Header And Logo
|
#include "nodes/plannodes.h"#include "nodes/relation.h"
Go to the source code of this file.
Definition at line 32 of file cost.h.
{
CONSTRAINT_EXCLUSION_OFF, /* do not use c_e */
CONSTRAINT_EXCLUSION_ON, /* apply c_e to all rels */
CONSTRAINT_EXCLUSION_PARTITION /* apply c_e to otherrels only */
} ConstraintExclusionType;
| double clamp_row_est | ( | double | nrows | ) |
Definition at line 154 of file costsize.c.
References rint().
Referenced by approx_tuple_count(), calc_joinrel_size_estimate(), cost_bitmap_heap_scan(), cost_index(), create_bitmap_subplan(), estimate_path_cost_size(), estimate_size(), expression_returns_set_rows(), final_cost_hashjoin(), get_parameterized_baserel_size(), initial_cost_mergejoin(), make_limit(), and set_baserel_size_estimates().
{
/*
* Force estimate to be at least one row, to make explain output look
* better and to avoid possible divide-by-zero when interpolating costs.
* Make it an integer, too.
*/
if (nrows <= 1.0)
nrows = 1.0;
else
nrows = rint(nrows);
return nrows;
}
| Selectivity clause_selectivity | ( | PlannerInfo * | root, | |
| Node * | clause, | |||
| int | varRelid, | |||
| JoinType | jointype, | |||
| SpecialJoinInfo * | sjinfo | |||
| ) |
Definition at line 484 of file clausesel.c.
References and_clause(), arg, OpExpr::args, bms_is_subset_singleton(), BooleanEqualOperator, booltestsel(), RestrictInfo::clause, RestrictInfo::clause_relids, clause_selectivity(), clauselist_selectivity(), Const::constisnull, Const::constvalue, CurrentOfExpr::cvarno, DatumGetBool, DEBUG4, elog, estimate_expression_value(), find_base_rel(), get_notclausearg(), OpExpr::inputcollid, InvalidOid, is_funcclause, is_opclause, IsA, JOIN_INNER, join_selectivity(), lfirst, list_make2, makeBoolConst(), RestrictInfo::norm_selec, not_clause(), NULL, nulltestsel(), OpExpr::opno, or_clause(), RestrictInfo::orclause, RestrictInfo::outer_selec, RestrictInfo::pseudoconstant, restriction_selectivity(), rowcomparesel(), s1, s2, scalararraysel(), treat_as_join_clause(), RelOptInfo::tuples, RangeQueryClause::var, Var::varlevelsup, and Var::varno.
Referenced by approx_tuple_count(), booltestsel(), clause_selectivity(), clauselist_selectivity(), and create_or_index_quals().
{
Selectivity s1 = 0.5; /* default for any unhandled clause type */
RestrictInfo *rinfo = NULL;
bool cacheable = false;
if (clause == NULL) /* can this still happen? */
return s1;
if (IsA(clause, RestrictInfo))
{
rinfo = (RestrictInfo *) clause;
/*
* If the clause is marked pseudoconstant, then it will be used as a
* gating qual and should not affect selectivity estimates; hence
* return 1.0. The only exception is that a constant FALSE may be
* taken as having selectivity 0.0, since it will surely mean no rows
* out of the plan. This case is simple enough that we need not
* bother caching the result.
*/
if (rinfo->pseudoconstant)
{
if (!IsA(rinfo->clause, Const))
return (Selectivity) 1.0;
}
/*
* If the clause is marked redundant, always return 1.0.
*/
if (rinfo->norm_selec > 1)
return (Selectivity) 1.0;
/*
* If possible, cache the result of the selectivity calculation for
* the clause. We can cache if varRelid is zero or the clause
* contains only vars of that relid --- otherwise varRelid will affect
* the result, so mustn't cache. Outer join quals might be examined
* with either their join's actual jointype or JOIN_INNER, so we need
* two cache variables to remember both cases. Note: we assume the
* result won't change if we are switching the input relations or
* considering a unique-ified case, so we only need one cache variable
* for all non-JOIN_INNER cases.
*/
if (varRelid == 0 ||
bms_is_subset_singleton(rinfo->clause_relids, varRelid))
{
/* Cacheable --- do we already have the result? */
if (jointype == JOIN_INNER)
{
if (rinfo->norm_selec >= 0)
return rinfo->norm_selec;
}
else
{
if (rinfo->outer_selec >= 0)
return rinfo->outer_selec;
}
cacheable = true;
}
/*
* Proceed with examination of contained clause. If the clause is an
* OR-clause, we want to look at the variant with sub-RestrictInfos,
* so that per-subclause selectivities can be cached.
*/
if (rinfo->orclause)
clause = (Node *) rinfo->orclause;
else
clause = (Node *) rinfo->clause;
}
if (IsA(clause, Var))
{
Var *var = (Var *) clause;
/*
* We probably shouldn't ever see an uplevel Var here, but if we do,
* return the default selectivity...
*/
if (var->varlevelsup == 0 &&
(varRelid == 0 || varRelid == (int) var->varno))
{
/*
* A Var at the top of a clause must be a bool Var. This is
* equivalent to the clause reln.attribute = 't', so we compute
* the selectivity as if that is what we have.
*/
s1 = restriction_selectivity(root,
BooleanEqualOperator,
list_make2(var,
makeBoolConst(true,
false)),
InvalidOid,
varRelid);
}
}
else if (IsA(clause, Const))
{
/* bool constant is pretty easy... */
Const *con = (Const *) clause;
s1 = con->constisnull ? 0.0 :
DatumGetBool(con->constvalue) ? 1.0 : 0.0;
}
else if (IsA(clause, Param))
{
/* see if we can replace the Param */
Node *subst = estimate_expression_value(root, clause);
if (IsA(subst, Const))
{
/* bool constant is pretty easy... */
Const *con = (Const *) subst;
s1 = con->constisnull ? 0.0 :
DatumGetBool(con->constvalue) ? 1.0 : 0.0;
}
else
{
/* XXX any way to do better than default? */
}
}
else if (not_clause(clause))
{
/* inverse of the selectivity of the underlying clause */
s1 = 1.0 - clause_selectivity(root,
(Node *) get_notclausearg((Expr *) clause),
varRelid,
jointype,
sjinfo);
}
else if (and_clause(clause))
{
/* share code with clauselist_selectivity() */
s1 = clauselist_selectivity(root,
((BoolExpr *) clause)->args,
varRelid,
jointype,
sjinfo);
}
else if (or_clause(clause))
{
/*
* Selectivities for an OR clause are computed as s1+s2 - s1*s2 to
* account for the probable overlap of selected tuple sets.
*
* XXX is this too conservative?
*/
ListCell *arg;
s1 = 0.0;
foreach(arg, ((BoolExpr *) clause)->args)
{
Selectivity s2 = clause_selectivity(root,
(Node *) lfirst(arg),
varRelid,
jointype,
sjinfo);
s1 = s1 + s2 - s1 * s2;
}
}
else if (is_opclause(clause) || IsA(clause, DistinctExpr))
{
OpExpr *opclause = (OpExpr *) clause;
Oid opno = opclause->opno;
if (treat_as_join_clause(clause, rinfo, varRelid, sjinfo))
{
/* Estimate selectivity for a join clause. */
s1 = join_selectivity(root, opno,
opclause->args,
opclause->inputcollid,
jointype,
sjinfo);
}
else
{
/* Estimate selectivity for a restriction clause. */
s1 = restriction_selectivity(root, opno,
opclause->args,
opclause->inputcollid,
varRelid);
}
/*
* DistinctExpr has the same representation as OpExpr, but the
* contained operator is "=" not "<>", so we must negate the result.
* This estimation method doesn't give the right behavior for nulls,
* but it's better than doing nothing.
*/
if (IsA(clause, DistinctExpr))
s1 = 1.0 - s1;
}
else if (is_funcclause(clause))
{
/*
* This is not an operator, so we guess at the selectivity. THIS IS A
* HACK TO GET V4 OUT THE DOOR. FUNCS SHOULD BE ABLE TO HAVE
* SELECTIVITIES THEMSELVES. -- JMH 7/9/92
*/
s1 = (Selectivity) 0.3333333;
}
#ifdef NOT_USED
else if (IsA(clause, SubPlan) ||
IsA(clause, AlternativeSubPlan))
{
/*
* Just for the moment! FIX ME! - vadim 02/04/98
*/
s1 = (Selectivity) 0.5;
}
#endif
else if (IsA(clause, ScalarArrayOpExpr))
{
/* Use node specific selectivity calculation function */
s1 = scalararraysel(root,
(ScalarArrayOpExpr *) clause,
treat_as_join_clause(clause, rinfo,
varRelid, sjinfo),
varRelid,
jointype,
sjinfo);
}
else if (IsA(clause, RowCompareExpr))
{
/* Use node specific selectivity calculation function */
s1 = rowcomparesel(root,
(RowCompareExpr *) clause,
varRelid,
jointype,
sjinfo);
}
else if (IsA(clause, NullTest))
{
/* Use node specific selectivity calculation function */
s1 = nulltestsel(root,
((NullTest *) clause)->nulltesttype,
(Node *) ((NullTest *) clause)->arg,
varRelid,
jointype,
sjinfo);
}
else if (IsA(clause, BooleanTest))
{
/* Use node specific selectivity calculation function */
s1 = booltestsel(root,
((BooleanTest *) clause)->booltesttype,
(Node *) ((BooleanTest *) clause)->arg,
varRelid,
jointype,
sjinfo);
}
else if (IsA(clause, CurrentOfExpr))
{
/* CURRENT OF selects at most one row of its table */
CurrentOfExpr *cexpr = (CurrentOfExpr *) clause;
RelOptInfo *crel = find_base_rel(root, cexpr->cvarno);
if (crel->tuples > 0)
s1 = 1.0 / crel->tuples;
}
else if (IsA(clause, RelabelType))
{
/* Not sure this case is needed, but it can't hurt */
s1 = clause_selectivity(root,
(Node *) ((RelabelType *) clause)->arg,
varRelid,
jointype,
sjinfo);
}
else if (IsA(clause, CoerceToDomain))
{
/* Not sure this case is needed, but it can't hurt */
s1 = clause_selectivity(root,
(Node *) ((CoerceToDomain *) clause)->arg,
varRelid,
jointype,
sjinfo);
}
/* Cache the result if possible */
if (cacheable)
{
if (jointype == JOIN_INNER)
rinfo->norm_selec = s1;
else
rinfo->outer_selec = s1;
}
#ifdef SELECTIVITY_DEBUG
elog(DEBUG4, "clause_selectivity: s1 %f", s1);
#endif /* SELECTIVITY_DEBUG */
return s1;
}
| Selectivity clauselist_selectivity | ( | PlannerInfo * | root, | |
| List * | clauses, | |||
| int | varRelid, | |||
| JoinType | jointype, | |||
| SpecialJoinInfo * | sjinfo | |||
| ) |
Definition at line 93 of file clausesel.c.
References addRangeClause(), OpExpr::args, bms_membership(), RestrictInfo::clause, RestrictInfo::clause_relids, clause_selectivity(), DEFAULT_INEQ_SEL, get_oprrest(), RangeQueryClause::have_hibound, RangeQueryClause::have_lobound, RangeQueryClause::hibound, IS_NULL, is_opclause, is_pseudo_constant_clause(), is_pseudo_constant_clause_relids(), IsA, RestrictInfo::left_relids, lfirst, linitial, list_length(), RangeQueryClause::lobound, lsecond, RangeQueryClause::next, NULL, nulltestsel(), NumRelids(), OpExpr::opno, pfree(), RestrictInfo::pseudoconstant, RestrictInfo::right_relids, s1, s2, and RangeQueryClause::var.
Referenced by btcostestimate(), calc_joinrel_size_estimate(), clause_selectivity(), compute_semi_anti_join_factors(), estimate_size(), genericcostestimate(), get_parameterized_baserel_size(), gincostestimate(), postgresGetForeignRelSize(), and set_baserel_size_estimates().
{
Selectivity s1 = 1.0;
RangeQueryClause *rqlist = NULL;
ListCell *l;
/*
* If there's exactly one clause, then no use in trying to match up pairs,
* so just go directly to clause_selectivity().
*/
if (list_length(clauses) == 1)
return clause_selectivity(root, (Node *) linitial(clauses),
varRelid, jointype, sjinfo);
/*
* Initial scan over clauses. Anything that doesn't look like a potential
* rangequery clause gets multiplied into s1 and forgotten. Anything that
* does gets inserted into an rqlist entry.
*/
foreach(l, clauses)
{
Node *clause = (Node *) lfirst(l);
RestrictInfo *rinfo;
Selectivity s2;
/* Always compute the selectivity using clause_selectivity */
s2 = clause_selectivity(root, clause, varRelid, jointype, sjinfo);
/*
* Check for being passed a RestrictInfo.
*
* If it's a pseudoconstant RestrictInfo, then s2 is either 1.0 or
* 0.0; just use that rather than looking for range pairs.
*/
if (IsA(clause, RestrictInfo))
{
rinfo = (RestrictInfo *) clause;
if (rinfo->pseudoconstant)
{
s1 = s1 * s2;
continue;
}
clause = (Node *) rinfo->clause;
}
else
rinfo = NULL;
/*
* See if it looks like a restriction clause with a pseudoconstant on
* one side. (Anything more complicated than that might not behave in
* the simple way we are expecting.) Most of the tests here can be
* done more efficiently with rinfo than without.
*/
if (is_opclause(clause) && list_length(((OpExpr *) clause)->args) == 2)
{
OpExpr *expr = (OpExpr *) clause;
bool varonleft = true;
bool ok;
if (rinfo)
{
ok = (bms_membership(rinfo->clause_relids) == BMS_SINGLETON) &&
(is_pseudo_constant_clause_relids(lsecond(expr->args),
rinfo->right_relids) ||
(varonleft = false,
is_pseudo_constant_clause_relids(linitial(expr->args),
rinfo->left_relids)));
}
else
{
ok = (NumRelids(clause) == 1) &&
(is_pseudo_constant_clause(lsecond(expr->args)) ||
(varonleft = false,
is_pseudo_constant_clause(linitial(expr->args))));
}
if (ok)
{
/*
* If it's not a "<" or ">" operator, just merge the
* selectivity in generically. But if it's the right oprrest,
* add the clause to rqlist for later processing.
*/
switch (get_oprrest(expr->opno))
{
case F_SCALARLTSEL:
addRangeClause(&rqlist, clause,
varonleft, true, s2);
break;
case F_SCALARGTSEL:
addRangeClause(&rqlist, clause,
varonleft, false, s2);
break;
default:
/* Just merge the selectivity in generically */
s1 = s1 * s2;
break;
}
continue; /* drop to loop bottom */
}
}
/* Not the right form, so treat it generically. */
s1 = s1 * s2;
}
/*
* Now scan the rangequery pair list.
*/
while (rqlist != NULL)
{
RangeQueryClause *rqnext;
if (rqlist->have_lobound && rqlist->have_hibound)
{
/* Successfully matched a pair of range clauses */
Selectivity s2;
/*
* Exact equality to the default value probably means the
* selectivity function punted. This is not airtight but should
* be good enough.
