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8f0a9e85b3
in RestrictInfo nodes, instead of recomputing on every use.
1361 lines
43 KiB
C
1361 lines
43 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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*
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* Portions Copyright (c) 1996-2001, 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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* $Header: /cvsroot/pgsql/src/backend/optimizer/path/costsize.c,v 1.81 2002/03/01 06:01:19 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 "parser/parsetree.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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#define LOG2(x) (log(x) / 0.693147180559945)
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#define LOG6(x) (log(x) / 1.79175946922805)
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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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Cost disable_cost = 100000000.0;
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bool enable_seqscan = true;
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bool enable_indexscan = true;
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bool enable_tidscan = true;
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bool enable_sort = 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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static Selectivity estimate_hash_bucketsize(Query *root, Var *var);
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static bool cost_qual_eval_walker(Node *node, Cost *total);
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static Selectivity approx_selectivity(Query *root, List *quals);
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static void set_rel_width(Query *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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* cost_seqscan
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* Determines and returns the cost of scanning a relation sequentially.
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*
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* Note: for historical reasons, this routine and the others in this module
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* use 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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*/
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void
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cost_seqscan(Path *path, Query *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(length(baserel->relids) == 1);
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Assert(!baserel->issubquery);
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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
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* reading a page from disk. We ignore that issue here, but must take
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* it into account when estimating the cost of non-sequential
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* 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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cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost;
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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:
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* for rel_size <= effective_cache_size:
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* 1 + (random_page_cost/2-1) * (rel_size/effective_cache_size) ** 2
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* for rel_size >= effective_cache_size:
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* random_page_cost * (1 - (effective_cache_size/rel_size)/2)
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* These give the right asymptotic behavior (=> 1.0 as rel_size 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 * (1.0 - 0.5 / relsize);
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else
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return 1.0 + (random_page_cost * 0.5 - 1.0) * relsize * relsize;
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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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* 'root' is the query root
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* 'baserel' is the base relation the index is for
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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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* 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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void
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cost_index(Path *path, Query *root,
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RelOptInfo *baserel,
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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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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) &&IsA(index, IndexOptInfo));
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Assert(length(baserel->relids) == 1);
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Assert(!baserel->issubquery);
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if (!enable_indexscan && !is_injoin)
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startup_cost += disable_cost;
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/*
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* Call index-access-method-specific code to estimate the processing
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* cost for scanning the index, as well as the selectivity of the
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* index (ie, the fraction of main-table tuples we will have to
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* retrieve) and its correlation to the main-table tuple order.
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*/
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OidFunctionCall8(index->amcostestimate,
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PointerGetDatum(root),
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PointerGetDatum(baserel),
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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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/* all costs for touching index itself included here */
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startup_cost += indexStartupCost;
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run_cost += indexTotalCost - indexStartupCost;
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/*----------
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* Estimate number of main-table tuples and pages fetched.
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*
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* When the index ordering is uncorrelated with the table ordering,
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* we use an approximation proposed by Mackert and Lohman, "Index Scans
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* Using a Finite LRU Buffer: A Validated I/O Model", ACM Transactions
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* on Database Systems, Vol. 14, No. 3, September 1989, Pages 401-424.
