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 1.103 2003/01/27 20:51:50 tgl Exp $
54 *-------------------------------------------------------------------------
61 #include "catalog/pg_statistic.h"
62 #include "executor/nodeHash.h"
63 #include "miscadmin.h"
64 #include "optimizer/clauses.h"
65 #include "optimizer/cost.h"
66 #include "optimizer/pathnode.h"
67 #include "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);
108 static void set_rel_width(Query *root, RelOptInfo *rel);
109 static double relation_byte_size(double tuples, int width);
110 static double page_size(double tuples, int width);
115 * Determines and returns the cost of scanning a relation sequentially.
118 cost_seqscan(Path *path, Query *root,
121 Cost startup_cost = 0;
125 /* Should only be applied to base relations */
126 Assert(length(baserel->relids) == 1);
127 Assert(baserel->rtekind == RTE_RELATION);
130 startup_cost += disable_cost;
135 * The cost of reading a page sequentially is 1.0, by definition. Note
136 * that the Unix kernel will typically do some amount of read-ahead
137 * optimization, so that this cost is less than the true cost of
138 * reading a page from disk. We ignore that issue here, but must take
139 * it into account when estimating the cost of non-sequential
142 run_cost += baserel->pages; /* sequential fetches with cost 1.0 */
145 startup_cost += baserel->baserestrictcost.startup;
146 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
147 run_cost += cpu_per_tuple * baserel->tuples;
149 path->startup_cost = startup_cost;
150 path->total_cost = startup_cost + run_cost;
154 * cost_nonsequential_access
155 * Estimate the cost of accessing one page at random from a relation
156 * (or sort temp file) of the given size in pages.
158 * The simplistic model that the cost is random_page_cost is what we want
159 * to use for large relations; but for small ones that is a serious
160 * overestimate because of the effects of caching. This routine tries to
163 * Unfortunately we don't have any good way of estimating the effective cache
164 * size we are working with --- we know that Postgres itself has NBuffers
165 * internal buffers, but the size of the kernel's disk cache is uncertain,
166 * and how much of it we get to use is even less certain. We punt the problem
167 * for now by assuming we are given an effective_cache_size parameter.
169 * Given a guesstimated cache size, we estimate the actual I/O cost per page
170 * with the entirely ad-hoc equations:
171 * if relpages >= effective_cache_size:
172 * random_page_cost * (1 - (effective_cache_size/relpages)/2)
173 * if relpages < effective_cache_size:
174 * 1 + (random_page_cost/2-1) * (relpages/effective_cache_size) ** 2
175 * These give the right asymptotic behavior (=> 1.0 as relpages becomes
176 * small, => random_page_cost as it becomes large) and meet in the middle
177 * with the estimate that the cache is about 50% effective for a relation
178 * of the same size as effective_cache_size. (XXX this is probably all
179 * wrong, but I haven't been able to find any theory about how effective
180 * a disk cache should be presumed to be.)
183 cost_nonsequential_access(double relpages)
187 /* don't crash on bad input data */
188 if (relpages <= 0.0 || effective_cache_size <= 0.0)
189 return random_page_cost;
191 relsize = relpages / effective_cache_size;
194 return random_page_cost * (1.0 - 0.5 / relsize);
196 return 1.0 + (random_page_cost * 0.5 - 1.0) * relsize * relsize;
201 * Determines and returns the cost of scanning a relation using an index.
203 * NOTE: an indexscan plan node can actually represent several passes,
204 * but here we consider the cost of just one pass.
206 * 'root' is the query root
207 * 'baserel' is the base relation the index is for
208 * 'index' is the index to be used
209 * 'indexQuals' is the list of applicable qual clauses (implicit AND semantics)
210 * 'is_injoin' is T if we are considering using the index scan as the inside
211 * of a nestloop join (hence, some of the indexQuals are join clauses)
213 * NOTE: 'indexQuals' must contain only clauses usable as index restrictions.
214 * Any additional quals evaluated as qpquals may reduce the number of returned
215 * tuples, but they won't reduce the number of tuples we have to fetch from
216 * the table, so they don't reduce the scan cost.
219 cost_index(Path *path, Query *root,
225 Cost startup_cost = 0;
227 Cost indexStartupCost;
229 Selectivity indexSelectivity;
230 double indexCorrelation,
235 double tuples_fetched;
236 double pages_fetched;
240 /* Should only be applied to base relations */
241 Assert(IsA(baserel, RelOptInfo) &&
242 IsA(index, IndexOptInfo));
243 Assert(length(baserel->relids) == 1);
244 Assert(baserel->rtekind == RTE_RELATION);
246 if (!enable_indexscan)
247 startup_cost += disable_cost;
250 * Call index-access-method-specific code to estimate the processing
251 * cost for scanning the index, as well as the selectivity of the
252 * index (ie, the fraction of main-table tuples we will have to
253 * retrieve) and its correlation to the main-table tuple order.
255 OidFunctionCall8(index->amcostestimate,
256 PointerGetDatum(root),
257 PointerGetDatum(baserel),
258 PointerGetDatum(index),
259 PointerGetDatum(indexQuals),
260 PointerGetDatum(&indexStartupCost),
261 PointerGetDatum(&indexTotalCost),
262 PointerGetDatum(&indexSelectivity),
263 PointerGetDatum(&indexCorrelation));
265 /* all costs for touching index itself included here */
266 startup_cost += indexStartupCost;
267 run_cost += indexTotalCost - indexStartupCost;
270 * Estimate number of main-table tuples and pages fetched.
272 * When the index ordering is uncorrelated with the table ordering,
273 * we use an approximation proposed by Mackert and Lohman, "Index Scans
274 * Using a Finite LRU Buffer: A Validated I/O Model", ACM Transactions
275 * on Database Systems, Vol. 14, No. 3, September 1989, Pages 401-424.
276 * The Mackert and Lohman approximation is that the number of pages
279 * min(2TNs/(2T+Ns), T) when T <= b
280 * 2TNs/(2T+Ns) when T > b and Ns <= 2Tb/(2T-b)
281 * b + (Ns - 2Tb/(2T-b))*(T-b)/T when T > b and Ns > 2Tb/(2T-b)
283 * T = # pages in table
284 * N = # tuples in table
285 * s = selectivity = fraction of table to be scanned
286 * b = # buffer pages available (we include kernel space here)
288 * When the index ordering is exactly correlated with the table ordering
289 * (just after a CLUSTER, for example), the number of pages fetched should
290 * be just sT. What's more, these will be sequential fetches, not the
291 * random fetches that occur in the uncorrelated case. So, depending on
292 * the extent of correlation, we should estimate the actual I/O cost
293 * somewhere between s * T * 1.0 and PF * random_cost. We currently
294 * interpolate linearly between these two endpoints based on the
295 * correlation squared (XXX is that appropriate?).
297 * In any case the number of tuples fetched is Ns.
