 - 1.0) * relsize * relsize;
202 * Determines and returns the cost of scanning a relation using an index.
204 * NOTE: an indexscan plan node can actually represent several passes,
205 * but here we consider the cost of just one pass.
207 * 'root' is the query root
208 * 'baserel' is the base relation the index is for
209 * 'index' is the index to be used
210 * 'indexQuals' is the list of applicable qual clauses (implicit AND semantics)
211 * 'is_injoin' is T if we are considering using the index scan as the inside
212 * of a nestloop join (hence, some of the indexQuals are join clauses)
214 * NOTE: 'indexQuals' must contain only clauses usable as index restrictions.
215 * Any additional quals evaluated as qpquals may reduce the number of returned
216 * tuples, but they won't reduce the number of tuples we have to fetch from
217 * the table, so they don't reduce the scan cost.
220 cost_index(Path *path, Query *root,
226 Cost startup_cost = 0;
228 Cost indexStartupCost;
230 Selectivity indexSelectivity;
231 double indexCorrelation,
236 double tuples_fetched;
237 double pages_fetched;
241 /* Should only be applied to base relations */
242 Assert(IsA(baserel, RelOptInfo) &&
243 IsA(index, IndexOptInfo));
244 Assert(baserel->relid > 0);
245 Assert(baserel->rtekind == RTE_RELATION);
247 if (!enable_indexscan)
248 startup_cost += disable_cost;
251 * Call index-access-method-specific code to estimate the processing
252 * cost for scanning the index, as well as the selectivity of the
253 * index (ie, the fraction of main-table tuples we will have to
254 * retrieve) and its correlation to the main-table tuple order.
256 OidFunctionCall8(index->amcostestimate,
257 PointerGetDatum(root),
258 PointerGetDatum(baserel),
259 PointerGetDatum(index),
260 PointerGetDatum(indexQuals),
261 PointerGetDatum(&indexStartupCost),
262 PointerGetDatum(&indexTotalCost),
263 PointerGetDatum(&indexSelectivity),
264 PointerGetDatum(&indexCorrelation));
266 /* all costs for touching index itself included here */
267 startup_cost += indexStartupCost;
268 run_cost += indexTotalCost - indexStartupCost;
271 * Estimate number of main-table tuples and pages fetched.
273 * When the index ordering is uncorrelated with the table ordering,
274 * we use an approximation proposed by Mackert and Lohman, "Index Scans
275 * Using a Finite LRU Buffer: A Validated I/O Model", ACM Transactions
276 * on Database Systems, Vol. 14, No. 3, September 1989, Pages 401-424.
277 * The Mackert and Lohman approximation is that the number of pages
280 * min(2TNs/(2T+Ns), T) when T <= b
281 * 2TNs/(2T+Ns) when T > b and Ns <= 2Tb/(2T-b)
282 * b + (Ns - 2Tb/(2T-b))*(T-b)/T when T > b and Ns > 2Tb/(2T-b)
284 * T = # pages in table
285 * N = # tuples in table
286 * s = selectivity = fraction of table to be scanned
287 * b = # buffer pages available (we include kernel space here)
289 * When the index ordering is exactly correlated with the table ordering
290 * (just after a CLUSTER, for example), the number of pages fetched should
291 * be just sT. What's more, these will be sequential fetches, not the
292 * random fetches that occur in the uncorrelated case. So, depending on
293 * the extent of correlation, we should estimate the actual I/O cost
294 * somewhere between s * T * 1.0 and PF * random_cost. We currently
295 * interpolate linearly between these two endpoints based on the
296 * correlation squared (XXX is that appropriate?).
298 * In any case the number of tuples fetched is Ns.
302 tuples_fetched = indexSelectivity * baserel->tuples;
303 /* Don't believe estimates less than 1... */
304 if (tuples_fetched < 1.0)
305 tuples_fetched = 1.0;
307 /* This part is the Mackert and Lohman formula */
309 T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
310 b = (effective_cache_size > 1) ? effective_cache_size : 1.0;
315 (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
316 if (pages_fetched > T)
323 lim = (2.0 * T * b) / (2.0 * T - b);
324 if (tuples_fetched <= lim)
327 (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
332 b + (tuples_fetched - lim) * (T - b) / T;
337 * min_IO_cost corresponds to the perfectly correlated case
338 * (csquared=1), max_IO_cost to the perfectly uncorrelated case
339 * (csquared=0). Note that we just charge random_page_cost per page
340 * in the uncorrelated case, rather than using
341 * cost_nonsequential_access, since we've already accounted for
342 * caching effects by using the Mackert model.
344 min_IO_cost = ceil(indexSelectivity * T);
345 max_IO_cost = pages_fetched * random_page_cost;
348 * Now interpolate based on estimated index order correlation to get
349 * total disk I/O cost for main table accesses.
351 csquared = indexCorrelation * indexCorrelation;
353 run_cost += max_IO_cost + csquared * (min_IO_cost - max_IO_cost);
356 * Estimate CPU costs per tuple.
358 * Normally the indexquals will be removed from the list of restriction
359 * clauses that we have to evaluate as qpquals, so we should subtract
360 * their costs from baserestrictcost. But if we are doing a join then
361 * some of the indexquals are join clauses and shouldn't be
362 * subtracted. Rather than work out exactly how much to subtract, we
363 * don't subtract anything.
365 * XXX For a lossy index, not all the quals will be removed and so we
366 * really shouldn't subtract their costs; but detecting that seems
367 * more expensive than it's worth.
369 startup_cost += baserel->baserestrictcost.startup;
370 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
374 QualCost index_qual_cost;
376 cost_qual_eval(&index_qual_cost, indexQuals);
377 cpu_per_tuple -= index_qual_cost.per_tuple;
380 run_cost += cpu_per_tuple * tuples_fetched;
382 path->startup_cost = startup_cost;
383 path->total_cost = startup_cost + run_cost;
388 * Determines and returns the cost of scanning a relation using TIDs.
391 cost_tidscan(Path *path, Query *root,
392 RelOptInfo *baserel, List *tideval)
394 Cost startup_cost = 0;
397 int ntuples = length(tideval);
399 /* Should only be applied to base relations */
400 Assert(baserel->relid > 0);
401 Assert(baserel->rtekind == RTE_RELATION);
404 startup_cost += disable_cost;
406 /* disk costs --- assume each tuple on a different page */
407 run_cost += random_page_cost * ntuples;
410 startup_cost += baserel->baserestrictcost.startup;
411 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
412 run_cost += cpu_per_tuple * ntuples;
414 path->startup_cost = startup_cost;
415 path->total_cost = startup_cost + run_cost;
420 * Determines and returns the cost of scanning a subquery RTE.