*/
if (rqlist->hibound == DEFAULT_INEQ_SEL ||
rqlist->lobound == DEFAULT_INEQ_SEL)
{
s2 = DEFAULT_RANGE_INEQ_SEL;
}
else
{
s2 = rqlist->hibound + rqlist->lobound - 1.0;
/* Adjust for double-exclusion of NULLs */
s2 += nulltestsel(root, IS_NULL, rqlist->var,
varRelid, jointype, sjinfo);
/*
* A zero or slightly negative s2 should be converted into a
* small positive value; we probably are dealing with a very
* tight range and got a bogus result due to roundoff errors.
* However, if s2 is very negative, then we probably have
* default selectivity estimates on one or both sides of the
* range that we failed to recognize above for some reason.
*/
if (s2 <= 0.0)
{
if (s2 < -0.01)
{
/*
* No data available --- use a default estimate that
* is small, but not real small.
*/
s2 = DEFAULT_RANGE_INEQ_SEL;
}
else
{
/*
* It's just roundoff error; use a small positive
* value
*/
s2 = 1.0e-10;
}
}
}
/* Merge in the selectivity of the pair of clauses */
s1 *= s2;
}
else
{
/* Only found one of a pair, merge it in generically */
if (rqlist->have_lobound)
s1 *= rqlist->lobound;
else
s1 *= rqlist->hibound;
}
/* release storage and advance */
rqnext = rqlist->next;
pfree(rqlist);
rqlist = rqnext;
}
return s1;
}
| void compute_semi_anti_join_factors | ( | PlannerInfo * | root, | |
| RelOptInfo * | outerrel, | |||
| RelOptInfo * | innerrel, | |||
| JoinType | jointype, | |||
| SpecialJoinInfo * | sjinfo, | |||
| List * | restrictlist, | |||
| SemiAntiJoinFactors * | semifactors | |||
| ) |
Definition at line 3191 of file costsize.c.
References Assert, clauselist_selectivity(), SpecialJoinInfo::delay_upper_joins, RestrictInfo::is_pushed_down, IsA, JOIN_ANTI, JOIN_INNER, SpecialJoinInfo::join_quals, JOIN_SEMI, SpecialJoinInfo::jointype, lappend(), lfirst, SpecialJoinInfo::lhs_strict, list_free(), SemiAntiJoinFactors::match_count, Max, SpecialJoinInfo::min_lefthand, SpecialJoinInfo::min_righthand, SemiAntiJoinFactors::outer_match_frac, RelOptInfo::relids, RelOptInfo::rows, SpecialJoinInfo::syn_lefthand, SpecialJoinInfo::syn_righthand, and SpecialJoinInfo::type.
Referenced by add_paths_to_joinrel().
{
Selectivity jselec;
Selectivity nselec;
Selectivity avgmatch;
SpecialJoinInfo norm_sjinfo;
List *joinquals;
ListCell *l;
/* Should only be called in these cases */
Assert(jointype == JOIN_SEMI || jointype == JOIN_ANTI);
/*
* In an ANTI join, we must ignore clauses that are "pushed down", since
* those won't affect the match logic. In a SEMI join, we do not
* distinguish joinquals from "pushed down" quals, so just use the whole
* restrictinfo list.
*/
if (jointype == JOIN_ANTI)
{
joinquals = NIL;
foreach(l, restrictlist)
{
RestrictInfo *rinfo = (RestrictInfo *) lfirst(l);
Assert(IsA(rinfo, RestrictInfo));
if (!rinfo->is_pushed_down)
joinquals = lappend(joinquals, rinfo);
}
}
else
joinquals = restrictlist;
/*
* Get the JOIN_SEMI or JOIN_ANTI selectivity of the join clauses.
*/
jselec = clauselist_selectivity(root,
joinquals,
0,
jointype,
sjinfo);
/*
* Also get the normal inner-join selectivity of the join clauses.
*/
norm_sjinfo.type = T_SpecialJoinInfo;
norm_sjinfo.min_lefthand = outerrel->relids;
norm_sjinfo.min_righthand = innerrel->relids;
norm_sjinfo.syn_lefthand = outerrel->relids;
norm_sjinfo.syn_righthand = innerrel->relids;
norm_sjinfo.jointype = JOIN_INNER;
/* we don't bother trying to make the remaining fields valid */
norm_sjinfo.lhs_strict = false;
norm_sjinfo.delay_upper_joins = false;
norm_sjinfo.join_quals = NIL;
nselec = clauselist_selectivity(root,
joinquals,
0,
JOIN_INNER,
&norm_sjinfo);
/* Avoid leaking a lot of ListCells */
if (jointype == JOIN_ANTI)
list_free(joinquals);
/*
* jselec can be interpreted as the fraction of outer-rel rows that have
* any matches (this is true for both SEMI and ANTI cases). And nselec is
* the fraction of the Cartesian product that matches. So, the average
* number of matches for each outer-rel row that has at least one match is
* nselec * inner_rows / jselec.
*
* Note: it is correct to use the inner rel's "rows" count here, even
* though we might later be considering a parameterized inner path with
* fewer rows. This is because we have included all the join clauses in
* the selectivity estimate.
*/
if (jselec > 0) /* protect against zero divide */
{
avgmatch = nselec * innerrel->rows / jselec;
/* Clamp to sane range */
avgmatch = Max(1.0, avgmatch);
}
else
avgmatch = 1.0;
semifactors->outer_match_frac = jselec;
semifactors->match_count = avgmatch;
}
| void cost_agg | ( | Path * | path, | |
| PlannerInfo * | root, | |||
| AggStrategy | aggstrategy, | |||
| const AggClauseCosts * | aggcosts, | |||
| int | numGroupCols, | |||
| double | numGroups, | |||
| Cost | input_startup_cost, | |||
| Cost | input_total_cost, | |||
| double | input_tuples | |||
| ) |
Definition at line 1467 of file costsize.c.
References AGG_HASHED, AGG_PLAIN, AGG_SORTED, Assert, cpu_operator_cost, cpu_tuple_cost, AggClauseCosts::finalCost, MemSet, NULL, QualCost::per_tuple, Path::rows, QualCost::startup, Path::startup_cost, Path::total_cost, and AggClauseCosts::transCost.
Referenced by choose_hashed_distinct(), choose_hashed_grouping(), choose_hashed_setop(), create_unique_path(), make_agg(), and optimize_minmax_aggregates().
{
double output_tuples;
Cost startup_cost;
Cost total_cost;
AggClauseCosts dummy_aggcosts;
/* Use all-zero per-aggregate costs if NULL is passed */
if (aggcosts == NULL)
{
Assert(aggstrategy == AGG_HASHED);
MemSet(&dummy_aggcosts, 0, sizeof(AggClauseCosts));
aggcosts = &dummy_aggcosts;
}
/*
* The transCost.per_tuple component of aggcosts should be charged once
* per input tuple, corresponding to the costs of evaluating the aggregate
* transfns and their input expressions (with any startup cost of course
* charged but once). The finalCost component is charged once per output
* tuple, corresponding to the costs of evaluating the finalfns.
*
* If we are grouping, we charge an additional cpu_operator_cost per
* grouping column per input tuple for grouping comparisons.
*
* We will produce a single output tuple if not grouping, and a tuple per
* group otherwise. We charge cpu_tuple_cost for each output tuple.
*
* Note: in this cost model, AGG_SORTED and AGG_HASHED have exactly the
* same total CPU cost, but AGG_SORTED has lower startup cost. If the
* input path is already sorted appropriately, AGG_SORTED should be
* preferred (since it has no risk of memory overflow). This will happen
* as long as the computed total costs are indeed exactly equal --- but if
* there's roundoff error we might do the wrong thing. So be sure that
* the computations below form the same intermediate values in the same
* order.
*/
if (aggstrategy == AGG_PLAIN)
{
startup_cost = input_total_cost;
startup_cost += aggcosts->transCost.startup;
startup_cost += aggcosts->transCost.per_tuple * input_tuples;
startup_cost += aggcosts->finalCost;
/* we aren't grouping */
total_cost = startup_cost + cpu_tuple_cost;
output_tuples = 1;
}
else if (aggstrategy == AGG_SORTED)
{
/* Here we are able to deliver output on-the-fly */
startup_cost = input_startup_cost;
total_cost = input_total_cost;
/* calcs phrased this way to match HASHED case, see note above */
total_cost += aggcosts->transCost.startup;
total_cost += aggcosts->transCost.per_tuple * input_tuples;
total_cost += (cpu_operator_cost * numGroupCols) * input_tuples;
total_cost += aggcosts->finalCost * numGroups;
total_cost += cpu_tuple_cost * numGroups;
output_tuples = numGroups;
}
else
{
/* must be AGG_HASHED */
startup_cost = input_total_cost;
startup_cost += aggcosts->transCost.startup;
startup_cost += aggcosts->transCost.per_tuple * input_tuples;
startup_cost += (cpu_operator_cost * numGroupCols) * input_tuples;
total_cost = startup_cost;
total_cost += aggcosts->finalCost * numGroups;
total_cost += cpu_tuple_cost * numGroups;
output_tuples = numGroups;
}
path->rows = output_tuples;
path->startup_cost = startup_cost;
path->total_cost = total_cost;
}
| void cost_bitmap_and_node | ( | BitmapAndPath * | path, | |
| PlannerInfo * | root | |||
| ) |
Definition at line 776 of file costsize.c.
References BitmapAndPath::bitmapquals, BitmapAndPath::bitmapselectivity, cost_bitmap_tree_node(), cpu_operator_cost, lfirst, list_head(), BitmapAndPath::path, Path::rows, Path::startup_cost, subpath(), and Path::total_cost.
Referenced by bitmap_and_cost_est(), and create_bitmap_and_path().
{
Cost totalCost;
Selectivity selec;
ListCell *l;
/*
* We estimate AND selectivity on the assumption that the inputs are
* independent. This is probably often wrong, but we don't have the info
* to do better.
*
* The runtime cost of the BitmapAnd itself is estimated at 100x
* cpu_operator_cost for each tbm_intersect needed. Probably too small,
* definitely too simplistic?
*/
totalCost = 0.0;
selec = 1.0;
foreach(l, path->bitmapquals)
{
Path *subpath = (Path *) lfirst(l);
Cost subCost;
Selectivity subselec;
cost_bitmap_tree_node(subpath, &subCost, &subselec);
selec *= subselec;
totalCost += subCost;
if (l != list_head(path->bitmapquals))
totalCost += 100.0 * cpu_operator_cost;
}
path->bitmapselectivity = selec;
path->path.rows = 0; /* per above, not used */
path->path.startup_cost = totalCost;
path->path.total_cost = totalCost;
}
| void cost_bitmap_heap_scan | ( | Path * | path, | |
| PlannerInfo * | root, | |||
| RelOptInfo * | baserel, | |||
| ParamPathInfo * | param_info, | |||
| Path * | bitmapqual, | |||
| double | loop_count | |||
| ) |
Definition at line 613 of file costsize.c.
References Assert, clamp_row_est(), cost_bitmap_tree_node(), cpu_tuple_cost, disable_cost, enable_bitmapscan, get_indexpath_pages(), get_restriction_qual_cost(), get_tablespace_page_costs(), index_pages_fetched(), IsA, RelOptInfo::pages, QualCost::per_tuple, ParamPathInfo::ppi_rows, RelOptInfo::relid, RelOptInfo::reltablespace, RelOptInfo::rows, Path::rows, RTE_RELATION, RelOptInfo::rtekind, QualCost::startup, Path::startup_cost, T, Path::total_cost, and RelOptInfo::tuples.
Referenced by bitmap_and_cost_est(), bitmap_scan_cost_est(), and create_bitmap_heap_path().
{
Cost startup_cost = 0;
Cost run_cost = 0;
Cost indexTotalCost;
Selectivity indexSelectivity;
QualCost qpqual_cost;
Cost cpu_per_tuple;
Cost cost_per_page;
double tuples_fetched;
double pages_fetched;
double spc_seq_page_cost,
spc_random_page_cost;
double T;
/* Should only be applied to base relations */
Assert(IsA(baserel, RelOptInfo));
Assert(baserel->relid > 0);
Assert(baserel->rtekind == RTE_RELATION);
/* Mark the path with the correct row estimate */
if (param_info)
path->rows = param_info->ppi_rows;
else
path->rows = baserel->rows;
if (!enable_bitmapscan)
startup_cost += disable_cost;
/*
* Fetch total cost of obtaining the bitmap, as well as its total
* selectivity.
*/
cost_bitmap_tree_node(bitmapqual, &indexTotalCost, &indexSelectivity);
startup_cost += indexTotalCost;
/* Fetch estimated page costs for tablespace containing table. */
get_tablespace_page_costs(baserel->reltablespace,
&spc_random_page_cost,
&spc_seq_page_cost);
/*
* Estimate number of main-table pages fetched.
*/
tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);
T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
if (loop_count > 1)
{
/*
* For repeated bitmap scans, scale up the number of tuples fetched in
* the Mackert and Lohman formula by the number of scans, so that we
* estimate the number of pages fetched by all the scans. Then
* pro-rate for one scan.
*/
pages_fetched = index_pages_fetched(tuples_fetched * loop_count,
baserel->pages,
get_indexpath_pages(bitmapqual),
root);
pages_fetched /= loop_count;
}
else
{
/*
* For a single scan, the number of heap pages that need to be fetched
* is the same as the Mackert and Lohman formula for the case T <= b
* (ie, no re-reads needed).
*/
pages_fetched = (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
}
if (pages_fetched >= T)
pages_fetched = T;
else
pages_fetched = ceil(pages_fetched);
/*
* For small numbers of pages we should charge spc_random_page_cost
* apiece, while if nearly all the table's pages are being read, it's more
* appropriate to charge spc_seq_page_cost apiece. The effect is
* nonlinear, too. For lack of a better idea, interpolate like this to
* determine the cost per page.
*/
if (pages_fetched >= 2.0)
cost_per_page = spc_random_page_cost -
(spc_random_page_cost - spc_seq_page_cost)
* sqrt(pages_fetched / T);
else
cost_per_page = spc_random_page_cost;
run_cost += pages_fetched * cost_per_page;
/*
* Estimate CPU costs per tuple.
*
* Often the indexquals don't need to be rechecked at each tuple ... but
* not always, especially not if there are enough tuples involved that the
* bitmaps become lossy. For the moment, just assume they will be
* rechecked always. This means we charge the full freight for all the
* scan clauses.
*/
get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
startup_cost += qpqual_cost.startup;
cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
run_cost += cpu_per_tuple * tuples_fetched;
path->startup_cost = startup_cost;
path->total_cost = startup_cost + run_cost;
}
| void cost_bitmap_or_node | ( | BitmapOrPath * | path, | |
| PlannerInfo * | root | |||
| ) |
Definition at line 820 of file costsize.c.
References BitmapOrPath::bitmapquals, BitmapOrPath::bitmapselectivity, cost_bitmap_tree_node(), cpu_operator_cost, IsA, lfirst, list_head(), Min, BitmapOrPath::path, Path::rows, Path::startup_cost, subpath(), and Path::total_cost.
Referenced by create_bitmap_or_path().