301 tuples_fetched = indexSelectivity * baserel->tuples;
302 /* Don't believe estimates less than 1... */
303 if (tuples_fetched < 1.0)
304 tuples_fetched = 1.0;
306 /* This part is the Mackert and Lohman formula */
308 T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
309 b = (effective_cache_size > 1) ? effective_cache_size : 1.0;
314 (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
315 if (pages_fetched > T)
322 lim = (2.0 * T * b) / (2.0 * T - b);
323 if (tuples_fetched <= lim)
326 (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
331 b + (tuples_fetched - lim) * (T - b) / T;
336 * min_IO_cost corresponds to the perfectly correlated case
337 * (csquared=1), max_IO_cost to the perfectly uncorrelated case
338 * (csquared=0). Note that we just charge random_page_cost per page
339 * in the uncorrelated case, rather than using
340 * cost_nonsequential_access, since we've already accounted for
341 * caching effects by using the Mackert model.
343 min_IO_cost = ceil(indexSelectivity * T);
344 max_IO_cost = pages_fetched * random_page_cost;
347 * Now interpolate based on estimated index order correlation to get
348 * total disk I/O cost for main table accesses.
350 csquared = indexCorrelation * indexCorrelation;
352 run_cost += max_IO_cost + csquared * (min_IO_cost - max_IO_cost);
355 * Estimate CPU costs per tuple.
357 * Normally the indexquals will be removed from the list of restriction
358 * clauses that we have to evaluate as qpquals, so we should subtract
359 * their costs from baserestrictcost. But if we are doing a join then
360 * some of the indexquals are join clauses and shouldn't be subtracted.
361 * Rather than work out exactly how much to subtract, we don't subtract
364 * XXX For a lossy index, not all the quals will be removed and so we
365 * really shouldn't subtract their costs; but detecting that seems more
366 * expensive than it's worth.
368 startup_cost += baserel->baserestrictcost.startup;
369 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
373 QualCost index_qual_cost;
375 cost_qual_eval(&index_qual_cost, indexQuals);
376 cpu_per_tuple -= index_qual_cost.per_tuple;
379 run_cost += cpu_per_tuple * tuples_fetched;
381 path->startup_cost = startup_cost;
382 path->total_cost = startup_cost + run_cost;
387 * Determines and returns the cost of scanning a relation using TIDs.
390 cost_tidscan(Path *path, Query *root,
391 RelOptInfo *baserel, List *tideval)
393 Cost startup_cost = 0;
396 int ntuples = length(tideval);
398 /* Should only be applied to base relations */
399 Assert(length(baserel->relids) == 1);
400 Assert(baserel->rtekind == RTE_RELATION);
403 startup_cost += disable_cost;
405 /* disk costs --- assume each tuple on a different page */
406 run_cost += random_page_cost * ntuples;
409 startup_cost += baserel->baserestrictcost.startup;
410 cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
411 run_cost += cpu_per_tuple * ntuples;
413 path->startup_cost = startup_cost;
414 path->total_cost = startup_cost + run_cost;
419 * Determines and returns the cost of scanning a function RTE.
422 cost_functionscan(Path *path, Query *root, RelOptInfo *baserel)
424 Cost startup_cost = 0;
428 /* Should only be applied to base relations that are functions */
429 Assert(length(baserel->relids) == 1);
430 Assert(baserel->rtekind == RTE_FUNCTION);
433 * For now, estimate function's cost at one operator eval per function
434 * call. Someday we should revive the function cost estimate columns
437 cpu_per_tuple = cpu_operator_cost;
439 /* Add scanning CPU costs */
440 startup_cost += baserel->baserestrictcost.startup;
441 cpu_per_tuple += cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
442 run_cost += cpu_per_tuple * baserel->tuples;
444 path->startup_cost = startup_cost;
445 path->total_cost = startup_cost + run_cost;
450 * Determines and returns the cost of sorting a relation, including
451 * the cost of reading the input data.
453 * If the total volume of data to sort is less than SortMem, we will do
454 * an in-memory sort, which requires no I/O and about t*log2(t) tuple
455 * comparisons for t tuples.
457 * If the total volume exceeds SortMem, we switch to a tape-style merge
458 * algorithm. There will still be about t*log2(t) tuple comparisons in
459 * total, but we will also need to write and read each tuple once per
460 * merge pass. We expect about ceil(log6(r)) merge passes where r is the
461 * number of initial runs formed (log6 because tuplesort.c uses six-tape
462 * merging). Since the average initial run should be about twice SortMem,
464 * disk traffic = 2 * relsize * ceil(log6(p / (2*SortMem)))
465 * cpu = comparison_cost * t * log2(t)
467 * The disk traffic is assumed to be half sequential and half random
468 * accesses (XXX can't we refine that guess?)
470 * We charge two operator evals per tuple comparison, which should be in
471 * the right ballpark in most cases.
473 * 'pathkeys' is a list of sort keys
474 * 'input_cost' is the total cost for reading the input data
475 * 'tuples' is the number of tuples in the relation
476 * 'width' is the average tuple width in bytes
478 * NOTE: some callers currently pass NIL for pathkeys because they
479 * can't conveniently supply the sort keys. Since this routine doesn't
480 * currently do anything with pathkeys anyway, that doesn't matter...
481 * but if it ever does, it should react gracefully to lack of key data.
482 * (Actually, the thing we'd most likely be interested in is just the number
483 * of sort keys, which all callers *could* supply.)
486 cost_sort(Path *path, Query *root,
487 List *pathkeys, Cost input_cost, double tuples, int width)
489 Cost startup_cost = input_cost;
491 double nbytes = relation_byte_size(tuples, width);
492 long sortmembytes = SortMem * 1024L;
495 startup_cost += disable_cost;
498 * We want to be sure the cost of a sort is never estimated as zero,
499 * even if passed-in tuple count is zero. Besides, mustn't do
508 * Assume about two operator evals per tuple comparison and N log2 N
511 startup_cost += 2.0 * cpu_operator_cost * tuples * LOG2(tuples);
514 if (nbytes > sortmembytes)
516 double npages = ceil(nbytes / BLCKSZ);
517 double nruns = nbytes / (sortmembytes * 2);
518 double log_runs = ceil(LOG6(nruns));
519 double npageaccesses;
523 npageaccesses = 2.0 * npages * log_runs;
524 /* Assume half are sequential (cost 1), half are not */
525 startup_cost += npageaccesses *
526 (1.0 + cost_nonsequential_access(npages)) * 0.5;
530 * Also charge a small amount (arbitrarily set equal to operator cost)
531 * per extracted tuple.
533 run_cost += cpu_operator_cost * tuples;
535 path->startup_cost = startup_cost;
536 path->total_cost = startup_cost + run_cost;
541 * Determines and returns the cost of materializing a relation, including
542 * the cost of reading the input data.
544 * If the total volume of data to materialize exceeds SortMem, we will need
545 * to write it to disk, so the cost is much higher in that case.
