6e07709760
(previously we only did = and <> correctly). Also, allow row comparisons with any operators that are in btree opclasses, not only those with these specific names. This gets rid of a whole lot of indefensible assumptions about the behavior of particular operators based on their names ... though it's still true that IN and NOT IN expand to "= ANY". The patch adds a RowCompareExpr expression node type, and makes some changes in the representation of ANY/ALL/ROWCOMPARE SubLinks so that they can share code with RowCompareExpr. I have not yet done anything about making RowCompareExpr an indexable operator, but will look at that soon. initdb forced due to changes in stored rules.
2063 lines
65 KiB
C
2063 lines
65 KiB
C
/*-------------------------------------------------------------------------
|
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*
|
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* costsize.c
|
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* Routines to compute (and set) relation sizes and path costs
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*
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* Path costs are measured in units of disk accesses: one sequential page
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* fetch has cost 1. All else is scaled relative to a page fetch, using
|
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* the scaling parameters
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*
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* random_page_cost Cost of a non-sequential page fetch
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* cpu_tuple_cost Cost of typical CPU time to process a tuple
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* cpu_index_tuple_cost Cost of typical CPU time to process an index tuple
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* cpu_operator_cost Cost of CPU time to process a typical WHERE operator
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*
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* We also use a rough estimate "effective_cache_size" of the number of
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* disk pages in Postgres + OS-level disk cache. (We can't simply use
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* NBuffers for this purpose because that would ignore the effects of
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* the kernel's disk cache.)
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*
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* Obviously, taking constants for these values is an oversimplification,
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* but it's tough enough to get any useful estimates even at this level of
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* detail. Note that all of these parameters are user-settable, in case
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* the default values are drastically off for a particular platform.
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*
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* We compute two separate costs for each path:
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* total_cost: total estimated cost to fetch all tuples
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* startup_cost: cost that is expended before first tuple is fetched
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* In some scenarios, such as when there is a LIMIT or we are implementing
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* an EXISTS(...) sub-select, it is not necessary to fetch all tuples of the
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* path's result. A caller can estimate the cost of fetching a partial
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* result by interpolating between startup_cost and total_cost. In detail:
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* actual_cost = startup_cost +
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* (total_cost - startup_cost) * tuples_to_fetch / path->parent->rows;
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* Note that a base relation's rows count (and, by extension, plan_rows for
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* plan nodes below the LIMIT node) are set without regard to any LIMIT, so
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* that this equation works properly. (Also, these routines guarantee not to
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* set the rows count to zero, so there will be no zero divide.) The LIMIT is
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* applied as a top-level plan node.
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*
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* For largely historical reasons, most of the routines in this module use
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* the passed result Path only to store their startup_cost and total_cost
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* results into. All the input data they need is passed as separate
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* parameters, even though much of it could be extracted from the Path.
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* An exception is made for the cost_XXXjoin() routines, which expect all
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* the non-cost fields of the passed XXXPath to be filled in.
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*
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*
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* Portions Copyright (c) 1996-2005, PostgreSQL Global Development Group
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* Portions Copyright (c) 1994, Regents of the University of California
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*
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* IDENTIFICATION
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* $PostgreSQL: pgsql/src/backend/optimizer/path/costsize.c,v 1.152 2005/12/28 01:29:59 tgl Exp $
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*
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*-------------------------------------------------------------------------
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*/
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#include "postgres.h"
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#include <math.h>
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#include "catalog/pg_statistic.h"
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#include "executor/nodeHash.h"
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#include "miscadmin.h"
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#include "optimizer/clauses.h"
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#include "optimizer/cost.h"
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#include "optimizer/pathnode.h"
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#include "optimizer/plancat.h"
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#include "parser/parsetree.h"
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#include "utils/array.h"
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#include "utils/selfuncs.h"
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#include "utils/lsyscache.h"
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#include "utils/syscache.h"
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|
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#define LOG2(x) (log(x) / 0.693147180559945)
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#define LOG6(x) (log(x) / 1.79175946922805)
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|
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/*
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* Some Paths return less than the nominal number of rows of their parent
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* relations; join nodes need to do this to get the correct input count:
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*/
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#define PATH_ROWS(path) \
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(IsA(path, UniquePath) ? \
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((UniquePath *) (path))->rows : \
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(path)->parent->rows)
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|
|
|
|
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double effective_cache_size = DEFAULT_EFFECTIVE_CACHE_SIZE;
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double random_page_cost = DEFAULT_RANDOM_PAGE_COST;
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double cpu_tuple_cost = DEFAULT_CPU_TUPLE_COST;
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|
double cpu_index_tuple_cost = DEFAULT_CPU_INDEX_TUPLE_COST;
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double cpu_operator_cost = DEFAULT_CPU_OPERATOR_COST;
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|
|
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Cost disable_cost = 100000000.0;
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|
|
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bool enable_seqscan = true;
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|
bool enable_indexscan = true;
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bool enable_bitmapscan = true;
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bool enable_tidscan = true;
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bool enable_sort = true;
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bool enable_hashagg = true;
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bool enable_nestloop = true;
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bool enable_mergejoin = true;
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bool enable_hashjoin = true;
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|
|
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static bool cost_qual_eval_walker(Node *node, QualCost *total);
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static int estimate_array_length(Node *arrayexpr);
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static Selectivity approx_selectivity(PlannerInfo *root, List *quals,
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JoinType jointype);
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static Selectivity join_in_selectivity(JoinPath *path, PlannerInfo *root);
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static void set_rel_width(PlannerInfo *root, RelOptInfo *rel);
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static double relation_byte_size(double tuples, int width);
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static double page_size(double tuples, int width);
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|
|
|
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/*
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* clamp_row_est
|
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* Force a row-count estimate to a sane value.
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*/
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double
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clamp_row_est(double nrows)
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{
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/*
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* Force estimate to be at least one row, to make explain output look
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* better and to avoid possible divide-by-zero when interpolating costs.
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* Make it an integer, too.
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*/
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if (nrows < 1.0)
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nrows = 1.0;
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else
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nrows = rint(nrows);
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return nrows;
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}
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|
|
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/*
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* cost_seqscan
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* Determines and returns the cost of scanning a relation sequentially.
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*/
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void
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cost_seqscan(Path *path, PlannerInfo *root,
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RelOptInfo *baserel)
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{
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Cost startup_cost = 0;
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Cost run_cost = 0;
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Cost cpu_per_tuple;
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/* Should only be applied to base relations */
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Assert(baserel->relid > 0);
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Assert(baserel->rtekind == RTE_RELATION);
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if (!enable_seqscan)
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startup_cost += disable_cost;
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/*
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* disk costs
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*
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* The cost of reading a page sequentially is 1.0, by definition. Note
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* that the Unix kernel will typically do some amount of read-ahead
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* optimization, so that this cost is less than the true cost of reading a
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* page from disk. We ignore that issue here, but must take it into
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* account when estimating the cost of non-sequential accesses!
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*/
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run_cost += baserel->pages; /* sequential fetches with cost 1.0 */
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/* CPU costs */
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startup_cost += baserel->baserestrictcost.startup;
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cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
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run_cost += cpu_per_tuple * baserel->tuples;
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path->startup_cost = startup_cost;
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path->total_cost = startup_cost + run_cost;
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}
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/*
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* cost_nonsequential_access
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* Estimate the cost of accessing one page at random from a relation
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* (or sort temp file) of the given size in pages.
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*
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* The simplistic model that the cost is random_page_cost is what we want
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* to use for large relations; but for small ones that is a serious
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* overestimate because of the effects of caching. This routine tries to
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* account for that.
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*
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* Unfortunately we don't have any good way of estimating the effective cache
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* size we are working with --- we know that Postgres itself has NBuffers
|
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* internal buffers, but the size of the kernel's disk cache is uncertain,
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* and how much of it we get to use is even less certain. We punt the problem
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* for now by assuming we are given an effective_cache_size parameter.
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*
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* Given a guesstimated cache size, we estimate the actual I/O cost per page
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* with the entirely ad-hoc equations (writing relsize for
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* relpages/effective_cache_size):
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* if relsize >= 1:
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* random_page_cost - (random_page_cost-1)/2 * (1/relsize)
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* if relsize < 1:
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* 1 + ((random_page_cost-1)/2) * relsize ** 2
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* These give the right asymptotic behavior (=> 1.0 as relpages becomes
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* small, => random_page_cost as it becomes large) and meet in the middle
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* with the estimate that the cache is about 50% effective for a relation
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* of the same size as effective_cache_size. (XXX this is probably all
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* wrong, but I haven't been able to find any theory about how effective
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* a disk cache should be presumed to be.)
