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.
41 * Portions Copyright (c) 1996-2001, PostgreSQL Global Development Group
42 * Portions Copyright (c) 1994, Regents of the University of California
45 * $Header: /cvsroot/pgsql/src/backend/optimizer/path/costsize.c,v 1.74 2001/05/20 20:28:18 tgl Exp $
47 *-------------------------------------------------------------------------
54 #include "catalog/pg_statistic.h"
55 #include "executor/nodeHash.h"
56 #include "miscadmin.h"
57 #include "optimizer/clauses.h"
58 #include "optimizer/cost.h"
59 #include "optimizer/pathnode.h"
60 #include "parser/parsetree.h"
61 #include "utils/lsyscache.h"
62 #include "utils/syscache.h"
65 #define LOG2(x) (log(x) / 0.693147180559945)
66 #define LOG6(x) (log(x) / 1.79175946922805)
69 double effective_cache_size = DEFAULT_EFFECTIVE_CACHE_SIZE;
70 double random_page_cost = DEFAULT_RANDOM_PAGE_COST;
71 double cpu_tuple_cost = DEFAULT_CPU_TUPLE_COST;
72 double cpu_index_tuple_cost = DEFAULT_CPU_INDEX_TUPLE_COST;
73 double cpu_operator_cost = DEFAULT_CPU_OPERATOR_COST;
75 Cost disable_cost = 100000000.0;
77 bool enable_seqscan = true;
78 bool enable_indexscan = true;
79 bool enable_tidscan = true;
80 bool enable_sort = true;
81 bool enable_nestloop = true;
82 bool enable_mergejoin = true;
83 bool enable_hashjoin = true;
86 static bool cost_qual_eval_walker(Node *node, Cost *total);
87 static void set_rel_width(Query *root, RelOptInfo *rel);
88 static double relation_byte_size(double tuples, int width);
89 static double page_size(double tuples, int width);
94 * Determines and returns the cost of scanning a relation sequentially.
96 * Note: for historical reasons, this routine and the others in this module
97 * use the passed result Path only to store their startup_cost and total_cost
98 * results into. All the input data they need is passed as separate
99 * parameters, even though much of it could be extracted from the Path.
102 cost_seqscan(Path *path, RelOptInfo *baserel)
104 Cost startup_cost = 0;
108 /* Should only be applied to base relations */
109 Assert(length(baserel->relids) == 1);
110 Assert(!baserel->issubquery);
113 startup_cost += disable_cost;
118 * The cost of reading a page sequentially is 1.0, by definition. Note
119 * that the Unix kernel will typically do some amount of read-ahead
120 * optimization, so that this cost is less than the true cost of
121 * reading a page from disk. We ignore that issue here, but must take
122 * it into account when estimating the cost of non-sequential
125 run_cost += baserel->pages; /* sequential fetches with cost 1.0 */
128 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost;
129 run_cost += cpu_per_tuple * baserel->tuples;
131 path->startup_cost = startup_cost;
132 path->total_cost = startup_cost + run_cost;
136 * cost_nonsequential_access
137 * Estimate the cost of accessing one page at random from a relation
138 * (or sort temp file) of the given size in pages.
140 * The simplistic model that the cost is random_page_cost is what we want
141 * to use for large relations; but for small ones that is a serious
142 * overestimate because of the effects of caching. This routine tries to
145 * Unfortunately we don't have any good way of estimating the effective cache
146 * size we are working with --- we know that Postgres itself has NBuffers
147 * internal buffers, but the size of the kernel's disk cache is uncertain,
148 * and how much of it we get to use is even less certain. We punt the problem
149 * for now by assuming we are given an effective_cache_size parameter.
151 * Given a guesstimated cache size, we estimate the actual I/O cost per page
152 * with the entirely ad-hoc equations:
153 * for rel_size <= effective_cache_size:
154 * 1 + (random_page_cost/2-1) * (rel_size/effective_cache_size) ** 2
155 * for rel_size >= effective_cache_size:
156 * random_page_cost * (1 - (effective_cache_size/rel_size)/2)
157 * These give the right asymptotic behavior (=> 1.0 as rel_size becomes
158 * small, => random_page_cost as it becomes large) and meet in the middle
159 * with the estimate that the cache is about 50% effective for a relation
160 * of the same size as effective_cache_size. (XXX this is probably all
161 * wrong, but I haven't been able to find any theory about how effective
162 * a disk cache should be presumed to be.)