548 cost_material(Path *path,
549 Cost input_cost, double tuples, int width)
551 Cost startup_cost = input_cost;
553 double nbytes = relation_byte_size(tuples, width);
554 long sortmembytes = SortMem * 1024L;
557 if (nbytes > sortmembytes)
559 double npages = ceil(nbytes / BLCKSZ);
561 /* We'll write during startup and read during retrieval */
562 startup_cost += npages;
567 * Also charge a small amount per extracted tuple. We use cpu_tuple_cost
568 * so that it doesn't appear worthwhile to materialize a bare seqscan.
570 run_cost += cpu_tuple_cost * tuples;
572 path->startup_cost = startup_cost;
573 path->total_cost = startup_cost + run_cost;
578 * Determines and returns the cost of performing an Agg plan node,
579 * including the cost of its input.
581 * Note: when aggstrategy == AGG_SORTED, caller must ensure that input costs
582 * are for appropriately-sorted input.
585 cost_agg(Path *path, Query *root,
586 AggStrategy aggstrategy, int numAggs,
587 int numGroupCols, double numGroups,
588 Cost input_startup_cost, Cost input_total_cost,
595 * We charge one cpu_operator_cost per aggregate function per input
596 * tuple, and another one per output tuple (corresponding to transfn
597 * and finalfn calls respectively). If we are grouping, we charge an
598 * additional cpu_operator_cost per grouping column per input tuple
599 * for grouping comparisons.
601 * We will produce a single output tuple if not grouping,
602 * and a tuple per group otherwise.
604 if (aggstrategy == AGG_PLAIN)
606 startup_cost = input_total_cost;
607 startup_cost += cpu_operator_cost * (input_tuples + 1) * numAggs;
608 /* we aren't grouping */
609 total_cost = startup_cost;
611 else if (aggstrategy == AGG_SORTED)
613 /* Here we are able to deliver output on-the-fly */
614 startup_cost = input_startup_cost;
615 total_cost = input_total_cost;
616 total_cost += cpu_operator_cost * (input_tuples + numGroups) * numAggs;
617 total_cost += cpu_operator_cost * input_tuples * numGroupCols;
621 /* must be AGG_HASHED */
622 startup_cost = input_total_cost;
623 startup_cost += cpu_operator_cost * input_tuples * numAggs;
624 startup_cost += cpu_operator_cost * input_tuples * numGroupCols;
625 total_cost = startup_cost;
626 total_cost += cpu_operator_cost * numGroups * numAggs;
629 path->startup_cost = startup_cost;
630 path->total_cost = total_cost;
635 * Determines and returns the cost of performing a Group plan node,
636 * including the cost of its input.
638 * Note: caller must ensure that input costs are for appropriately-sorted
642 cost_group(Path *path, Query *root,
643 int numGroupCols, double numGroups,
644 Cost input_startup_cost, Cost input_total_cost,
650 startup_cost = input_startup_cost;
651 total_cost = input_total_cost;
654 * Charge one cpu_operator_cost per comparison per input tuple. We
655 * assume all columns get compared at most of the tuples.
657 total_cost += cpu_operator_cost * input_tuples * numGroupCols;
659 path->startup_cost = startup_cost;
660 path->total_cost = total_cost;
665 * Determines and returns the cost of joining two relations using the
666 * nested loop algorithm.
668 * 'path' is already filled in except for the cost fields
671 cost_nestloop(NestPath *path, Query *root)
673 Path *outer_path = path->outerjoinpath;
674 Path *inner_path = path->innerjoinpath;
675 List *restrictlist = path->joinrestrictinfo;
676 Cost startup_cost = 0;
679 QualCost restrict_qual_cost;
680 double outer_path_rows = PATH_ROWS(outer_path);
681 double inner_path_rows = PATH_ROWS(inner_path);
683 Selectivity joininfactor;
685 if (!enable_nestloop)
686 startup_cost += disable_cost;
689 * If we're doing JOIN_IN then we will stop scanning inner tuples for an
690 * outer tuple as soon as we have one match. Account for the effects of
691 * this by scaling down the cost estimates in proportion to the expected
692 * output size. (This assumes that all the quals attached to the join are
693 * IN quals, which should be true.)
695 * Note: it's probably bogus to use the normal selectivity calculation
696 * here when either the outer or inner path is a UniquePath.
698 if (path->jointype == JOIN_IN)
700 Selectivity qual_selec = approx_selectivity(root, restrictlist);
703 qptuples = ceil(qual_selec * outer_path_rows * inner_path_rows);
704 if (qptuples > path->path.parent->rows)
705 joininfactor = path->path.parent->rows / qptuples;
712 /* cost of source data */
715 * NOTE: clearly, we must pay both outer and inner paths' startup_cost
716 * before we can start returning tuples, so the join's startup cost is
717 * their sum. What's not so clear is whether the inner path's
718 * startup_cost must be paid again on each rescan of the inner path.
719 * This is not true if the inner path is materialized or is a hashjoin,
720 * but probably is true otherwise.
722 startup_cost += outer_path->startup_cost + inner_path->startup_cost;
723 run_cost += outer_path->total_cost - outer_path->startup_cost;
724 if (IsA(inner_path, MaterialPath) ||
725 IsA(inner_path, HashPath))
727 /* charge only run cost for each iteration of inner path */
732 * charge startup cost for each iteration of inner path, except we
733 * already charged the first startup_cost in our own startup
735 run_cost += (outer_path_rows - 1) * inner_path->startup_cost;
737 run_cost += outer_path_rows *
738 (inner_path->total_cost - inner_path->startup_cost) * joininfactor;
741 * Compute number of tuples processed (not number emitted!).
742 * If inner path is an indexscan, be sure to use its estimated output row
743 * count, which may be lower than the restriction-clause-only row count of
744 * its parent. (We don't include this case in the PATH_ROWS macro because
745 * it applies *only* to a nestloop's inner relation.) Note: it is correct
746 * to use the unadjusted inner_path_rows in the above calculation for
747 * joininfactor, since otherwise we'd be double-counting the selectivity
748 * of the join clause being used for the index.
750 if (IsA(inner_path, IndexPath))
751 inner_path_rows = ((IndexPath *) inner_path)->rows;
753 ntuples = inner_path_rows * outer_path_rows;
756 cost_qual_eval(&restrict_qual_cost, restrictlist);
757 startup_cost += restrict_qual_cost.startup;
758 cpu_per_tuple = cpu_tuple_cost + restrict_qual_cost.per_tuple;
759 run_cost += cpu_per_tuple * ntuples;
761 path->path.startup_cost = startup_cost;
762 path->path.total_cost = startup_cost + run_cost;
767 * Determines and returns the cost of joining two relations using the
768 * merge join algorithm.
770 * 'path' is already filled in except for the cost fields
772 * Notes: path's mergeclauses should be a subset of the joinrestrictinfo list;
773 * outersortkeys and innersortkeys are lists of the keys to be used
774 * to sort the outer and inner relations, or NIL if no explicit
775 * sort is needed because the source path is already ordered.