423 cost_subqueryscan(Path *path, RelOptInfo *baserel)
429 /* Should only be applied to base relations that are subqueries */
430 Assert(baserel->relid > 0);
431 Assert(baserel->rtekind == RTE_SUBQUERY);
434 * Cost of path is cost of evaluating the subplan, plus cost of
435 * evaluating any restriction clauses that will be attached to the
436 * SubqueryScan node, plus cpu_tuple_cost to account for selection and
437 * projection overhead.
439 path->startup_cost = baserel->subplan->startup_cost;
440 path->total_cost = baserel->subplan->total_cost;
442 startup_cost = baserel->baserestrictcost.startup;
443 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
444 run_cost = cpu_per_tuple * baserel->tuples;
446 path->startup_cost += startup_cost;
447 path->total_cost += startup_cost + run_cost;
452 * Determines and returns the cost of scanning a function RTE.
455 cost_functionscan(Path *path, Query *root, RelOptInfo *baserel)
457 Cost startup_cost = 0;
461 /* Should only be applied to base relations that are functions */
462 Assert(baserel->relid > 0);
463 Assert(baserel->rtekind == RTE_FUNCTION);
466 * For now, estimate function's cost at one operator eval per function
467 * call. Someday we should revive the function cost estimate columns
470 cpu_per_tuple = cpu_operator_cost;
472 /* Add scanning CPU costs */
473 startup_cost += baserel->baserestrictcost.startup;
474 cpu_per_tuple += cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
475 run_cost += cpu_per_tuple * baserel->tuples;
477 path->startup_cost = startup_cost;
478 path->total_cost = startup_cost + run_cost;
483 * Determines and returns the cost of sorting a relation, including
484 * the cost of reading the input data.
486 * If the total volume of data to sort is less than SortMem, we will do
487 * an in-memory sort, which requires no I/O and about t*log2(t) tuple
488 * comparisons for t tuples.
490 * If the total volume exceeds SortMem, we switch to a tape-style merge
491 * algorithm. There will still be about t*log2(t) tuple comparisons in
492 * total, but we will also need to write and read each tuple once per
493 * merge pass. We expect about ceil(log6(r)) merge passes where r is the
494 * number of initial runs formed (log6 because tuplesort.c uses six-tape
495 * merging). Since the average initial run should be about twice SortMem,
497 * disk traffic = 2 * relsize * ceil(log6(p / (2*SortMem)))
498 * cpu = comparison_cost * t * log2(t)
500 * The disk traffic is assumed to be half sequential and half random
501 * accesses (XXX can't we refine that guess?)
503 * We charge two operator evals per tuple comparison, which should be in
504 * the right ballpark in most cases.
506 * 'pathkeys' is a list of sort keys
507 * 'input_cost' is the total cost for reading the input data
508 * 'tuples' is the number of tuples in the relation
509 * 'width' is the average tuple width in bytes
511 * NOTE: some callers currently pass NIL for pathkeys because they
512 * can't conveniently supply the sort keys. Since this routine doesn't
513 * currently do anything with pathkeys anyway, that doesn't matter...
514 * but if it ever does, it should react gracefully to lack of key data.
515 * (Actually, the thing we'd most likely be interested in is just the number
516 * of sort keys, which all callers *could* supply.)
519 cost_sort(Path *path, Query *root,
520 List *pathkeys, Cost input_cost, double tuples, int width)
522 Cost startup_cost = input_cost;
524 double nbytes = relation_byte_size(tuples, width);
525 long sortmembytes = SortMem * 1024L;
528 startup_cost += disable_cost;
531 * We want to be sure the cost of a sort is never estimated as zero,
532 * even if passed-in tuple count is zero. Besides, mustn't do
541 * Assume about two operator evals per tuple comparison and N log2 N
544 startup_cost += 2.0 * cpu_operator_cost * tuples * LOG2(tuples);
547 if (nbytes > sortmembytes)
549 double npages = ceil(nbytes / BLCKSZ);
550 double nruns = nbytes / (sortmembytes * 2);
551 double log_runs = ceil(LOG6(nruns));
552 double npageaccesses;
556 npageaccesses = 2.0 * npages * log_runs;
557 /* Assume half are sequential (cost 1), half are not */
558 startup_cost += npageaccesses *
559 (1.0 + cost_nonsequential_access(npages)) * 0.5;
563 * Also charge a small amount (arbitrarily set equal to operator cost)
564 * per extracted tuple.
566 run_cost += cpu_operator_cost * tuples;
568 path->startup_cost = startup_cost;
569 path->total_cost = startup_cost + run_cost;
574 * Determines and returns the cost of materializing a relation, including
575 * the cost of reading the input data.
577 * If the total volume of data to materialize exceeds SortMem, we will need
578 * to write it to disk, so the cost is much higher in that case.
581 cost_material(Path *path,
582 Cost input_cost, double tuples, int width)
584 Cost startup_cost = input_cost;
586 double nbytes = relation_byte_size(tuples, width);
587 long sortmembytes = SortMem * 1024L;
590 if (nbytes > sortmembytes)
592 double npages = ceil(nbytes / BLCKSZ);
594 /* We'll write during startup and read during retrieval */
595 startup_cost += npages;
600 * Also charge a small amount per extracted tuple. We use
601 * cpu_tuple_cost so that it doesn't appear worthwhile to materialize
604 run_cost += cpu_tuple_cost * tuples;
606 path->startup_cost = startup_cost;
607 path->total_cost = startup_cost + run_cost;
612 * Determines and returns the cost of performing an Agg plan node,
613 * including the cost of its input.
615 * Note: when aggstrategy == AGG_SORTED, caller must ensure that input costs
616 * are for appropriately-sorted input.
619 cost_agg(Path *path, Query *root,
620 AggStrategy aggstrategy, int numAggs,
621 int numGroupCols, double numGroups,
622 Cost input_startup_cost, Cost input_total_cost,
629 * We charge one cpu_operator_cost per aggregate function per input
630 * tuple, and another one per output tuple (corresponding to transfn
631 * and finalfn calls respectively). If we are grouping, we charge an
632 * additional cpu_operator_cost per grouping column per input tuple
633 * for grouping comparisons.
635 * We will produce a single output tuple if not grouping, and a tuple per
638 * Note: in this cost model, AGG_SORTED and AGG_HASHED have exactly the
639 * same total CPU cost, but AGG_SORTED has lower startup cost. If the
640 * input path is already sorted appropriately, AGG_SORTED should be
641 * preferred (since it has no risk of memory overflow). This will
642 * happen as long as the computed total costs are indeed exactly equal
643 * --- but if there's roundoff error we might do the wrong thing. So
644 * be sure that the computations below form the same intermediate
645 * values in the same order.