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* The Mackert and Lohman approximation is that the number of pages
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* fetched is
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* PF =
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* min(2TNs/(2T+Ns), T) when T <= b
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* 2TNs/(2T+Ns) when T > b and Ns <= 2Tb/(2T-b)
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* b + (Ns - 2Tb/(2T-b))*(T-b)/T when T > b and Ns > 2Tb/(2T-b)
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* where
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* T = # pages in table
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* N = # tuples in table
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* s = selectivity = fraction of table to be scanned
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* b = # buffer pages available (we include kernel space here)
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*
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* When the index ordering is exactly correlated with the table ordering
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* (just after a CLUSTER, for example), the number of pages fetched should
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* 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
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* somewhere between s * T * 1.0 and PF * random_cost. We currently
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* 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 = indexSelectivity * baserel->tuples;
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/* Don't believe estimates less than 1... */
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if (tuples_fetched < 1.0)
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tuples_fetched = 1.0;
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/* 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;
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if (T <= b)
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{
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pages_fetched =
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(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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}
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else
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{
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double lim;
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lim = (2.0 * T * b) / (2.0 * T - b);
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if (tuples_fetched <= lim)
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{
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pages_fetched =
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(2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
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}
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else
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{
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pages_fetched =
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b + (tuples_fetched - lim) * (T - b) / T;
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}
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}
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/*
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* min_IO_cost corresponds to the perfectly correlated case
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* (csquared=1), max_IO_cost to the perfectly uncorrelated case
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* (csquared=0). Note that we just charge random_page_cost per page
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* in the uncorrelated case, rather than using
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* cost_nonsequential_access, since we've already accounted for
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* caching effects by using the Mackert model.
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*/
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min_IO_cost = ceil(indexSelectivity * T);
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max_IO_cost = pages_fetched * random_page_cost;
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/*
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* Now interpolate based on estimated index order correlation to get
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* total disk I/O cost for main table accesses.
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*/
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csquared = indexCorrelation * indexCorrelation;
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run_cost += max_IO_cost + csquared * (min_IO_cost - max_IO_cost);
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/*
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* Estimate CPU costs per tuple.
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*
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* Normally the indexquals will be removed from the list of restriction
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* clauses that we have to evaluate as qpquals, so we should subtract
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* their costs from baserestrictcost. XXX For a lossy index, not all
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* the quals will be removed and so we really shouldn't subtract their
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* costs; but detecting that seems more expensive than it's worth.
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* Also, if we are doing a join then some of the indexquals are join
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* clauses and shouldn't be subtracted. Rather than work out exactly
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* how much to subtract, we don't subtract anything.
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*/
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cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost;
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if (!is_injoin)
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cpu_per_tuple -= cost_qual_eval(indexQuals);
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run_cost += cpu_per_tuple * tuples_fetched;
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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_tidscan
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* Determines and returns the cost of scanning a relation using TIDs.
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*/
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void
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cost_tidscan(Path *path, Query *root,
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RelOptInfo *baserel, List *tideval)
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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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int ntuples = length(tideval);
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if (!enable_tidscan)
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startup_cost += disable_cost;
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/* disk costs --- assume each tuple on a different page */
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run_cost += random_page_cost * ntuples;
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/* CPU costs */
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cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost;
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run_cost += cpu_per_tuple * ntuples;
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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_sort
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* Determines and returns the cost of sorting a relation.
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*
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* The cost of supplying the input data is NOT included; the caller should
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* add that cost to both startup and total costs returned from this routine!
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*
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* If the total volume of data to sort is less than SortMem, we will do
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* an in-memory sort, which requires no I/O and about t*log2(t) tuple
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* comparisons for t tuples.
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*
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* If the total volume exceeds SortMem, we switch to a tape-style merge
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* algorithm. There will still be about t*log2(t) tuple comparisons in
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* total, but we will also need to write and read each tuple once per
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* merge pass. We expect about ceil(log6(r)) merge passes where r is the
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* number of initial runs formed (log6 because tuplesort.c uses six-tape
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* merging). Since the average initial run should be about twice SortMem,
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* we have
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* disk traffic = 2 * relsize * ceil(log6(p / (2*SortMem)))
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* cpu = comparison_cost * t * log2(t)
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*
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* The disk traffic is assumed to be half sequential and half random
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* accesses (XXX can't we refine that guess?)
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*
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* We charge two operator evals per tuple comparison, which should be in
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* the right ballpark in most cases.
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*
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* 'pathkeys' is a list of sort keys
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* 'tuples' is the number of tuples in the relation
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* 'width' is the average tuple width in bytes
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*
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* NOTE: some callers currently pass NIL for pathkeys because they
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* can't conveniently supply the sort keys. Since this routine doesn't
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* currently do anything with pathkeys anyway, that doesn't matter...