778 cost_mergejoin(MergePath *path, Query *root)
780 Path *outer_path = path->jpath.outerjoinpath;
781 Path *inner_path = path->jpath.innerjoinpath;
782 List *restrictlist = path->jpath.joinrestrictinfo;
783 List *mergeclauses = path->path_mergeclauses;
784 List *outersortkeys = path->outersortkeys;
785 List *innersortkeys = path->innersortkeys;
786 Cost startup_cost = 0;
789 Selectivity merge_selec;
790 Selectivity qp_selec;
791 QualCost merge_qual_cost;
792 QualCost qp_qual_cost;
793 RestrictInfo *firstclause;
795 double outer_path_rows = PATH_ROWS(outer_path);
796 double inner_path_rows = PATH_ROWS(inner_path);
799 double mergejointuples,
803 Selectivity outerscansel,
805 Selectivity joininfactor;
806 Path sort_path; /* dummy for result of cost_sort */
808 if (!enable_mergejoin)
809 startup_cost += disable_cost;
812 * Compute cost and selectivity of the mergequals and qpquals (other
813 * restriction clauses) separately. We use approx_selectivity here
814 * for speed --- in most cases, any errors won't affect the result much.
816 * Note: it's probably bogus to use the normal selectivity calculation
817 * here when either the outer or inner path is a UniquePath.
819 merge_selec = approx_selectivity(root, mergeclauses);
820 cost_qual_eval(&merge_qual_cost, mergeclauses);
821 qpquals = set_ptrDifference(restrictlist, mergeclauses);
822 qp_selec = approx_selectivity(root, qpquals);
823 cost_qual_eval(&qp_qual_cost, qpquals);
826 /* approx # tuples passing the merge quals */
827 mergejointuples = ceil(merge_selec * outer_path_rows * inner_path_rows);
828 /* approx # tuples passing qpquals as well */
829 qptuples = ceil(mergejointuples * qp_selec);
832 * When there are equal merge keys in the outer relation, the mergejoin
833 * must rescan any matching tuples in the inner relation. This means
834 * re-fetching inner tuples. Our cost model for this is that a re-fetch
835 * costs the same as an original fetch, which is probably an overestimate;
836 * but on the other hand we ignore the bookkeeping costs of mark/restore.
837 * Not clear if it's worth developing a more refined model.
839 * The number of re-fetches can be estimated approximately as size of
840 * merge join output minus size of inner relation. Assume that the
841 * distinct key values are 1, 2, ..., and denote the number of values of
842 * each key in the outer relation as m1, m2, ...; in the inner relation,
843 * n1, n2, ... Then we have
845 * size of join = m1 * n1 + m2 * n2 + ...
847 * number of rescanned tuples = (m1 - 1) * n1 + (m2 - 1) * n2 + ...
848 * = m1 * n1 + m2 * n2 + ... - (n1 + n2 + ...)
849 * = size of join - size of inner relation
851 * This equation works correctly for outer tuples having no inner match
852 * (nk = 0), but not for inner tuples having no outer match (mk = 0);
853 * we are effectively subtracting those from the number of rescanned
854 * tuples, when we should not. Can we do better without expensive
855 * selectivity computations?
857 if (IsA(outer_path, UniquePath))
861 rescannedtuples = mergejointuples - inner_path_rows;
862 /* Must clamp because of possible underestimate */
863 if (rescannedtuples < 0)
866 /* We'll inflate inner run cost this much to account for rescanning */
867 rescanratio = 1.0 + (rescannedtuples / inner_path_rows);
870 * A merge join will stop as soon as it exhausts either input stream.
871 * Estimate fraction of the left and right inputs that will actually
872 * need to be scanned. We use only the first (most significant) merge
873 * clause for this purpose.
875 * Since this calculation is somewhat expensive, and will be the same for
876 * all mergejoin paths associated with the merge clause, we cache the
877 * results in the RestrictInfo node.
879 firstclause = (RestrictInfo *) lfirst(mergeclauses);
880 if (firstclause->left_mergescansel < 0) /* not computed yet? */
881 mergejoinscansel(root, (Node *) firstclause->clause,
882 &firstclause->left_mergescansel,
883 &firstclause->right_mergescansel);
885 if (is_subseti(firstclause->left_relids, outer_path->parent->relids))
887 /* left side of clause is outer */
888 outerscansel = firstclause->left_mergescansel;
889 innerscansel = firstclause->right_mergescansel;
893 /* left side of clause is inner */
894 outerscansel = firstclause->right_mergescansel;
895 innerscansel = firstclause->left_mergescansel;
898 /* convert selectivity to row count; must scan at least one row */
900 outer_rows = ceil(outer_path_rows * outerscansel);
903 inner_rows = ceil(inner_path_rows * innerscansel);
908 * Readjust scan selectivities to account for above rounding. This is
909 * normally an insignificant effect, but when there are only a few rows
910 * in the inputs, failing to do this makes for a large percentage error.
912 outerscansel = outer_rows / outer_path_rows;
913 innerscansel = inner_rows / inner_path_rows;
915 /* cost of source data */
917 if (outersortkeys) /* do we need to sort outer? */
919 cost_sort(&sort_path,
922 outer_path->total_cost,
924 outer_path->parent->width);
925 startup_cost += sort_path.startup_cost;
926 run_cost += (sort_path.total_cost - sort_path.startup_cost)
931 startup_cost += outer_path->startup_cost;
932 run_cost += (outer_path->total_cost - outer_path->startup_cost)
936 if (innersortkeys) /* do we need to sort inner? */
938 cost_sort(&sort_path,
941 inner_path->total_cost,
943 inner_path->parent->width);
944 startup_cost += sort_path.startup_cost;
945 run_cost += (sort_path.total_cost - sort_path.startup_cost)
946 * innerscansel * rescanratio;
950 startup_cost += inner_path->startup_cost;
951 run_cost += (inner_path->total_cost - inner_path->startup_cost)
952 * innerscansel * rescanratio;
958 * If we're doing JOIN_IN then we will stop outputting inner
959 * tuples for an outer tuple as soon as we have one match. Account for
960 * the effects of this by scaling down the cost estimates in proportion
961 * to the expected output size. (This assumes that all the quals attached
962 * to the join are IN quals, which should be true.)
964 if (path->jpath.jointype == JOIN_IN &&
965 qptuples > path->jpath.path.parent->rows)
966 joininfactor = path->jpath.path.parent->rows / qptuples;
971 * The number of tuple comparisons needed is approximately number of
972 * outer rows plus number of inner rows plus number of rescanned
973 * tuples (can we refine this?). At each one, we need to evaluate
974 * the mergejoin quals. NOTE: JOIN_IN mode does not save any work
975 * here, so do NOT include joininfactor.
977 startup_cost += merge_qual_cost.startup;
978 run_cost += merge_qual_cost.per_tuple *
979 (outer_rows + inner_rows * rescanratio);
982 * For each tuple that gets through the mergejoin proper, we charge
983 * cpu_tuple_cost plus the cost of evaluating additional restriction
984 * clauses that are to be applied at the join. (This is pessimistic
985 * since not all of the quals may get evaluated at each tuple.) This
986 * work is skipped in JOIN_IN mode, so apply the factor.