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*/
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static Cost
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cost_nonsequential_access(double relpages)
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{
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double relsize;
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/* don't crash on bad input data */
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if (relpages <= 0.0 || effective_cache_size <= 0.0)
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return random_page_cost;
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relsize = relpages / effective_cache_size;
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if (relsize >= 1.0)
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return random_page_cost - (random_page_cost - 1.0) * 0.5 / relsize;
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else
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return 1.0 + (random_page_cost - 1.0) * 0.5 * relsize * relsize;
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}
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|
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/*
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* cost_index
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|
* Determines and returns the cost of scanning a relation using an index.
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*
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* NOTE: an indexscan plan node can actually represent several passes,
|
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* but here we consider the cost of just one pass.
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*
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* 'index' is the index to be used
|
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* 'indexQuals' is the list of applicable qual clauses (implicit AND semantics)
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* 'is_injoin' is T if we are considering using the index scan as the inside
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* of a nestloop join (hence, some of the indexQuals are join clauses)
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*
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* cost_index() takes an IndexPath not just a Path, because it sets a few
|
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* additional fields of the IndexPath besides startup_cost and total_cost.
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* These fields are needed if the IndexPath is used in a BitmapIndexScan.
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*
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* NOTE: 'indexQuals' must contain only clauses usable as index restrictions.
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* Any additional quals evaluated as qpquals may reduce the number of returned
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* tuples, but they won't reduce the number of tuples we have to fetch from
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* the table, so they don't reduce the scan cost.
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*
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* NOTE: as of 8.0, indexQuals is a list of RestrictInfo nodes, where formerly
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* it was a list of bare clause expressions.
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*/
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void
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cost_index(IndexPath *path, PlannerInfo *root,
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IndexOptInfo *index,
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List *indexQuals,
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bool is_injoin)
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{
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RelOptInfo *baserel = index->rel;
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Cost startup_cost = 0;
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Cost run_cost = 0;
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Cost indexStartupCost;
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Cost indexTotalCost;
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Selectivity indexSelectivity;
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double indexCorrelation,
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csquared;
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Cost min_IO_cost,
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max_IO_cost;
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Cost cpu_per_tuple;
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double tuples_fetched;
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double pages_fetched;
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double T,
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b;
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/* Should only be applied to base relations */
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Assert(IsA(baserel, RelOptInfo) &&
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IsA(index, IndexOptInfo));
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Assert(baserel->relid > 0);
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Assert(baserel->rtekind == RTE_RELATION);
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|
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if (!enable_indexscan)
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startup_cost += disable_cost;
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|
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/*
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* Call index-access-method-specific code to estimate the processing cost
|
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* for scanning the index, as well as the selectivity of the index (ie,
|
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* the fraction of main-table tuples we will have to retrieve) and its
|
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* correlation to the main-table tuple order.
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*/
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OidFunctionCall7(index->amcostestimate,
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PointerGetDatum(root),
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PointerGetDatum(index),
|
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PointerGetDatum(indexQuals),
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PointerGetDatum(&indexStartupCost),
|
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PointerGetDatum(&indexTotalCost),
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PointerGetDatum(&indexSelectivity),
|
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PointerGetDatum(&indexCorrelation));
|
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|
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/*
|
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* Save amcostestimate's results for possible use in bitmap scan planning.
|
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* We don't bother to save indexStartupCost or indexCorrelation, because a
|
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* bitmap scan doesn't care about either.
|
|
*/
|
|
path->indextotalcost = indexTotalCost;
|
|
path->indexselectivity = indexSelectivity;
|
|
|
|
/* all costs for touching index itself included here */
|
|
startup_cost += indexStartupCost;
|
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run_cost += indexTotalCost - indexStartupCost;
|
|
|
|
/*----------
|
|
* Estimate number of main-table tuples and pages fetched.
|
|
*
|
|
* When the index ordering is uncorrelated with the table ordering,
|
|
* we use an approximation proposed by Mackert and Lohman, "Index Scans
|
|
* Using a Finite LRU Buffer: A Validated I/O Model", ACM Transactions
|
|
* on Database Systems, Vol. 14, No. 3, September 1989, Pages 401-424.
|
|
* The Mackert and Lohman approximation is that the number of pages
|
|
* fetched is
|
|
* PF =
|
|
* min(2TNs/(2T+Ns), T) when T <= b
|
|
* 2TNs/(2T+Ns) when T > b and Ns <= 2Tb/(2T-b)
|
|
* b + (Ns - 2Tb/(2T-b))*(T-b)/T when T > b and Ns > 2Tb/(2T-b)
|
|
* where
|
|
* T = # pages in table
|
|
* N = # tuples in table
|
|
* s = selectivity = fraction of table to be scanned
|
|
* b = # buffer pages available (we include kernel space here)
|
|
*
|
|
* When the index ordering is exactly correlated with the table ordering
|
|
* (just after a CLUSTER, for example), the number of pages fetched should
|
|
* be just sT. What's more, these will be sequential fetches, not the
|
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* random fetches that occur in the uncorrelated case. So, depending on
|
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* the extent of correlation, we should estimate the actual I/O cost
|
|
* somewhere between s * T * 1.0 and PF * random_cost. We currently
|
|
* interpolate linearly between these two endpoints based on the
|
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* correlation squared (XXX is that appropriate?).
|
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*
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* In any case the number of tuples fetched is Ns.
|
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*----------
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|
*/
|
|
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tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);
|
|
|
|
/* This part is the Mackert and Lohman formula */
|
|
|
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T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
|
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b = (effective_cache_size > 1) ? effective_cache_size : 1.0;
|
|
|
|
if (T <= b)
|
|
{
|
|
pages_fetched =
|
|
(2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
|
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if (pages_fetched > T)
|
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pages_fetched = T;
|
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}
|
|
else
|
|
{
|
|
double lim;
|
|
|
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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;
|
|
}
|
|
}
|
|
|
|
/*
|
|
* min_IO_cost corresponds to the perfectly correlated case (csquared=1),
|
|
* max_IO_cost to the perfectly uncorrelated case (csquared=0). Note that
|
|
* we just charge random_page_cost per page in the uncorrelated case,
|
|
* rather than using cost_nonsequential_access, since we've already
|
|
* accounted for caching effects by using the Mackert model.
|
|
*/
|
|
min_IO_cost = ceil(indexSelectivity * T);
|
|
max_IO_cost = pages_fetched * random_page_cost;
|
|
|
|
/*
|
|
* 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.
|
|
*
|
|
* Normally the indexquals will be removed from the list of restriction
|
|
* clauses that we have to evaluate as qpquals, so we should subtract
|
|
* their costs from baserestrictcost. But if we are doing a join then
|
|
* some of the indexquals are join clauses and shouldn't be subtracted.
|
|
* Rather than work out exactly how much to subtract, we don't subtract
|
|
* anything.
|
|
*/
|
|
startup_cost += baserel->baserestrictcost.startup;
|
|
cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
|
|
|
|
if (!is_injoin)
|
|
{
|
|
QualCost index_qual_cost;
|
|
|
|
cost_qual_eval(&index_qual_cost, indexQuals);
|
|
/* any startup cost still has to be paid ... */
|
|
cpu_per_tuple -= index_qual_cost.per_tuple;
|
|
}
|
|
|
|
run_cost += cpu_per_tuple * tuples_fetched;
|
|
|
|
path->path.startup_cost = startup_cost;
|
|
path->path.total_cost = startup_cost + run_cost;
|
|
}
|
|
|
|
/*
|
|
* cost_bitmap_heap_scan
|
|
* Determines and returns the cost of scanning a relation using a bitmap
|
|
* index-then-heap plan.