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* but if it ever does, it should react gracefully to lack of key data.
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*/
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void
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cost_sort(Path *path, Query *root,
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List *pathkeys, double tuples, int width)
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{
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Cost startup_cost = 0;
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Cost run_cost = 0;
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double nbytes = relation_byte_size(tuples, width);
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long sortmembytes = SortMem * 1024L;
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if (!enable_sort)
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startup_cost += disable_cost;
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|
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/*
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* We want to be sure the cost of a sort is never estimated as zero,
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* even if passed-in tuple count is zero. Besides, mustn't do
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* log(0)...
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*/
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if (tuples < 2.0)
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tuples = 2.0;
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/*
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* CPU costs
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*
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* Assume about two operator evals per tuple comparison and N log2 N
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* comparisons
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*/
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startup_cost += 2.0 * cpu_operator_cost * tuples * LOG2(tuples);
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/* disk costs */
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if (nbytes > sortmembytes)
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{
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double npages = ceil(nbytes / BLCKSZ);
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double nruns = nbytes / (sortmembytes * 2);
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double log_runs = ceil(LOG6(nruns));
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double npageaccesses;
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if (log_runs < 1.0)
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log_runs = 1.0;
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npageaccesses = 2.0 * npages * log_runs;
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/* Assume half are sequential (cost 1), half are not */
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startup_cost += npageaccesses *
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(1.0 + cost_nonsequential_access(npages)) * 0.5;
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}
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/*
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* Note: should we bother to assign a nonzero run_cost to reflect the
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* overhead of extracting tuples from the sort result? Probably not
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* worth worrying about.
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*/
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path->startup_cost = startup_cost;
|
|
path->total_cost = startup_cost + run_cost;
|
|
}
|
|
|
|
|
|
/*
|
|
* cost_nestloop
|
|
* Determines and returns the cost of joining two relations using the
|
|
* nested loop algorithm.
|
|
*
|
|
* 'outer_path' is the path for the outer relation
|
|
* 'inner_path' is the path for the inner relation
|
|
* 'restrictlist' are the RestrictInfo nodes to be applied at the join
|
|
*/
|
|
void
|
|
cost_nestloop(Path *path, Query *root,
|
|
Path *outer_path,
|
|
Path *inner_path,
|
|
List *restrictlist)
|
|
{
|
|
Cost startup_cost = 0;
|
|
Cost run_cost = 0;
|
|
Cost cpu_per_tuple;
|
|
double ntuples;
|
|
|
|
if (!enable_nestloop)
|
|
startup_cost += disable_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. 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, but probably is
|
|
* true otherwise. Since we don't yet have clean handling of the
|
|
* decision whether to materialize a path, we can't tell here which
|
|
* will happen. As a compromise, charge 50% of the inner startup cost
|
|
* for each restart.
|
|
*/
|
|
startup_cost += outer_path->startup_cost + inner_path->startup_cost;
|
|
run_cost += outer_path->total_cost - outer_path->startup_cost;
|
|
run_cost += outer_path->parent->rows *
|
|
(inner_path->total_cost - inner_path->startup_cost);
|
|
if (outer_path->parent->rows > 1)
|
|
run_cost += (outer_path->parent->rows - 1) *
|
|
inner_path->startup_cost * 0.5;
|
|
|
|
/*
|
|
* Number of tuples processed (not number emitted!). 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.