988 startup_cost += qp_qual_cost.startup;
989 cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
990 run_cost += cpu_per_tuple * mergejointuples * joininfactor;
992 path->jpath.path.startup_cost = startup_cost;
993 path->jpath.path.total_cost = startup_cost + run_cost;
998 * Determines and returns the cost of joining two relations using the
999 * hash join algorithm.
1001 * 'path' is already filled in except for the cost fields
1003 * Note: path's hashclauses should be a subset of the joinrestrictinfo list
1006 cost_hashjoin(HashPath *path, Query *root)
1008 Path *outer_path = path->jpath.outerjoinpath;
1009 Path *inner_path = path->jpath.innerjoinpath;
1010 List *restrictlist = path->jpath.joinrestrictinfo;
1011 List *hashclauses = path->path_hashclauses;
1012 Cost startup_cost = 0;
1015 Selectivity hash_selec;
1016 Selectivity qp_selec;
1017 QualCost hash_qual_cost;
1018 QualCost qp_qual_cost;
1019 double hashjointuples;
1021 double outer_path_rows = PATH_ROWS(outer_path);
1022 double inner_path_rows = PATH_ROWS(inner_path);
1023 double outerbytes = relation_byte_size(outer_path_rows,
1024 outer_path->parent->width);
1025 double innerbytes = relation_byte_size(inner_path_rows,
1026 inner_path->parent->width);
1027 int num_hashclauses = length(hashclauses);
1029 int physicalbuckets;
1031 Selectivity innerbucketsize;
1032 Selectivity joininfactor;
1036 if (!enable_hashjoin)
1037 startup_cost += disable_cost;
1040 * Compute cost and selectivity of the hashquals and qpquals (other
1041 * restriction clauses) separately. We use approx_selectivity here
1042 * for speed --- in most cases, any errors won't affect the result much.
1044 * Note: it's probably bogus to use the normal selectivity calculation
1045 * here when either the outer or inner path is a UniquePath.
1047 hash_selec = approx_selectivity(root, hashclauses);
1048 cost_qual_eval(&hash_qual_cost, hashclauses);
1049 qpquals = set_ptrDifference(restrictlist, hashclauses);
1050 qp_selec = approx_selectivity(root, qpquals);
1051 cost_qual_eval(&qp_qual_cost, qpquals);
1054 /* approx # tuples passing the hash quals */
1055 hashjointuples = ceil(hash_selec * outer_path_rows * inner_path_rows);
1056 /* approx # tuples passing qpquals as well */
1057 qptuples = ceil(hashjointuples * qp_selec);
1059 /* cost of source data */
1060 startup_cost += outer_path->startup_cost;
1061 run_cost += outer_path->total_cost - outer_path->startup_cost;
1062 startup_cost += inner_path->total_cost;
1065 * Cost of computing hash function: must do it once per input tuple.
1066 * We charge one cpu_operator_cost for each column's hash function.
1068 * XXX when a hashclause is more complex than a single operator,
1069 * we really should charge the extra eval costs of the left or right
1070 * side, as appropriate, here. This seems more work than it's worth
1073 startup_cost += cpu_operator_cost * num_hashclauses * inner_path_rows;
1074 run_cost += cpu_operator_cost * num_hashclauses * outer_path_rows;
1076 /* Get hash table size that executor would use for inner relation */
1077 ExecChooseHashTableSize(inner_path_rows,
1078 inner_path->parent->width,
1084 * Determine bucketsize fraction for inner relation. We use the
1085 * smallest bucketsize estimated for any individual hashclause;
1086 * this is undoubtedly conservative.
1088 innerbucketsize = 1.0;
1089 foreach(hcl, hashclauses)
1091 RestrictInfo *restrictinfo = (RestrictInfo *) lfirst(hcl);
1092 Selectivity thisbucketsize;
1094 Assert(IsA(restrictinfo, RestrictInfo));
1097 * First we have to figure out which side of the hashjoin clause is the
1100 * Since we tend to visit the same clauses over and over when planning
1101 * a large query, we cache the bucketsize estimate in the RestrictInfo
1102 * node to avoid repeated lookups of statistics.
1104 if (is_subseti(restrictinfo->right_relids, inner_path->parent->relids))
1106 /* righthand side is inner */
1107 thisbucketsize = restrictinfo->right_bucketsize;
1108 if (thisbucketsize < 0)
1110 /* not cached yet */
1111 thisbucketsize = estimate_hash_bucketsize(root,
1112 (Var *) get_rightop(restrictinfo->clause),
1114 restrictinfo->right_bucketsize = thisbucketsize;
1119 Assert(is_subseti(restrictinfo->left_relids,
1120 inner_path->parent->relids));
1121 /* lefthand side is inner */
1122 thisbucketsize = restrictinfo->left_bucketsize;
1123 if (thisbucketsize < 0)
1125 /* not cached yet */
1126 thisbucketsize = estimate_hash_bucketsize(root,
1127 (Var *) get_leftop(restrictinfo->clause),
1129 restrictinfo->left_bucketsize = thisbucketsize;
1133 if (innerbucketsize > thisbucketsize)
1134 innerbucketsize = thisbucketsize;
1138 * if inner relation is too big then we will need to "batch" the join,
1139 * which implies writing and reading most of the tuples to disk an
1140 * extra time. Charge one cost unit per page of I/O (correct since it
1141 * should be nice and sequential...). Writing the inner rel counts as
1142 * startup cost, all the rest as run cost.
1146 double outerpages = page_size(outer_path_rows,
1147 outer_path->parent->width);
1148 double innerpages = page_size(inner_path_rows,
1149 inner_path->parent->width);
1151 startup_cost += innerpages;
1152 run_cost += innerpages + 2 * outerpages;
1158 * If we're doing JOIN_IN then we will stop comparing inner
1159 * tuples to an outer tuple as soon as we have one match. Account for
1160 * the effects of this by scaling down the cost estimates in proportion
1161 * to the expected output size. (This assumes that all the quals attached
1162 * to the join are IN quals, which should be true.)
1164 if (path->jpath.jointype == JOIN_IN &&
1165 qptuples > path->jpath.path.parent->rows)
1166 joininfactor = path->jpath.path.parent->rows / qptuples;
1171 * The number of tuple comparisons needed is the number of outer
1172 * tuples times the typical number of tuples in a hash bucket, which
1173 * is the inner relation size times its bucketsize fraction. At each
1174 * one, we need to evaluate the hashjoin quals.
1176 startup_cost += hash_qual_cost.startup;
1177 run_cost += hash_qual_cost.per_tuple *
1178 outer_path_rows * ceil(inner_path_rows * innerbucketsize) *
1182 * For each tuple that gets through the hashjoin proper, we charge
1183 * cpu_tuple_cost plus the cost of evaluating additional restriction
1184 * clauses that are to be applied at the join. (This is pessimistic
1185 * since not all of the quals may get evaluated at each tuple.)