|
|
*
|
|
* 'baserel' is the relation to be scanned
|
|
* 'bitmapqual' is a tree of IndexPaths, BitmapAndPaths, and BitmapOrPaths
|
|
* 'is_injoin' is T if we are considering using the scan as the inside
|
|
* of a nestloop join (hence, some of the quals are join clauses)
|
|
*/
|
|
void
|
|
cost_bitmap_heap_scan(Path *path, PlannerInfo *root, RelOptInfo *baserel,
|
|
Path *bitmapqual, bool is_injoin)
|
|
{
|
|
Cost startup_cost = 0;
|
|
Cost run_cost = 0;
|
|
Cost indexTotalCost;
|
|
Selectivity indexSelectivity;
|
|
Cost cpu_per_tuple;
|
|
Cost cost_per_page;
|
|
double tuples_fetched;
|
|
double pages_fetched;
|
|
double T;
|
|
|
|
/* Should only be applied to base relations */
|
|
Assert(IsA(baserel, RelOptInfo));
|
|
Assert(baserel->relid > 0);
|
|
Assert(baserel->rtekind == RTE_RELATION);
|
|
|
|
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;
|
|
|
|
/*
|
|
* 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).
|
|
*/
|
|
tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);
|
|
|
|
T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
|
|
pages_fetched = (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
|
|
if (pages_fetched > T)
|
|
pages_fetched = T;
|
|
|
|
/*
|
|
* For small numbers of pages we should charge random_page_cost apiece,
|
|
* while if nearly all the table's pages are being read, it's more
|
|
* appropriate to charge 1.0 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 = random_page_cost -
|
|
(random_page_cost - 1.0) * sqrt(pages_fetched / T);
|
|
else
|
|
cost_per_page = 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.
|
|
*/
|
|
startup_cost += baserel->baserestrictcost.startup;
|
|
cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
|
|
|
|
run_cost += cpu_per_tuple * tuples_fetched;
|
|
|
|
path->startup_cost = startup_cost;
|
|
path->total_cost = startup_cost + run_cost;
|
|
}
|
|
|
|
/*
|
|
* cost_bitmap_tree_node
|
|
* Extract cost and selectivity from a bitmap tree node (index/and/or)
|
|
*/
|
|
void
|
|
cost_bitmap_tree_node(Path *path, Cost *cost, Selectivity *selec)
|
|
{
|
|
if (IsA(path, IndexPath))
|
|
{
|
|
*cost = ((IndexPath *) path)->indextotalcost;
|
|
*selec = ((IndexPath *) path)->indexselectivity;
|
|
}
|
|
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_bitmap_and_node
|
|
* Estimate the cost of a BitmapAnd node
|
|
*
|
|
* Note that this considers only the costs of index scanning and bitmap
|
|
* creation, not the eventual heap access. In that sense the object isn't
|
|
* truly a Path, but it has enough path-like properties (costs in particular)
|
|
* to warrant treating it as one.
|
|
*/
|
|
void
|
|
cost_bitmap_and_node(BitmapAndPath *path, PlannerInfo *root)
|
|
{
|
|
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.startup_cost = totalCost;
|
|
path->path.total_cost = totalCost;
|
|
}
|
|
|
|
/*
|
|
* cost_bitmap_or_node
|
|
* Estimate the cost of a BitmapOr node
|
|
*
|
|
* See comments for cost_bitmap_and_node.
|
|
*/
|
|
void
|
|
cost_bitmap_or_node(BitmapOrPath *path, PlannerInfo *root)
|
|
{
|
|
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.startup_cost = totalCost;
|
|
path->path.total_cost = totalCost;
|
|
}
|
|
|
|
/*
|
|
* cost_tidscan
|
|
* Determines and returns the cost of scanning a relation using TIDs.
|
|
*/
|
|
void
|
|
cost_tidscan(Path *path, PlannerInfo *root,
|
|
RelOptInfo *baserel, List *tidquals)
|
|
{
|
|
Cost startup_cost = 0;
|
|
Cost run_cost = 0;
|
|
Cost cpu_per_tuple;
|
|
int ntuples;
|
|
ListCell *l;
|
|
|
|
/* Should only be applied to base relations */
|
|
Assert(baserel->relid > 0);
|
|
Assert(baserel->rtekind == RTE_RELATION);
|
|
|
|
if (!enable_tidscan)
|
|
startup_cost += disable_cost;
|
|
|
|
/* 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
|
|
{
|
|
/* It's just CTID = something, count 1 tuple */
|
|
ntuples++;
|
|
}
|
|
}
|
|
|
|
/* disk costs --- assume each tuple on a different page */
|
|
run_cost += random_page_cost * ntuples;
|
|
|
|
/* CPU costs */
|
|
startup_cost += baserel->baserestrictcost.startup;
|
|
cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
|
|
run_cost += cpu_per_tuple * ntuples;
|
|
|
|
path->startup_cost = startup_cost;
|
|
path->total_cost = startup_cost + run_cost;
|
|
}
|
|
|
|
/*
|
|
* Estimate number of elements in the array yielded by an expression.
|
|
*/
|
|
static int
|
|
estimate_array_length(Node *arrayexpr)
|
|
{
|
|
if (arrayexpr && IsA(arrayexpr, Const))
|
|
{
|
|
Datum arraydatum = ((Const *) arrayexpr)->constvalue;
|
|
bool arrayisnull = ((Const *) arrayexpr)->constisnull;
|
|
ArrayType *arrayval;
|
|
|
|
if (arrayisnull)
|
|
return 0;
|
|
arrayval = DatumGetArrayTypeP(arraydatum);
|
|
return ArrayGetNItems(ARR_NDIM(arrayval), ARR_DIMS(arrayval));
|
|
}
|
|
else if (arrayexpr && IsA(arrayexpr, ArrayExpr))
|
|
{
|
|
return list_length(((ArrayExpr *) arrayexpr)->elements);
|
|
}
|
|
else
|
|
{
|
|
/* default guess */
|
|
return 10;
|
|
}
|
|
}
|
|
|
|
/*
|
|
* cost_subqueryscan
|
|
* Determines and returns the cost of scanning a subquery RTE.
|
|
*/
|
|
void
|
|
cost_subqueryscan(Path *path, RelOptInfo *baserel)
|
|
{
|
|
Cost startup_cost;
|
|
Cost run_cost;
|
|
Cost cpu_per_tuple;
|
|
|
|
/* Should only be applied to base relations that are subqueries */
|
|
Assert(baserel->relid > 0);
|
|
Assert(baserel->rtekind == RTE_SUBQUERY);
|
|
|
|
/*
|
|
* 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;
|
|
|
|
startup_cost = baserel->baserestrictcost.startup;
|
|
cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
|
|
run_cost = cpu_per_tuple * baserel->tuples;
|
|
|
|
path->startup_cost += startup_cost;
|
|
path->total_cost += startup_cost + run_cost;
|
|
}
|
|
|
|
/*
|
|
* cost_functionscan
|
|
* Determines and returns the cost of scanning a function RTE.
|
|
*/
|
|
void
|
|
cost_functionscan(Path *path, PlannerInfo *root, RelOptInfo *baserel)
|
|
{
|
|
Cost startup_cost = 0;
|
|
Cost run_cost = 0;
|
|
Cost cpu_per_tuple;
|
|
|
|
/* Should only be applied to base relations that are functions */
|
|
Assert(baserel->relid > 0);
|
|
Assert(baserel->rtekind == RTE_FUNCTION);
|
|
|
|
/*
|
|
* For now, estimate function's cost at one operator eval per function
|
|
* call. Someday we should revive the function cost estimate columns in
|
|
* pg_proc...
|
|
*/
|
|
cpu_per_tuple = cpu_operator_cost;
|
|
|
|
/* Add scanning CPU costs */
|
|
startup_cost += baserel->baserestrictcost.startup;
|
|
cpu_per_tuple += cpu_tuple_cost + baserel->baserestrictcost.per_tuple;
|
|
run_cost += cpu_per_tuple * baserel->tuples;
|
|
|
|
path->startup_cost = startup_cost;
|
|
path->total_cost = startup_cost + run_cost;
|
|
}
|
|
|
|
/*
|
|
* cost_sort
|
|
* Determines and returns the cost of sorting a relation, including
|
|
* the cost of reading the input data.
|
|
*
|
|
* If the total volume of data to sort is less than work_mem, we will do
|
|
* an in-memory sort, which requires no I/O and about t*log2(t) tuple
|
|
* comparisons for t tuples.
|
|
*
|
|
* If the total volume exceeds work_mem, we switch to a tape-style merge
|
|
* algorithm. There will still be about t*log2(t) tuple comparisons in
|
|
* total, but we will also need to write and read each tuple once per
|
|
* merge pass. We expect about ceil(log6(r)) merge passes where r is the
|
|
* number of initial runs formed (log6 because tuplesort.c uses six-tape
|
|
* merging). Since the average initial run should be about twice work_mem,
|
|
* we have
|
|
* disk traffic = 2 * relsize * ceil(log6(p / (2*work_mem)))
|
|
* cpu = comparison_cost * t * log2(t)
|
|
*
|
|
* The disk traffic is assumed to be half sequential and half random
|
|
* accesses (XXX can't we refine that guess?)