647 if (aggstrategy == AGG_PLAIN)
649 startup_cost = input_total_cost;
650 startup_cost += cpu_operator_cost * (input_tuples + 1) * numAggs;
651 /* we aren't grouping */
652 total_cost = startup_cost;
654 else if (aggstrategy == AGG_SORTED)
656 /* Here we are able to deliver output on-the-fly */
657 startup_cost = input_startup_cost;
658 total_cost = input_total_cost;
659 /* calcs phrased this way to match HASHED case, see note above */
660 total_cost += cpu_operator_cost * input_tuples * numGroupCols;
661 total_cost += cpu_operator_cost * input_tuples * numAggs;
662 total_cost += cpu_operator_cost * numGroups * numAggs;
666 /* must be AGG_HASHED */
667 startup_cost = input_total_cost;
668 startup_cost += cpu_operator_cost * input_tuples * numGroupCols;
669 startup_cost += cpu_operator_cost * input_tuples * numAggs;
670 total_cost = startup_cost;
671 total_cost += cpu_operator_cost * numGroups * numAggs;
674 path->startup_cost = startup_cost;
675 path->total_cost = total_cost;
680 * Determines and returns the cost of performing a Group plan node,
681 * including the cost of its input.
683 * Note: caller must ensure that input costs are for appropriately-sorted
687 cost_group(Path *path, Query *root,
688 int numGroupCols, double numGroups,
689 Cost input_startup_cost, Cost input_total_cost,
695 startup_cost = input_startup_cost;
696 total_cost = input_total_cost;
699 * Charge one cpu_operator_cost per comparison per input tuple. We
700 * assume all columns get compared at most of the tuples.
702 total_cost += cpu_operator_cost * input_tuples * numGroupCols;
704 path->startup_cost = startup_cost;
705 path->total_cost = total_cost;
710 * Determines and returns the cost of joining two relations using the
711 * nested loop algorithm.
713 * 'path' is already filled in except for the cost fields
716 cost_nestloop(NestPath *path, Query *root)
718 Path *outer_path = path->outerjoinpath;
719 Path *inner_path = path->innerjoinpath;
720 List *restrictlist = path->joinrestrictinfo;
721 Cost startup_cost = 0;
724 QualCost restrict_qual_cost;
725 double outer_path_rows = PATH_ROWS(outer_path);
726 double inner_path_rows = PATH_ROWS(inner_path);
728 Selectivity joininfactor;
730 if (!enable_nestloop)
731 startup_cost += disable_cost;
734 * If we're doing JOIN_IN then we will stop scanning inner tuples for
735 * an outer tuple as soon as we have one match. Account for the
736 * effects of this by scaling down the cost estimates in proportion to
737 * the expected output size. (This assumes that all the quals
738 * attached to the join are IN quals, which should be true.)
740 * Note: it's probably bogus to use the normal selectivity calculation
741 * here when either the outer or inner path is a UniquePath.
743 if (path->jointype == JOIN_IN)
745 Selectivity qual_selec = approx_selectivity(root, restrictlist,
749 qptuples = ceil(qual_selec * outer_path_rows * inner_path_rows);
750 if (qptuples > path->path.parent->rows)
751 joininfactor = path->path.parent->rows / qptuples;
758 /* cost of source data */
761 * NOTE: clearly, we must pay both outer and inner paths' startup_cost
762 * before we can start returning tuples, so the join's startup cost is
763 * their sum. What's not so clear is whether the inner path's
764 * startup_cost must be paid again on each rescan of the inner path.
765 * This is not true if the inner path is materialized or is a
766 * hashjoin, but probably is true otherwise.
768 startup_cost += outer_path->startup_cost + inner_path->startup_cost;
769 run_cost += outer_path->total_cost - outer_path->startup_cost;
770 if (IsA(inner_path, MaterialPath) ||
771 IsA(inner_path, HashPath))
773 /* charge only run cost for each iteration of inner path */
778 * charge startup cost for each iteration of inner path, except we
779 * already charged the first startup_cost in our own startup
781 run_cost += (outer_path_rows - 1) * inner_path->startup_cost;
783 run_cost += outer_path_rows *
784 (inner_path->total_cost - inner_path->startup_cost) * joininfactor;
787 * Compute number of tuples processed (not number emitted!). If inner
788 * path is an indexscan, be sure to use its estimated output row
789 * count, which may be lower than the restriction-clause-only row
790 * count of its parent. (We don't include this case in the PATH_ROWS
791 * macro because it applies *only* to a nestloop's inner relation.)
792 * Note: it is correct to use the unadjusted inner_path_rows in the
793 * above calculation for joininfactor, since otherwise we'd be
794 * double-counting the selectivity of the join clause being used for
797 if (IsA(inner_path, IndexPath))
798 inner_path_rows = ((IndexPath *) inner_path)->rows;
800 ntuples = inner_path_rows * outer_path_rows;
803 cost_qual_eval(&restrict_qual_cost, restrictlist);
804 startup_cost += restrict_qual_cost.startup;
805 cpu_per_tuple = cpu_tuple_cost + restrict_qual_cost.per_tuple;
806 run_cost += cpu_per_tuple * ntuples;
808 path->path.startup_cost = startup_cost;
809 path->path.total_cost = startup_cost + run_cost;
814 * Determines and returns the cost of joining two relations using the
815 * merge join algorithm.
817 * 'path' is already filled in except for the cost fields
819 * Notes: path's mergeclauses should be a subset of the joinrestrictinfo list;
820 * outersortkeys and innersortkeys are lists of the keys to be used
821 * to sort the outer and inner relations, or NIL if no explicit
822 * sort is needed because the source path is already ordered.
825 cost_mergejoin(MergePath *path, Query *root)
827 Path *outer_path = path->jpath.outerjoinpath;
828 Path *inner_path = path->jpath.innerjoinpath;
829 List *restrictlist = path->jpath.joinrestrictinfo;
830 List *mergeclauses = path->path_mergeclauses;
831 List *outersortkeys = path->outersortkeys;
832 List *innersortkeys = path->innersortkeys;
833 Cost startup_cost = 0;
836 Selectivity merge_selec;
837 Selectivity qp_selec;
838 QualCost merge_qual_cost;
839 QualCost qp_qual_cost;
840 RestrictInfo *firstclause;
842 double outer_path_rows = PATH_ROWS(outer_path);
843 double inner_path_rows = PATH_ROWS(inner_path);
846 double mergejointuples,
850 Selectivity outerscansel,
852 Selectivity joininfactor;
853 Path sort_path; /* dummy for result of cost_sort */
855 if (!enable_mergejoin)
856 startup_cost += disable_cost;
859 * Compute cost and selectivity of the mergequals and qpquals (other
860 * restriction clauses) separately. We use approx_selectivity here
861 * for speed --- in most cases, any errors won't affect the result
864 * Note: it's probably bogus to use the normal selectivity calculation
865 * here when either the outer or inner path is a UniquePath.