{
Cost totalCost;
Selectivity selec;
ListCell *l;
/*
* We estimate OR selectivity on the assumption that the inputs are
* non-overlapping, since that's often the case in "x IN (list)" type
* situations. Of course, we clamp to 1.0 at the end.
*
* The runtime cost of the BitmapOr itself is estimated at 100x
* cpu_operator_cost for each tbm_union needed. Probably too small,
* definitely too simplistic? We are aware that the tbm_unions are
* optimized out when the inputs are BitmapIndexScans.
*/
totalCost = 0.0;
selec = 0.0;
foreach(l, path->bitmapquals)
{
Path *subpath = (Path *) lfirst(l);
Cost subCost;
Selectivity subselec;
cost_bitmap_tree_node(subpath, &subCost, &subselec);
selec += subselec;
totalCost += subCost;
if (l != list_head(path->bitmapquals) &&
!IsA(subpath, IndexPath))
totalCost += 100.0 * cpu_operator_cost;
}
path->bitmapselectivity = Min(selec, 1.0);
path->path.rows = 0; /* per above, not used */
path->path.startup_cost = totalCost;
path->path.total_cost = totalCost;
}
| void cost_bitmap_tree_node | ( | Path * | path, | |
| Cost * | cost, | |||
| Selectivity * | selec | |||
| ) |
Definition at line 733 of file costsize.c.
References cpu_operator_cost, elog, ERROR, IsA, nodeTag, Path::rows, and Path::total_cost.
Referenced by choose_bitmap_and(), cost_bitmap_and_node(), cost_bitmap_heap_scan(), cost_bitmap_or_node(), and path_usage_comparator().
{
if (IsA(path, IndexPath))
{
*cost = ((IndexPath *) path)->indextotalcost;
*selec = ((IndexPath *) path)->indexselectivity;
/*
* Charge a small amount per retrieved tuple to reflect the costs of
* manipulating the bitmap. This is mostly to make sure that a bitmap
* scan doesn't look to be the same cost as an indexscan to retrieve a
* single tuple.
*/
*cost += 0.1 * cpu_operator_cost * path->rows;
}
else if (IsA(path, BitmapAndPath))
{
*cost = path->total_cost;
*selec = ((BitmapAndPath *) path)->bitmapselectivity;
}
else if (IsA(path, BitmapOrPath))
{
*cost = path->total_cost;
*selec = ((BitmapOrPath *) path)->bitmapselectivity;
}
else
{
elog(ERROR, "unrecognized node type: %d", nodeTag(path));
*cost = *selec = 0; /* keep compiler quiet */
}
}
| void cost_ctescan | ( | Path * | path, | |
| PlannerInfo * | root, | |||
| RelOptInfo * | baserel, | |||
| ParamPathInfo * | param_info | |||
| ) |
Definition at line 1114 of file costsize.c.
References Assert, cpu_tuple_cost, get_restriction_qual_cost(), QualCost::per_tuple, ParamPathInfo::ppi_rows, RelOptInfo::relid, RelOptInfo::rows, Path::rows, RTE_CTE, RelOptInfo::rtekind, QualCost::startup, Path::startup_cost, Path::total_cost, and RelOptInfo::tuples.
Referenced by create_ctescan_path(), and create_worktablescan_path().
{
Cost startup_cost = 0;
Cost run_cost = 0;
QualCost qpqual_cost;
Cost cpu_per_tuple;
/* Should only be applied to base relations that are CTEs */
Assert(baserel->relid > 0);
Assert(baserel->rtekind == RTE_CTE);
/* Mark the path with the correct row estimate */
if (param_info)
path->rows = param_info->ppi_rows;
else
path->rows = baserel->rows;
/* Charge one CPU tuple cost per row for tuplestore manipulation */
cpu_per_tuple = cpu_tuple_cost;
/* Add scanning CPU costs */
get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
startup_cost += qpqual_cost.startup;
cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
run_cost += cpu_per_tuple * baserel->tuples;
path->startup_cost = startup_cost;
path->total_cost = startup_cost + run_cost;
}
| void cost_functionscan | ( | Path * | path, | |
| PlannerInfo * | root, | |||
| RelOptInfo * | baserel, | |||
| ParamPathInfo * | param_info | |||
| ) |
Definition at line 1011 of file costsize.c.
References Assert, cost_qual_eval_node(), cpu_tuple_cost, RangeTblEntry::funcexpr, get_restriction_qual_cost(), QualCost::per_tuple, planner_rt_fetch, ParamPathInfo::ppi_rows, RelOptInfo::relid, RelOptInfo::rows, Path::rows, RTE_FUNCTION, RangeTblEntry::rtekind, QualCost::startup, Path::startup_cost, Path::total_cost, and RelOptInfo::tuples.
Referenced by create_functionscan_path().
{
Cost startup_cost = 0;
Cost run_cost = 0;
QualCost qpqual_cost;
Cost cpu_per_tuple;
RangeTblEntry *rte;
QualCost exprcost;
/* Should only be applied to base relations that are functions */
Assert(baserel->relid > 0);
rte = planner_rt_fetch(baserel->relid, root);
Assert(rte->rtekind == RTE_FUNCTION);
/* Mark the path with the correct row estimate */
if (param_info)
path->rows = param_info->ppi_rows;
else
path->rows = baserel->rows;
/*
* Estimate costs of executing the function expression.
*
* Currently, nodeFunctionscan.c always executes the function to
* completion before returning any rows, and caches the results in a
* tuplestore. So the function eval cost is all startup cost, and per-row
* costs are minimal.
*
* XXX in principle we ought to charge tuplestore spill costs if the
* number of rows is large. However, given how phony our rowcount
* estimates for functions tend to be, there's not a lot of point in that
* refinement right now.
*/
cost_qual_eval_node(&exprcost, rte->funcexpr, root);
startup_cost += exprcost.startup + exprcost.per_tuple;
/* Add scanning CPU costs */
get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
startup_cost += qpqual_cost.startup;
cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
run_cost += cpu_per_tuple * baserel->tuples;
path->startup_cost = startup_cost;
path->total_cost = startup_cost + run_cost;
}
| void cost_group | ( | Path * | path, | |
| PlannerInfo * | root, | |||
| int | numGroupCols, | |||
| double | numGroups, | |||
| Cost | input_startup_cost, | |||
| Cost | input_total_cost, | |||
| double | input_tuples | |||
| ) |
Definition at line 1621 of file costsize.c.
References cpu_operator_cost, Path::rows, Path::startup_cost, and Path::total_cost.
Referenced by choose_hashed_distinct(), choose_hashed_grouping(), choose_hashed_setop(), and make_group().
{
Cost startup_cost;
Cost total_cost;
startup_cost = input_startup_cost;
total_cost = input_total_cost;
/*
* Charge one cpu_operator_cost per comparison per input tuple. We assume
* all columns get compared at most of the tuples.
*/
total_cost += cpu_operator_cost * input_tuples * numGroupCols;
path->rows = numGroups;
path->startup_cost = startup_cost;
path->total_cost = total_cost;
}
| void cost_index | ( | IndexPath * | path, | |
| PlannerInfo * | root, | |||
| double | loop_count | |||
| ) |
Definition at line 240 of file costsize.c.
References RelOptInfo::allvisfrac, IndexOptInfo::amcostestimate, Assert, RelOptInfo::baserestrictinfo, clamp_row_est(), cost_qual_eval(), cpu_tuple_cost, disable_cost, enable_indexscan, Float8GetDatum(), get_tablespace_page_costs(), index_pages_fetched(), IndexPath::indexinfo, IndexPath::indexquals, IndexPath::indexselectivity, IndexPath::indextotalcost, IsA, list_concat(), list_copy(), list_difference_ptr(), OidFunctionCall7, IndexOptInfo::pages, RelOptInfo::pages, Path::param_info, IndexPath::path, Path::pathtype, QualCost::per_tuple, PointerGetDatum, ParamPathInfo::ppi_clauses, ParamPathInfo::ppi_rows, IndexOptInfo::rel, RelOptInfo::relid, RelOptInfo::reltablespace, RelOptInfo::rows, Path::rows, RTE_RELATION, RelOptInfo::rtekind, QualCost::startup, Path::startup_cost, Path::total_cost, and RelOptInfo::tuples.
Referenced by create_index_path(), and reparameterize_path().
{
IndexOptInfo *index = path->indexinfo;
RelOptInfo *baserel = index->rel;
bool indexonly = (path->path.pathtype == T_IndexOnlyScan);
List *allclauses;
Cost startup_cost = 0;
Cost run_cost = 0;
Cost indexStartupCost;
Cost indexTotalCost;
Selectivity indexSelectivity;
double indexCorrelation,
csquared;
double spc_seq_page_cost,
spc_random_page_cost;
Cost min_IO_cost,
max_IO_cost;
QualCost qpqual_cost;
Cost cpu_per_tuple;
double tuples_fetched;
double pages_fetched;
/* Should only be applied to base relations */
Assert(IsA(baserel, RelOptInfo) &&
IsA(index, IndexOptInfo));
Assert(baserel->relid > 0);
Assert(baserel->rtekind == RTE_RELATION);
/* Mark the path with the correct row estimate */
if (path->path.param_info)
{
path->path.rows = path->path.param_info->ppi_rows;
/* also get the set of clauses that should be enforced by the scan */
allclauses = list_concat(list_copy(path->path.param_info->ppi_clauses),
baserel->baserestrictinfo);
}
else
{
path->path.rows = baserel->rows;
/* allclauses should just be the rel's restriction clauses */
allclauses = baserel->baserestrictinfo;
}
if (!enable_indexscan)
startup_cost += disable_cost;
/* we don't need to check enable_indexonlyscan; indxpath.c does that */
/*
* Call index-access-method-specific code to estimate the processing cost
* for scanning the index, as well as the selectivity of the index (ie,
* the fraction of main-table tuples we will have to retrieve) and its
* correlation to the main-table tuple order.
*/
OidFunctionCall7(index->amcostestimate,
PointerGetDatum(root),
PointerGetDatum(path),
Float8GetDatum(loop_count),
PointerGetDatum(&indexStartupCost),
PointerGetDatum(&indexTotalCost),
PointerGetDatum(&indexSelectivity),
PointerGetDatum(&indexCorrelation));
/*
* Save amcostestimate's results for possible use in bitmap scan planning.
* We don't bother to save indexStartupCost or indexCorrelation, because a
* bitmap scan doesn't care about either.
*/
path->indextotalcost = indexTotalCost;
path->indexselectivity = indexSelectivity;
/* all costs for touching index itself included here */
startup_cost += indexStartupCost;
run_cost += indexTotalCost - indexStartupCost;
/* estimate number of main-table tuples fetched */
tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);
/* fetch estimated page costs for tablespace containing table */
get_tablespace_page_costs(baserel->reltablespace,
&spc_random_page_cost,
&spc_seq_page_cost);
/*----------
* Estimate number of main-table pages fetched, and compute I/O cost.
*
* When the index ordering is uncorrelated with the table ordering,
* we use an approximation proposed by Mackert and Lohman (see
* index_pages_fetched() for details) to compute the number of pages
* fetched, and then charge spc_random_page_cost per page fetched.
*
* When the index ordering is exactly correlated with the table ordering
* (just after a CLUSTER, for example), the number of pages fetched should
* be exactly selectivity * table_size. What's more, all but the first
* will be sequential fetches, not the random fetches that occur in the
* uncorrelated case. So if the number of pages is more than 1, we
* ought to charge
* spc_random_page_cost + (pages_fetched - 1) * spc_seq_page_cost
* For partially-correlated indexes, we ought to charge somewhere between
* these two estimates. We currently interpolate linearly between the
* estimates based on the correlation squared (XXX is that appropriate?).
*
* If it's an index-only scan, then we will not need to fetch any heap
* pages for which the visibility map shows all tuples are visible.
* Hence, reduce the estimated number of heap fetches accordingly.
* We use the measured fraction of the entire heap that is all-visible,
* which might not be particularly relevant to the subset of the heap
* that this query will fetch; but it's not clear how to do better.
*----------
*/
if (loop_count > 1)
{
/*
* For repeated indexscans, the appropriate estimate for the
* uncorrelated case is to scale up the number of tuples fetched in
* the Mackert and Lohman formula by the number of scans, so that we
* estimate the number of pages fetched by all the scans; then
* pro-rate the costs for one scan. In this case we assume all the
* fetches are random accesses.
*/
pages_fetched = index_pages_fetched(tuples_fetched * loop_count,
baserel->pages,
(double) index->pages,
root);
if (indexonly)
pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
max_IO_cost = (pages_fetched * spc_random_page_cost) / loop_count;
/*
* In the perfectly correlated case, the number of pages touched by
* each scan is selectivity * table_size, and we can use the Mackert
* and Lohman formula at the page level to estimate how much work is
* saved by caching across scans. We still assume all the fetches are
* random, though, which is an overestimate that's hard to correct for
* without double-counting the cache effects. (But in most cases
* where such a plan is actually interesting, only one page would get
* fetched per scan anyway, so it shouldn't matter much.)
*/
pages_fetched = ceil(indexSelectivity * (double) baserel->pages);
pages_fetched = index_pages_fetched(pages_fetched * loop_count,
baserel->pages,
(double) index->pages,
root);
if (indexonly)
pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
min_IO_cost = (pages_fetched * spc_random_page_cost) / loop_count;
}
else
{
/*
* Normal case: apply the Mackert and Lohman formula, and then
* interpolate between that and the correlation-derived result.
*/
pages_fetched = index_pages_fetched(tuples_fetched,
baserel->pages,
(double) index->pages,
root);
if (indexonly)
pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
/* max_IO_cost is for the perfectly uncorrelated case (csquared=0) */
max_IO_cost = pages_fetched * spc_random_page_cost;
/* min_IO_cost is for the perfectly correlated case (csquared=1) */
pages_fetched = ceil(indexSelectivity * (double) baserel->pages);
if (indexonly)
pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
if (pages_fetched > 0)
{
min_IO_cost = spc_random_page_cost;
if (pages_fetched > 1)
min_IO_cost += (pages_fetched - 1) * spc_seq_page_cost;
}
else
min_IO_cost = 0;
}
/*
* Now interpolate based on estimated index order correlation to get total
* disk I/O cost for main table accesses.
*/
csquared = indexCorrelation * indexCorrelation;
run_cost += max_IO_cost + csquared * (min_IO_cost - max_IO_cost);
/*
* Estimate CPU costs per tuple.
*
* What we want here is cpu_tuple_cost plus the evaluation costs of any
* qual clauses that we have to evaluate as qpquals. We approximate that
* list as allclauses minus any clauses appearing in indexquals. (We
* assume that pointer equality is enough to recognize duplicate
* RestrictInfos.) This method neglects some considerations such as
* clauses that needn't be checked because they are implied by a partial
* index's predicate. It does not seem worth the cycles to try to factor
* those things in at this stage, even though createplan.c will take pains
* to remove such unnecessary clauses from the qpquals list if this path
* is selected for use.