|
|
*
|
|
* We charge two operator evals per tuple comparison, which should be in
|
|
* the right ballpark in most cases.
|
|
*
|
|
* 'pathkeys' is a list of sort keys
|
|
* 'input_cost' is the total cost for reading the input data
|
|
* 'tuples' is the number of tuples in the relation
|
|
* 'width' is the average tuple width in bytes
|
|
*
|
|
* NOTE: some callers currently pass NIL for pathkeys because they
|
|
* can't conveniently supply the sort keys. Since this routine doesn't
|
|
* currently do anything with pathkeys anyway, that doesn't matter...
|
|
* but if it ever does, it should react gracefully to lack of key data.
|
|
* (Actually, the thing we'd most likely be interested in is just the number
|
|
* of sort keys, which all callers *could* supply.)
|
|
*/
|
|
void
|
|
cost_sort(Path *path, PlannerInfo *root,
|
|
List *pathkeys, Cost input_cost, double tuples, int width)
|
|
{
|
|
Cost startup_cost = input_cost;
|
|
Cost run_cost = 0;
|
|
double nbytes = relation_byte_size(tuples, width);
|
|
long work_mem_bytes = work_mem * 1024L;
|
|
|
|
if (!enable_sort)
|
|
startup_cost += disable_cost;
|
|
|
|
/*
|
|
* 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;
|
|
|
|
/*
|
|
* CPU costs
|
|
*
|
|
* Assume about two operator evals per tuple comparison and N log2 N
|
|
* comparisons
|
|
*/
|
|
startup_cost += 2.0 * cpu_operator_cost * tuples * LOG2(tuples);
|
|
|
|
/* disk costs */
|
|
if (nbytes > work_mem_bytes)
|
|
{
|
|
double npages = ceil(nbytes / BLCKSZ);
|
|
double nruns = (nbytes / work_mem_bytes) * 0.5;
|
|
double log_runs = ceil(LOG6(nruns));
|
|
double npageaccesses;
|
|
|
|
if (log_runs < 1.0)
|
|
log_runs = 1.0;
|
|
npageaccesses = 2.0 * npages * log_runs;
|
|
/* Assume half are sequential (cost 1), half are not */
|
|
startup_cost += npageaccesses *
|
|
(1.0 + cost_nonsequential_access(npages)) * 0.5;
|
|
}
|
|
|
|
/*
|
|
* Also charge a small amount (arbitrarily set equal to operator cost) per
|
|
* extracted tuple.
|
|
*/
|
|
run_cost += cpu_operator_cost * tuples;
|
|
|
|
path->startup_cost = startup_cost;
|
|
path->total_cost = startup_cost + run_cost;
|
|
}
|
|
|
|
/*
|
|
* cost_material
|
|
* Determines and returns the cost of materializing a relation, including
|
|
* the cost of reading the input data.
|
|
*
|
|
* If the total volume of data to materialize exceeds work_mem, we will need
|
|
* to write it to disk, so the cost is much higher in that case.
|
|
*/
|
|
void
|
|
cost_material(Path *path,
|
|
Cost input_cost, double tuples, int width)
|
|
{
|
|
Cost startup_cost = input_cost;
|
|
Cost run_cost = 0;
|
|
double nbytes = relation_byte_size(tuples, width);
|
|
long work_mem_bytes = work_mem * 1024L;
|
|
|
|
/* disk costs */
|
|
if (nbytes > work_mem_bytes)
|
|
{
|
|
double npages = ceil(nbytes / BLCKSZ);
|
|
|
|
/* We'll write during startup and read during retrieval */
|
|
startup_cost += npages;
|
|
run_cost += npages;
|
|
}
|
|
|
|
/*
|
|
* Charge a very small amount per inserted tuple, to reflect bookkeeping
|
|
* costs. We use cpu_tuple_cost/10 for this. This is needed to break the
|
|
* tie that would otherwise exist 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.
|
|
*/
|
|
startup_cost += cpu_tuple_cost * 0.1 * tuples;
|
|
|
|
/*
|
|
* Also charge a small amount per extracted tuple. We use cpu_tuple_cost
|
|
* so that it doesn't appear worthwhile to materialize a bare seqscan.
|
|
*/
|
|
run_cost += cpu_tuple_cost * tuples;
|
|
|
|
path->startup_cost = startup_cost;
|
|
path->total_cost = startup_cost + run_cost;
|
|
}
|
|
|
|
/*
|
|
* cost_agg
|
|
* Determines and returns the cost of performing an Agg plan node,
|
|
* including the cost of its input.
|
|
*
|
|
* Note: when aggstrategy == AGG_SORTED, caller must ensure that input costs
|
|
* are for appropriately-sorted input.
|
|
*/
|
|
void
|
|
cost_agg(Path *path, PlannerInfo *root,
|
|
AggStrategy aggstrategy, int numAggs,
|
|
int numGroupCols, double numGroups,
|
|
Cost input_startup_cost, Cost input_total_cost,
|
|
double input_tuples)
|
|
{
|
|
Cost startup_cost;
|
|
Cost total_cost;
|
|
|
|
/*
|
|
* We charge one cpu_operator_cost per aggregate function per input tuple,
|
|
* and another one per output tuple (corresponding to transfn and finalfn
|
|
* calls respectively). 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 += cpu_operator_cost * (input_tuples + 1) * numAggs;
|
|
/* we aren't grouping */
|
|
total_cost = startup_cost + cpu_tuple_cost;
|
|
}
|
|
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 += cpu_operator_cost * input_tuples * numGroupCols;
|
|
total_cost += cpu_operator_cost * input_tuples * numAggs;
|
|
total_cost += cpu_operator_cost * numGroups * numAggs;
|
|
total_cost += cpu_tuple_cost * numGroups;
|
|
}
|
|
else
|
|
{
|
|
/* must be AGG_HASHED */
|
|
startup_cost = input_total_cost;
|
|
startup_cost += cpu_operator_cost * input_tuples * numGroupCols;
|
|
startup_cost += cpu_operator_cost * input_tuples * numAggs;
|
|
total_cost = startup_cost;
|
|
total_cost += cpu_operator_cost * numGroups * numAggs;
|
|
total_cost += cpu_tuple_cost * numGroups;
|
|
}
|
|
|
|
path->startup_cost = startup_cost;
|
|
path->total_cost = total_cost;
|
|
}
|
|
|
|
/*
|
|
* cost_group
|
|
* Determines and returns the cost of performing a Group plan node,
|
|
* including the cost of its input.
|
|
*
|
|
* Note: caller must ensure that input costs are for appropriately-sorted
|
|
* input.
|
|
*/
|
|
void
|
|
cost_group(Path *path, PlannerInfo *root,
|
|
int numGroupCols, double numGroups,
|
|
Cost input_startup_cost, Cost input_total_cost,
|
|
double input_tuples)
|
|
{
|
|
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->startup_cost = startup_cost;
|
|
path->total_cost = total_cost;
|
|
}
|
|
|
|
/*
|
|
* cost_nestloop
|
|
* Determines and returns the cost of joining two relations using the
|
|
* nested loop algorithm.
|
|
*
|
|
* 'path' is already filled in except for the cost fields
|
|
*/
|
|
void
|
|
cost_nestloop(NestPath *path, PlannerInfo *root)
|
|
{
|
|
Path *outer_path = path->outerjoinpath;
|
|
Path *inner_path = path->innerjoinpath;
|
|
Cost startup_cost = 0;
|
|
Cost run_cost = 0;
|
|
Cost cpu_per_tuple;
|
|
QualCost restrict_qual_cost;
|
|
double outer_path_rows = PATH_ROWS(outer_path);
|
|
double inner_path_rows = PATH_ROWS(inner_path);
|
|
double ntuples;
|
|
Selectivity joininfactor;
|
|
|
|
/*
|
|
* If inner path is an indexscan, be sure to use its estimated output row
|
|
* count, which may be lower than the restriction-clause-only row count of
|
|
* its parent. (We don't include this case in the PATH_ROWS macro because
|
|
* it applies *only* to a nestloop's inner relation.)