867 merge_selec = approx_selectivity(root, mergeclauses,
868 path->jpath.jointype);
869 cost_qual_eval(&merge_qual_cost, mergeclauses);
870 qpquals = set_ptrDifference(restrictlist, mergeclauses);
871 qp_selec = approx_selectivity(root, qpquals,
872 path->jpath.jointype);
873 cost_qual_eval(&qp_qual_cost, qpquals);
876 /* approx # tuples passing the merge quals */
877 mergejointuples = ceil(merge_selec * outer_path_rows * inner_path_rows);
878 /* approx # tuples passing qpquals as well */
879 qptuples = ceil(mergejointuples * qp_selec);
882 * When there are equal merge keys in the outer relation, the
883 * mergejoin must rescan any matching tuples in the inner relation.
884 * This means re-fetching inner tuples. Our cost model for this is
885 * that a re-fetch costs the same as an original fetch, which is
886 * probably an overestimate; but on the other hand we ignore the
887 * bookkeeping costs of mark/restore. Not clear if it's worth
888 * developing a more refined model.
890 * The number of re-fetches can be estimated approximately as size of
891 * merge join output minus size of inner relation. Assume that the
892 * distinct key values are 1, 2, ..., and denote the number of values
893 * of each key in the outer relation as m1, m2, ...; in the inner
894 * relation, n1, n2, ... Then we have
896 * size of join = m1 * n1 + m2 * n2 + ...
898 * number of rescanned tuples = (m1 - 1) * n1 + (m2 - 1) * n2 + ... = m1 *
899 * n1 + m2 * n2 + ... - (n1 + n2 + ...) = size of join - size of inner
902 * This equation works correctly for outer tuples having no inner match
903 * (nk = 0), but not for inner tuples having no outer match (mk = 0);
904 * we are effectively subtracting those from the number of rescanned
905 * tuples, when we should not. Can we do better without expensive
906 * selectivity computations?
908 if (IsA(outer_path, UniquePath))
912 rescannedtuples = mergejointuples - inner_path_rows;
913 /* Must clamp because of possible underestimate */
914 if (rescannedtuples < 0)
917 /* We'll inflate inner run cost this much to account for rescanning */
918 rescanratio = 1.0 + (rescannedtuples / inner_path_rows);
921 * A merge join will stop as soon as it exhausts either input stream.
922 * Estimate fraction of the left and right inputs that will actually
923 * need to be scanned. We use only the first (most significant) merge
924 * 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 firstclause = (RestrictInfo *) lfirst(mergeclauses);
931 if (firstclause->left_mergescansel < 0) /* not computed yet? */
932 mergejoinscansel(root, (Node *) firstclause->clause,
933 &firstclause->left_mergescansel,
934 &firstclause->right_mergescansel);
936 if (bms_is_subset(firstclause->left_relids, outer_path->parent->relids))
938 /* left side of clause is outer */
939 outerscansel = firstclause->left_mergescansel;
940 innerscansel = firstclause->right_mergescansel;
944 /* left side of clause is inner */
945 outerscansel = firstclause->right_mergescansel;
946 innerscansel = firstclause->left_mergescansel;
949 /* convert selectivity to row count; must scan at least one row */
951 outer_rows = ceil(outer_path_rows * outerscansel);
954 inner_rows = ceil(inner_path_rows * innerscansel);
959 * Readjust scan selectivities to account for above rounding. This is
960 * normally an insignificant effect, but when there are only a few
961 * rows in the inputs, failing to do this makes for a large percentage
964 outerscansel = outer_rows / outer_path_rows;
965 innerscansel = inner_rows / inner_path_rows;
967 /* cost of source data */
969 if (outersortkeys) /* do we need to sort outer? */
971 cost_sort(&sort_path,
974 outer_path->total_cost,
976 outer_path->parent->width);
977 startup_cost += sort_path.startup_cost;
978 run_cost += (sort_path.total_cost - sort_path.startup_cost)
983 startup_cost += outer_path->startup_cost;
984 run_cost += (outer_path->total_cost - outer_path->startup_cost)
988 if (innersortkeys) /* do we need to sort inner? */
990 cost_sort(&sort_path,
993 inner_path->total_cost,
995 inner_path->parent->width);
996 startup_cost += sort_path.startup_cost;
997 run_cost += (sort_path.total_cost - sort_path.startup_cost)
998 * innerscansel * rescanratio;
1002 startup_cost += inner_path->startup_cost;
1003 run_cost += (inner_path->total_cost - inner_path->startup_cost)
1004 * innerscansel * rescanratio;
1010 * If we're doing JOIN_IN then we will stop outputting inner tuples
1011 * for an outer tuple as soon as we have one match. Account for the
1012 * effects of this by scaling down the cost estimates in proportion to
1013 * the expected output size. (This assumes that all the quals
1014 * attached to the join are IN quals, which should be true.)
1016 if (path->jpath.jointype == JOIN_IN &&
1017 qptuples > path->jpath.path.parent->rows)
1018 joininfactor = path->jpath.path.parent->rows / qptuples;
1023 * The number of tuple comparisons needed is approximately number of
1024 * outer rows plus number of inner rows plus number of rescanned
1025 * tuples (can we refine this?). At each one, we need to evaluate the
1026 * mergejoin quals. NOTE: JOIN_IN mode does not save any work here,
1027 * so do NOT include joininfactor.
1029 startup_cost += merge_qual_cost.startup;
1030 run_cost += merge_qual_cost.per_tuple *
1031 (outer_rows + inner_rows * rescanratio);
1034 * For each tuple that gets through the mergejoin proper, we charge
1035 * cpu_tuple_cost plus the cost of evaluating additional restriction
1036 * clauses that are to be applied at the join. (This is pessimistic
1037 * since not all of the quals may get evaluated at each tuple.) This
1038 * work is skipped in JOIN_IN mode, so apply the factor.
1040 startup_cost += qp_qual_cost.startup;
1041 cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
1042 run_cost += cpu_per_tuple * mergejointuples * joininfactor;
1044 path->jpath.path.startup_cost = startup_cost;
1045 path->jpath.path.total_cost = startup_cost + run_cost;
1050 * Determines and returns the cost of joining two relations using the
1051 * hash join algorithm.