165 cost_nonsequential_access(double relpages)
169 /* don't crash on bad input data */
170 if (relpages <= 0.0 || effective_cache_size <= 0.0)
171 return random_page_cost;
173 relsize = relpages / effective_cache_size;
176 return random_page_cost * (1.0 - 0.5 / relsize);
178 return 1.0 + (random_page_cost * 0.5 - 1.0) * relsize * relsize;
183 * Determines and returns the cost of scanning a relation using an index.
185 * NOTE: an indexscan plan node can actually represent several passes,
186 * but here we consider the cost of just one pass.
188 * 'root' is the query root
189 * 'baserel' is the base relation the index is for
190 * 'index' is the index to be used
191 * 'indexQuals' is the list of applicable qual clauses (implicit AND semantics)
192 * 'is_injoin' is T if we are considering using the index scan as the inside
193 * of a nestloop join (hence, some of the indexQuals are join clauses)
195 * NOTE: 'indexQuals' must contain only clauses usable as index restrictions.
196 * Any additional quals evaluated as qpquals may reduce the number of returned
197 * tuples, but they won't reduce the number of tuples we have to fetch from
198 * the table, so they don't reduce the scan cost.
201 cost_index(Path *path, Query *root,
207 Cost startup_cost = 0;
209 Cost indexStartupCost;
211 Selectivity indexSelectivity;
212 double indexCorrelation,
217 double tuples_fetched;
218 double pages_fetched;
222 /* Should only be applied to base relations */
223 Assert(IsA(baserel, RelOptInfo) &&IsA(index, IndexOptInfo));
224 Assert(length(baserel->relids) == 1);
225 Assert(!baserel->issubquery);
227 if (!enable_indexscan && !is_injoin)
228 startup_cost += disable_cost;
231 * Call index-access-method-specific code to estimate the processing
232 * cost for scanning the index, as well as the selectivity of the
233 * index (ie, the fraction of main-table tuples we will have to
234 * retrieve) and its correlation to the main-table tuple order.
236 OidFunctionCall8(index->amcostestimate,
237 PointerGetDatum(root),
238 PointerGetDatum(baserel),
239 PointerGetDatum(index),
240 PointerGetDatum(indexQuals),
241 PointerGetDatum(&indexStartupCost),
242 PointerGetDatum(&indexTotalCost),
243 PointerGetDatum(&indexSelectivity),
244 PointerGetDatum(&indexCorrelation));
246 /* all costs for touching index itself included here */
247 startup_cost += indexStartupCost;
248 run_cost += indexTotalCost - indexStartupCost;
251 * Estimate number of main-table tuples and pages fetched.
253 * When the index ordering is uncorrelated with the table ordering,
254 * we use an approximation proposed by Mackert and Lohman, "Index Scans
255 * Using a Finite LRU Buffer: A Validated I/O Model", ACM Transactions
256 * on Database Systems, Vol. 14, No. 3, September 1989, Pages 401-424.
257 * The Mackert and Lohman approximation is that the number of pages
260 * min(2TNs/(2T+Ns), T) when T <= b
261 * 2TNs/(2T+Ns) when T > b and Ns <= 2Tb/(2T-b)
262 * b + (Ns - 2Tb/(2T-b))*(T-b)/T when T > b and Ns > 2Tb/(2T-b)
264 * T = # pages in table
265 * N = # tuples in table
266 * s = selectivity = fraction of table to be scanned
267 * b = # buffer pages available (we include kernel space here)
269 * When the index ordering is exactly correlated with the table ordering
270 * (just after a CLUSTER, for example), the number of pages fetched should
271 * be just sT. What's more, these will be sequential fetches, not the
272 * random fetches that occur in the uncorrelated case. So, depending on
273 * the extent of correlation, we should estimate the actual I/O cost
274 * somewhere between s * T * 1.0 and PF * random_cost. We currently
275 * interpolate linearly between these two endpoints based on the
276 * correlation squared (XXX is that appropriate?).
278 * In any case the number of tuples fetched is Ns.