|
|
*/
|
|
if (IsA(inner_path, IndexPath))
|
|
inner_path_rows = ((IndexPath *) inner_path)->rows;
|
|
else if (IsA(inner_path, BitmapHeapPath))
|
|
inner_path_rows = ((BitmapHeapPath *) inner_path)->rows;
|
|
|
|
if (!enable_nestloop)
|
|
startup_cost += disable_cost;
|
|
|
|
/*
|
|
* If we're doing JOIN_IN then we will stop scanning inner tuples for an
|
|
* outer tuple as soon as we have one match. Account for the effects of
|
|
* this by scaling down the cost estimates in proportion to the JOIN_IN
|
|
* selectivity. (This assumes that all the quals attached to the join are
|
|
* IN quals, which should be true.)
|
|
*/
|
|
joininfactor = join_in_selectivity(path, root);
|
|
|
|
/* 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. What's not so clear is whether the inner path's
|
|
* startup_cost must be paid again on each rescan of the inner path. This
|
|
* is not true if the inner path is materialized or is a hashjoin, but
|
|
* probably is true otherwise.
|
|
*/
|
|
startup_cost += outer_path->startup_cost + inner_path->startup_cost;
|
|
run_cost += outer_path->total_cost - outer_path->startup_cost;
|
|
if (IsA(inner_path, MaterialPath) ||
|
|
IsA(inner_path, HashPath))
|
|
{
|
|
/* charge only run cost for each iteration of inner path */
|
|
}
|
|
else
|
|
{
|
|
/*
|
|
* charge startup cost for each iteration of inner path, except we
|
|
* already charged the first startup_cost in our own startup
|
|
*/
|
|
run_cost += (outer_path_rows - 1) * inner_path->startup_cost;
|
|
}
|
|
run_cost += outer_path_rows *
|
|
(inner_path->total_cost - inner_path->startup_cost) * joininfactor;
|
|
|
|
/*
|
|
* Compute number of tuples processed (not number emitted!)
|
|
*/
|
|
ntuples = outer_path_rows * inner_path_rows * joininfactor;
|
|
|
|
/* CPU costs */
|
|
cost_qual_eval(&restrict_qual_cost, path->joinrestrictinfo);
|
|
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;
|
|
}
|
|
|
|
/*
|
|
* cost_mergejoin
|
|
* Determines and returns the cost of joining two relations using the
|
|
* merge join algorithm.
|
|
*
|
|
* 'path' is already filled in except for the cost fields
|
|
*
|
|
* Notes: path's mergeclauses should be a subset of the joinrestrictinfo list;
|
|
* outersortkeys and innersortkeys are lists of the keys to be used
|
|
* to sort the outer and inner relations, or NIL if no explicit
|
|
* sort is needed because the source path is already ordered.
|
|
*/
|
|
void
|
|
cost_mergejoin(MergePath *path, PlannerInfo *root)
|
|
{
|
|
Path *outer_path = path->jpath.outerjoinpath;
|
|
Path *inner_path = path->jpath.innerjoinpath;
|
|
List *mergeclauses = path->path_mergeclauses;
|
|
List *outersortkeys = path->outersortkeys;
|
|
List *innersortkeys = path->innersortkeys;
|
|
Cost startup_cost = 0;
|
|
Cost run_cost = 0;
|
|
Cost cpu_per_tuple;
|
|
Selectivity merge_selec;
|
|
QualCost merge_qual_cost;
|
|
QualCost qp_qual_cost;
|
|
RestrictInfo *firstclause;
|
|
double outer_path_rows = PATH_ROWS(outer_path);
|
|
double inner_path_rows = PATH_ROWS(inner_path);
|
|
double outer_rows,
|
|
inner_rows;
|
|
double mergejointuples,
|
|
rescannedtuples;
|
|
double rescanratio;
|
|
Selectivity outerscansel,
|
|
innerscansel;
|
|
Selectivity joininfactor;
|
|
Path sort_path; /* dummy for result of cost_sort */
|
|
|
|
if (!enable_mergejoin)
|
|
startup_cost += disable_cost;
|
|
|
|
/*
|
|
* Compute cost and selectivity of the mergequals and qpquals (other
|
|
* restriction clauses) separately. We use approx_selectivity here for
|
|
* speed --- in most cases, any errors won't affect the result much.
|
|
*
|
|
* Note: it's probably bogus to use the normal selectivity calculation
|
|
* here when either the outer or inner path is a UniquePath.
|
|
*/
|
|
merge_selec = approx_selectivity(root, mergeclauses,
|
|
path->jpath.jointype);
|
|
cost_qual_eval(&merge_qual_cost, mergeclauses);
|
|
cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo);
|
|
qp_qual_cost.startup -= merge_qual_cost.startup;
|
|
qp_qual_cost.per_tuple -= merge_qual_cost.per_tuple;
|
|
|
|
/* approx # tuples passing the merge quals */
|
|
mergejointuples = clamp_row_est(merge_selec * outer_path_rows * inner_path_rows);
|
|
|
|
/*
|
|
* 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. Our cost model for this 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.
|
|
*
|
|
* 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?
|
|
*/
|
|
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 inner run cost this much to account for rescanning */
|
|
rescanratio = 1.0 + (rescannedtuples / inner_path_rows);
|
|
|
|
/*
|
|
* 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. We use only the first
|
|
* (most significant) merge clause for this purpose.
|
|
*
|
|
* Since this calculation is somewhat expensive, and will be the same for
|
|
* all mergejoin paths associated with the merge clause, we cache the
|
|
* results in the RestrictInfo node.
|
|
*/
|
|
if (mergeclauses && path->jpath.jointype != JOIN_FULL)
|
|
{
|
|
firstclause = (RestrictInfo *) linitial(mergeclauses);
|
|
if (firstclause->left_mergescansel < 0) /* not computed yet? */
|
|
mergejoinscansel(root, (Node *) firstclause->clause,
|
|
&firstclause->left_mergescansel,
|
|
&firstclause->right_mergescansel);
|
|
|
|
if (bms_is_subset(firstclause->left_relids, outer_path->parent->relids))
|
|
{
|
|
/* left side of clause is outer */
|
|
outerscansel = firstclause->left_mergescansel;
|
|
innerscansel = firstclause->right_mergescansel;
|
|
}
|
|
else
|
|
{
|
|
/* left side of clause is inner */
|
|
outerscansel = firstclause->right_mergescansel;
|
|
innerscansel = firstclause->left_mergescansel;
|
|
}
|
|
if (path->jpath.jointype == JOIN_LEFT)
|
|
outerscansel = 1.0;
|
|
else if (path->jpath.jointype == JOIN_RIGHT)
|
|
innerscansel = 1.0;
|
|
}
|
|
else
|
|
{
|
|
/* cope with clauseless or full mergejoin */
|
|
outerscansel = innerscansel = 1.0;
|
|
}
|
|
|
|
/* convert selectivity to row count; must scan at least one row */
|
|
outer_rows = clamp_row_est(outer_path_rows * outerscansel);
|
|
inner_rows = clamp_row_est(inner_path_rows * innerscansel);
|
|
|
|
/*
|
|
* 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.
|
|
*/
|
|
outerscansel = outer_rows / outer_path_rows;
|
|
innerscansel = inner_rows / inner_path_rows;
|
|
|
|
/* 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);
|
|
startup_cost += sort_path.startup_cost;
|
|
run_cost += (sort_path.total_cost - sort_path.startup_cost)
|
|
* outerscansel;
|
|
}
|
|
else
|
|
{
|
|
startup_cost += outer_path->startup_cost;
|
|
run_cost += (outer_path->total_cost - outer_path->startup_cost)
|
|
* outerscansel;
|
|
}
|
|
|
|
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);
|
|
startup_cost += sort_path.startup_cost;
|
|
run_cost += (sort_path.total_cost - sort_path.startup_cost)
|
|
* innerscansel * rescanratio;
|
|
}
|
|
else
|
|
{
|
|
startup_cost += inner_path->startup_cost;
|
|
run_cost += (inner_path->total_cost - inner_path->startup_cost)
|
|
* innerscansel * rescanratio;
|
|
}
|
|
|
|
/* CPU costs */
|
|
|
|
/*
|
|
* If we're doing JOIN_IN then we will stop outputting inner tuples for an
|
|
* outer tuple as soon as we have one match. Account for the effects of
|
|
* this by scaling down the cost estimates in proportion to the expected
|
|
* output size. (This assumes that all the quals attached to the join are
|
|
* IN quals, which should be true.)