1053 * 'path' is already filled in except for the cost fields
1055 * Note: path's hashclauses should be a subset of the joinrestrictinfo list
1058 cost_hashjoin(HashPath *path, Query *root)
1060 Path *outer_path = path->jpath.outerjoinpath;
1061 Path *inner_path = path->jpath.innerjoinpath;
1062 List *restrictlist = path->jpath.joinrestrictinfo;
1063 List *hashclauses = path->path_hashclauses;
1064 Cost startup_cost = 0;
1067 Selectivity hash_selec;
1068 Selectivity qp_selec;
1069 QualCost hash_qual_cost;
1070 QualCost qp_qual_cost;
1071 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 = length(hashclauses);
1081 int physicalbuckets;
1083 Selectivity innerbucketsize;
1084 Selectivity joininfactor;
1088 if (!enable_hashjoin)
1089 startup_cost += disable_cost;
1092 * Compute cost and selectivity of the hashquals and qpquals (other
1093 * restriction clauses) separately. We use approx_selectivity here
1094 * for speed --- in most cases, any errors won't affect the result
1097 * Note: it's probably bogus to use the normal selectivity calculation
1098 * here when either the outer or inner path is a UniquePath.
1100 hash_selec = approx_selectivity(root, hashclauses,
1101 path->jpath.jointype);
1102 cost_qual_eval(&hash_qual_cost, hashclauses);
1103 qpquals = set_ptrDifference(restrictlist, hashclauses);
1104 qp_selec = approx_selectivity(root, qpquals,
1105 path->jpath.jointype);
1106 cost_qual_eval(&qp_qual_cost, qpquals);
1109 /* approx # tuples passing the hash quals */
1110 hashjointuples = ceil(hash_selec * outer_path_rows * inner_path_rows);
1111 /* approx # tuples passing qpquals as well */
1112 qptuples = ceil(hashjointuples * qp_selec);
1114 /* cost of source data */
1115 startup_cost += outer_path->startup_cost;
1116 run_cost += outer_path->total_cost - outer_path->startup_cost;
1117 startup_cost += inner_path->total_cost;
1120 * Cost of computing hash function: must do it once per input tuple.
1121 * We charge one cpu_operator_cost for each column's hash function.
1123 * XXX when a hashclause is more complex than a single operator, we
1124 * really should charge the extra eval costs of the left or right
1125 * side, as appropriate, here. This seems more work than it's worth
1128 startup_cost += cpu_operator_cost * num_hashclauses * inner_path_rows;
1129 run_cost += cpu_operator_cost * num_hashclauses * outer_path_rows;
1131 /* Get hash table size that executor would use for inner relation */
1132 ExecChooseHashTableSize(inner_path_rows,
1133 inner_path->parent->width,
1139 * Determine bucketsize fraction for inner relation. We use the
1140 * smallest bucketsize estimated for any individual hashclause; this
1141 * is undoubtedly conservative.
1143 * BUT: if inner relation has been unique-ified, we can assume it's good
1144 * for hashing. This is important both because it's the right answer,
1145 * and because we avoid contaminating the cache with a value that's
1146 * wrong for non-unique-ified paths.
1148 if (IsA(inner_path, UniquePath))
1149 innerbucketsize = 1.0 / virtualbuckets;
1152 innerbucketsize = 1.0;
1153 foreach(hcl, hashclauses)
1155 RestrictInfo *restrictinfo = (RestrictInfo *) lfirst(hcl);
1156 Selectivity thisbucketsize;
1158 Assert(IsA(restrictinfo, RestrictInfo));
1161 * First we have to figure out which side of the hashjoin
1162 * clause is the inner side.
1164 * Since we tend to visit the same clauses over and over when
1165 * planning a large query, we cache the bucketsize estimate in
1166 * the RestrictInfo node to avoid repeated lookups of
1169 if (bms_is_subset(restrictinfo->right_relids,
1170 inner_path->parent->relids))
1172 /* righthand side is inner */
1173 thisbucketsize = restrictinfo->right_bucketsize;
1174 if (thisbucketsize < 0)
1176 /* not cached yet */
1178 estimate_hash_bucketsize(root,
1179 (Var *) get_rightop(restrictinfo->clause),
1181 restrictinfo->right_bucketsize = thisbucketsize;
1186 Assert(bms_is_subset(restrictinfo->left_relids,
1187 inner_path->parent->relids));
1188 /* lefthand side is inner */
1189 thisbucketsize = restrictinfo->left_bucketsize;
1190 if (thisbucketsize < 0)
1192 /* not cached yet */
1194 estimate_hash_bucketsize(root,
1195 (Var *) get_leftop(restrictinfo->clause),
1197 restrictinfo->left_bucketsize = thisbucketsize;
1201 if (innerbucketsize > thisbucketsize)
1202 innerbucketsize = thisbucketsize;
1207 * if inner relation is too big then we will need to "batch" the join,
1208 * which implies writing and reading most of the tuples to disk an
1209 * extra time. Charge one cost unit per page of I/O (correct since it
1210 * should be nice and sequential...). Writing the inner rel counts as
1211 * startup cost, all the rest as run cost.
1215 double outerpages = page_size(outer_path_rows,
1216 outer_path->parent->width);
1217 double innerpages = page_size(inner_path_rows,
1218 inner_path->parent->width);
1220 startup_cost += innerpages;
1221 run_cost += innerpages + 2 * outerpages;
1227 * If we're doing JOIN_IN then we will stop comparing inner tuples to
1228 * an outer tuple as soon as we have one match. Account for the
1229 * effects of this by scaling down the cost estimates in proportion to
1230 * the expected output size. (This assumes that all the quals
1231 * attached to the join are IN quals, which should be true.)
1233 if (path->jpath.jointype == JOIN_IN &&
1234 qptuples > path->jpath.path.parent->rows)
1235 joininfactor = path->jpath.path.parent->rows / qptuples;
1240 * The number of tuple comparisons needed is the number of outer
1241 * tuples times the typical number of tuples in a hash bucket, which
1242 * is the inner relation size times its bucketsize fraction. At each
1243 * one, we need to evaluate the hashjoin quals.
1245 startup_cost += hash_qual_cost.startup;
1246 run_cost += hash_qual_cost.per_tuple *
1247 outer_path_rows * ceil(inner_path_rows * innerbucketsize) *
1251 * For each tuple that gets through the hashjoin proper, we charge
1252 * cpu_tuple_cost plus the cost of evaluating additional restriction
1253 * clauses that are to be applied at the join. (This is pessimistic
1254 * since not all of the quals may get evaluated at each tuple.)
1256 startup_cost += qp_qual_cost.startup;
1257 cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
1258 run_cost += cpu_per_tuple * hashjointuples * joininfactor;
1261 * Bias against putting larger relation on inside. We don't want an
1262 * absolute prohibition, though, since larger relation might have
1263 * better bucketsize --- and we can't trust the size estimates
1264 * unreservedly, anyway. Instead, inflate the run cost by the square
1265 * root of the size ratio. (Why square root? No real good reason,
1266 * but it seems reasonable...)