*/
cost_qual_eval(&qpqual_cost,
list_difference_ptr(allclauses, path->indexquals),
root);
startup_cost += qpqual_cost.startup;
cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
run_cost += cpu_per_tuple * tuples_fetched;
path->path.startup_cost = startup_cost;
path->path.total_cost = startup_cost + run_cost;
}
| void cost_material | ( | Path * | path, | |
| Cost | input_startup_cost, | |||
| Cost | input_total_cost, | |||
| double | tuples, | |||
| int | width | |||
| ) |
Definition at line 1413 of file costsize.c.
References cpu_operator_cost, relation_byte_size(), Path::rows, seq_page_cost, Path::startup_cost, Path::total_cost, and work_mem.
Referenced by create_material_path(), and materialize_finished_plan().
{
Cost startup_cost = input_startup_cost;
Cost run_cost = input_total_cost - input_startup_cost;
double nbytes = relation_byte_size(tuples, width);
long work_mem_bytes = work_mem * 1024L;
path->rows = tuples;
/*
* Whether spilling or not, charge 2x cpu_operator_cost per tuple to
* reflect bookkeeping overhead. (This rate must be more than what
* cost_rescan charges for materialize, ie, cpu_operator_cost per tuple;
* if it is exactly the same then there will be a cost tie between
* nestloop with A outer, materialized B inner and nestloop with B outer,
* materialized A inner. The extra cost ensures we'll prefer
* materializing the smaller rel.) Note that this is normally a good deal
* less than cpu_tuple_cost; which is OK because a Material plan node
* doesn't do qual-checking or projection, so it's got less overhead than
* most plan nodes.
*/
run_cost += 2 * cpu_operator_cost * tuples;
/*
* If we will spill to disk, charge at the rate of seq_page_cost per page.
* This cost is assumed to be evenly spread through the plan run phase,
* which isn't exactly accurate but our cost model doesn't allow for
* nonuniform costs within the run phase.
*/
if (nbytes > work_mem_bytes)
{
double npages = ceil(nbytes / BLCKSZ);
run_cost += seq_page_cost * npages;
}
path->startup_cost = startup_cost;
path->total_cost = startup_cost + run_cost;
}
| void cost_merge_append | ( | Path * | path, | |
| PlannerInfo * | root, | |||
| List * | pathkeys, | |||
| int | n_streams, | |||
| Cost | input_startup_cost, | |||
| Cost | input_total_cost, | |||
| double | tuples | |||
| ) |
Definition at line 1362 of file costsize.c.
References cpu_operator_cost, LOG2, Path::startup_cost, and Path::total_cost.
Referenced by create_merge_append_path().
{
Cost startup_cost = 0;
Cost run_cost = 0;
Cost comparison_cost;
double N;
double logN;
/*
* Avoid log(0)...
*/
N = (n_streams < 2) ? 2.0 : (double) n_streams;
logN = LOG2(N);
/* Assumed cost per tuple comparison */
comparison_cost = 2.0 * cpu_operator_cost;
/* Heap creation cost */
startup_cost += comparison_cost * N * logN;
/* Per-tuple heap maintenance cost */
run_cost += tuples * comparison_cost * 2.0 * logN;
/*
* Also charge a small amount (arbitrarily set equal to operator cost) per
* extracted tuple. We don't charge cpu_tuple_cost because a MergeAppend
* node doesn't do qual-checking or projection, so it has less overhead
* than most plan nodes.
*/
run_cost += cpu_operator_cost * tuples;
path->startup_cost = startup_cost + input_startup_cost;
path->total_cost = startup_cost + run_cost + input_total_cost;
}
| void cost_qual_eval | ( | QualCost * | cost, | |
| List * | quals, | |||
| PlannerInfo * | root | |||
| ) |
Definition at line 2898 of file costsize.c.
References cost_qual_eval_walker(), lfirst, QualCost::per_tuple, cost_qual_eval_context::root, QualCost::startup, and cost_qual_eval_context::total.
Referenced by add_tlist_costs_to_plan(), cost_index(), cost_subplan(), cost_tidscan(), final_cost_hashjoin(), final_cost_mergejoin(), final_cost_nestloop(), genericcostestimate(), get_restriction_qual_cost(), gincostestimate(), inline_function(), make_agg(), make_group(), make_result(), plan_cluster_use_sort(), postgresGetForeignRelSize(), set_baserel_size_estimates(), and set_foreign_size_estimates().
{
cost_qual_eval_context context;
ListCell *l;
context.root = root;
context.total.startup = 0;
context.total.per_tuple = 0;
/* We don't charge any cost for the implicit ANDing at top level ... */
foreach(l, quals)
{
Node *qual = (Node *) lfirst(l);
cost_qual_eval_walker(qual, &context);
}
*cost = context.total;
}
| void cost_qual_eval_node | ( | QualCost * | cost, | |
| Node * | qual, | |||
| PlannerInfo * | root | |||
| ) |
Definition at line 2924 of file costsize.c.
References cost_qual_eval_walker(), QualCost::per_tuple, cost_qual_eval_context::root, QualCost::startup, and cost_qual_eval_context::total.
Referenced by cost_functionscan(), cost_windowagg(), count_agg_clauses_walker(), and order_qual_clauses().
{
cost_qual_eval_context context;
context.root = root;
context.total.startup = 0;
context.total.per_tuple = 0;
cost_qual_eval_walker(qual, &context);
*cost = context.total;
}
Definition at line 1158 of file costsize.c.
References cpu_tuple_cost, Max, Plan::plan_rows, Plan::plan_width, Plan::startup_cost, and Plan::total_cost.
Referenced by make_recursive_union().
{
Cost startup_cost;
Cost total_cost;
double total_rows;
/* We probably have decent estimates for the non-recursive term */
startup_cost = nrterm->startup_cost;
total_cost = nrterm->total_cost;
total_rows = nrterm->plan_rows;
/*
* We arbitrarily assume that about 10 recursive iterations will be
* needed, and that we've managed to get a good fix on the cost and output
* size of each one of them. These are mighty shaky assumptions but it's
* hard to see how to do better.
*/
total_cost += 10 * rterm->total_cost;
total_rows += 10 * rterm->plan_rows;
/*
* Also charge cpu_tuple_cost per row to account for the costs of
* manipulating the tuplestores. (We don't worry about possible
* spill-to-disk costs.)
*/
total_cost += cpu_tuple_cost * total_rows;
runion->startup_cost = startup_cost;
runion->total_cost = total_cost;
runion->plan_rows = total_rows;
runion->plan_width = Max(nrterm->plan_width, rterm->plan_width);
}
| void cost_seqscan | ( | Path * | path, | |
| PlannerInfo * | root, | |||
| RelOptInfo * | baserel, | |||
| ParamPathInfo * | param_info | |||
| ) |
Definition at line 178 of file costsize.c.
References Assert, cpu_tuple_cost, disable_cost, enable_seqscan, get_restriction_qual_cost(), get_tablespace_page_costs(), NULL, RelOptInfo::pages, QualCost::per_tuple, ParamPathInfo::ppi_rows, RelOptInfo::relid, RelOptInfo::reltablespace, RelOptInfo::rows, Path::rows, RTE_RELATION, RelOptInfo::rtekind, QualCost::startup, Path::startup_cost, Path::total_cost, and RelOptInfo::tuples.
Referenced by create_seqscan_path().
{
Cost startup_cost = 0;
Cost run_cost = 0;
double spc_seq_page_cost;
QualCost qpqual_cost;
Cost cpu_per_tuple;
/* Should only be applied to base relations */
Assert(baserel->relid > 0);
Assert(baserel->rtekind == RTE_RELATION);
/* Mark the path with the correct row estimate */
if (param_info)
path->rows = param_info->ppi_rows;
else
path->rows = baserel->rows;
if (!enable_seqscan)
startup_cost += disable_cost;
/* fetch estimated page cost for tablespace containing table */
get_tablespace_page_costs(baserel->reltablespace,
NULL,
&spc_seq_page_cost);
/*
* disk costs
*/
run_cost += spc_seq_page_cost * baserel->pages;
/* CPU costs */
get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
startup_cost += qpqual_cost.startup;
cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
run_cost += cpu_per_tuple * baserel->tuples;
path->startup_cost = startup_cost;
path->total_cost = startup_cost + run_cost;
}
| void cost_sort | ( | Path * | path, | |
| PlannerInfo * | root, | |||
| List * | pathkeys, | |||
| Cost | input_cost, | |||
| double | tuples, | |||
| int | width, | |||
| Cost | comparison_cost, | |||
| int | sort_mem, | |||
| double | limit_tuples | |||
| ) |
Definition at line 1237 of file costsize.c.
References cpu_operator_cost, disable_cost, enable_sort, LOG2, random_page_cost, relation_byte_size(), Path::rows, seq_page_cost, Path::startup_cost, Path::total_cost, and tuplesort_merge_order().
Referenced by choose_hashed_distinct(), choose_hashed_grouping(), choose_hashed_setop(), create_merge_append_path(), create_unique_path(), initial_cost_mergejoin(), make_sort(), plan_cluster_use_sort(), and query_planner().
{
Cost startup_cost = input_cost;
Cost run_cost = 0;
double input_bytes = relation_byte_size(tuples, width);
double output_bytes;
double output_tuples;
long sort_mem_bytes = sort_mem * 1024L;
if (!enable_sort)
startup_cost += disable_cost;
path->rows = tuples;
/*
* We want to be sure the cost of a sort is never estimated as zero, even
* if passed-in tuple count is zero. Besides, mustn't do log(0)...
*/
if (tuples < 2.0)
tuples = 2.0;
/* Include the default cost-per-comparison */
comparison_cost += 2.0 * cpu_operator_cost;
/* Do we have a useful LIMIT? */
if (limit_tuples > 0 && limit_tuples < tuples)
{
output_tuples = limit_tuples;
output_bytes = relation_byte_size(output_tuples, width);
}
else
{
output_tuples = tuples;
output_bytes = input_bytes;
}
if (output_bytes > sort_mem_bytes)
{
/*
* We'll have to use a disk-based sort of all the tuples
*/
double npages = ceil(input_bytes / BLCKSZ);
double nruns = (input_bytes / sort_mem_bytes) * 0.5;
double mergeorder = tuplesort_merge_order(sort_mem_bytes);
double log_runs;
double npageaccesses;
/*
* CPU costs
*
* Assume about N log2 N comparisons
*/
startup_cost += comparison_cost * tuples * LOG2(tuples);
/* Disk costs */
/* Compute logM(r) as log(r) / log(M) */
if (nruns > mergeorder)
log_runs = ceil(log(nruns) / log(mergeorder));
else
log_runs = 1.0;
npageaccesses = 2.0 * npages * log_runs;
/* Assume 3/4ths of accesses are sequential, 1/4th are not */
startup_cost += npageaccesses *
(seq_page_cost * 0.75 + random_page_cost * 0.25);
}
else if (tuples > 2 * output_tuples || input_bytes > sort_mem_bytes)
{
/*
* We'll use a bounded heap-sort keeping just K tuples in memory, for
* a total number of tuple comparisons of N log2 K; but the constant
* factor is a bit higher than for quicksort. Tweak it so that the
* cost curve is continuous at the crossover point.
*/
startup_cost += comparison_cost * tuples * LOG2(2.0 * output_tuples);
}
else
{
/* We'll use plain quicksort on all the input tuples */
startup_cost += comparison_cost * tuples * LOG2(tuples);
}
/*
* Also charge a small amount (arbitrarily set equal to operator cost) per
* extracted tuple. We don't charge cpu_tuple_cost because a Sort node
* doesn't do qual-checking or projection, so it has less overhead than
* most plan nodes. Note it's correct to use tuples not output_tuples
* here --- the upper LIMIT will pro-rate the run cost so we'd be double
* counting the LIMIT otherwise.
*/
run_cost += cpu_operator_cost * tuples;
path->startup_cost = startup_cost;
path->total_cost = startup_cost + run_cost;
}
| void cost_subplan | ( | PlannerInfo * | root, | |
| SubPlan * | subplan, | |||
| Plan * | plan | |||
| ) |
Definition at line 2708 of file costsize.c.
References ALL_SUBLINK, ANY_SUBLINK, cost_qual_eval(), cpu_operator_cost, ExecMaterializesOutput(), EXISTS_SUBLINK, make_ands_implicit(), NIL, nodeTag, SubPlan::parParam, SubPlan::per_call_cost, QualCost::per_tuple, Plan::plan_rows, QualCost::startup, SubPlan::startup_cost, Plan::startup_cost, SubPlan::subLinkType, SubPlan::testexpr, Plan::total_cost, and SubPlan::useHashTable.
Referenced by build_subplan(), SS_make_initplan_from_plan(), and SS_process_ctes().
{
QualCost sp_cost;
/* Figure any cost for evaluating the testexpr */
cost_qual_eval(&sp_cost,
make_ands_implicit((Expr *) subplan->testexpr),
root);
if (subplan->useHashTable)
{
/*
* If we are using a hash table for the subquery outputs, then the
* cost of evaluating the query is a one-time cost. We charge one
* cpu_operator_cost per tuple for the work of loading the hashtable,
* too.
*/
sp_cost.startup += plan->total_cost +
cpu_operator_cost * plan->plan_rows;
/*
* The per-tuple costs include the cost of evaluating the lefthand
* expressions, plus the cost of probing the hashtable. We already
* accounted for the lefthand expressions as part of the testexpr, and
* will also have counted one cpu_operator_cost for each comparison
* operator. That is probably too low for the probing cost, but it's
* hard to make a better estimate, so live with it for now.
*/
}
else
{
/*
* Otherwise we will be rescanning the subplan output on each
* evaluation. We need to estimate how much of the output we will
* actually need to scan. NOTE: this logic should agree with the
* tuple_fraction estimates used by make_subplan() in
* plan/subselect.c.
*/
Cost plan_run_cost = plan->total_cost - plan->startup_cost;
if (subplan->subLinkType == EXISTS_SUBLINK)
{
/* we only need to fetch 1 tuple */
sp_cost.per_tuple += plan_run_cost / plan->plan_rows;
}
else if (subplan->subLinkType == ALL_SUBLINK ||
subplan->subLinkType == ANY_SUBLINK)
{
/* assume we need 50% of the tuples */
sp_cost.per_tuple += 0.50 * plan_run_cost;
/* also charge a cpu_operator_cost per row examined */
sp_cost.per_tuple += 0.50 * plan->plan_rows * cpu_operator_cost;
}
else
{
/* assume we need all tuples */
sp_cost.per_tuple += plan_run_cost;
}
/*
* Also account for subplan's startup cost. If the subplan is
* uncorrelated or undirect correlated, AND its topmost node is one
* that materializes its output, assume that we'll only need to pay
* its startup cost once; otherwise assume we pay the startup cost
* every time.
*/
if (subplan->parParam == NIL &&
ExecMaterializesOutput(nodeTag(plan)))
sp_cost.startup += plan->startup_cost;
else
sp_cost.per_tuple += plan->startup_cost;
}
subplan->startup_cost = sp_cost.startup;
subplan->per_call_cost = sp_cost.per_tuple;
}
| void cost_subqueryscan | ( | Path * | path, | |
| PlannerInfo * | root, | |||
| RelOptInfo * | baserel, | |||
| ParamPathInfo * | param_info | |||
| ) |
Definition at line 967 of file costsize.c.