|
|
*/
|
|
if (IsA(inner_path, IndexPath))
|
|
ntuples = ((IndexPath *) inner_path)->rows;
|
|
else
|
|
ntuples = inner_path->parent->rows;
|
|
ntuples *= outer_path->parent->rows;
|
|
|
|
/* CPU costs */
|
|
cpu_per_tuple = cpu_tuple_cost + cost_qual_eval(restrictlist);
|
|
run_cost += cpu_per_tuple * ntuples;
|
|
|
|
path->startup_cost = startup_cost;
|
|
path->total_cost = startup_cost + run_cost;
|
|
}
|
|
|
|
/*
|
|
* cost_mergejoin
|
|
* Determines and returns the cost of joining two relations using the
|
|
* merge join algorithm.
|
|
*
|
|
* 'outer_path' is the path for the outer relation
|
|
* 'inner_path' is the path for the inner relation
|
|
* 'restrictlist' are the RestrictInfo nodes to be applied at the join
|
|
* 'mergeclauses' are the RestrictInfo nodes to use as merge clauses
|
|
* (this should be a subset of the restrictlist)
|
|
* '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(Path *path, Query *root,
|
|
Path *outer_path,
|
|
Path *inner_path,
|
|
List *restrictlist,
|
|
List *mergeclauses,
|
|
List *outersortkeys,
|
|
List *innersortkeys)
|
|
{
|
|
Cost startup_cost = 0;
|
|
Cost run_cost = 0;
|
|
Cost cpu_per_tuple;
|
|
RestrictInfo *firstclause;
|
|
double outer_rows,
|
|
inner_rows;
|
|
double ntuples;
|
|
Selectivity leftscan,
|
|
rightscan;
|
|
Path sort_path; /* dummy for result of cost_sort */
|
|
|
|
if (!enable_mergejoin)
|
|
startup_cost += disable_cost;
|
|
|
|
/*
|
|
* A merge join will stop as soon as it exhausts either input stream.
|
|
* 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.
|
|
*/
|
|
firstclause = (RestrictInfo *) lfirst(mergeclauses);
|
|
if (firstclause->left_mergescansel < 0) /* not computed yet? */
|
|
mergejoinscansel(root, (Node *) firstclause->clause,
|
|
&firstclause->left_mergescansel,
|
|
&firstclause->right_mergescansel);
|
|
leftscan = firstclause->left_mergescansel;
|
|
rightscan = firstclause->right_mergescansel;
|
|
|
|
outer_rows = outer_path->parent->rows * leftscan;
|
|
inner_rows = inner_path->parent->rows * rightscan;
|
|
|
|
/* cost of source data */
|
|
|
|
/*
|
|
* Note we are assuming that each source tuple is fetched just once,
|
|
* which is not right in the presence of equal keys. If we had a way
|
|
* of estimating the proportion of equal keys, we could apply a
|
|
* correction factor...
|
|
*/
|
|
if (outersortkeys) /* do we need to sort outer? */
|
|
{
|
|
startup_cost += outer_path->total_cost;
|
|
cost_sort(&sort_path,
|
|
root,
|
|
outersortkeys,
|
|
outer_path->parent->rows,
|
|
outer_path->parent->width);
|
|
startup_cost += sort_path.startup_cost;
|
|
run_cost += (sort_path.total_cost - sort_path.startup_cost)
|
|
* leftscan;
|
|
}
|
|
else
|
|
{
|
|
startup_cost += outer_path->startup_cost;
|
|
run_cost += (outer_path->total_cost - outer_path->startup_cost)
|
|
* leftscan;
|
|
}
|
|
|
|
if (innersortkeys) /* do we need to sort inner? */
|
|
{
|
|
startup_cost += inner_path->total_cost;
|
|
cost_sort(&sort_path,
|
|
root,
|
|
innersortkeys,
|
|
inner_path->parent->rows,
|
|
inner_path->parent->width);
|
|
startup_cost += sort_path.startup_cost;
|
|
run_cost += (sort_path.total_cost - sort_path.startup_cost)
|
|
* rightscan;
|
|
}
|
|
else
|
|
{
|
|
startup_cost += inner_path->startup_cost;
|
|
run_cost += (inner_path->total_cost - inner_path->startup_cost)
|
|
* rightscan;
|
|
}
|
|
|
|
/*
|
|
* The number of tuple comparisons needed depends drastically on the
|
|
* number of equal keys in the two source relations, which we have no
|
|
* good way of estimating. (XXX could the MCV statistics help?)