1187 startup_cost += qp_qual_cost.startup;
1188 cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
1189 run_cost += cpu_per_tuple * hashjointuples * joininfactor;
1192 * Bias against putting larger relation on inside. We don't want an
1193 * absolute prohibition, though, since larger relation might have
1194 * better bucketsize --- and we can't trust the size estimates
1195 * unreservedly, anyway. Instead, inflate the run cost by the
1196 * square root of the size ratio. (Why square root? No real good
1197 * reason, but it seems reasonable...)
1199 * Note: before 7.4 we implemented this by inflating startup cost;
1200 * but if there's a disable_cost component in the input paths'
1201 * startup cost, that unfairly penalizes the hash. Probably it'd
1202 * be better to keep track of disable penalty separately from cost.
1204 if (innerbytes > outerbytes && outerbytes > 0)
1205 run_cost *= sqrt(innerbytes / outerbytes);
1207 path->jpath.path.startup_cost = startup_cost;
1208 path->jpath.path.total_cost = startup_cost + run_cost;
1212 * Estimate hash bucketsize fraction (ie, number of entries in a bucket
1213 * divided by total tuples in relation) if the specified Var is used
1216 * XXX This is really pretty bogus since we're effectively assuming that the
1217 * distribution of hash keys will be the same after applying restriction
1218 * clauses as it was in the underlying relation. However, we are not nearly
1219 * smart enough to figure out how the restrict clauses might change the
1220 * distribution, so this will have to do for now.
1222 * We are passed the number of buckets the executor will use for the given
1223 * input relation. If the data were perfectly distributed, with the same
1224 * number of tuples going into each available bucket, then the bucketsize
1225 * fraction would be 1/nbuckets. But this happy state of affairs will occur
1226 * only if (a) there are at least nbuckets distinct data values, and (b)
1227 * we have a not-too-skewed data distribution. Otherwise the buckets will
1228 * be nonuniformly occupied. If the other relation in the join has a key
1229 * distribution similar to this one's, then the most-loaded buckets are
1230 * exactly those that will be probed most often. Therefore, the "average"
1231 * bucket size for costing purposes should really be taken as something close
1232 * to the "worst case" bucket size. We try to estimate this by adjusting the
1233 * fraction if there are too few distinct data values, and then scaling up
1234 * by the ratio of the most common value's frequency to the average frequency.
1236 * If no statistics are available, use a default estimate of 0.1. This will
1237 * discourage use of a hash rather strongly if the inner relation is large,
1238 * which is what we want. We do not want to hash unless we know that the
1239 * inner rel is well-dispersed (or the alternatives seem much worse).
1242 estimate_hash_bucketsize(Query *root, Var *var, int nbuckets)
1247 Form_pg_statistic stats;
1256 * Lookup info about var's relation and attribute; if none available,
1257 * return default estimate.
1259 if (var == NULL || !IsA(var, Var))
1262 relid = getrelid(var->varno, root->rtable);
1263 if (relid == InvalidOid)
1266 rel = find_base_rel(root, var->varno);
1268 if (rel->tuples <= 0.0 || rel->rows <= 0.0)
1269 return 0.1; /* ensure we can divide below */
1271 tuple = SearchSysCache(STATRELATT,
1272 ObjectIdGetDatum(relid),
1273 Int16GetDatum(var->varattno),
1275 if (!HeapTupleIsValid(tuple))
1278 * Perhaps the Var is a system attribute; if so, it will have no
1279 * entry in pg_statistic, but we may be able to guess something
1280 * about its distribution anyway.
1282 switch (var->varattno)
1284 case ObjectIdAttributeNumber:
1285 case SelfItemPointerAttributeNumber:
1286 /* these are unique, so buckets should be well-distributed */
1287 return 1.0 / (double) nbuckets;
1288 case TableOidAttributeNumber:
1289 /* hashing this is a terrible idea... */
1294 stats = (Form_pg_statistic) GETSTRUCT(tuple);
1297 * Obtain number of distinct data values in raw relation.
1299 ndistinct = stats->stadistinct;
1300 if (ndistinct < 0.0)
1301 ndistinct = -ndistinct * rel->tuples;
1303 if (ndistinct <= 0.0) /* ensure we can divide */
1305 ReleaseSysCache(tuple);
1309 /* Also compute avg freq of all distinct data values in raw relation */
1310 avgfreq = (1.0 - stats->stanullfrac) / ndistinct;
1313 * Adjust ndistinct to account for restriction clauses. Observe we
1314 * are assuming that the data distribution is affected uniformly by
1315 * the restriction clauses!
1317 * XXX Possibly better way, but much more expensive: multiply by
1318 * selectivity of rel's restriction clauses that mention the target
1321 ndistinct *= rel->rows / rel->tuples;
1324 * Initial estimate of bucketsize fraction is 1/nbuckets as long as
1325 * the number of buckets is less than the expected number of distinct
1326 * values; otherwise it is 1/ndistinct.
1328 if (ndistinct > (double) nbuckets)
1329 estfract = 1.0 / (double) nbuckets;
1331 estfract = 1.0 / ndistinct;
1334 * Look up the frequency of the most common value, if available.
1338 if (get_attstatsslot(tuple, var->vartype, var->vartypmod,
1339 STATISTIC_KIND_MCV, InvalidOid,
1340 NULL, NULL, &numbers, &nnumbers))
1343 * The first MCV stat is for the most common value.
1346 mcvfreq = numbers[0];
1347 free_attstatsslot(var->vartype, NULL, 0,
1352 * Adjust estimated bucketsize upward to account for skewed
1355 if (avgfreq > 0.0 && mcvfreq > avgfreq)
1356 estfract *= mcvfreq / avgfreq;
1359 * Clamp bucketsize to sane range (the above adjustment could easily
1360 * produce an out-of-range result). We set the lower bound a little
1361 * above zero, since zero isn't a very sane result.
1363 if (estfract < 1.0e-6)
1365 else if (estfract > 1.0)
1368 ReleaseSysCache(tuple);
1370 return (Selectivity) estfract;
1376 * Estimate the CPU costs of evaluating a WHERE clause.
1377 * The input can be either an implicitly-ANDed list of boolean
1378 * expressions, or a list of RestrictInfo nodes.
1379 * The result includes both a one-time (startup) component,
1380 * and a per-evaluation component.
1383 cost_qual_eval(QualCost *cost, List *quals)
1388 cost->per_tuple = 0;
1390 /* We don't charge any cost for the implicit ANDing at top level ... */
1394 Node *qual = (Node *) lfirst(l);
1397 * RestrictInfo nodes contain an eval_cost field reserved for this
1398 * routine's use, so that it's not necessary to evaluate the qual
1399 * clause's cost more than once. If the clause's cost hasn't been
1400 * computed yet, the field's startup value will contain -1.