282 tuples_fetched = indexSelectivity * baserel->tuples;
283 /* Don't believe estimates less than 1... */
284 if (tuples_fetched < 1.0)
285 tuples_fetched = 1.0;
287 /* This part is the Mackert and Lohman formula */
289 T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
290 b = (effective_cache_size > 1) ? effective_cache_size : 1.0;
295 (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
296 if (pages_fetched > T)
303 lim = (2.0 * T * b) / (2.0 * T - b);
304 if (tuples_fetched <= lim)
307 (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
312 b + (tuples_fetched - lim) * (T - b) / T;
317 * min_IO_cost corresponds to the perfectly correlated case (csquared=1),
318 * max_IO_cost to the perfectly uncorrelated case (csquared=0). Note
319 * that we just charge random_page_cost per page in the uncorrelated
320 * case, rather than using cost_nonsequential_access, since we've already
321 * accounted for caching effects by using the Mackert model.
323 min_IO_cost = ceil(indexSelectivity * T);
324 max_IO_cost = pages_fetched * random_page_cost;
327 * Now interpolate based on estimated index order correlation
328 * to get total disk I/O cost for main table accesses.
330 csquared = indexCorrelation * indexCorrelation;
332 run_cost += max_IO_cost + csquared * (min_IO_cost - max_IO_cost);
335 * Estimate CPU costs per tuple.
337 * Normally the indexquals will be removed from the list of
338 * restriction clauses that we have to evaluate as qpquals, so we
339 * should subtract their costs from baserestrictcost. For a lossy
340 * index, however, we will have to recheck all the quals and so
341 * mustn't subtract anything. Also, if we are doing a join then some
342 * of the indexquals are join clauses and shouldn't be subtracted.
343 * Rather than work out exactly how much to subtract, we don't
344 * subtract anything in that case either.
346 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost;
348 if (!index->lossy && !is_injoin)
349 cpu_per_tuple -= cost_qual_eval(indexQuals);
351 run_cost += cpu_per_tuple * tuples_fetched;
353 path->startup_cost = startup_cost;
354 path->total_cost = startup_cost + run_cost;
359 * Determines and returns the cost of scanning a relation using tid-s.
362 cost_tidscan(Path *path, RelOptInfo *baserel, List *tideval)
364 Cost startup_cost = 0;
367 int ntuples = length(tideval);
370 startup_cost += disable_cost;
372 /* disk costs --- assume each tuple on a different page */
373 run_cost += random_page_cost * ntuples;
376 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost;
377 run_cost += cpu_per_tuple * ntuples;
379 path->startup_cost = startup_cost;
380 path->total_cost = startup_cost + run_cost;
385 * Determines and returns the cost of sorting a relation.
387 * The cost of supplying the input data is NOT included; the caller should
388 * add that cost to both startup and total costs returned from this routine!
390 * If the total volume of data to sort is less than SortMem, we will do
391 * an in-memory sort, which requires no I/O and about t*log2(t) tuple
392 * comparisons for t tuples.
394 * If the total volume exceeds SortMem, we switch to a tape-style merge
395 * algorithm. There will still be about t*log2(t) tuple comparisons in
396 * total, but we will also need to write and read each tuple once per
397 * merge pass. We expect about ceil(log6(r)) merge passes where r is the
398 * number of initial runs formed (log6 because tuplesort.c uses six-tape
399 * merging). Since the average initial run should be about twice SortMem,
401 * disk traffic = 2 * relsize * ceil(log6(p / (2*SortMem)))
402 * cpu = comparison_cost * t * log2(t)
404 * The disk traffic is assumed to be half sequential and half random
405 * accesses (XXX can't we refine that guess?)
407 * We charge two operator evals per tuple comparison, which should be in
408 * the right ballpark in most cases.
410 * 'pathkeys' is a list of sort keys
411 * 'tuples' is the number of tuples in the relation
412 * 'width' is the average tuple width in bytes
414 * NOTE: some callers currently pass NIL for pathkeys because they
415 * can't conveniently supply the sort keys. Since this routine doesn't
416 * currently do anything with pathkeys anyway, that doesn't matter...
417 * but if it ever does, it should react gracefully to lack of key data.