|
|
*/
|
|
joininfactor = join_in_selectivity(&path->jpath, root);
|
|
|
|
/*
|
|
* 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.
|
|
* NOTE: JOIN_IN mode does not save any work here, so do NOT include
|
|
* joininfactor.
|
|
*/
|
|
startup_cost += merge_qual_cost.startup;
|
|
run_cost += merge_qual_cost.per_tuple *
|
|
(outer_rows + inner_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.) This work is
|
|
* skipped in JOIN_IN mode, so apply the factor.
|
|
*/
|
|
startup_cost += qp_qual_cost.startup;
|
|
cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
|
|
run_cost += cpu_per_tuple * mergejointuples * joininfactor;
|
|
|
|
path->jpath.path.startup_cost = startup_cost;
|
|
path->jpath.path.total_cost = startup_cost + run_cost;
|
|
}
|
|
|
|
/*
|
|
* cost_hashjoin
|
|
* Determines and returns the cost of joining two relations using the
|
|
* hash join algorithm.
|
|
*
|
|
* 'path' is already filled in except for the cost fields
|
|
*
|
|
* Note: path's hashclauses should be a subset of the joinrestrictinfo list
|
|
*/
|
|
void
|
|
cost_hashjoin(HashPath *path, PlannerInfo *root)
|
|
{
|
|
Path *outer_path = path->jpath.outerjoinpath;
|
|
Path *inner_path = path->jpath.innerjoinpath;
|
|
List *hashclauses = path->path_hashclauses;
|
|
Cost startup_cost = 0;
|
|
Cost run_cost = 0;
|
|
Cost cpu_per_tuple;
|
|
Selectivity hash_selec;
|
|
QualCost hash_qual_cost;
|
|
QualCost qp_qual_cost;
|
|
double hashjointuples;
|
|
double outer_path_rows = PATH_ROWS(outer_path);
|
|
double inner_path_rows = PATH_ROWS(inner_path);
|
|
double outerbytes = relation_byte_size(outer_path_rows,
|
|
outer_path->parent->width);
|
|
double innerbytes = relation_byte_size(inner_path_rows,
|
|
inner_path->parent->width);
|
|
int num_hashclauses = list_length(hashclauses);
|
|
int numbuckets;
|
|
int numbatches;
|
|
double virtualbuckets;
|
|
Selectivity innerbucketsize;
|
|
Selectivity joininfactor;
|
|
ListCell *hcl;
|
|
|
|
if (!enable_hashjoin)
|
|
startup_cost += disable_cost;
|
|
|
|
/*
|
|
* Compute cost and selectivity of the hashquals and qpquals (other
|
|
* restriction clauses) separately. We use approx_selectivity here for
|
|
* speed --- in most cases, any errors won't affect the result much.
|
|
*
|
|
* Note: it's probably bogus to use the normal selectivity calculation
|
|
* here when either the outer or inner path is a UniquePath.
|
|
*/
|
|
hash_selec = approx_selectivity(root, hashclauses,
|
|
path->jpath.jointype);
|
|
cost_qual_eval(&hash_qual_cost, hashclauses);
|
|
cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo);
|
|
qp_qual_cost.startup -= hash_qual_cost.startup;
|
|
qp_qual_cost.per_tuple -= hash_qual_cost.per_tuple;
|
|
|
|
/* approx # tuples passing the hash quals */
|
|
hashjointuples = clamp_row_est(hash_selec * outer_path_rows * inner_path_rows);
|
|
|
|
/* 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.
|
|
*
|
|
* 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 * inner_path_rows;
|
|
run_cost += cpu_operator_cost * num_hashclauses * outer_path_rows;
|
|
|
|
/* Get hash table size that executor would use for inner relation */
|
|
ExecChooseHashTableSize(inner_path_rows,
|
|
inner_path->parent->width,
|
|
&numbuckets,
|
|
&numbatches);
|
|
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;
|
|
}
|
|
}
|
|
|
|
/*
|
|
* 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 one cost unit per page of I/O (correct since it 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 += innerpages;
|
|
run_cost += innerpages + 2 * outerpages;
|
|
}
|
|
|
|
/* CPU costs */
|
|
|
|
/*
|
|
* If we're doing JOIN_IN then we will stop comparing inner tuples to an
|
|
* outer tuple as soon as we have one match. Account for the effects of
|
|
* this by scaling down the cost estimates in proportion to the expected
|
|
* output size. (This assumes that all the quals attached to the join are
|
|
* IN quals, which should be true.)
|
|
*/
|
|
joininfactor = join_in_selectivity(&path->jpath, root);
|
|
|
|
/*
|
|
* 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. (Note: charging the full qual eval cost
|
|
* at each tuple is pessimistic, since we don't evaluate the quals unless
|
|
* the hash values match exactly.)
|
|
*/
|
|
startup_cost += hash_qual_cost.startup;
|
|
run_cost += hash_qual_cost.per_tuple *
|
|
outer_path_rows * clamp_row_est(inner_path_rows * innerbucketsize) *
|
|
joininfactor;
|
|
|
|
/*
|
|
* 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 * joininfactor;
|
|
|
|
/*
|
|
* Bias against putting larger relation on inside. We don't want an
|
|
* absolute prohibition, though, since larger relation might have better
|
|
* bucketsize --- and we can't trust the size estimates unreservedly,
|
|
* anyway. Instead, inflate the run cost by the square root of the size
|
|
* ratio. (Why square root? No real good reason, but it seems
|
|
* reasonable...)
|
|
*
|
|
* Note: before 7.4 we implemented this by inflating startup cost; but if
|
|
* there's a disable_cost component in the input paths' startup cost, that
|
|
* unfairly penalizes the hash. Probably it'd be better to keep track of
|
|
* disable penalty separately from cost.
|
|
*/
|
|
if (innerbytes > outerbytes && outerbytes > 0)
|
|
run_cost *= sqrt(innerbytes / outerbytes);
|
|
|
|
path->jpath.path.startup_cost = startup_cost;
|
|
path->jpath.path.total_cost = startup_cost + run_cost;
|
|
}
|
|
|
|
|
|
/*
|
|
* cost_qual_eval
|
|
* Estimate the CPU costs of evaluating a WHERE clause.
|
|
* The input can be either an implicitly-ANDed list of boolean
|
|
* expressions, or a list of RestrictInfo nodes.
|
|
* The result includes both a one-time (startup) component,
|
|
* and a per-evaluation component.
|
|
*/
|
|
void
|
|
cost_qual_eval(QualCost *cost, List *quals)
|
|
{
|
|
ListCell *l;
|
|
|
|
cost->startup = 0;
|
|
cost->per_tuple = 0;
|
|
|
|
/* We don't charge any cost for the implicit ANDing at top level ... */
|
|
|
|
foreach(l, quals)
|
|
{
|
|
Node *qual = (Node *) lfirst(l);
|
|
|
|
/*
|
|
* RestrictInfo nodes contain an eval_cost field reserved for this
|
|
* routine's use, so that it's not necessary to evaluate the qual
|
|
* clause's cost more than once. If the clause's cost hasn't been
|
|
* computed yet, the field's startup value will contain -1.
|
|
*/
|
|
if (qual && IsA(qual, RestrictInfo))
|
|
{
|
|
RestrictInfo *restrictinfo = (RestrictInfo *) qual;
|
|
|
|
if (restrictinfo->eval_cost.startup < 0)
|
|
{
|
|
restrictinfo->eval_cost.startup = 0;
|
|
restrictinfo->eval_cost.per_tuple = 0;
|
|
cost_qual_eval_walker((Node *) restrictinfo->clause,
|
|
&restrictinfo->eval_cost);
|
|
}
|
|
cost->startup += restrictinfo->eval_cost.startup;
|
|
cost->per_tuple += restrictinfo->eval_cost.per_tuple;
|
|
}
|
|
else
|
|
{
|
|
/* If it's a bare expression, must always do it the hard way */
|
|
cost_qual_eval_walker(qual, cost);
|
|
}
|
|
}
|
|
}
|
|
|
|
static bool
|
|
cost_qual_eval_walker(Node *node, QualCost *total)
|
|
{
|
|
if (node == NULL)
|
|
return false;
|
|
|
|
/*
|
|
* Our basic strategy is to charge one cpu_operator_cost for each operator
|
|
* or function node in the given tree. Vars and Consts are charged zero,
|
|
* and so are boolean operators (AND, OR, NOT). Simplistic, but a lot
|
|
* better than no model at all.