References Assert, cpu_tuple_cost, get_restriction_qual_cost(), QualCost::per_tuple, ParamPathInfo::ppi_rows, RelOptInfo::relid, RelOptInfo::rows, Path::rows, RTE_SUBQUERY, RelOptInfo::rtekind, QualCost::startup, Plan::startup_cost, Path::startup_cost, RelOptInfo::subplan, Plan::total_cost, Path::total_cost, and RelOptInfo::tuples.
Referenced by create_subqueryscan_path().
{
Cost startup_cost;
Cost run_cost;
QualCost qpqual_cost;
Cost cpu_per_tuple;
/* Should only be applied to base relations that are subqueries */
Assert(baserel->relid > 0);
Assert(baserel->rtekind == RTE_SUBQUERY);
/* Mark the path with the correct row estimate */
if (param_info)
path->rows = param_info->ppi_rows;
else
path->rows = baserel->rows;
/*
* Cost of path is cost of evaluating the subplan, plus cost of evaluating
* any restriction clauses that will be attached to the SubqueryScan node,
* plus cpu_tuple_cost to account for selection and projection overhead.
*/
path->startup_cost = baserel->subplan->startup_cost;
path->total_cost = baserel->subplan->total_cost;
get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
startup_cost = qpqual_cost.startup;
cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
run_cost = cpu_per_tuple * baserel->tuples;
path->startup_cost += startup_cost;
path->total_cost += startup_cost + run_cost;
}
| void cost_tidscan | ( | Path * | path, | |
| PlannerInfo * | root, | |||
| RelOptInfo * | baserel, | |||
| List * | tidquals, | |||
| ParamPathInfo * | param_info | |||
| ) |
Definition at line 868 of file costsize.c.
References ScalarArrayOpExpr::args, Assert, RelOptInfo::baserestrictcost, cost_qual_eval(), cpu_tuple_cost, disable_cost, enable_tidscan, estimate_array_length(), get_restriction_qual_cost(), get_tablespace_page_costs(), IsA, lfirst, lsecond, NULL, QualCost::per_tuple, ParamPathInfo::ppi_rows, RelOptInfo::relid, RelOptInfo::reltablespace, RelOptInfo::rows, Path::rows, RTE_RELATION, RelOptInfo::rtekind, QualCost::startup, Path::startup_cost, and Path::total_cost.
Referenced by create_tidscan_path().
{
Cost startup_cost = 0;
Cost run_cost = 0;
bool isCurrentOf = false;
QualCost qpqual_cost;
Cost cpu_per_tuple;
QualCost tid_qual_cost;
int ntuples;
ListCell *l;
double spc_random_page_cost;
/* Should only be applied to base relations */
Assert(baserel->relid > 0);
Assert(baserel->rtekind == RTE_RELATION);
/* Mark the path with the correct row estimate */
if (param_info)
path->rows = param_info->ppi_rows;
else
path->rows = baserel->rows;
/* Count how many tuples we expect to retrieve */
ntuples = 0;
foreach(l, tidquals)
{
if (IsA(lfirst(l), ScalarArrayOpExpr))
{
/* Each element of the array yields 1 tuple */
ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) lfirst(l);
Node *arraynode = (Node *) lsecond(saop->args);
ntuples += estimate_array_length(arraynode);
}
else if (IsA(lfirst(l), CurrentOfExpr))
{
/* CURRENT OF yields 1 tuple */
isCurrentOf = true;
ntuples++;
}
else
{
/* It's just CTID = something, count 1 tuple */
ntuples++;
}
}
/*
* We must force TID scan for WHERE CURRENT OF, because only nodeTidscan.c
* understands how to do it correctly. Therefore, honor enable_tidscan
* only when CURRENT OF isn't present. Also note that cost_qual_eval
* counts a CurrentOfExpr as having startup cost disable_cost, which we
* subtract off here; that's to prevent other plan types such as seqscan
* from winning.
*/
if (isCurrentOf)
{
Assert(baserel->baserestrictcost.startup >= disable_cost);
startup_cost -= disable_cost;
}
else if (!enable_tidscan)
startup_cost += disable_cost;
/*
* The TID qual expressions will be computed once, any other baserestrict
* quals once per retrived tuple.
*/
cost_qual_eval(&tid_qual_cost, tidquals, root);
/* fetch estimated page cost for tablespace containing table */
get_tablespace_page_costs(baserel->reltablespace,
&spc_random_page_cost,
NULL);
/* disk costs --- assume each tuple on a different page */
run_cost += spc_random_page_cost * ntuples;
/* Add scanning CPU costs */
get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
/* XXX currently we assume TID quals are a subset of qpquals */
startup_cost += qpqual_cost.startup + tid_qual_cost.per_tuple;
cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple -
tid_qual_cost.per_tuple;
run_cost += cpu_per_tuple * ntuples;
path->startup_cost = startup_cost;
path->total_cost = startup_cost + run_cost;
}
| void cost_valuesscan | ( | Path * | path, | |
| PlannerInfo * | root, | |||
| RelOptInfo * | baserel, | |||
| ParamPathInfo * | param_info | |||
| ) |
Definition at line 1068 of file costsize.c.
References Assert, cpu_operator_cost, cpu_tuple_cost, get_restriction_qual_cost(), QualCost::per_tuple, ParamPathInfo::ppi_rows, RelOptInfo::relid, RelOptInfo::rows, Path::rows, RTE_VALUES, RelOptInfo::rtekind, QualCost::startup, Path::startup_cost, Path::total_cost, and RelOptInfo::tuples.
Referenced by create_valuesscan_path().
{
Cost startup_cost = 0;
Cost run_cost = 0;
QualCost qpqual_cost;
Cost cpu_per_tuple;
/* Should only be applied to base relations that are values lists */
Assert(baserel->relid > 0);
Assert(baserel->rtekind == RTE_VALUES);
/* Mark the path with the correct row estimate */
if (param_info)
path->rows = param_info->ppi_rows;
else
path->rows = baserel->rows;
/*
* For now, estimate list evaluation cost at one operator eval per list
* (probably pretty bogus, but is it worth being smarter?)
*/
cpu_per_tuple = cpu_operator_cost;
/* Add scanning CPU costs */
get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
startup_cost += qpqual_cost.startup;
cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
run_cost += cpu_per_tuple * baserel->tuples;
path->startup_cost = startup_cost;
path->total_cost = startup_cost + run_cost;
}
| void cost_windowagg | ( | Path * | path, | |
| PlannerInfo * | root, | |||
| List * | windowFuncs, | |||
| int | numPartCols, | |||
| int | numOrderCols, | |||
| Cost | input_startup_cost, | |||
| Cost | input_total_cost, | |||
| double | input_tuples | |||
| ) |
Definition at line 1557 of file costsize.c.
References WindowFunc::args, Assert, cost_qual_eval_node(), cpu_operator_cost, cpu_tuple_cost, get_func_cost(), IsA, lfirst, QualCost::per_tuple, Path::rows, QualCost::startup, Path::startup_cost, Path::total_cost, and WindowFunc::winfnoid.
Referenced by make_windowagg().
{
Cost startup_cost;
Cost total_cost;
ListCell *lc;
startup_cost = input_startup_cost;
total_cost = input_total_cost;
/*
* Window functions are assumed to cost their stated execution cost, plus
* the cost of evaluating their input expressions, per tuple. Since they
* may in fact evaluate their inputs at multiple rows during each cycle,
* this could be a drastic underestimate; but without a way to know how
* many rows the window function will fetch, it's hard to do better. In
* any case, it's a good estimate for all the built-in window functions,
* so we'll just do this for now.
*/
foreach(lc, windowFuncs)
{
WindowFunc *wfunc = (WindowFunc *) lfirst(lc);
Cost wfunccost;
QualCost argcosts;
Assert(IsA(wfunc, WindowFunc));
wfunccost = get_func_cost(wfunc->winfnoid) * cpu_operator_cost;
/* also add the input expressions' cost to per-input-row costs */
cost_qual_eval_node(&argcosts, (Node *) wfunc->args, root);
startup_cost += argcosts.startup;
wfunccost += argcosts.per_tuple;
total_cost += wfunccost * input_tuples;
}
/*
* We also charge cpu_operator_cost per grouping column per tuple for
* grouping comparisons, plus cpu_tuple_cost per tuple for general
* overhead.
*
* XXX this neglects costs of spooling the data to disk when it overflows
* work_mem. Sooner or later that should get accounted for.
*/
total_cost += cpu_operator_cost * (numPartCols + numOrderCols) * input_tuples;
total_cost += cpu_tuple_cost * input_tuples;
path->rows = input_tuples;
path->startup_cost = startup_cost;
path->total_cost = total_cost;
}
| void final_cost_hashjoin | ( | PlannerInfo * | root, | |
| HashPath * | path, | |||
| JoinCostWorkspace * | workspace, | |||
| SpecialJoinInfo * | sjinfo, | |||
| SemiAntiJoinFactors * | semifactors | |||
| ) |
Definition at line 2495 of file costsize.c.
References approx_tuple_count(), Assert, bms_is_subset(), clamp_row_est(), RestrictInfo::clause, cost_qual_eval(), cpu_tuple_cost, disable_cost, enable_hashjoin, estimate_hash_bucketsize(), get_leftop(), get_rightop(), JoinPath::innerjoinpath, IsA, JOIN_ANTI, JOIN_SEMI, JoinPath::joinrestrictinfo, JoinPath::jointype, HashPath::jpath, RestrictInfo::left_bucketsize, RestrictInfo::left_relids, lfirst, SemiAntiJoinFactors::match_count, HashPath::num_batches, JoinCostWorkspace::numbatches, JoinCostWorkspace::numbuckets, SemiAntiJoinFactors::outer_match_frac, JoinPath::outerjoinpath, Path::param_info, Path::parent, JoinPath::path, HashPath::path_hashclauses, QualCost::per_tuple, ParamPathInfo::ppi_rows, RelOptInfo::relids, RestrictInfo::right_bucketsize, RestrictInfo::right_relids, rint(), RelOptInfo::rows, Path::rows, JoinCostWorkspace::run_cost, QualCost::startup, Path::startup_cost, JoinCostWorkspace::startup_cost, and Path::total_cost.
Referenced by create_hashjoin_path().
{
Path *outer_path = path->jpath.outerjoinpath;
Path *inner_path = path->jpath.innerjoinpath;
double outer_path_rows = outer_path->rows;
double inner_path_rows = inner_path->rows;
List *hashclauses = path->path_hashclauses;
Cost startup_cost = workspace->startup_cost;
Cost run_cost = workspace->run_cost;
int numbuckets = workspace->numbuckets;
int numbatches = workspace->numbatches;
Cost cpu_per_tuple;
QualCost hash_qual_cost;
QualCost qp_qual_cost;
double hashjointuples;
double virtualbuckets;
Selectivity innerbucketsize;
ListCell *hcl;
/* Mark the path with the correct row estimate */
if (path->jpath.path.param_info)
path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
else
path->jpath.path.rows = path->jpath.path.parent->rows;
/*
* We could include disable_cost in the preliminary estimate, but that
* would amount to optimizing for the case where the join method is
* disabled, which doesn't seem like the way to bet.
*/
if (!enable_hashjoin)
startup_cost += disable_cost;
/* mark the path with estimated # of batches */
path->num_batches = numbatches;
/* and compute the number of "virtual" buckets in the whole join */
virtualbuckets = (double) numbuckets *(double) numbatches;
/*
* Determine bucketsize fraction for inner relation. We use the smallest
* bucketsize estimated for any individual hashclause; this is undoubtedly
* conservative.
*
* BUT: if inner relation has been unique-ified, we can assume it's good
* for hashing. This is important both because it's the right answer, and
* because we avoid contaminating the cache with a value that's wrong for
* non-unique-ified paths.
*/
if (IsA(inner_path, UniquePath))
innerbucketsize = 1.0 / virtualbuckets;
else
{
innerbucketsize = 1.0;
foreach(hcl, hashclauses)
{
RestrictInfo *restrictinfo = (RestrictInfo *) lfirst(hcl);
Selectivity thisbucketsize;
Assert(IsA(restrictinfo, RestrictInfo));
/*
* First we have to figure out which side of the hashjoin clause
* is the inner side.
*
* Since we tend to visit the same clauses over and over when
* planning a large query, we cache the bucketsize estimate in the
* RestrictInfo node to avoid repeated lookups of statistics.
*/
if (bms_is_subset(restrictinfo->right_relids,
inner_path->parent->relids))
{
/* righthand side is inner */
thisbucketsize = restrictinfo->right_bucketsize;
if (thisbucketsize < 0)
{
/* not cached yet */
thisbucketsize =
estimate_hash_bucketsize(root,
get_rightop(restrictinfo->clause),
virtualbuckets);
restrictinfo->right_bucketsize = thisbucketsize;
}
}
else
{
Assert(bms_is_subset(restrictinfo->left_relids,
inner_path->parent->relids));
/* lefthand side is inner */
thisbucketsize = restrictinfo->left_bucketsize;
if (thisbucketsize < 0)
{
/* not cached yet */
thisbucketsize =
estimate_hash_bucketsize(root,
get_leftop(restrictinfo->clause),
virtualbuckets);
restrictinfo->left_bucketsize = thisbucketsize;
}
}
if (innerbucketsize > thisbucketsize)
innerbucketsize = thisbucketsize;
}
}
/*
* Compute cost of the hashquals and qpquals (other restriction clauses)
* separately.
*/
cost_qual_eval(&hash_qual_cost, hashclauses, root);
cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo, root);
qp_qual_cost.startup -= hash_qual_cost.startup;
qp_qual_cost.per_tuple -= hash_qual_cost.per_tuple;
/* CPU costs */
if (path->jpath.jointype == JOIN_SEMI || path->jpath.jointype == JOIN_ANTI)
{
double outer_matched_rows;
Selectivity inner_scan_frac;
/*
* SEMI or ANTI join: executor will stop after first match.
*
* For an outer-rel row that has at least one match, we can expect the
* bucket scan to stop after a fraction 1/(match_count+1) of the
* bucket's rows, if the matches are evenly distributed. Since they
* probably aren't quite evenly distributed, we apply a fuzz factor of
* 2.0 to that fraction. (If we used a larger fuzz factor, we'd have
* to clamp inner_scan_frac to at most 1.0; but since match_count is
* at least 1, no such clamp is needed now.)
*/
outer_matched_rows = rint(outer_path_rows * semifactors->outer_match_frac);
inner_scan_frac = 2.0 / (semifactors->match_count + 1.0);
startup_cost += hash_qual_cost.startup;
run_cost += hash_qual_cost.per_tuple * outer_matched_rows *
clamp_row_est(inner_path_rows * innerbucketsize * inner_scan_frac) * 0.5;
/*
* For unmatched outer-rel rows, the picture is quite a lot different.
* In the first place, there is no reason to assume that these rows
* preferentially hit heavily-populated buckets; instead assume they
* are uncorrelated with the inner distribution and so they see an
* average bucket size of inner_path_rows / virtualbuckets. In the
* second place, it seems likely that they will have few if any exact
* hash-code matches and so very few of the tuples in the bucket will
* actually require eval of the hash quals. We don't have any good
* way to estimate how many will, but for the moment assume that the
* effective cost per bucket entry is one-tenth what it is for
* matchable tuples.