|
|
* Somewhat arbitrarily, we charge one tuple
|
|
* comparison (one cpu_operator_cost) for each tuple in the two source
|
|
* relations. This is probably a lower bound.
|
|
*/
|
|
run_cost += cpu_operator_cost * (outer_rows + inner_rows);
|
|
|
|
/*
|
|
* 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. It's OK to use an
|
|
* approximate selectivity here, since in most cases this is a minor
|
|
* component of the cost. NOTE: it's correct to use the unscaled rows
|
|
* counts here, not the scaled-down counts we obtained above.
|
|
*/
|
|
ntuples = approx_selectivity(root, mergeclauses) *
|
|
outer_path->parent->rows * inner_path->parent->rows;
|
|
|
|
/* CPU costs */
|
|
cpu_per_tuple = cpu_tuple_cost + cost_qual_eval(restrictlist);
|
|
run_cost += cpu_per_tuple * ntuples;
|
|
|
|
path->startup_cost = startup_cost;
|
|
path->total_cost = startup_cost + run_cost;
|
|
}
|
|
|
|
/*
|
|
* cost_hashjoin
|
|
* Determines and returns the cost of joining two relations using the
|
|
* hash join algorithm.
|
|
*
|
|
* 'outer_path' is the path for the outer relation
|
|
* 'inner_path' is the path for the inner relation
|
|
* 'restrictlist' are the RestrictInfo nodes to be applied at the join
|
|
* 'hashclauses' is a list of the hash join clause (always a 1-element list)
|
|
* (this should be a subset of the restrictlist)
|
|
*/
|
|
void
|
|
cost_hashjoin(Path *path, Query *root,
|
|
Path *outer_path,
|
|
Path *inner_path,
|
|
List *restrictlist,
|
|
List *hashclauses)
|
|
{
|
|
Cost startup_cost = 0;
|
|
Cost run_cost = 0;
|
|
Cost cpu_per_tuple;
|
|
double ntuples;
|
|
double outerbytes = relation_byte_size(outer_path->parent->rows,
|
|
outer_path->parent->width);
|
|
double innerbytes = relation_byte_size(inner_path->parent->rows,
|
|
inner_path->parent->width);
|
|
long hashtablebytes = SortMem * 1024L;
|
|
RestrictInfo *restrictinfo;
|
|
Var *left,
|
|
*right;
|
|
Selectivity innerbucketsize;
|
|
|
|
if (!enable_hashjoin)
|
|
startup_cost += disable_cost;
|
|
|
|
/* 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 */
|
|
startup_cost += cpu_operator_cost * inner_path->parent->rows;
|
|
run_cost += cpu_operator_cost * outer_path->parent->rows;
|
|
|
|
/*
|
|
* Determine bucketsize fraction for inner relation. First we have to
|
|
* figure out which side of the hashjoin clause is the inner side.
|
|
*/
|
|
Assert(length(hashclauses) == 1);
|
|
Assert(IsA(lfirst(hashclauses), RestrictInfo));
|
|
restrictinfo = (RestrictInfo *) lfirst(hashclauses);
|
|
/* these must be OK, since check_hashjoinable accepted the clause */
|
|
left = get_leftop(restrictinfo->clause);
|
|
right = get_rightop(restrictinfo->clause);
|
|
|
|
/*
|
|
* 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 (intMember(right->varno, inner_path->parent->relids))
|
|
{
|
|
/* righthand side is inner */
|
|
innerbucketsize = restrictinfo->right_bucketsize;
|
|
if (innerbucketsize < 0)
|
|
{
|
|
/* not cached yet */
|
|
innerbucketsize = estimate_hash_bucketsize(root, right);
|
|
restrictinfo->right_bucketsize = innerbucketsize;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
Assert(intMember(left->varno, inner_path->parent->relids));
|
|
/* lefthand side is inner */
|
|
innerbucketsize = restrictinfo->left_bucketsize;
|
|
if (innerbucketsize < 0)
|
|
{
|
|
/* not cached yet */
|
|
innerbucketsize = estimate_hash_bucketsize(root, left);
|
|
restrictinfo->left_bucketsize = innerbucketsize;
|
|
}
|
|
}
|
|
|
|
/*
|
|
* 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. We charge
|
|
* one cpu_operator_cost per tuple comparison.