1268 * Note: before 7.4 we implemented this by inflating startup cost; but if
1269 * there's a disable_cost component in the input paths' startup cost,
1270 * that unfairly penalizes the hash. Probably it'd be better to keep
1271 * track of disable penalty separately from cost.
1273 if (innerbytes > outerbytes && outerbytes > 0)
1274 run_cost *= sqrt(innerbytes / outerbytes);
1276 path->jpath.path.startup_cost = startup_cost;
1277 path->jpath.path.total_cost = startup_cost + run_cost;
1281 * Estimate hash bucketsize fraction (ie, number of entries in a bucket
1282 * divided by total tuples in relation) if the specified Var is used
1285 * XXX This is really pretty bogus since we're effectively assuming that the
1286 * distribution of hash keys will be the same after applying restriction
1287 * clauses as it was in the underlying relation. However, we are not nearly
1288 * smart enough to figure out how the restrict clauses might change the
1289 * distribution, so this will have to do for now.
1291 * We are passed the number of buckets the executor will use for the given
1292 * input relation. If the data were perfectly distributed, with the same
1293 * number of tuples going into each available bucket, then the bucketsize
1294 * fraction would be 1/nbuckets. But this happy state of affairs will occur
1295 * only if (a) there are at least nbuckets distinct data values, and (b)
1296 * we have a not-too-skewed data distribution. Otherwise the buckets will
1297 * be nonuniformly occupied. If the other relation in the join has a key
1298 * distribution similar to this one's, then the most-loaded buckets are
1299 * exactly those that will be probed most often. Therefore, the "average"
1300 * bucket size for costing purposes should really be taken as something close
1301 * to the "worst case" bucket size. We try to estimate this by adjusting the
1302 * fraction if there are too few distinct data values, and then scaling up
1303 * by the ratio of the most common value's frequency to the average frequency.
1305 * If no statistics are available, use a default estimate of 0.1. This will
1306 * discourage use of a hash rather strongly if the inner relation is large,
1307 * which is what we want. We do not want to hash unless we know that the
1308 * inner rel is well-dispersed (or the alternatives seem much worse).
1311 estimate_hash_bucketsize(Query *root, Var *var, int nbuckets)
1316 Form_pg_statistic stats;
1325 * Lookup info about var's relation and attribute; if none available,
1326 * return default estimate.
1328 if (var == NULL || !IsA(var, Var))
1331 relid = getrelid(var->varno, root->rtable);
1332 if (relid == InvalidOid)
1335 rel = find_base_rel(root, var->varno);
1337 if (rel->tuples <= 0.0 || rel->rows <= 0.0)
1338 return 0.1; /* ensure we can divide below */
1340 tuple = SearchSysCache(STATRELATT,
1341 ObjectIdGetDatum(relid),
1342 Int16GetDatum(var->varattno),
1344 if (!HeapTupleIsValid(tuple))
1347 * Perhaps the Var is a system attribute; if so, it will have no
1348 * entry in pg_statistic, but we may be able to guess something
1349 * about its distribution anyway.
1351 switch (var->varattno)
1353 case ObjectIdAttributeNumber:
1354 case SelfItemPointerAttributeNumber:
1355 /* these are unique, so buckets should be well-distributed */
1356 return 1.0 / (double) nbuckets;
1357 case TableOidAttributeNumber:
1358 /* hashing this is a terrible idea... */
1363 stats = (Form_pg_statistic) GETSTRUCT(tuple);
1366 * Obtain number of distinct data values in raw relation.
1368 ndistinct = stats->stadistinct;
1369 if (ndistinct < 0.0)
1370 ndistinct = -ndistinct * rel->tuples;
1372 if (ndistinct <= 0.0) /* ensure we can divide */
1374 ReleaseSysCache(tuple);
1378 /* Also compute avg freq of all distinct data values in raw relation */
1379 avgfreq = (1.0 - stats->stanullfrac) / ndistinct;
1382 * Adjust ndistinct to account for restriction clauses. Observe we
1383 * are assuming that the data distribution is affected uniformly by
1384 * the restriction clauses!
1386 * XXX Possibly better way, but much more expensive: multiply by
1387 * selectivity of rel's restriction clauses that mention the target
1390 ndistinct *= rel->rows / rel->tuples;
1393 * Initial estimate of bucketsize fraction is 1/nbuckets as long as
1394 * the number of buckets is less than the expected number of distinct
1395 * values; otherwise it is 1/ndistinct.
1397 if (ndistinct > (double) nbuckets)
1398 estfract = 1.0 / (double) nbuckets;
1400 estfract = 1.0 / ndistinct;
1403 * Look up the frequency of the most common value, if available.
1407 if (get_attstatsslot(tuple, var->vartype, var->vartypmod,
1408 STATISTIC_KIND_MCV, InvalidOid,
1409 NULL, NULL, &numbers, &nnumbers))
1412 * The first MCV stat is for the most common value.
1415 mcvfreq = numbers[0];
1416 free_attstatsslot(var->vartype, NULL, 0,
1421 * Adjust estimated bucketsize upward to account for skewed
1424 if (avgfreq > 0.0 && mcvfreq > avgfreq)
1425 estfract *= mcvfreq / avgfreq;
1428 * Clamp bucketsize to sane range (the above adjustment could easily
1429 * produce an out-of-range result). We set the lower bound a little
1430 * above zero, since zero isn't a very sane result.
1432 if (estfract < 1.0e-6)
1434 else if (estfract > 1.0)
1437 ReleaseSysCache(tuple);
1439 return (Selectivity) estfract;
1445 * Estimate the CPU costs of evaluating a WHERE clause.
1446 * The input can be either an implicitly-ANDed list of boolean
1447 * expressions, or a list of RestrictInfo nodes.
1448 * The result includes both a one-time (startup) component,
1449 * and a per-evaluation component.
1452 cost_qual_eval(QualCost * cost, List *quals)
1457 cost->per_tuple = 0;
1459 /* We don't charge any cost for the implicit ANDing at top level ... */
1463 Node *qual = (Node *) lfirst(l);
1466 * RestrictInfo nodes contain an eval_cost field reserved for this
1467 * routine's use, so that it's not necessary to evaluate the qual
1468 * clause's cost more than once. If the clause's cost hasn't been
1469 * computed yet, the field's startup value will contain -1.