1402 if (qual && IsA(qual, RestrictInfo))
1404 RestrictInfo *restrictinfo = (RestrictInfo *) qual;
1406 if (restrictinfo->eval_cost.startup < 0)
1408 restrictinfo->eval_cost.startup = 0;
1409 restrictinfo->eval_cost.per_tuple = 0;
1410 cost_qual_eval_walker((Node *) restrictinfo->clause,
1411 &restrictinfo->eval_cost);
1413 cost->startup += restrictinfo->eval_cost.startup;
1414 cost->per_tuple += restrictinfo->eval_cost.per_tuple;
1418 /* If it's a bare expression, must always do it the hard way */
1419 cost_qual_eval_walker(qual, cost);
1425 cost_qual_eval_walker(Node *node, QualCost *total)
1431 * Our basic strategy is to charge one cpu_operator_cost for each
1432 * operator or function node in the given tree. Vars and Consts are
1433 * charged zero, and so are boolean operators (AND, OR, NOT).
1434 * Simplistic, but a lot better than no model at all.
1436 * Should we try to account for the possibility of short-circuit
1437 * evaluation of AND/OR?
1439 if (IsA(node, FuncExpr) ||
1440 IsA(node, OpExpr) ||
1441 IsA(node, DistinctExpr))
1443 total->per_tuple += cpu_operator_cost;
1445 else if (IsA(node, SubLink))
1447 /* This routine should not be applied to un-planned expressions */
1448 elog(ERROR, "cost_qual_eval: can't handle unplanned sub-select");
1450 else if (IsA(node, SubPlan))
1453 * A subplan node in an expression typically indicates that the
1454 * subplan will be executed on each evaluation, so charge accordingly.
1455 * (Sub-selects that can be executed as InitPlans have already been
1456 * removed from the expression.)
1458 * An exception occurs when we have decided we can implement the
1459 * subplan by hashing.
1462 SubPlan *subplan = (SubPlan *) node;
1463 Plan *plan = subplan->plan;
1465 if (subplan->useHashTable)
1468 * If we are using a hash table for the subquery outputs, then
1469 * the cost of evaluating the query is a one-time cost.
1470 * We charge one cpu_operator_cost per tuple for the work of
1471 * loading the hashtable, too.
1473 total->startup += plan->total_cost +
1474 cpu_operator_cost * plan->plan_rows;
1476 * The per-tuple costs include the cost of evaluating the
1477 * lefthand expressions, plus the cost of probing the hashtable.
1478 * Recursion into the exprs list will handle the lefthand
1479 * expressions properly, and will count one cpu_operator_cost
1480 * for each comparison operator. That is probably too low for
1481 * the probing cost, but it's hard to make a better estimate,
1482 * so live with it for now.
1488 * Otherwise we will be rescanning the subplan output on each
1489 * evaluation. We need to estimate how much of the output
1490 * we will actually need to scan. NOTE: this logic should
1491 * agree with the estimates used by make_subplan() in
1494 Cost plan_run_cost = plan->total_cost - plan->startup_cost;
1496 if (subplan->subLinkType == EXISTS_SUBLINK)
1498 /* we only need to fetch 1 tuple */
1499 total->per_tuple += plan_run_cost / plan->plan_rows;
1501 else if (subplan->subLinkType == ALL_SUBLINK ||
1502 subplan->subLinkType == ANY_SUBLINK)
1504 /* assume we need 50% of the tuples */
1505 total->per_tuple += 0.50 * plan_run_cost;
1506 /* also charge a cpu_operator_cost per row examined */
1507 total->per_tuple += 0.50 * plan->plan_rows * cpu_operator_cost;
1511 /* assume we need all tuples */
1512 total->per_tuple += plan_run_cost;
1515 * Also account for subplan's startup cost.
1516 * If the subplan is uncorrelated or undirect correlated,
1517 * AND its topmost node is a Sort or Material node, assume
1518 * that we'll only need to pay its startup cost once;
1519 * otherwise assume we pay the startup cost every time.
1521 if (subplan->parParam == NIL &&
1523 IsA(plan, Material)))
1525 total->startup += plan->startup_cost;
1529 total->per_tuple += plan->startup_cost;
1534 return expression_tree_walker(node, cost_qual_eval_walker,
1540 * approx_selectivity
1541 * Quick-and-dirty estimation of clause selectivities.
1542 * The input can be either an implicitly-ANDed list of boolean
1543 * expressions, or a list of RestrictInfo nodes (typically the latter).
1545 * The "quick" part comes from caching the selectivity estimates so we can
1546 * avoid recomputing them later. (Since the same clauses are typically
1547 * examined over and over in different possible join trees, this makes a
1550 * The "dirty" part comes from the fact that the selectivities of multiple
1551 * clauses are estimated independently and multiplied together. Now
1552 * clauselist_selectivity often can't do any better than that anyhow, but
1553 * for some situations (such as range constraints) it is smarter.
1555 * Since we are only using the results to estimate how many potential
1556 * output tuples are generated and passed through qpqual checking, it
1557 * seems OK to live with the approximation.
1560 approx_selectivity(Query *root, List *quals)
1562 Selectivity total = 1.0;
1567 Node *qual = (Node *) lfirst(l);
1571 * RestrictInfo nodes contain a this_selec field reserved for this
1572 * routine's use, so that it's not necessary to evaluate the qual
1573 * clause's selectivity more than once. If the clause's
1574 * selectivity hasn't been computed yet, the field will contain
1577 if (qual && IsA(qual, RestrictInfo))
1579 RestrictInfo *restrictinfo = (RestrictInfo *) qual;
1581 if (restrictinfo->this_selec < 0)
1582 restrictinfo->this_selec =
1583 clause_selectivity(root,
1584 (Node *) restrictinfo->clause,
1586 selec = restrictinfo->this_selec;
1590 /* If it's a bare expression, must always do it the hard way */
1591 selec = clause_selectivity(root, qual, 0);
1600 * set_baserel_size_estimates
1601 * Set the size estimates for the given base relation.
1603 * The rel's targetlist and restrictinfo list must have been constructed
1606 * We set the following fields of the rel node:
1607 * rows: the estimated number of output tuples (after applying
1608 * restriction clauses).
1609 * width: the estimated average output tuple width in bytes.
1610 * baserestrictcost: estimated cost of evaluating baserestrictinfo clauses.
1613 set_baserel_size_estimates(Query *root, RelOptInfo *rel)
1617 /* Should only be applied to base relations */
1618 Assert(length(rel->relids) == 1);
1620 temp = rel->tuples *
1621 restrictlist_selectivity(root,
1622 rel->baserestrictinfo,
1623 lfirsti(rel->relids));
1626 * Force estimate to be at least one row, to make explain output look
1627 * better and to avoid possible divide-by-zero when interpolating
1628 * cost. Make it an integer, too.
1637 cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo);
1639 set_rel_width(root, rel);
1643 * set_joinrel_size_estimates
1644 * Set the size estimates for the given join relation.