420 cost_sort(Path *path, List *pathkeys, double tuples, int width)
422 Cost startup_cost = 0;
424 double nbytes = relation_byte_size(tuples, width);
425 long sortmembytes = SortMem * 1024L;
428 startup_cost += disable_cost;
431 * We want to be sure the cost of a sort is never estimated as zero,
432 * even if passed-in tuple count is zero. Besides, mustn't do
441 * Assume about two operator evals per tuple comparison and N log2 N
444 startup_cost += 2.0 * cpu_operator_cost * tuples * LOG2(tuples);
447 if (nbytes > sortmembytes)
449 double npages = ceil(nbytes / BLCKSZ);
450 double nruns = nbytes / (sortmembytes * 2);
451 double log_runs = ceil(LOG6(nruns));
452 double npageaccesses;
456 npageaccesses = 2.0 * npages * log_runs;
457 /* Assume half are sequential (cost 1), half are not */
458 startup_cost += npageaccesses *
459 (1.0 + cost_nonsequential_access(npages)) * 0.5;
463 * Note: should we bother to assign a nonzero run_cost to reflect the
464 * overhead of extracting tuples from the sort result? Probably not
465 * worth worrying about.
467 path->startup_cost = startup_cost;
468 path->total_cost = startup_cost + run_cost;
474 * Determines and returns the cost of joining two relations using the
475 * nested loop algorithm.
477 * 'outer_path' is the path for the outer relation
478 * 'inner_path' is the path for the inner relation
479 * 'restrictlist' are the RestrictInfo nodes to be applied at the join
482 cost_nestloop(Path *path,
487 Cost startup_cost = 0;
492 if (!enable_nestloop)
493 startup_cost += disable_cost;
495 /* cost of source data */
498 * NOTE: clearly, we must pay both outer and inner paths' startup_cost
499 * before we can start returning tuples, so the join's startup cost
500 * is their sum. What's not so clear is whether the inner path's
501 * startup_cost must be paid again on each rescan of the inner path.
502 * This is not true if the inner path is materialized, but probably
503 * is true otherwise. Since we don't yet have clean handling of the
504 * decision whether to materialize a path, we can't tell here which
505 * will happen. As a compromise, charge 50% of the inner startup cost
508 startup_cost += outer_path->startup_cost + inner_path->startup_cost;
509 run_cost += outer_path->total_cost - outer_path->startup_cost;
510 run_cost += outer_path->parent->rows *
511 (inner_path->total_cost - inner_path->startup_cost);
512 if (outer_path->parent->rows > 1)
513 run_cost += (outer_path->parent->rows - 1) * inner_path->startup_cost;
516 * Number of tuples processed (not number emitted!). If inner path is
517 * an indexscan, be sure to use its estimated output row count, which
518 * may be lower than the restriction-clause-only row count of its
521 if (IsA(inner_path, IndexPath))
522 ntuples = ((IndexPath *) inner_path)->rows;
524 ntuples = inner_path->parent->rows;
525 ntuples *= outer_path->parent->rows;
528 cpu_per_tuple = cpu_tuple_cost + cost_qual_eval(restrictlist);
529 run_cost += cpu_per_tuple * ntuples;
531 path->startup_cost = startup_cost;
532 path->total_cost = startup_cost + run_cost;
537 * Determines and returns the cost of joining two relations using the
538 * merge join algorithm.
540 * 'outer_path' is the path for the outer relation
541 * 'inner_path' is the path for the inner relation
542 * 'restrictlist' are the RestrictInfo nodes to be applied at the join
543 * 'outersortkeys' and 'innersortkeys' are lists of the keys to be used
544 * to sort the outer and inner relations, or NIL if no explicit
545 * sort is needed because the source path is already ordered
548 cost_mergejoin(Path *path,
555 Cost startup_cost = 0;
559 Path sort_path; /* dummy for result of cost_sort */
561 if (!enable_mergejoin)
562 startup_cost += disable_cost;
564 /* cost of source data */
567 * Note we are assuming that each source tuple is fetched just once,
568 * which is not right in the presence of equal keys. If we had a way
569 * of estimating the proportion of equal keys, we could apply a
570 * correction factor...