|
|
*/
|
|
run_cost += cpu_operator_cost * outer_path->parent->rows *
|
|
ceil(inner_path->parent->rows * innerbucketsize);
|
|
|
|
/*
|
|
* 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. It's OK to use an
|
|
* approximate selectivity here, since in most cases this is a minor
|
|
* component of the cost.
|
|
*/
|
|
ntuples = approx_selectivity(root, hashclauses) *
|
|
outer_path->parent->rows * inner_path->parent->rows;
|
|
|
|
/* CPU costs */
|
|
cpu_per_tuple = cpu_tuple_cost + cost_qual_eval(restrictlist);
|
|
run_cost += cpu_per_tuple * ntuples;
|
|
|
|
/*
|
|
* 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 (innerbytes > hashtablebytes)
|
|
{
|
|
double outerpages = page_size(outer_path->parent->rows,
|
|
outer_path->parent->width);
|
|
double innerpages = page_size(inner_path->parent->rows,
|
|
inner_path->parent->width);
|
|
|
|
startup_cost += innerpages;
|
|
run_cost += innerpages + 2 * outerpages;
|
|
}
|
|
|
|
/*
|
|
* 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 startup cost by the
|
|
* square root of the size ratio. (Why square root? No real good
|
|
* reason, but it seems reasonable...)
|
|
*/
|
|
if (innerbytes > outerbytes && outerbytes > 0)
|
|
startup_cost *= sqrt(innerbytes / outerbytes);
|
|
|
|
path->startup_cost = startup_cost;
|
|
path->total_cost = startup_cost + run_cost;
|
|
}
|
|
|
|
/*
|
|
* Estimate hash bucketsize fraction (ie, number of entries in a bucket
|
|
* divided by total tuples in relation) if the specified Var is used
|
|
* as a hash key.
|
|
*
|
|
* XXX This is really pretty bogus since we're effectively assuming that the
|
|
* distribution of hash keys will be the same after applying restriction
|
|
* clauses as it was in the underlying relation. However, we are not nearly
|
|
* smart enough to figure out how the restrict clauses might change the
|
|
* distribution, so this will have to do for now.
|
|
*
|
|
* We can get the number of buckets the executor will use for the given
|
|
* input relation. If the data were perfectly distributed, with the same
|
|
* number of tuples going into each available bucket, then the bucketsize
|
|
* fraction would be 1/nbuckets. But this happy state of affairs will occur
|
|
* only if (a) there are at least nbuckets distinct data values, and (b)
|
|
* we have a not-too-skewed data distribution. Otherwise the buckets will
|
|
* be nonuniformly occupied. If the other relation in the join has a key
|
|
* distribution similar to this one's, then the most-loaded buckets are
|
|
* exactly those that will be probed most often. Therefore, the "average"
|
|
* bucket size for costing purposes should really be taken as something close
|
|
* to the "worst case" bucket size. We try to estimate this by adjusting the
|
|
* fraction if there are too few distinct data values, and then scaling up
|
|
* by the ratio of the most common value's frequency to the average frequency.
|
|
*
|
|
* If no statistics are available, use a default estimate of 0.1. This will
|
|
* discourage use of a hash rather strongly if the inner relation is large,
|
|
* which is what we want. We do not want to hash unless we know that the
|
|
* inner rel is well-dispersed (or the alternatives seem much worse).