|
|
*
|
|
* Should we try to account for the possibility of short-circuit
|
|
* evaluation of AND/OR?
|
|
*/
|
|
if (IsA(node, FuncExpr) ||
|
|
IsA(node, OpExpr) ||
|
|
IsA(node, DistinctExpr) ||
|
|
IsA(node, NullIfExpr))
|
|
total->per_tuple += cpu_operator_cost;
|
|
else if (IsA(node, ScalarArrayOpExpr))
|
|
{
|
|
/*
|
|
* Estimate that the operator will be applied to about half of the
|
|
* array elements before the answer is determined.
|
|
*/
|
|
ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) node;
|
|
Node *arraynode = (Node *) lsecond(saop->args);
|
|
|
|
total->per_tuple +=
|
|
cpu_operator_cost * estimate_array_length(arraynode) * 0.5;
|
|
}
|
|
else if (IsA(node, RowCompareExpr))
|
|
{
|
|
/* Conservatively assume we will check all the columns */
|
|
RowCompareExpr *rcexpr = (RowCompareExpr *) node;
|
|
|
|
total->per_tuple += cpu_operator_cost * list_length(rcexpr->opnos);
|
|
}
|
|
else if (IsA(node, SubLink))
|
|
{
|
|
/* This routine should not be applied to un-planned expressions */
|
|
elog(ERROR, "cannot handle unplanned sub-select");
|
|
}
|
|
else if (IsA(node, SubPlan))
|
|
{
|
|
/*
|
|
* A subplan node in an expression typically indicates that the
|
|
* subplan will be executed on each evaluation, so charge accordingly.
|
|
* (Sub-selects that can be executed as InitPlans have already been
|
|
* removed from the expression.)
|
|
*
|
|
* An exception occurs when we have decided we can implement the
|
|
* subplan by hashing.
|
|
*/
|
|
SubPlan *subplan = (SubPlan *) node;
|
|
Plan *plan = subplan->plan;
|
|
|
|
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.
|
|
*/
|
|
total->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. Recursion
|
|
* into the testexpr will handle the lefthand expressions
|
|
* properly, and will count 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
|
|
* 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 */
|
|
total->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 */
|
|
total->per_tuple += 0.50 * plan_run_cost;
|
|
/* also charge a cpu_operator_cost per row examined */
|
|
total->per_tuple += 0.50 * plan->plan_rows * cpu_operator_cost;
|
|
}
|
|
else
|
|
{
|
|
/* assume we need all tuples */
|
|
total->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 a
|
|
* Sort or Material node, 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 &&
|
|
(IsA(plan, Sort) ||
|
|
IsA(plan, Material)))
|
|
total->startup += plan->startup_cost;
|
|
else
|
|
total->per_tuple += plan->startup_cost;
|
|
}
|
|
}
|
|
|
|
return expression_tree_walker(node, cost_qual_eval_walker,
|
|
(void *) total);
|
|
}
|
|
|
|
|
|
/*
|
|
* approx_selectivity
|
|
* Quick-and-dirty estimation of clause selectivities.
|
|
* The input can be either an implicitly-ANDed list of boolean
|
|
* expressions, or a list of RestrictInfo nodes (typically the latter).
|
|
*
|
|
* This is quick-and-dirty because we bypass clauselist_selectivity, and
|
|
* simply multiply the independent clause selectivities together. Now
|
|
* clauselist_selectivity often can't do any better than that anyhow, but
|
|
* for some situations (such as range constraints) it is smarter. However,
|
|
* we can't effectively cache the results of clauselist_selectivity, whereas
|
|
* the individual clause selectivities can be and are cached.
|
|
*
|
|
* Since we are only using the results to estimate how many potential
|
|
* output tuples are generated and passed through qpqual checking, it
|
|
* seems OK to live with the approximation.
|
|
*/
|
|
static Selectivity
|
|
approx_selectivity(PlannerInfo *root, List *quals, JoinType jointype)
|
|
{
|
|
Selectivity total = 1.0;
|
|
ListCell *l;
|
|
|
|
foreach(l, quals)
|
|
{
|
|
Node *qual = (Node *) lfirst(l);
|
|
|
|
/* Note that clause_selectivity will be able to cache its result */
|
|
total *= clause_selectivity(root, qual, 0, jointype);
|
|
}
|
|
return total;
|
|
}
|
|
|
|
|
|
/*
|
|
* set_baserel_size_estimates
|
|
* Set the size estimates for the given base relation.
|
|
*
|
|
* The rel's targetlist and restrictinfo list must have been constructed
|
|
* already.
|
|
*
|
|
* We set the following fields of the rel node:
|
|
* rows: the estimated number of output tuples (after applying
|
|
* restriction clauses).
|
|
* width: the estimated average output tuple width in bytes.
|
|
* baserestrictcost: estimated cost of evaluating baserestrictinfo clauses.
|
|
*/
|
|
void
|
|
set_baserel_size_estimates(PlannerInfo *root, RelOptInfo *rel)
|
|
{
|
|
double nrows;
|
|
|
|
/* Should only be applied to base relations */
|
|
Assert(rel->relid > 0);
|
|
|
|
nrows = rel->tuples *
|
|
clauselist_selectivity(root,
|
|
rel->baserestrictinfo,
|
|
0,
|
|
JOIN_INNER);
|
|
|
|
rel->rows = clamp_row_est(nrows);
|
|
|
|
cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo);
|
|
|
|
set_rel_width(root, rel);
|
|
}
|
|
|
|
/*
|
|
* set_joinrel_size_estimates
|
|
* Set the size estimates for the given join relation.
|
|
*
|
|
* The rel's targetlist must have been constructed already, and a
|
|
* restriction clause list that matches the given component rels must
|
|
* be provided.
|
|
*
|
|
* Since there is more than one way to make a joinrel for more than two
|
|
* base relations, the results we get here could depend on which component
|
|
* rel pair is provided. In theory we should get the same answers no matter
|
|
* which pair is provided; in practice, since the selectivity estimation
|
|
* routines don't handle all cases equally well, we might not. But there's
|
|
* not much to be done about it. (Would it make sense to repeat the
|
|
* calculations for each pair of input rels that's encountered, and somehow
|
|
* average the results? Probably way more trouble than it's worth.)
|
|
*
|
|
* It's important that the results for symmetric JoinTypes be symmetric,
|
|
* eg, (rel1, rel2, JOIN_LEFT) should produce the same result as (rel2,
|
|
* rel1, JOIN_RIGHT). Also, JOIN_IN should produce the same result as
|
|
* JOIN_UNIQUE_INNER, likewise JOIN_REVERSE_IN == JOIN_UNIQUE_OUTER.
|
|
*
|
|
* We set only the rows field here. The width field was already set by
|
|
* build_joinrel_tlist, and baserestrictcost is not used for join rels.
|
|
*/
|
|
void
|
|
set_joinrel_size_estimates(PlannerInfo *root, RelOptInfo *rel,
|
|
RelOptInfo *outer_rel,
|
|
RelOptInfo *inner_rel,
|
|
JoinType jointype,
|
|
List *restrictlist)
|
|
{
|
|
Selectivity selec;
|
|
double nrows;
|
|
UniquePath *upath;
|
|
|
|
/*
|
|
* Compute joinclause selectivity. Note that we are only considering
|
|
* clauses that become restriction clauses at this join level; we are not
|
|
* double-counting them because they were not considered in estimating the
|
|
* sizes of the component rels.
|
|
*/
|
|
selec = clauselist_selectivity(root,
|
|
restrictlist,
|
|
0,
|
|
jointype);
|
|
|
|
/*
|
|
* Basically, we multiply size of Cartesian product by selectivity.
|
|
*
|
|
* If we are doing an outer join, take that into account: the output must
|
|
* be at least as large as the non-nullable input. (Is there any chance
|
|
* of being even smarter?)
|
|
*
|
|
* For JOIN_IN and variants, the Cartesian product is figured with respect
|
|
* to a unique-ified input, and then we can clamp to the size of the other
|
|
* input.