572 if (outersortkeys) /* do we need to sort outer? */
574 startup_cost += outer_path->total_cost;
575 cost_sort(&sort_path,
577 outer_path->parent->rows,
578 outer_path->parent->width);
579 startup_cost += sort_path.startup_cost;
580 run_cost += sort_path.total_cost - sort_path.startup_cost;
584 startup_cost += outer_path->startup_cost;
585 run_cost += outer_path->total_cost - outer_path->startup_cost;
588 if (innersortkeys) /* do we need to sort inner? */
590 startup_cost += inner_path->total_cost;
591 cost_sort(&sort_path,
593 inner_path->parent->rows,
594 inner_path->parent->width);
595 startup_cost += sort_path.startup_cost;
596 run_cost += sort_path.total_cost - sort_path.startup_cost;
600 startup_cost += inner_path->startup_cost;
601 run_cost += inner_path->total_cost - inner_path->startup_cost;
605 * Estimate the number of tuples to be processed in the mergejoin
606 * itself as one per tuple in the two source relations. This could be
607 * a drastic underestimate if there are many equal-keyed tuples in
608 * either relation, but we have no good way of estimating that...
610 ntuples = outer_path->parent->rows + inner_path->parent->rows;
613 cpu_per_tuple = cpu_tuple_cost + cost_qual_eval(restrictlist);
614 run_cost += cpu_per_tuple * ntuples;
616 path->startup_cost = startup_cost;
617 path->total_cost = startup_cost + run_cost;
622 * Determines and returns the cost of joining two relations using the
623 * hash join algorithm.
625 * 'outer_path' is the path for the outer relation
626 * 'inner_path' is the path for the inner relation
627 * 'restrictlist' are the RestrictInfo nodes to be applied at the join
628 * 'innerbucketsize' is an estimate of the bucketsize statistic
629 * for the inner hash key.
632 cost_hashjoin(Path *path,
636 Selectivity innerbucketsize)
638 Cost startup_cost = 0;
642 double outerbytes = relation_byte_size(outer_path->parent->rows,
643 outer_path->parent->width);
644 double innerbytes = relation_byte_size(inner_path->parent->rows,
645 inner_path->parent->width);
646 long hashtablebytes = SortMem * 1024L;
648 if (!enable_hashjoin)
649 startup_cost += disable_cost;
651 /* cost of source data */
652 startup_cost += outer_path->startup_cost;
653 run_cost += outer_path->total_cost - outer_path->startup_cost;
654 startup_cost += inner_path->total_cost;
656 /* cost of computing hash function: must do it once per input tuple */
657 startup_cost += cpu_operator_cost * inner_path->parent->rows;
658 run_cost += cpu_operator_cost * outer_path->parent->rows;
661 * The number of tuple comparisons needed is the number of outer
662 * tuples times the typical number of tuples in a hash bucket,
663 * which is the inner relation size times its bucketsize fraction.
664 * We charge one cpu_operator_cost per tuple comparison.
666 run_cost += cpu_operator_cost * outer_path->parent->rows *
667 ceil(inner_path->parent->rows * innerbucketsize);
670 * Estimate the number of tuples that get through the hashing filter
671 * as one per tuple in the two source relations. This could be a
672 * drastic underestimate if there are many equal-keyed tuples in
673 * either relation, but we have no simple way of estimating that;
674 * and since this is only a second-order parameter, it's probably
675 * not worth expending a lot of effort on the estimate.
677 ntuples = outer_path->parent->rows + inner_path->parent->rows;
680 cpu_per_tuple = cpu_tuple_cost + cost_qual_eval(restrictlist);
681 run_cost += cpu_per_tuple * ntuples;
684 * if inner relation is too big then we will need to "batch" the join,
685 * which implies writing and reading most of the tuples to disk an
686 * extra time. Charge one cost unit per page of I/O (correct since it
687 * should be nice and sequential...). Writing the inner rel counts as
688 * startup cost, all the rest as run cost.
690 if (innerbytes > hashtablebytes)
692 double outerpages = page_size(outer_path->parent->rows,
693 outer_path->parent->width);
694 double innerpages = page_size(inner_path->parent->rows,
695 inner_path->parent->width);
697 startup_cost += innerpages;
698 run_cost += innerpages + 2 * outerpages;
702 * Bias against putting larger relation on inside. We don't want an
703 * absolute prohibition, though, since larger relation might have
704 * better bucketsize --- and we can't trust the size estimates
705 * unreservedly, anyway. Instead, inflate the startup cost by the
706 * square root of the size ratio. (Why square root? No real good
707 * reason, but it seems reasonable...)