1471 if (qual && IsA(qual, RestrictInfo))
1473 RestrictInfo *restrictinfo = (RestrictInfo *) qual;
1475 if (restrictinfo->eval_cost.startup < 0)
1477 restrictinfo->eval_cost.startup = 0;
1478 restrictinfo->eval_cost.per_tuple = 0;
1479 cost_qual_eval_walker((Node *) restrictinfo->clause,
1480 &restrictinfo->eval_cost);
1482 cost->startup += restrictinfo->eval_cost.startup;
1483 cost->per_tuple += restrictinfo->eval_cost.per_tuple;
1487 /* If it's a bare expression, must always do it the hard way */
1488 cost_qual_eval_walker(qual, cost);
1494 cost_qual_eval_walker(Node *node, QualCost * total)
1500 * Our basic strategy is to charge one cpu_operator_cost for each
1501 * operator or function node in the given tree. Vars and Consts are
1502 * charged zero, and so are boolean operators (AND, OR, NOT).
1503 * Simplistic, but a lot better than no model at all.
1505 * Should we try to account for the possibility of short-circuit
1506 * evaluation of AND/OR?
1508 if (IsA(node, FuncExpr) ||
1509 IsA(node, OpExpr) ||
1510 IsA(node, DistinctExpr) ||
1511 IsA(node, NullIfExpr))
1512 total->per_tuple += cpu_operator_cost;
1513 else if (IsA(node, ScalarArrayOpExpr))
1515 /* should charge more than 1 op cost, but how many? */
1516 total->per_tuple += cpu_operator_cost * 10;
1518 else if (IsA(node, SubLink))
1520 /* This routine should not be applied to un-planned expressions */
1521 elog(ERROR, "cannot handle unplanned sub-select");
1523 else if (IsA(node, SubPlan))
1526 * A subplan node in an expression typically indicates that the
1527 * subplan will be executed on each evaluation, so charge
1528 * accordingly. (Sub-selects that can be executed as InitPlans
1529 * have already been removed from the expression.)
1531 * An exception occurs when we have decided we can implement the
1532 * subplan by hashing.
1535 SubPlan *subplan = (SubPlan *) node;
1536 Plan *plan = subplan->plan;
1538 if (subplan->useHashTable)
1541 * If we are using a hash table for the subquery outputs, then
1542 * the cost of evaluating the query is a one-time cost. We
1543 * charge one cpu_operator_cost per tuple for the work of
1544 * loading the hashtable, too.
1546 total->startup += plan->total_cost +
1547 cpu_operator_cost * plan->plan_rows;
1550 * The per-tuple costs include the cost of evaluating the
1551 * lefthand expressions, plus the cost of probing the
1552 * hashtable. Recursion into the exprs list will handle the
1553 * lefthand expressions properly, and will count one
1554 * cpu_operator_cost for each comparison operator. That is
1555 * probably too low for the probing cost, but it's hard to
1556 * make a better estimate, so live with it for now.
1562 * Otherwise we will be rescanning the subplan output on each
1563 * evaluation. We need to estimate how much of the output we
1564 * will actually need to scan. NOTE: this logic should agree
1565 * with the estimates used by make_subplan() in
1568 Cost plan_run_cost = plan->total_cost - plan->startup_cost;
1570 if (subplan->subLinkType == EXISTS_SUBLINK)
1572 /* we only need to fetch 1 tuple */
1573 total->per_tuple += plan_run_cost / plan->plan_rows;
1575 else if (subplan->subLinkType == ALL_SUBLINK ||
1576 subplan->subLinkType == ANY_SUBLINK)
1578 /* assume we need 50% of the tuples */
1579 total->per_tuple += 0.50 * plan_run_cost;
1580 /* also charge a cpu_operator_cost per row examined */
1581 total->per_tuple += 0.50 * plan->plan_rows * cpu_operator_cost;
1585 /* assume we need all tuples */
1586 total->per_tuple += plan_run_cost;
1590 * Also account for subplan's startup cost. If the subplan is
1591 * uncorrelated or undirect correlated, AND its topmost node
1592 * is a Sort or Material node, assume that we'll only need to
1593 * pay its startup cost once; otherwise assume we pay the
1594 * startup cost every time.
1596 if (subplan->parParam == NIL &&
1598 IsA(plan, Material)))
1599 total->startup += plan->startup_cost;
1601 total->per_tuple += plan->startup_cost;
1605 return expression_tree_walker(node, cost_qual_eval_walker,
1611 * approx_selectivity
1612 * Quick-and-dirty estimation of clause selectivities.
1613 * The input can be either an implicitly-ANDed list of boolean
1614 * expressions, or a list of RestrictInfo nodes (typically the latter).
1616 * The "quick" part comes from caching the selectivity estimates so we can
1617 * avoid recomputing them later. (Since the same clauses are typically
1618 * examined over and over in different possible join trees, this makes a
1621 * The "dirty" part comes from the fact that the selectivities of multiple
1622 * clauses are estimated independently and multiplied together. Now
1623 * clauselist_selectivity often can't do any better than that anyhow, but
1624 * for some situations (such as range constraints) it is smarter.
1626 * Since we are only using the results to estimate how many potential
1627 * output tuples are generated and passed through qpqual checking, it
1628 * seems OK to live with the approximation.
1631 approx_selectivity(Query *root, List *quals, JoinType jointype)
1633 Selectivity total = 1.0;
1638 Node *qual = (Node *) lfirst(l);
1642 * RestrictInfo nodes contain a this_selec field reserved for this
1643 * routine's use, so that it's not necessary to evaluate the qual
1644 * clause's selectivity more than once. If the clause's
1645 * selectivity hasn't been computed yet, the field will contain
1648 if (qual && IsA(qual, RestrictInfo))
1650 RestrictInfo *restrictinfo = (RestrictInfo *) qual;
1652 if (restrictinfo->this_selec < 0)
1653 restrictinfo->this_selec =
1654 clause_selectivity(root,
1655 (Node *) restrictinfo->clause,
1658 selec = restrictinfo->this_selec;
1662 /* If it's a bare expression, must always do it the hard way */
1663 selec = clause_selectivity(root, qual, 0, jointype);
1672 * set_baserel_size_estimates
1673 * Set the size estimates for the given base relation.
1675 * The rel's targetlist and restrictinfo list must have been constructed
1678 * We set the following fields of the rel node:
1679 * rows: the estimated number of output tuples (after applying
1680 * restriction clauses).
1681 * width: the estimated average output tuple width in bytes.
1682 * baserestrictcost: estimated cost of evaluating baserestrictinfo clauses.
1685 set_baserel_size_estimates(Query *root, RelOptInfo *rel)
1689 /* Should only be applied to base relations */
1690 Assert(rel->relid > 0);
1692 temp = rel->tuples *
1693 restrictlist_selectivity(root,
1694 rel->baserestrictinfo,
1699 * Force estimate to be at least one row, to make explain output look
1700 * better and to avoid possible divide-by-zero when interpolating
1701 * cost. Make it an integer, too.
1710 cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo);
1712 set_rel_width(root, rel);
1716 * set_joinrel_size_estimates
1717 * Set the size estimates for the given join relation.