*/
run_cost += hash_qual_cost.per_tuple *
(outer_path_rows - outer_matched_rows) *
clamp_row_est(inner_path_rows / virtualbuckets) * 0.05;
/* Get # of tuples that will pass the basic join */
if (path->jpath.jointype == JOIN_SEMI)
hashjointuples = outer_matched_rows;
else
hashjointuples = outer_path_rows - outer_matched_rows;
}
else
{
/*
* The number of tuple comparisons needed is the number of outer
* tuples times the typical number of tuples in a hash bucket, which
* is the inner relation size times its bucketsize fraction. At each
* one, we need to evaluate the hashjoin quals. But actually,
* charging the full qual eval cost at each tuple is pessimistic,
* since we don't evaluate the quals unless the hash values match
* exactly. For lack of a better idea, halve the cost estimate to
* allow for that.
*/
startup_cost += hash_qual_cost.startup;
run_cost += hash_qual_cost.per_tuple * outer_path_rows *
clamp_row_est(inner_path_rows * innerbucketsize) * 0.5;
/*
* Get approx # tuples passing the hashquals. We use
* approx_tuple_count here because we need an estimate done with
* JOIN_INNER semantics.
*/
hashjointuples = approx_tuple_count(root, &path->jpath, hashclauses);
}
/*
* For each tuple that gets through the hashjoin proper, we charge
* cpu_tuple_cost plus the cost of evaluating additional restriction
* clauses that are to be applied at the join. (This is pessimistic since
* not all of the quals may get evaluated at each tuple.)
*/
startup_cost += qp_qual_cost.startup;
cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
run_cost += cpu_per_tuple * hashjointuples;
path->jpath.path.startup_cost = startup_cost;
path->jpath.path.total_cost = startup_cost + run_cost;
}
| void final_cost_mergejoin | ( | PlannerInfo * | root, | |
| MergePath * | path, | |||
| JoinCostWorkspace * | workspace, | |||
| SpecialJoinInfo * | sjinfo | |||
| ) |
Definition at line 2114 of file costsize.c.
References approx_tuple_count(), cost_qual_eval(), cpu_operator_cost, cpu_tuple_cost, disable_cost, enable_material, enable_mergejoin, ExecSupportsMarkRestore(), JoinCostWorkspace::inner_rows, JoinCostWorkspace::inner_run_cost, JoinCostWorkspace::inner_skip_rows, JoinPath::innerjoinpath, MergePath::innersortkeys, IsA, JoinPath::joinrestrictinfo, MergePath::jpath, MergePath::materialize_inner, NIL, JoinCostWorkspace::outer_rows, JoinCostWorkspace::outer_skip_rows, JoinPath::outerjoinpath, Path::param_info, Path::parent, JoinPath::path, MergePath::path_mergeclauses, Path::pathtype, QualCost::per_tuple, ParamPathInfo::ppi_rows, relation_byte_size(), RelOptInfo::rows, Path::rows, JoinCostWorkspace::run_cost, QualCost::startup, Path::startup_cost, JoinCostWorkspace::startup_cost, Path::total_cost, RelOptInfo::width, and work_mem.
Referenced by create_mergejoin_path().
{
Path *outer_path = path->jpath.outerjoinpath;
Path *inner_path = path->jpath.innerjoinpath;
double inner_path_rows = inner_path->rows;
List *mergeclauses = path->path_mergeclauses;
List *innersortkeys = path->innersortkeys;
Cost startup_cost = workspace->startup_cost;
Cost run_cost = workspace->run_cost;
Cost inner_run_cost = workspace->inner_run_cost;
double outer_rows = workspace->outer_rows;
double inner_rows = workspace->inner_rows;
double outer_skip_rows = workspace->outer_skip_rows;
double inner_skip_rows = workspace->inner_skip_rows;
Cost cpu_per_tuple,
bare_inner_cost,
mat_inner_cost;
QualCost merge_qual_cost;
QualCost qp_qual_cost;
double mergejointuples,
rescannedtuples;
double rescanratio;
/* Protect some assumptions below that rowcounts aren't zero or NaN */
if (inner_path_rows <= 0 || isnan(inner_path_rows))
inner_path_rows = 1;
/* Mark the path with the correct row estimate */
if (path->jpath.path.param_info)
path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
else
path->jpath.path.rows = path->jpath.path.parent->rows;
/*
* We could include disable_cost in the preliminary estimate, but that
* would amount to optimizing for the case where the join method is
* disabled, which doesn't seem like the way to bet.
*/
if (!enable_mergejoin)
startup_cost += disable_cost;
/*
* Compute cost of the mergequals and qpquals (other restriction clauses)
* separately.
*/
cost_qual_eval(&merge_qual_cost, mergeclauses, root);
cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo, root);
qp_qual_cost.startup -= merge_qual_cost.startup;
qp_qual_cost.per_tuple -= merge_qual_cost.per_tuple;
/*
* Get approx # tuples passing the mergequals. We use approx_tuple_count
* here because we need an estimate done with JOIN_INNER semantics.
*/
mergejointuples = approx_tuple_count(root, &path->jpath, mergeclauses);
/*
* When there are equal merge keys in the outer relation, the mergejoin
* must rescan any matching tuples in the inner relation. This means
* re-fetching inner tuples; we have to estimate how often that happens.
*
* For regular inner and outer joins, the number of re-fetches can be
* estimated approximately as size of merge join output minus size of
* inner relation. Assume that the distinct key values are 1, 2, ..., and
* denote the number of values of each key in the outer relation as m1,
* m2, ...; in the inner relation, n1, n2, ... Then we have
*
* size of join = m1 * n1 + m2 * n2 + ...
*
* number of rescanned tuples = (m1 - 1) * n1 + (m2 - 1) * n2 + ... = m1 *
* n1 + m2 * n2 + ... - (n1 + n2 + ...) = size of join - size of inner
* relation
*
* This equation works correctly for outer tuples having no inner match
* (nk = 0), but not for inner tuples having no outer match (mk = 0); we
* are effectively subtracting those from the number of rescanned tuples,
* when we should not. Can we do better without expensive selectivity
* computations?
*
* The whole issue is moot if we are working from a unique-ified outer
* input.
*/
if (IsA(outer_path, UniquePath))
rescannedtuples = 0;
else
{
rescannedtuples = mergejointuples - inner_path_rows;
/* Must clamp because of possible underestimate */
if (rescannedtuples < 0)
rescannedtuples = 0;
}
/* We'll inflate various costs this much to account for rescanning */
rescanratio = 1.0 + (rescannedtuples / inner_path_rows);
/*
* Decide whether we want to materialize the inner input to shield it from
* mark/restore and performing re-fetches. Our cost model for regular
* re-fetches is that a re-fetch costs the same as an original fetch,
* which is probably an overestimate; but on the other hand we ignore the
* bookkeeping costs of mark/restore. Not clear if it's worth developing
* a more refined model. So we just need to inflate the inner run cost by
* rescanratio.
*/
bare_inner_cost = inner_run_cost * rescanratio;
/*
* When we interpose a Material node the re-fetch cost is assumed to be
* just cpu_operator_cost per tuple, independently of the underlying
* plan's cost; and we charge an extra cpu_operator_cost per original
* fetch as well. Note that we're assuming the materialize node will
* never spill to disk, since it only has to remember tuples back to the
* last mark. (If there are a huge number of duplicates, our other cost
* factors will make the path so expensive that it probably won't get
* chosen anyway.) So we don't use cost_rescan here.
*
* Note: keep this estimate in sync with create_mergejoin_plan's labeling
* of the generated Material node.
*/
mat_inner_cost = inner_run_cost +
cpu_operator_cost * inner_path_rows * rescanratio;
/*
* Prefer materializing if it looks cheaper, unless the user has asked to
* suppress materialization.
*/
if (enable_material && mat_inner_cost < bare_inner_cost)
path->materialize_inner = true;
/*
* Even if materializing doesn't look cheaper, we *must* do it if the
* inner path is to be used directly (without sorting) and it doesn't
* support mark/restore.
*
* Since the inner side must be ordered, and only Sorts and IndexScans can
* create order to begin with, and they both support mark/restore, you
* might think there's no problem --- but you'd be wrong. Nestloop and
* merge joins can *preserve* the order of their inputs, so they can be
* selected as the input of a mergejoin, and they don't support
* mark/restore at present.
*
* We don't test the value of enable_material here, because
* materialization is required for correctness in this case, and turning
* it off does not entitle us to deliver an invalid plan.
*/
else if (innersortkeys == NIL &&
!ExecSupportsMarkRestore(inner_path->pathtype))
path->materialize_inner = true;
/*
* Also, force materializing if the inner path is to be sorted and the
* sort is expected to spill to disk. This is because the final merge
* pass can be done on-the-fly if it doesn't have to support mark/restore.
* We don't try to adjust the cost estimates for this consideration,
* though.
*
* Since materialization is a performance optimization in this case,
* rather than necessary for correctness, we skip it if enable_material is
* off.
*/
else if (enable_material && innersortkeys != NIL &&
relation_byte_size(inner_path_rows, inner_path->parent->width) >
(work_mem * 1024L))
path->materialize_inner = true;
else
path->materialize_inner = false;
/* Charge the right incremental cost for the chosen case */
if (path->materialize_inner)
run_cost += mat_inner_cost;
else
run_cost += bare_inner_cost;
/* CPU costs */
/*
* The number of tuple comparisons needed is approximately number of outer
* rows plus number of inner rows plus number of rescanned tuples (can we
* refine this?). At each one, we need to evaluate the mergejoin quals.
*/
startup_cost += merge_qual_cost.startup;
startup_cost += merge_qual_cost.per_tuple *
(outer_skip_rows + inner_skip_rows * rescanratio);
run_cost += merge_qual_cost.per_tuple *
((outer_rows - outer_skip_rows) +
(inner_rows - inner_skip_rows) * rescanratio);
/*
* For each tuple that gets through the mergejoin proper, we charge
* cpu_tuple_cost plus the cost of evaluating additional restriction
* clauses that are to be applied at the join. (This is pessimistic since
* not all of the quals may get evaluated at each tuple.)
*
* Note: we could adjust for SEMI/ANTI joins skipping some qual
* evaluations here, but it's probably not worth the trouble.
*/
startup_cost += qp_qual_cost.startup;
cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
run_cost += cpu_per_tuple * mergejointuples;
path->jpath.path.startup_cost = startup_cost;
path->jpath.path.total_cost = startup_cost + run_cost;
}
| void final_cost_nestloop | ( | PlannerInfo * | root, | |
| NestPath * | path, | |||
| JoinCostWorkspace * | workspace, | |||
| SpecialJoinInfo * | sjinfo, | |||
| SemiAntiJoinFactors * | semifactors | |||
| ) |
Definition at line 1772 of file costsize.c.
References cost_qual_eval(), cpu_tuple_cost, disable_cost, enable_nestloop, has_indexed_join_quals(), JoinCostWorkspace::inner_rescan_run_cost, JoinCostWorkspace::inner_scan_frac, JoinPath::innerjoinpath, JOIN_ANTI, JOIN_SEMI, JoinPath::joinrestrictinfo, JoinPath::jointype, JoinCostWorkspace::outer_matched_rows, JoinPath::outerjoinpath, Path::param_info, Path::parent, JoinPath::path, QualCost::per_tuple, ParamPathInfo::ppi_rows, RelOptInfo::rows, Path::rows, JoinCostWorkspace::run_cost, QualCost::startup, Path::startup_cost, JoinCostWorkspace::startup_cost, and Path::total_cost.
Referenced by create_nestloop_path().
{
Path *outer_path = path->outerjoinpath;
Path *inner_path = path->innerjoinpath;
double outer_path_rows = outer_path->rows;
double inner_path_rows = inner_path->rows;
Cost startup_cost = workspace->startup_cost;
Cost run_cost = workspace->run_cost;
Cost inner_rescan_run_cost = workspace->inner_rescan_run_cost;
Cost cpu_per_tuple;
QualCost restrict_qual_cost;
double ntuples;
/* Mark the path with the correct row estimate */
if (path->path.param_info)
path->path.rows = path->path.param_info->ppi_rows;
else
path->path.rows = path->path.parent->rows;
/*
* We could include disable_cost in the preliminary estimate, but that
* would amount to optimizing for the case where the join method is
* disabled, which doesn't seem like the way to bet.
*/
if (!enable_nestloop)
startup_cost += disable_cost;
/* cost of source data */
if (path->jointype == JOIN_SEMI || path->jointype == JOIN_ANTI)
{
double outer_matched_rows = workspace->outer_matched_rows;
Selectivity inner_scan_frac = workspace->inner_scan_frac;
/*
* SEMI or ANTI join: executor will stop after first match.
*/
/* Compute number of tuples processed (not number emitted!) */
ntuples = outer_matched_rows * inner_path_rows * inner_scan_frac;
/*
* For unmatched outer-rel rows, there are two cases. If the inner
* path is an indexscan using all the joinquals as indexquals, then an
* unmatched row results in an indexscan returning no rows, which is
* probably quite cheap. We estimate this case as the same cost to
* return the first tuple of a nonempty scan. Otherwise, the executor
* will have to scan the whole inner rel; not so cheap.
*/
if (has_indexed_join_quals(path))
{
run_cost += (outer_path_rows - outer_matched_rows) *
inner_rescan_run_cost / inner_path_rows;
/*
* We won't be evaluating any quals at all for these rows, so
* don't add them to ntuples.
*/
}
else
{
run_cost += (outer_path_rows - outer_matched_rows) *
inner_rescan_run_cost;
ntuples += (outer_path_rows - outer_matched_rows) *
inner_path_rows;
}
}
else
{
/* Normal-case source costs were included in preliminary estimate */
/* Compute number of tuples processed (not number emitted!) */
ntuples = outer_path_rows * inner_path_rows;
}
/* CPU costs */
cost_qual_eval(&restrict_qual_cost, path->joinrestrictinfo, root);
startup_cost += restrict_qual_cost.startup;
cpu_per_tuple = cpu_tuple_cost + restrict_qual_cost.per_tuple;
run_cost += cpu_per_tuple * ntuples;
path->path.startup_cost = startup_cost;
path->path.total_cost = startup_cost + run_cost;
}
| double get_parameterized_baserel_size | ( | PlannerInfo * | root, | |
| RelOptInfo * | rel, | |||
| List * | param_clauses | |||
| ) |
Definition at line 3475 of file costsize.c.
References RelOptInfo::baserestrictinfo, clamp_row_est(), clauselist_selectivity(), JOIN_INNER, list_concat(), list_copy(), NULL, RelOptInfo::relid, RelOptInfo::rows, and RelOptInfo::tuples.
Referenced by get_baserel_parampathinfo().
{
List *allclauses;
double nrows;
/*
* Estimate the number of rows returned by the parameterized scan, knowing
* that it will apply all the extra join clauses as well as the rel's own
* restriction clauses. Note that we force the clauses to be treated as
* non-join clauses during selectivity estimation.