1646 * The rel's targetlist must have been constructed already, and a
1647 * restriction clause list that matches the given component rels must
1650 * Since there is more than one way to make a joinrel for more than two
1651 * base relations, the results we get here could depend on which component
1652 * rel pair is provided. In theory we should get the same answers no matter
1653 * which pair is provided; in practice, since the selectivity estimation
1654 * routines don't handle all cases equally well, we might not. But there's
1655 * not much to be done about it. (Would it make sense to repeat the
1656 * calculations for each pair of input rels that's encountered, and somehow
1657 * average the results? Probably way more trouble than it's worth.)
1659 * It's important that the results for symmetric JoinTypes be symmetric,
1660 * eg, (rel1, rel2, JOIN_LEFT) should produce the same result as (rel2,
1661 * rel1, JOIN_RIGHT). Also, JOIN_IN should produce the same result as
1662 * JOIN_UNIQUE_INNER, likewise JOIN_REVERSE_IN == JOIN_UNIQUE_OUTER.
1664 * We set the same relnode fields as set_baserel_size_estimates() does.
1667 set_joinrel_size_estimates(Query *root, RelOptInfo *rel,
1668 RelOptInfo *outer_rel,
1669 RelOptInfo *inner_rel,
1678 * Compute joinclause selectivity. Note that we are only considering
1679 * clauses that become restriction clauses at this join level; we are
1680 * not double-counting them because they were not considered in
1681 * estimating the sizes of the component rels.
1683 selec = restrictlist_selectivity(root,
1688 * Basically, we multiply size of Cartesian product by selectivity.
1690 * If we are doing an outer join, take that into account: the output
1691 * must be at least as large as the non-nullable input. (Is there any
1692 * chance of being even smarter?)
1694 * For JOIN_IN and variants, the Cartesian product is figured with
1695 * respect to a unique-ified input, and then we can clamp to the size
1696 * of the other input.
1697 * XXX it's not at all clear that the ordinary selectivity calculation
1698 * is appropriate in this case.
1703 temp = outer_rel->rows * inner_rel->rows * selec;
1706 temp = outer_rel->rows * inner_rel->rows * selec;
1707 if (temp < outer_rel->rows)
1708 temp = outer_rel->rows;
1711 temp = outer_rel->rows * inner_rel->rows * selec;
1712 if (temp < inner_rel->rows)
1713 temp = inner_rel->rows;
1716 temp = outer_rel->rows * inner_rel->rows * selec;
1717 if (temp < outer_rel->rows)
1718 temp = outer_rel->rows;
1719 if (temp < inner_rel->rows)
1720 temp = inner_rel->rows;
1723 case JOIN_UNIQUE_INNER:
1724 upath = create_unique_path(root, inner_rel,
1725 inner_rel->cheapest_total_path);
1726 temp = outer_rel->rows * upath->rows * selec;
1727 if (temp > outer_rel->rows)
1728 temp = outer_rel->rows;
1730 case JOIN_REVERSE_IN:
1731 case JOIN_UNIQUE_OUTER:
1732 upath = create_unique_path(root, outer_rel,
1733 outer_rel->cheapest_total_path);
1734 temp = upath->rows * inner_rel->rows * selec;
1735 if (temp > inner_rel->rows)
1736 temp = inner_rel->rows;
1739 elog(ERROR, "set_joinrel_size_estimates: unsupported join type %d",
1741 temp = 0; /* keep compiler quiet */
1746 * Force estimate to be at least one row, to make explain output look
1747 * better and to avoid possible divide-by-zero when interpolating
1748 * cost. Make it an integer, too.
1758 * We could apply set_rel_width() to compute the output tuple width
1759 * from scratch, but at present it's always just the sum of the input
1760 * widths, so why work harder than necessary? If relnode.c is ever
1761 * taught to remove unneeded columns from join targetlists, go back to
1762 * using set_rel_width here.
1764 rel->width = outer_rel->width + inner_rel->width;
1768 * set_function_size_estimates
1769 * Set the size estimates for a base relation that is a function call.
1771 * The rel's targetlist and restrictinfo list must have been constructed
1774 * We set the following fields of the rel node:
1775 * rows: the estimated number of output tuples (after applying
1776 * restriction clauses).
1777 * width: the estimated average output tuple width in bytes.
1778 * baserestrictcost: estimated cost of evaluating baserestrictinfo clauses.
1781 set_function_size_estimates(Query *root, RelOptInfo *rel)
1785 /* Should only be applied to base relations that are functions */
1786 Assert(length(rel->relids) == 1);
1787 Assert(rel->rtekind == RTE_FUNCTION);
1790 * Estimate number of rows the function itself will return.
1792 * XXX no idea how to do this yet; but should at least check whether
1793 * function returns set or not...
1797 /* Now estimate number of output rows */
1798 temp = rel->tuples *
1799 restrictlist_selectivity(root,
1800 rel->baserestrictinfo,
1801 lfirsti(rel->relids));
1804 * Force estimate to be at least one row, to make explain output look
1805 * better and to avoid possible divide-by-zero when interpolating
1806 * cost. Make it an integer, too.
1815 cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo);
1817 set_rel_width(root, rel);
1823 * Set the estimated output width of the relation.
1825 * NB: this works best on base relations because it prefers to look at
1826 * real Vars. It will fail to make use of pg_statistic info when applied
1827 * to a subquery relation, even if the subquery outputs are simple vars
1828 * that we could have gotten info for. Is it worth trying to be smarter
1832 set_rel_width(Query *root, RelOptInfo *rel)
1834 int32 tuple_width = 0;
1837 foreach(tllist, rel->targetlist)
1839 TargetEntry *tle = (TargetEntry *) lfirst(tllist);
1843 * If it's a Var, try to get statistical info from pg_statistic.
1845 if (tle->expr && IsA(tle->expr, Var))
1847 Var *var = (Var *) tle->expr;
1850 relid = getrelid(var->varno, root->rtable);
1851 if (relid != InvalidOid)
1853 item_width = get_attavgwidth(relid, var->varattno);
1856 tuple_width += item_width;
1863 * Not a Var, or can't find statistics for it. Estimate using
1864 * just the type info.
1866 item_width = get_typavgwidth(tle->resdom->restype,
1867 tle->resdom->restypmod);
1868 Assert(item_width > 0);
1869 tuple_width += item_width;
1871 Assert(tuple_width >= 0);
1872 rel->width = tuple_width;
1876 * relation_byte_size
1877 * Estimate the storage space in bytes for a given number of tuples
1878 * of a given width (size in bytes).
1881 relation_byte_size(double tuples, int width)
1883 return tuples * (MAXALIGN(width) + MAXALIGN(sizeof(HeapTupleData)));
1888 * Returns an estimate of the number of pages covered by a given
1889 * number of tuples of a given width (size in bytes).
1892 page_size(double tuples, int width)
1894 return ceil(relation_byte_size(tuples, width) / BLCKSZ);