|
|
*/
|
|
switch (jointype)
|
|
{
|
|
case JOIN_INNER:
|
|
nrows = outer_rel->rows * inner_rel->rows * selec;
|
|
break;
|
|
case JOIN_LEFT:
|
|
nrows = outer_rel->rows * inner_rel->rows * selec;
|
|
if (nrows < outer_rel->rows)
|
|
nrows = outer_rel->rows;
|
|
break;
|
|
case JOIN_RIGHT:
|
|
nrows = outer_rel->rows * inner_rel->rows * selec;
|
|
if (nrows < inner_rel->rows)
|
|
nrows = inner_rel->rows;
|
|
break;
|
|
case JOIN_FULL:
|
|
nrows = outer_rel->rows * inner_rel->rows * selec;
|
|
if (nrows < outer_rel->rows)
|
|
nrows = outer_rel->rows;
|
|
if (nrows < inner_rel->rows)
|
|
nrows = inner_rel->rows;
|
|
break;
|
|
case JOIN_IN:
|
|
case JOIN_UNIQUE_INNER:
|
|
upath = create_unique_path(root, inner_rel,
|
|
inner_rel->cheapest_total_path);
|
|
nrows = outer_rel->rows * upath->rows * selec;
|
|
if (nrows > outer_rel->rows)
|
|
nrows = outer_rel->rows;
|
|
break;
|
|
case JOIN_REVERSE_IN:
|
|
case JOIN_UNIQUE_OUTER:
|
|
upath = create_unique_path(root, outer_rel,
|
|
outer_rel->cheapest_total_path);
|
|
nrows = upath->rows * inner_rel->rows * selec;
|
|
if (nrows > inner_rel->rows)
|
|
nrows = inner_rel->rows;
|
|
break;
|
|
default:
|
|
elog(ERROR, "unrecognized join type: %d", (int) jointype);
|
|
nrows = 0; /* keep compiler quiet */
|
|
break;
|
|
}
|
|
|
|
rel->rows = clamp_row_est(nrows);
|
|
}
|
|
|
|
/*
|
|
* join_in_selectivity
|
|
* Determines the factor by which a JOIN_IN join's result is expected
|
|
* to be smaller than an ordinary inner join.
|
|
*
|
|
* 'path' is already filled in except for the cost fields
|
|
*/
|
|
static Selectivity
|
|
join_in_selectivity(JoinPath *path, PlannerInfo *root)
|
|
{
|
|
RelOptInfo *innerrel;
|
|
UniquePath *innerunique;
|
|
Selectivity selec;
|
|
double nrows;
|
|
|
|
/* Return 1.0 whenever it's not JOIN_IN */
|
|
if (path->jointype != JOIN_IN)
|
|
return 1.0;
|
|
|
|
/*
|
|
* Return 1.0 if the inner side is already known unique. The case where
|
|
* the inner path is already a UniquePath probably cannot happen in
|
|
* current usage, but check it anyway for completeness. The interesting
|
|
* case is where we've determined the inner relation itself is unique,
|
|
* which we can check by looking at the rows estimate for its UniquePath.
|
|
*/
|
|
if (IsA(path->innerjoinpath, UniquePath))
|
|
return 1.0;
|
|
innerrel = path->innerjoinpath->parent;
|
|
innerunique = create_unique_path(root,
|
|
innerrel,
|
|
innerrel->cheapest_total_path);
|
|
if (innerunique->rows >= innerrel->rows)
|
|
return 1.0;
|
|
|
|
/*
|
|
* Compute same result set_joinrel_size_estimates would compute for
|
|
* JOIN_INNER. Note that we use the input rels' absolute size estimates,
|
|
* not PATH_ROWS() which might be less; if we used PATH_ROWS() we'd be
|
|
* double-counting the effects of any join clauses used in input scans.
|
|
*/
|
|
selec = clauselist_selectivity(root,
|
|
path->joinrestrictinfo,
|
|
0,
|
|
JOIN_INNER);
|
|
nrows = path->outerjoinpath->parent->rows * innerrel->rows * selec;
|
|
|
|
nrows = clamp_row_est(nrows);
|
|
|
|
/* See if it's larger than the actual JOIN_IN size estimate */
|
|
if (nrows > path->path.parent->rows)
|
|
return path->path.parent->rows / nrows;
|
|
else
|
|
return 1.0;
|
|
}
|
|
|
|
/*
|
|
* set_function_size_estimates
|
|
* Set the size estimates for a base relation that is a function call.
|
|
*
|
|
* The rel's targetlist and restrictinfo list must have been constructed
|
|
* already.
|
|
*
|
|
* We set the same fields as set_baserel_size_estimates.
|
|
*/
|
|
void
|
|
set_function_size_estimates(PlannerInfo *root, RelOptInfo *rel)
|
|
{
|
|
RangeTblEntry *rte;
|
|
|
|
/* Should only be applied to base relations that are functions */
|
|
Assert(rel->relid > 0);
|
|
rte = rt_fetch(rel->relid, root->parse->rtable);
|
|
Assert(rte->rtekind == RTE_FUNCTION);
|
|
|
|
/*
|
|
* Estimate number of rows the function itself will return.
|
|
*
|
|
* XXX no idea how to do this yet; but we can at least check whether
|
|
* function returns set or not...
|
|
*/
|
|
if (expression_returns_set(rte->funcexpr))
|
|
rel->tuples = 1000;
|
|
else
|
|
rel->tuples = 1;
|
|
|
|
/* Now estimate number of output rows, etc */
|
|
set_baserel_size_estimates(root, rel);
|
|
}
|
|
|
|
|
|
/*
|
|
* set_rel_width
|
|
* Set the estimated output width of a base relation.
|
|
*
|
|
* NB: this works best on plain relations because it prefers to look at
|
|
* real Vars. It will fail to make use of pg_statistic info when applied
|
|
* to a subquery relation, even if the subquery outputs are simple vars
|
|
* that we could have gotten info for. Is it worth trying to be smarter
|
|
* about subqueries?
|
|
*
|
|
* The per-attribute width estimates are cached for possible re-use while
|
|
* building join relations.
|
|
*/
|
|
static void
|
|
set_rel_width(PlannerInfo *root, RelOptInfo *rel)
|
|
{
|
|
int32 tuple_width = 0;
|
|
ListCell *tllist;
|
|
|
|
foreach(tllist, rel->reltargetlist)
|
|
{
|
|
Var *var = (Var *) lfirst(tllist);
|
|
int ndx;
|
|
Oid relid;
|
|
int32 item_width;
|
|
|
|
/* For now, punt on whole-row child Vars */
|
|
if (!IsA(var, Var))
|
|
{
|
|
tuple_width += 32; /* arbitrary */
|
|
continue;
|
|
}
|
|
|
|
ndx = var->varattno - rel->min_attr;
|
|
|
|
/*
|
|
* The width probably hasn't been cached yet, but may as well check
|
|
*/
|
|
if (rel->attr_widths[ndx] > 0)
|
|
{
|
|
tuple_width += rel->attr_widths[ndx];
|
|
continue;
|
|
}
|
|
|
|
relid = getrelid(var->varno, root->parse->rtable);
|
|
if (relid != InvalidOid)
|
|
{
|
|
item_width = get_attavgwidth(relid, var->varattno);
|
|
if (item_width > 0)
|
|
{
|
|
rel->attr_widths[ndx] = item_width;
|
|
tuple_width += item_width;
|
|
continue;
|
|
}
|
|
}
|
|
|
|
/*
|
|
* Not a plain relation, or can't find statistics for it. Estimate
|
|
* using just the type info.
|
|
*/
|
|
item_width = get_typavgwidth(var->vartype, var->vartypmod);
|
|
Assert(item_width > 0);
|
|
rel->attr_widths[ndx] = item_width;
|
|
tuple_width += item_width;
|
|
}
|
|
Assert(tuple_width >= 0);
|
|
rel->width = tuple_width;
|
|
}
|
|
|
|
/*
|
|
* relation_byte_size
|
|
* Estimate the storage space in bytes for a given number of tuples
|
|
* of a given width (size in bytes).
|
|
*/
|
|
static double
|
|
relation_byte_size(double tuples, int width)
|
|
{
|
|
return tuples * (MAXALIGN(width) + MAXALIGN(sizeof(HeapTupleHeaderData)));
|
|
}
|
|
|
|
/*
|
|
* page_size
|
|
* Returns an estimate of the number of pages covered by a given
|
|
* number of tuples of a given width (size in bytes).
|
|
*/
|
|
static double
|
|
page_size(double tuples, int width)
|
|
{
|
|
return ceil(relation_byte_size(tuples, width) / BLCKSZ);
|
|
}
|