*/
allclauses = list_concat(list_copy(param_clauses),
rel->baserestrictinfo);
nrows = rel->tuples *
clauselist_selectivity(root,
allclauses,
rel->relid, /* do not use 0! */
JOIN_INNER,
NULL);
nrows = clamp_row_est(nrows);
/* For safety, make sure result is not more than the base estimate */
if (nrows > rel->rows)
nrows = rel->rows;
return nrows;
}
| double get_parameterized_joinrel_size | ( | PlannerInfo * | root, | |
| RelOptInfo * | rel, | |||
| double | outer_rows, | |||
| double | inner_rows, | |||
| SpecialJoinInfo * | sjinfo, | |||
| List * | restrict_clauses | |||
| ) |
Definition at line 3554 of file costsize.c.
References calc_joinrel_size_estimate(), and RelOptInfo::rows.
Referenced by get_joinrel_parampathinfo().
{
double nrows;
/*
* Estimate the number of rows returned by the parameterized join as the
* sizes of the input paths times the selectivity of the clauses that have
* ended up at this join node.
*
* As with set_joinrel_size_estimates, the rowcount estimate could depend
* on the pair of input paths provided, though ideally we'd get the same
* estimate for any pair with the same parameterization.
*/
nrows = calc_joinrel_size_estimate(root,
outer_rows,
inner_rows,
sjinfo,
restrict_clauses);
/* For safety, make sure result is not more than the base estimate */
if (nrows > rel->rows)
nrows = rel->rows;
return nrows;
}
| double index_pages_fetched | ( | double | tuples_fetched, | |
| BlockNumber | pages, | |||
| double | index_pages, | |||
| PlannerInfo * | root | |||
| ) |
Definition at line 498 of file costsize.c.
References Assert, effective_cache_size, Max, T, and PlannerInfo::total_table_pages.
Referenced by cost_bitmap_heap_scan(), cost_index(), genericcostestimate(), and gincostestimate().
{
double pages_fetched;
double total_pages;
double T,
b;
/* T is # pages in table, but don't allow it to be zero */
T = (pages > 1) ? (double) pages : 1.0;
/* Compute number of pages assumed to be competing for cache space */
total_pages = root->total_table_pages + index_pages;
total_pages = Max(total_pages, 1.0);
Assert(T <= total_pages);
/* b is pro-rated share of effective_cache_size */
b = (double) effective_cache_size *T / total_pages;
/* force it positive and integral */
if (b <= 1.0)
b = 1.0;
else
b = ceil(b);
/* This part is the Mackert and Lohman formula */
if (T <= b)
{
pages_fetched =
(2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
if (pages_fetched >= T)
pages_fetched = T;
else
pages_fetched = ceil(pages_fetched);
}
else
{
double lim;
lim = (2.0 * T * b) / (2.0 * T - b);
if (tuples_fetched <= lim)
{
pages_fetched =
(2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
}
else
{
pages_fetched =
b + (tuples_fetched - lim) * (T - b) / T;
}
pages_fetched = ceil(pages_fetched);
}
return pages_fetched;
}
| void initial_cost_hashjoin | ( | PlannerInfo * | root, | |
| JoinCostWorkspace * | workspace, | |||
| JoinType | jointype, | |||
| List * | hashclauses, | |||
| Path * | outer_path, | |||
| Path * | inner_path, | |||
| SpecialJoinInfo * | sjinfo, | |||
| SemiAntiJoinFactors * | semifactors | |||
| ) |
Definition at line 2401 of file costsize.c.
References cpu_operator_cost, cpu_tuple_cost, ExecChooseHashTableSize(), list_length(), JoinCostWorkspace::numbatches, JoinCostWorkspace::numbuckets, page_size(), Path::parent, Path::rows, JoinCostWorkspace::run_cost, seq_page_cost, JoinCostWorkspace::startup_cost, Path::startup_cost, JoinCostWorkspace::total_cost, Path::total_cost, and RelOptInfo::width.
Referenced by try_hashjoin_path().
{
Cost startup_cost = 0;
Cost run_cost = 0;
double outer_path_rows = outer_path->rows;
double inner_path_rows = inner_path->rows;
int num_hashclauses = list_length(hashclauses);
int numbuckets;
int numbatches;
int num_skew_mcvs;
/* cost of source data */
startup_cost += outer_path->startup_cost;
run_cost += outer_path->total_cost - outer_path->startup_cost;
startup_cost += inner_path->total_cost;
/*
* Cost of computing hash function: must do it once per input tuple. We
* charge one cpu_operator_cost for each column's hash function. Also,
* tack on one cpu_tuple_cost per inner row, to model the costs of
* inserting the row into the hashtable.
*
* XXX when a hashclause is more complex than a single operator, we really
* should charge the extra eval costs of the left or right side, as
* appropriate, here. This seems more work than it's worth at the moment.
*/
startup_cost += (cpu_operator_cost * num_hashclauses + cpu_tuple_cost)
* inner_path_rows;
run_cost += cpu_operator_cost * num_hashclauses * outer_path_rows;
/*
* Get hash table size that executor would use for inner relation.
*
* XXX for the moment, always assume that skew optimization will be
* performed. As long as SKEW_WORK_MEM_PERCENT is small, it's not worth
* trying to determine that for sure.
*
* XXX at some point it might be interesting to try to account for skew
* optimization in the cost estimate, but for now, we don't.
*/
ExecChooseHashTableSize(inner_path_rows,
inner_path->parent->width,
true, /* useskew */
&numbuckets,
&numbatches,
&num_skew_mcvs);
/*
* If inner relation is too big then we will need to "batch" the join,
* which implies writing and reading most of the tuples to disk an extra
* time. Charge seq_page_cost per page, since the I/O should be nice and
* sequential. Writing the inner rel counts as startup cost, all the rest
* as run cost.
*/
if (numbatches > 1)
{
double outerpages = page_size(outer_path_rows,
outer_path->parent->width);
double innerpages = page_size(inner_path_rows,
inner_path->parent->width);
startup_cost += seq_page_cost * innerpages;
run_cost += seq_page_cost * (innerpages + 2 * outerpages);
}
/* CPU costs left for later */
/* Public result fields */
workspace->startup_cost = startup_cost;
workspace->total_cost = startup_cost + run_cost;
/* Save private data for final_cost_hashjoin */
workspace->run_cost = run_cost;
workspace->numbuckets = numbuckets;
workspace->numbatches = numbatches;
}
| void initial_cost_mergejoin | ( | PlannerInfo * | root, | |
| JoinCostWorkspace * | workspace, | |||
| JoinType | jointype, | |||
| List * | mergeclauses, | |||
| Path * | outer_path, | |||
| Path * | inner_path, | |||
| List * | outersortkeys, | |||
| List * | innersortkeys, | |||
| SpecialJoinInfo * | sjinfo | |||
| ) |
Definition at line 1891 of file costsize.c.
References Assert, bms_is_subset(), cached_scansel(), clamp_row_est(), cost_sort(), EquivalenceClass::ec_collation, elog, ERROR, JoinCostWorkspace::inner_rows, JoinCostWorkspace::inner_run_cost, JoinCostWorkspace::inner_skip_rows, JOIN_ANTI, JOIN_FULL, JOIN_LEFT, JOIN_RIGHT, RestrictInfo::left_relids, MergeScanSelCache::leftendsel, MergeScanSelCache::leftstartsel, linitial, JoinCostWorkspace::outer_rows, JoinCostWorkspace::outer_skip_rows, Path::parent, Path::pathkeys, PathKey::pk_eclass, PathKey::pk_nulls_first, PathKey::pk_opfamily, PathKey::pk_strategy, RelOptInfo::relids, MergeScanSelCache::rightendsel, MergeScanSelCache::rightstartsel, rint(), Path::rows, JoinCostWorkspace::run_cost, JoinCostWorkspace::startup_cost, Path::startup_cost, JoinCostWorkspace::total_cost, Path::total_cost, RelOptInfo::width, and work_mem.
Referenced by try_mergejoin_path().
{
Cost startup_cost = 0;
Cost run_cost = 0;
double outer_path_rows = outer_path->rows;
double inner_path_rows = inner_path->rows;
Cost inner_run_cost;
double outer_rows,
inner_rows,
outer_skip_rows,
inner_skip_rows;
Selectivity outerstartsel,
outerendsel,
innerstartsel,
innerendsel;
Path sort_path; /* dummy for result of cost_sort */
/* Protect some assumptions below that rowcounts aren't zero or NaN */
if (outer_path_rows <= 0 || isnan(outer_path_rows))
outer_path_rows = 1;
if (inner_path_rows <= 0 || isnan(inner_path_rows))
inner_path_rows = 1;
/*
* A merge join will stop as soon as it exhausts either input stream
* (unless it's an outer join, in which case the outer side has to be
* scanned all the way anyway). Estimate fraction of the left and right
* inputs that will actually need to be scanned. Likewise, we can
* estimate the number of rows that will be skipped before the first join
* pair is found, which should be factored into startup cost. We use only
* the first (most significant) merge clause for this purpose. Since
* mergejoinscansel() is a fairly expensive computation, we cache the
* results in the merge clause RestrictInfo.
*/
if (mergeclauses && jointype != JOIN_FULL)
{
RestrictInfo *firstclause = (RestrictInfo *) linitial(mergeclauses);
List *opathkeys;
List *ipathkeys;
PathKey *opathkey;
PathKey *ipathkey;
MergeScanSelCache *cache;
/* Get the input pathkeys to determine the sort-order details */
opathkeys = outersortkeys ? outersortkeys : outer_path->pathkeys;
ipathkeys = innersortkeys ? innersortkeys : inner_path->pathkeys;
Assert(opathkeys);
Assert(ipathkeys);
opathkey = (PathKey *) linitial(opathkeys);
ipathkey = (PathKey *) linitial(ipathkeys);
/* debugging check */
if (opathkey->pk_opfamily != ipathkey->pk_opfamily ||
opathkey->pk_eclass->ec_collation != ipathkey->pk_eclass->ec_collation ||
opathkey->pk_strategy != ipathkey->pk_strategy ||
opathkey->pk_nulls_first != ipathkey->pk_nulls_first)
elog(ERROR, "left and right pathkeys do not match in mergejoin");
/* Get the selectivity with caching */
cache = cached_scansel(root, firstclause, opathkey);
if (bms_is_subset(firstclause->left_relids,
outer_path->parent->relids))
{
/* left side of clause is outer */
outerstartsel = cache->leftstartsel;
outerendsel = cache->leftendsel;
innerstartsel = cache->rightstartsel;
innerendsel = cache->rightendsel;
}
else
{
/* left side of clause is inner */
outerstartsel = cache->rightstartsel;
outerendsel = cache->rightendsel;
innerstartsel = cache->leftstartsel;
innerendsel = cache->leftendsel;
}
if (jointype == JOIN_LEFT ||
jointype == JOIN_ANTI)
{
outerstartsel = 0.0;
outerendsel = 1.0;
}
else if (jointype == JOIN_RIGHT)
{
innerstartsel = 0.0;
innerendsel = 1.0;
}
}
else
{
/* cope with clauseless or full mergejoin */
outerstartsel = innerstartsel = 0.0;
outerendsel = innerendsel = 1.0;
}
/*
* Convert selectivities to row counts. We force outer_rows and
* inner_rows to be at least 1, but the skip_rows estimates can be zero.
*/
outer_skip_rows = rint(outer_path_rows * outerstartsel);
inner_skip_rows = rint(inner_path_rows * innerstartsel);
outer_rows = clamp_row_est(outer_path_rows * outerendsel);
inner_rows = clamp_row_est(inner_path_rows * innerendsel);
Assert(outer_skip_rows <= outer_rows);
Assert(inner_skip_rows <= inner_rows);
/*
* Readjust scan selectivities to account for above rounding. This is
* normally an insignificant effect, but when there are only a few rows in
* the inputs, failing to do this makes for a large percentage error.
*/
outerstartsel = outer_skip_rows / outer_path_rows;
innerstartsel = inner_skip_rows / inner_path_rows;
outerendsel = outer_rows / outer_path_rows;
innerendsel = inner_rows / inner_path_rows;
Assert(outerstartsel <= outerendsel);
Assert(innerstartsel <= innerendsel);
/* cost of source data */
if (outersortkeys) /* do we need to sort outer? */
{
cost_sort(&sort_path,
root,
outersortkeys,
outer_path->total_cost,
outer_path_rows,
outer_path->parent->width,
0.0,
work_mem,
-1.0);
startup_cost += sort_path.startup_cost;
startup_cost += (sort_path.total_cost - sort_path.startup_cost)
* outerstartsel;
run_cost += (sort_path.total_cost - sort_path.startup_cost)
* (outerendsel - outerstartsel);
}
else
{
startup_cost += outer_path->startup_cost;
startup_cost += (outer_path->total_cost - outer_path->startup_cost)
* outerstartsel;
run_cost += (outer_path->total_cost - outer_path->startup_cost)
* (outerendsel - outerstartsel);
}
if (innersortkeys) /* do we need to sort inner? */
{
cost_sort(&sort_path,
root,
innersortkeys,
inner_path->total_cost,
inner_path_rows,
inner_path->parent->width,
0.0,
work_mem,
-1.0);
startup_cost += sort_path.startup_cost;
startup_cost += (sort_path.total_cost - sort_path.startup_cost)
* innerstartsel;
inner_run_cost = (sort_path.total_cost - sort_path.startup_cost)
* (innerendsel - innerstartsel);
}
else
{
startup_cost += inner_path->startup_cost;
startup_cost += (inner_path->total_cost - inner_path->startup_cost)
* innerstartsel;
inner_run_cost = (inner_path->total_cost - inner_path->startup_cost)
* (innerendsel - innerstartsel);
}
/*
* We can't yet determine whether rescanning occurs, or whether
* materialization of the inner input should be done. The minimum
* possible inner input cost, regardless of rescan and materialization
* considerations, is inner_run_cost. We include that in
* workspace->total_cost, but not yet in run_cost.
*/
/* CPU costs left for later */
/* Public result fields */
workspace->startup_cost = startup_cost;
workspace->total_cost = startup_cost + run_cost + inner_run_cost;
/* Save private data for final_cost_mergejoin */
workspace->run_cost = run_cost;
workspace->inner_run_cost = inner_run_cost;
workspace->outer_rows = outer_rows;
workspace->inner_rows = inner_rows;
workspace->outer_skip_rows = outer_skip_rows;
workspace->inner_skip_rows = inner_skip_rows;
}
| void initial_cost_nestloop | ( | PlannerInfo * | root, | |
| JoinCostWorkspace * | workspace, | |||
| JoinType | jointype, | |||
| Path * | outer_path, | |||
| Path * | inner_path, | |||
| SpecialJoinInfo * | sjinfo, | |||
| SemiAntiJoinFactors * | semifactors | |||
| ) |
Definition at line 1668 of file costsize.c.
References cost_rescan(), JoinCostWorkspace::inner_rescan_run_cost, JoinCostWorkspace::inner_scan_frac, JOIN_ANTI, JOIN_SEMI, SemiAntiJoinFactors::match_count, SemiAntiJoinFactors::outer_match_frac, JoinCostWorkspace::outer_matched_rows, rint(), Path::rows, JoinCostWorkspace::run_cost, JoinCostWorkspace::startup_cost, Path::startup_cost, JoinCostWorkspace::total_cost, and Path::total_cost.