1719 * The rel's targetlist must have been constructed already, and a
1720 * restriction clause list that matches the given component rels must
1723 * Since there is more than one way to make a joinrel for more than two
1724 * base relations, the results we get here could depend on which component
1725 * rel pair is provided. In theory we should get the same answers no matter
1726 * which pair is provided; in practice, since the selectivity estimation
1727 * routines don't handle all cases equally well, we might not. But there's
1728 * not much to be done about it. (Would it make sense to repeat the
1729 * calculations for each pair of input rels that's encountered, and somehow
1730 * average the results? Probably way more trouble than it's worth.)
1732 * It's important that the results for symmetric JoinTypes be symmetric,
1733 * eg, (rel1, rel2, JOIN_LEFT) should produce the same result as (rel2,
1734 * rel1, JOIN_RIGHT). Also, JOIN_IN should produce the same result as
1735 * JOIN_UNIQUE_INNER, likewise JOIN_REVERSE_IN == JOIN_UNIQUE_OUTER.
1737 * We set the same relnode fields as set_baserel_size_estimates() does.
1740 set_joinrel_size_estimates(Query *root, RelOptInfo *rel,
1741 RelOptInfo *outer_rel,
1742 RelOptInfo *inner_rel,
1751 * Compute joinclause selectivity. Note that we are only considering
1752 * clauses that become restriction clauses at this join level; we are
1753 * not double-counting them because they were not considered in
1754 * estimating the sizes of the component rels.
1756 selec = restrictlist_selectivity(root,
1762 * Basically, we multiply size of Cartesian product by selectivity.
1764 * If we are doing an outer join, take that into account: the output must
1765 * be at least as large as the non-nullable input. (Is there any
1766 * chance of being even smarter?)
1768 * For JOIN_IN and variants, the Cartesian product is figured with
1769 * respect to a unique-ified input, and then we can clamp to the size
1770 * of the other input.
1775 temp = outer_rel->rows * inner_rel->rows * selec;
1778 temp = outer_rel->rows * inner_rel->rows * selec;
1779 if (temp < outer_rel->rows)
1780 temp = outer_rel->rows;
1783 temp = outer_rel->rows * inner_rel->rows * selec;
1784 if (temp < inner_rel->rows)
1785 temp = inner_rel->rows;
1788 temp = outer_rel->rows * inner_rel->rows * selec;
1789 if (temp < outer_rel->rows)
1790 temp = outer_rel->rows;
1791 if (temp < inner_rel->rows)
1792 temp = inner_rel->rows;
1795 case JOIN_UNIQUE_INNER:
1796 upath = create_unique_path(root, inner_rel,
1797 inner_rel->cheapest_total_path);
1798 temp = outer_rel->rows * upath->rows * selec;
1799 if (temp > outer_rel->rows)
1800 temp = outer_rel->rows;
1802 case JOIN_REVERSE_IN:
1803 case JOIN_UNIQUE_OUTER:
1804 upath = create_unique_path(root, outer_rel,
1805 outer_rel->cheapest_total_path);
1806 temp = upath->rows * inner_rel->rows * selec;
1807 if (temp > inner_rel->rows)
1808 temp = inner_rel->rows;
1811 elog(ERROR, "unrecognized join type: %d", (int) jointype);
1812 temp = 0; /* keep compiler quiet */
1817 * Force estimate to be at least one row, to make explain output look
1818 * better and to avoid possible divide-by-zero when interpolating
1819 * cost. Make it an integer, too.
1829 * We need not compute the output width here, because
1830 * build_joinrel_tlist already did.
1835 * set_function_size_estimates
1836 * Set the size estimates for a base relation that is a function call.
1838 * The rel's targetlist and restrictinfo list must have been constructed
1841 * We set the following fields of the rel node:
1842 * rows: the estimated number of output tuples (after applying
1843 * restriction clauses).
1844 * width: the estimated average output tuple width in bytes.
1845 * baserestrictcost: estimated cost of evaluating baserestrictinfo clauses.
1848 set_function_size_estimates(Query *root, RelOptInfo *rel)
1852 /* Should only be applied to base relations that are functions */
1853 Assert(rel->relid > 0);
1854 Assert(rel->rtekind == RTE_FUNCTION);
1857 * Estimate number of rows the function itself will return.
1859 * XXX no idea how to do this yet; but should at least check whether
1860 * function returns set or not...
1864 /* Now estimate number of output rows */
1865 temp = rel->tuples *
1866 restrictlist_selectivity(root,
1867 rel->baserestrictinfo,
1872 * Force estimate to be at least one row, to make explain output look
1873 * better and to avoid possible divide-by-zero when interpolating
1874 * cost. Make it an integer, too.
1883 cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo);
1885 set_rel_width(root, rel);
1891 * Set the estimated output width of a base relation.
1893 * NB: this works best on plain relations because it prefers to look at
1894 * real Vars. It will fail to make use of pg_statistic info when applied
1895 * to a subquery relation, even if the subquery outputs are simple vars
1896 * that we could have gotten info for. Is it worth trying to be smarter
1899 * The per-attribute width estimates are cached for possible re-use while
1900 * building join relations.
1903 set_rel_width(Query *root, RelOptInfo *rel)
1905 int32 tuple_width = 0;
1908 foreach(tllist, FastListValue(&rel->reltargetlist))
1910 Var *var = (Var *) lfirst(tllist);
1911 int ndx = var->varattno - rel->min_attr;
1915 Assert(IsA(var, Var));
1918 * The width probably hasn't been cached yet, but may as well
1921 if (rel->attr_widths[ndx] > 0)
1923 tuple_width += rel->attr_widths[ndx];
1927 relid = getrelid(var->varno, root->rtable);
1928 if (relid != InvalidOid)
1930 item_width = get_attavgwidth(relid, var->varattno);
1933 rel->attr_widths[ndx] = item_width;
1934 tuple_width += item_width;
1940 * Not a plain relation, or can't find statistics for it. Estimate
1941 * using just the type info.
1943 item_width = get_typavgwidth(var->vartype, var->vartypmod);
1944 Assert(item_width > 0);
1945 rel->attr_widths[ndx] = item_width;
1946 tuple_width += item_width;
1948 Assert(tuple_width >= 0);
1949 rel->width = tuple_width;
1953 * relation_byte_size
1954 * Estimate the storage space in bytes for a given number of tuples
1955 * of a given width (size in bytes).
1958 relation_byte_size(double tuples, int width)
1960 return tuples * (MAXALIGN(width) + MAXALIGN(sizeof(HeapTupleData)));
1965 * Returns an estimate of the number of pages covered by a given
1966 * number of tuples of a given width (size in bytes).
1969 page_size(double tuples, int width)
1971 return ceil(relation_byte_size(tuples, width) / BLCKSZ);