709 if (innerbytes > outerbytes && outerbytes > 0)
710 startup_cost *= sqrt(innerbytes / outerbytes);
712 path->startup_cost = startup_cost;
713 path->total_cost = startup_cost + run_cost;
717 * Estimate hash bucketsize fraction (ie, number of entries in a bucket
718 * divided by total tuples in relation) if the specified Var is used
721 * This statistic is used by cost_hashjoin. We split out the calculation
722 * because it's useful to cache the result for re-use across multiple path
725 * XXX This is really pretty bogus since we're effectively assuming that the
726 * distribution of hash keys will be the same after applying restriction
727 * clauses as it was in the underlying relation. However, we are not nearly
728 * smart enough to figure out how the restrict clauses might change the
729 * distribution, so this will have to do for now.
731 * The executor tries for average bucket loading of NTUP_PER_BUCKET by setting
732 * number of buckets equal to ntuples / NTUP_PER_BUCKET, which would yield
733 * a bucketsize fraction of NTUP_PER_BUCKET / ntuples. But that goal will
734 * be reached only if the data values are uniformly distributed among the
735 * buckets, which requires (a) at least ntuples / NTUP_PER_BUCKET distinct
736 * data values, and (b) a not-too-skewed data distribution. Otherwise the
737 * buckets will be nonuniformly occupied. If the other relation in the join
738 * has a similar distribution, the most-loaded buckets are exactly those
739 * that will be probed most often. Therefore, the "average" bucket size for
740 * costing purposes should really be taken as something close to the "worst
741 * case" bucket size. We try to estimate this by first scaling up if there
742 * are too few distinct data values, and then scaling up again by the
743 * ratio of the most common value's frequency to the average frequency.
745 * If no statistics are available, use a default estimate of 0.1. This will
746 * discourage use of a hash rather strongly if the inner relation is large,
747 * which is what we want. We do not want to hash unless we know that the
748 * inner rel is well-dispersed (or the alternatives seem much worse).
751 estimate_hash_bucketsize(Query *root, Var *var)
756 Form_pg_statistic stats;
766 * Lookup info about var's relation and attribute;
767 * if none available, return default estimate.
772 relid = getrelid(var->varno, root->rtable);
773 if (relid == InvalidOid)
776 rel = find_base_rel(root, var->varno);
778 if (rel->tuples <= 0.0 || rel->rows <= 0.0)
779 return 0.1; /* ensure we can divide below */
781 tuple = SearchSysCache(STATRELATT,
782 ObjectIdGetDatum(relid),
783 Int16GetDatum(var->varattno),
785 if (!HeapTupleIsValid(tuple))
788 * Perhaps the Var is a system attribute; if so, it will have no
789 * entry in pg_statistic, but we may be able to guess something
790 * about its distribution anyway.
792 switch (var->varattno)
794 case ObjectIdAttributeNumber:
795 case SelfItemPointerAttributeNumber:
796 /* these are unique, so buckets should be well-distributed */
797 return (double) NTUP_PER_BUCKET / rel->rows;
798 case TableOidAttributeNumber:
799 /* hashing this is a terrible idea... */
804 stats = (Form_pg_statistic) GETSTRUCT(tuple);
807 * Obtain number of distinct data values in raw relation.
809 ndistinct = stats->stadistinct;
811 ndistinct = -ndistinct * rel->tuples;
814 * Adjust ndistinct to account for restriction clauses. Observe we are
815 * assuming that the data distribution is affected uniformly by the
816 * restriction clauses!
818 * XXX Possibly better way, but much more expensive: multiply by
819 * selectivity of rel's restriction clauses that mention the target Var.
821 ndistinct *= rel->rows / rel->tuples;
824 * Discourage use of hash join if there seem not to be very many distinct
825 * data values. The threshold here is somewhat arbitrary, as is the
826 * fraction used to "discourage" the choice.
828 if (ndistinct < 50.0)
830 ReleaseSysCache(tuple);
835 * Form initial estimate of bucketsize fraction. Here we use rel->rows,
836 * ie the number of rows after applying restriction clauses, because
837 * that's what the fraction will eventually be multiplied by in
840 estfract = (double) NTUP_PER_BUCKET / rel->rows;
843 * Adjust estimated bucketsize if too few distinct values to fill
846 needdistinct = rel->rows / (double) NTUP_PER_BUCKET;
847 if (ndistinct < needdistinct)
848 estfract *= needdistinct / ndistinct;
851 * Look up the frequency of the most common value, if available.