Referenced by try_nestloop_path().
{
Cost startup_cost = 0;
Cost run_cost = 0;
double outer_path_rows = outer_path->rows;
Cost inner_rescan_start_cost;
Cost inner_rescan_total_cost;
Cost inner_run_cost;
Cost inner_rescan_run_cost;
/* estimate costs to rescan the inner relation */
cost_rescan(root, inner_path,
&inner_rescan_start_cost,
&inner_rescan_total_cost);
/* cost of source data */
/*
* NOTE: clearly, we must pay both outer and inner paths' startup_cost
* before we can start returning tuples, so the join's startup cost is
* their sum. We'll also pay the inner path's rescan startup cost
* multiple times.
*/
startup_cost += outer_path->startup_cost + inner_path->startup_cost;
run_cost += outer_path->total_cost - outer_path->startup_cost;
if (outer_path_rows > 1)
run_cost += (outer_path_rows - 1) * inner_rescan_start_cost;
inner_run_cost = inner_path->total_cost - inner_path->startup_cost;
inner_rescan_run_cost = inner_rescan_total_cost - inner_rescan_start_cost;
if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
{
double outer_matched_rows;
Selectivity inner_scan_frac;
/*
* SEMI or ANTI join: executor will stop after first match.
*
* For an outer-rel row that has at least one match, we can expect the
* inner scan to stop after a fraction 1/(match_count+1) of the inner
* rows, if the matches are evenly distributed. Since they probably
* aren't quite evenly distributed, we apply a fuzz factor of 2.0 to
* that fraction. (If we used a larger fuzz factor, we'd have to
* clamp inner_scan_frac to at most 1.0; but since match_count is at
* least 1, no such clamp is needed now.)
*
* A complicating factor is that rescans may be cheaper than first
* scans. If we never scan all the way to the end of the inner rel,
* it might be (depending on the plan type) that we'd never pay the
* whole inner first-scan run cost. However it is difficult to
* estimate whether that will happen, so be conservative and always
* charge the whole first-scan cost once.
*/
run_cost += inner_run_cost;
outer_matched_rows = rint(outer_path_rows * semifactors->outer_match_frac);
inner_scan_frac = 2.0 / (semifactors->match_count + 1.0);
/* Add inner run cost for additional outer tuples having matches */
if (outer_matched_rows > 1)
run_cost += (outer_matched_rows - 1) * inner_rescan_run_cost * inner_scan_frac;
/*
* The cost of processing unmatched rows varies depending on the
* details of the joinclauses, so we leave that part for later.
*/
/* Save private data for final_cost_nestloop */
workspace->outer_matched_rows = outer_matched_rows;
workspace->inner_scan_frac = inner_scan_frac;
}
else
{
/* Normal case; we'll scan whole input rel for each outer row */
run_cost += inner_run_cost;
if (outer_path_rows > 1)
run_cost += (outer_path_rows - 1) * inner_rescan_run_cost;
}
/* CPU costs left for later */
/* Public result fields */
workspace->startup_cost = startup_cost;
workspace->total_cost = startup_cost + run_cost;
/* Save private data for final_cost_nestloop */
workspace->run_cost = run_cost;
workspace->inner_rescan_run_cost = inner_rescan_run_cost;
}
| void set_baserel_size_estimates | ( | PlannerInfo * | root, | |
| RelOptInfo * | rel | |||
| ) |
Definition at line 3445 of file costsize.c.
References Assert, RelOptInfo::baserestrictcost, RelOptInfo::baserestrictinfo, clamp_row_est(), clauselist_selectivity(), cost_qual_eval(), JOIN_INNER, NULL, RelOptInfo::relid, RelOptInfo::rows, set_rel_width(), and RelOptInfo::tuples.
Referenced by postgresGetForeignRelSize(), set_cte_size_estimates(), set_function_size_estimates(), set_plain_rel_size(), set_subquery_size_estimates(), and set_values_size_estimates().
{
double nrows;
/* Should only be applied to base relations */
Assert(rel->relid > 0);
nrows = rel->tuples *
clauselist_selectivity(root,
rel->baserestrictinfo,
0,
JOIN_INNER,
NULL);
rel->rows = clamp_row_est(nrows);
cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo, root);
set_rel_width(root, rel);
}
| void set_cte_size_estimates | ( | PlannerInfo * | root, | |
| RelOptInfo * | rel, | |||
| Plan * | cteplan | |||
| ) |
Definition at line 3849 of file costsize.c.
References Assert, Plan::plan_rows, planner_rt_fetch, RelOptInfo::relid, RTE_CTE, RangeTblEntry::rtekind, RangeTblEntry::self_reference, set_baserel_size_estimates(), and RelOptInfo::tuples.
Referenced by set_cte_pathlist(), and set_worktable_pathlist().
{
RangeTblEntry *rte;
/* Should only be applied to base relations that are CTE references */
Assert(rel->relid > 0);
rte = planner_rt_fetch(rel->relid, root);
Assert(rte->rtekind == RTE_CTE);
if (rte->self_reference)
{
/*
* In a self-reference, arbitrarily assume the average worktable size
* is about 10 times the nonrecursive term's size.
*/
rel->tuples = 10 * cteplan->plan_rows;
}
else
{
/* Otherwise just believe the CTE plan's output estimate */
rel->tuples = cteplan->plan_rows;
}
/* Now estimate number of output rows, etc */
set_baserel_size_estimates(root, rel);
}
| void set_foreign_size_estimates | ( | PlannerInfo * | root, | |
| RelOptInfo * | rel | |||
| ) |
Definition at line 3892 of file costsize.c.
References Assert, RelOptInfo::baserestrictcost, RelOptInfo::baserestrictinfo, cost_qual_eval(), RelOptInfo::relid, RelOptInfo::rows, and set_rel_width().
Referenced by set_foreign_size().
{
/* Should only be applied to base relations */
Assert(rel->relid > 0);
rel->rows = 1000; /* entirely bogus default estimate */
cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo, root);
set_rel_width(root, rel);
}
| void set_function_size_estimates | ( | PlannerInfo * | root, | |
| RelOptInfo * | rel | |||
| ) |
Definition at line 3791 of file costsize.c.
References Assert, expression_returns_set_rows(), RangeTblEntry::funcexpr, planner_rt_fetch, RelOptInfo::relid, RTE_FUNCTION, RangeTblEntry::rtekind, set_baserel_size_estimates(), and RelOptInfo::tuples.
Referenced by set_rel_size().
{
RangeTblEntry *rte;
/* Should only be applied to base relations that are functions */
Assert(rel->relid > 0);
rte = planner_rt_fetch(rel->relid, root);
Assert(rte->rtekind == RTE_FUNCTION);
/* Estimate number of rows the function itself will return */
rel->tuples = expression_returns_set_rows(rte->funcexpr);
/* Now estimate number of output rows, etc */
set_baserel_size_estimates(root, rel);
}
| void set_joinrel_size_estimates | ( | PlannerInfo * | root, | |
| RelOptInfo * | rel, | |||
| RelOptInfo * | outer_rel, | |||
| RelOptInfo * | inner_rel, | |||
| SpecialJoinInfo * | sjinfo, | |||
| List * | restrictlist | |||
| ) |
Definition at line 3525 of file costsize.c.
References calc_joinrel_size_estimate(), and RelOptInfo::rows.
Referenced by build_join_rel().
{
rel->rows = calc_joinrel_size_estimate(root,
outer_rel->rows,
inner_rel->rows,
sjinfo,
restrictlist);
}
| void set_subquery_size_estimates | ( | PlannerInfo * | root, | |
| RelOptInfo * | rel | |||
| ) |
Definition at line 3713 of file costsize.c.
References Assert, RelOptInfo::attr_widths, TargetEntry::expr, find_base_rel(), IsA, lfirst, RelOptInfo::max_attr, RelOptInfo::min_attr, PlannerInfo::parse, Plan::plan_rows, planner_rt_fetch, RelOptInfo::relid, TargetEntry::resjunk, TargetEntry::resno, RTE_SUBQUERY, set_baserel_size_estimates(), Query::setOperations, RelOptInfo::subplan, RelOptInfo::subroot, Query::targetList, RelOptInfo::tuples, RangeQueryClause::var, Var::varattno, and Var::varno.
Referenced by set_subquery_pathlist().
{
PlannerInfo *subroot = rel->subroot;
RangeTblEntry *rte PG_USED_FOR_ASSERTS_ONLY;
ListCell *lc;
/* Should only be applied to base relations that are subqueries */
Assert(rel->relid > 0);
rte = planner_rt_fetch(rel->relid, root);
Assert(rte->rtekind == RTE_SUBQUERY);
/* Copy raw number of output rows from subplan */
rel->tuples = rel->subplan->plan_rows;
/*
* Compute per-output-column width estimates by examining the subquery's
* targetlist. For any output that is a plain Var, get the width estimate
* that was made while planning the subquery. Otherwise, we leave it to
* set_rel_width to fill in a datatype-based default estimate.
*/
foreach(lc, subroot->parse->targetList)
{
TargetEntry *te = (TargetEntry *) lfirst(lc);
Node *texpr = (Node *) te->expr;
int32 item_width = 0;
Assert(IsA(te, TargetEntry));
/* junk columns aren't visible to upper query */
if (te->resjunk)
continue;
/*
* The subquery could be an expansion of a view that's had columns
* added to it since the current query was parsed, so that there are
* non-junk tlist columns in it that don't correspond to any column
* visible at our query level. Ignore such columns.
*/
if (te->resno < rel->min_attr || te->resno > rel->max_attr)
continue;
/*
* XXX This currently doesn't work for subqueries containing set
* operations, because the Vars in their tlists are bogus references
* to the first leaf subquery, which wouldn't give the right answer
* even if we could still get to its PlannerInfo.
*
* Also, the subquery could be an appendrel for which all branches are
* known empty due to constraint exclusion, in which case
* set_append_rel_pathlist will have left the attr_widths set to zero.
*
* In either case, we just leave the width estimate zero until
* set_rel_width fixes it.
*/
if (IsA(texpr, Var) &&
subroot->parse->setOperations == NULL)
{
Var *var = (Var *) texpr;
RelOptInfo *subrel = find_base_rel(subroot, var->varno);
item_width = subrel->attr_widths[var->varattno - subrel->min_attr];
}
rel->attr_widths[te->resno - rel->min_attr] = item_width;
}
/* Now estimate number of output rows, etc */
set_baserel_size_estimates(root, rel);
}
| void set_values_size_estimates | ( | PlannerInfo * | root, | |
| RelOptInfo * | rel | |||
| ) |
Definition at line 3817 of file costsize.c.
References Assert, list_length(), planner_rt_fetch, RelOptInfo::relid, RTE_VALUES, RangeTblEntry::rtekind, set_baserel_size_estimates(), RelOptInfo::tuples, and RangeTblEntry::values_lists.
Referenced by set_rel_size().
{
RangeTblEntry *rte;
/* Should only be applied to base relations that are values lists */
Assert(rel->relid > 0);
rte = planner_rt_fetch(rel->relid, root);
Assert(rte->rtekind == RTE_VALUES);
/*
* Estimate number of rows the values list will return. We know this
* precisely based on the list length (well, barring set-returning
* functions in list items, but that's a refinement not catered for
* anywhere else either).
*/
rel->tuples = list_length(rte->values_lists);
/* Now estimate number of output rows, etc */
set_baserel_size_estimates(root, rel);
}
Definition at line 47 of file plancat.c.
Referenced by relation_excluded_by_constraints().
| PGDLLIMPORT double cpu_index_tuple_cost |
Definition at line 102 of file costsize.c.
Referenced by genericcostestimate(), and gincostestimate().
| PGDLLIMPORT double cpu_operator_cost |
Definition at line 103 of file costsize.c.
Referenced by btcostestimate(), cost_agg(), cost_bitmap_and_node(), cost_bitmap_or_node(), cost_bitmap_tree_node(), cost_group(), cost_material(), cost_merge_append(), cost_qual_eval_walker(), cost_rescan(), cost_sort(), cost_subplan(), cost_valuesscan(), cost_windowagg(), count_agg_clauses_walker(), create_mergejoin_plan(), create_unique_path(), final_cost_mergejoin(), genericcostestimate(), gincostestimate(), gistcostestimate(), initial_cost_hashjoin(), inline_function(), make_setop(), make_unique(), and spgcostestimate().
| PGDLLIMPORT double cpu_tuple_cost |
Definition at line 101 of file costsize.c.
Referenced by add_tlist_costs_to_plan(), cost_agg(), cost_bitmap_heap_scan(), cost_ctescan(), cost_functionscan(), cost_index(), cost_recursive_union(), cost_rescan(), cost_seqscan(), cost_subqueryscan(), cost_tidscan(), cost_valuesscan(), cost_windowagg(), create_result_path(), estimate_costs(), estimate_path_cost_size(), final_cost_hashjoin(), final_cost_mergejoin(), final_cost_nestloop(), initial_cost_hashjoin(), make_lockrows(), make_result(), and make_subqueryscan().
Definition at line 107 of file costsize.c.
Referenced by cost_bitmap_heap_scan(), cost_index(), cost_qual_eval_walker(), cost_seqscan(), cost_sort(), cost_tidscan(), final_cost_hashjoin(), final_cost_mergejoin(), and final_cost_nestloop().
| PGDLLIMPORT int effective_cache_size |
Definition at line 105 of file costsize.c.
Referenced by gistBuildCallback(), gistInitBuffering(), and index_pages_fetched().
Definition at line 112 of file costsize.c.
Referenced by cost_bitmap_heap_scan().
Definition at line 115 of file costsize.c.
Referenced by choose_hashed_distinct(), choose_hashed_grouping(), choose_hashed_setop(), and create_unique_path().
Definition at line 119 of file costsize.c.
Referenced by add_paths_to_joinrel(), and final_cost_hashjoin().
Definition at line 111 of file costsize.c.
Referenced by check_index_only().
Definition at line 110 of file costsize.c.
Referenced by cost_index().
Definition at line 117 of file costsize.c.
Referenced by build_subplan(), final_cost_mergejoin(), and match_unsorted_outer().
Definition at line 118 of file costsize.c.
Referenced by add_paths_to_joinrel(), and final_cost_mergejoin().
Definition at line 116 of file costsize.c.
Referenced by final_cost_nestloop().
Definition at line 109 of file costsize.c.
Referenced by cost_seqscan().
Definition at line 114 of file costsize.c.
Referenced by cost_sort().
Definition at line 113 of file costsize.c.
Referenced by cost_tidscan().
| PGDLLIMPORT double random_page_cost |
Definition at line 100 of file costsize.c.
Referenced by cost_sort(), get_tablespace_page_costs(), and tablespace_reloptions().
| PGDLLIMPORT double seq_page_cost |
Definition at line 99 of file costsize.c.
Referenced by cost_material(), cost_rescan(), cost_sort(), estimate_costs(), estimate_path_cost_size(), get_tablespace_page_costs(), initial_cost_hashjoin(), and tablespace_reloptions().
1.7.1