855 if (get_attstatsslot(tuple, var->vartype, var->vartypmod,
856 STATISTIC_KIND_MCV, InvalidOid,
857 NULL, NULL, &numbers, &nnumbers))
860 * The first MCV stat is for the most common value.
863 mcvfreq = numbers[0];
864 free_attstatsslot(var->vartype, NULL, 0,
869 * Adjust estimated bucketsize upward to account for skewed distribution.
871 avgfreq = (1.0 - stats->stanullfrac) / ndistinct;
873 if (avgfreq > 0.0 && mcvfreq > avgfreq)
874 estfract *= mcvfreq / avgfreq;
876 ReleaseSysCache(tuple);
878 return (Selectivity) estfract;
884 * Estimate the CPU cost of evaluating a WHERE clause (once).
885 * The input can be either an implicitly-ANDed list of boolean
886 * expressions, or a list of RestrictInfo nodes.
889 cost_qual_eval(List *quals)
894 /* We don't charge any cost for the implicit ANDing at top level ... */
898 Node *qual = (Node *) lfirst(l);
901 * RestrictInfo nodes contain an eval_cost field reserved for this
902 * routine's use, so that it's not necessary to evaluate the qual
903 * clause's cost more than once. If the clause's cost hasn't been
904 * computed yet, the field will contain -1.
906 if (qual && IsA(qual, RestrictInfo))
908 RestrictInfo *restrictinfo = (RestrictInfo *) qual;
910 if (restrictinfo->eval_cost < 0)
912 restrictinfo->eval_cost = 0;
913 cost_qual_eval_walker((Node *) restrictinfo->clause,
914 &restrictinfo->eval_cost);
916 total += restrictinfo->eval_cost;
920 /* If it's a bare expression, must always do it the hard way */
921 cost_qual_eval_walker(qual, &total);
928 cost_qual_eval_walker(Node *node, Cost *total)
934 * Our basic strategy is to charge one cpu_operator_cost for each
935 * operator or function node in the given tree. Vars and Consts are
936 * charged zero, and so are boolean operators (AND, OR, NOT).
937 * Simplistic, but a lot better than no model at all.
939 * Should we try to account for the possibility of short-circuit
940 * evaluation of AND/OR?
944 Expr *expr = (Expr *) node;
946 switch (expr->opType)
950 *total += cpu_operator_cost;
959 * A subplan node in an expression indicates that the
960 * subplan will be executed on each evaluation, so charge
961 * accordingly. (We assume that sub-selects that can be
962 * executed as InitPlans have already been removed from
965 * NOTE: this logic should agree with the estimates used by
966 * make_subplan() in plan/subselect.c.
969 SubPlan *subplan = (SubPlan *) expr->oper;
970 Plan *plan = subplan->plan;
973 if (subplan->sublink->subLinkType == EXISTS_SUBLINK)
975 /* we only need to fetch 1 tuple */
976 subcost = plan->startup_cost +
977 (plan->total_cost - plan->startup_cost) / plan->plan_rows;
979 else if (subplan->sublink->subLinkType == ALL_SUBLINK ||
980 subplan->sublink->subLinkType == ANY_SUBLINK)
982 /* assume we need 50% of the tuples */
983 subcost = plan->startup_cost +
984 0.50 * (plan->total_cost - plan->startup_cost);
985 /* XXX what if subplan has been materialized? */
989 /* assume we need all tuples */
990 subcost = plan->total_cost;
996 /* fall through to examine args of Expr node */
998 return expression_tree_walker(node, cost_qual_eval_walker,
1004 * set_baserel_size_estimates
1005 * Set the size estimates for the given base relation.
1007 * The rel's targetlist and restrictinfo list must have been constructed
1010 * We set the following fields of the rel node:
1011 * rows: the estimated number of output tuples (after applying
1012 * restriction clauses).
1013 * width: the estimated average output tuple width in bytes.
1014 * baserestrictcost: estimated cost of evaluating baserestrictinfo clauses.
1017 set_baserel_size_estimates(Query *root, RelOptInfo *rel)
1019 /* Should only be applied to base relations */
1020 Assert(length(rel->relids) == 1);
1022 rel->rows = rel->tuples *
1023 restrictlist_selectivity(root,
1024 rel->baserestrictinfo,
1025 lfirsti(rel->relids));
1028 * Force estimate to be at least one row, to make explain output look
1029 * better and to avoid possible divide-by-zero when interpolating
1032 if (rel->rows < 1.0)
1035 rel->baserestrictcost = cost_qual_eval(rel->baserestrictinfo);
1037 set_rel_width(root, rel);
1041 * set_joinrel_size_estimates
1042 * Set the size estimates for the given join relation.
1044 * The rel's targetlist must have been constructed already, and a
1045 * restriction clause list that matches the given component rels must
1048 * Since there is more than one way to make a joinrel for more than two
1049 * base relations, the results we get here could depend on which component
1050 * rel pair is provided. In theory we should get the same answers no matter
1051 * which pair is provided; in practice, since the selectivity estimation
1052 * routines don't handle all cases equally well, we might not. But there's
1053 * not much to be done about it. (Would it make sense to repeat the
1054 * calculations for each pair of input rels that's encountered, and somehow
1055 * average the results? Probably way more trouble than it's worth.)
1057 * We set the same relnode fields as set_baserel_size_estimates() does.
1060 set_joinrel_size_estimates(Query *root, RelOptInfo *rel,
1061 RelOptInfo *outer_rel,
1062 RelOptInfo *inner_rel,
1068 /* Start with the Cartesian product */
1069 temp = outer_rel->rows * inner_rel->rows;
1072 * Apply join restrictivity. Note that we are only considering
1073 * clauses that become restriction clauses at this join level; we are
1074 * not double-counting them because they were not considered in
1075 * estimating the sizes of the component rels.
1077 temp *= restrictlist_selectivity(root,
1082 * If we are doing an outer join, take that into account: the output
1083 * must be at least as large as the non-nullable input. (Is there any
1084 * chance of being even smarter?)
1091 if (temp < outer_rel->rows)
1092 temp = outer_rel->rows;
1095 if (temp < inner_rel->rows)
1096 temp = inner_rel->rows;
1099 if (temp < outer_rel->rows)
1100 temp = outer_rel->rows;
1101 if (temp < inner_rel->rows)
1102 temp = inner_rel->rows;
1105 elog(ERROR, "set_joinrel_size_estimates: unsupported join type %d",
1111 * Force estimate to be at least one row, to make explain output look
1112 * better and to avoid possible divide-by-zero when interpolating
1121 * We could apply set_rel_width() to compute the output tuple width
1122 * from scratch, but at present it's always just the sum of the input
1123 * widths, so why work harder than necessary? If relnode.c is ever
1124 * taught to remove unneeded columns from join targetlists, go back to
1125 * using set_rel_width here.
1127 rel->width = outer_rel->width + inner_rel->width;
1132 * Set the estimated output width of the relation.
1134 * NB: this works best on base relations because it prefers to look at
1135 * real Vars. It will fail to make use of pg_statistic info when applied
1136 * to a subquery relation, even if the subquery outputs are simple vars
1137 * that we could have gotten info for. Is it worth trying to be smarter
1141 set_rel_width(Query *root, RelOptInfo *rel)
1143 int32 tuple_width = 0;
1146 foreach(tllist, rel->targetlist)
1148 TargetEntry *tle = (TargetEntry *) lfirst(tllist);
1152 * If it's a Var, try to get statistical info from pg_statistic.
1154 if (tle->expr && IsA(tle->expr, Var))
1156 Var *var = (Var *) tle->expr;
1159 relid = getrelid(var->varno, root->rtable);
1160 if (relid != InvalidOid)
1162 item_width = get_attavgwidth(relid, var->varattno);
1165 tuple_width += item_width;
1171 * Not a Var, or can't find statistics for it. Estimate using
1172 * just the type info.
1174 item_width = get_typavgwidth(tle->resdom->restype,
1175 tle->resdom->restypmod);
1176 Assert(item_width > 0);
1177 tuple_width += item_width;
1179 Assert(tuple_width >= 0);
1180 rel->width = tuple_width;
1184 * relation_byte_size
1185 * Estimate the storage space in bytes for a given number of tuples
1186 * of a given width (size in bytes).
1189 relation_byte_size(double tuples, int width)
1191 return tuples * ((double) MAXALIGN(width + sizeof(HeapTupleData)));
1196 * Returns an estimate of the number of pages covered by a given
1197 * number of tuples of a given width (size in bytes).
1200 page_size(double tuples, int width)
1202 return ceil(relation_byte_size(tuples, width) / BLCKSZ);