|
|
*/
|
|
static Selectivity
|
|
estimate_hash_bucketsize(Query *root, Var *var)
|
|
{
|
|
Oid relid;
|
|
RelOptInfo *rel;
|
|
int virtualbuckets;
|
|
int physicalbuckets;
|
|
int numbatches;
|
|
HeapTuple tuple;
|
|
Form_pg_statistic stats;
|
|
double estfract,
|
|
ndistinct,
|
|
mcvfreq,
|
|
avgfreq;
|
|
float4 *numbers;
|
|
int nnumbers;
|
|
|
|
/*
|
|
* Lookup info about var's relation and attribute; if none available,
|
|
* return default estimate.
|
|
*/
|
|
if (!IsA(var, Var))
|
|
return 0.1;
|
|
|
|
relid = getrelid(var->varno, root->rtable);
|
|
if (relid == InvalidOid)
|
|
return 0.1;
|
|
|
|
rel = find_base_rel(root, var->varno);
|
|
|
|
if (rel->tuples <= 0.0 || rel->rows <= 0.0)
|
|
return 0.1; /* ensure we can divide below */
|
|
|
|
/* Get hash table size that executor would use for this relation */
|
|
ExecChooseHashTableSize(rel->rows, rel->width,
|
|
&virtualbuckets,
|
|
&physicalbuckets,
|
|
&numbatches);
|
|
|
|
tuple = SearchSysCache(STATRELATT,
|
|
ObjectIdGetDatum(relid),
|
|
Int16GetDatum(var->varattno),
|
|
0, 0);
|
|
if (!HeapTupleIsValid(tuple))
|
|
{
|
|
/*
|
|
* Perhaps the Var is a system attribute; if so, it will have no
|
|
* entry in pg_statistic, but we may be able to guess something
|
|
* about its distribution anyway.
|
|
*/
|
|
switch (var->varattno)
|
|
{
|
|
case ObjectIdAttributeNumber:
|
|
case SelfItemPointerAttributeNumber:
|
|
/* these are unique, so buckets should be well-distributed */
|
|
return 1.0 / (double) virtualbuckets;
|
|
case TableOidAttributeNumber:
|
|
/* hashing this is a terrible idea... */
|
|
return 1.0;
|
|
}
|
|
return 0.1;
|
|
}
|
|
stats = (Form_pg_statistic) GETSTRUCT(tuple);
|
|
|
|
/*
|
|
* Obtain number of distinct data values in raw relation.
|
|
*/
|
|
ndistinct = stats->stadistinct;
|
|
if (ndistinct < 0.0)
|
|
ndistinct = -ndistinct * rel->tuples;
|
|
|
|
if (ndistinct <= 0.0) /* ensure we can divide */
|
|
{
|
|
ReleaseSysCache(tuple);
|
|
return 0.1;
|
|
}
|
|
|
|
/* Also compute avg freq of all distinct data values in raw relation */
|
|
avgfreq = (1.0 - stats->stanullfrac) / ndistinct;
|
|
|
|
/*
|
|
* Adjust ndistinct to account for restriction clauses. Observe we
|
|
* are assuming that the data distribution is affected uniformly by
|
|
* the restriction clauses!
|
|
*
|
|
* XXX Possibly better way, but much more expensive: multiply by
|
|
* selectivity of rel's restriction clauses that mention the target
|
|
* Var.
|
|
*/
|
|
ndistinct *= rel->rows / rel->tuples;
|
|
|
|
/*
|
|
* Initial estimate of bucketsize fraction is 1/nbuckets as long as
|
|
* the number of buckets is less than the expected number of distinct
|
|
* values; otherwise it is 1/ndistinct.
|
|
*/
|
|
if (ndistinct > (double) virtualbuckets)
|
|
estfract = 1.0 / (double) virtualbuckets;
|
|
else
|
|
estfract = 1.0 / ndistinct;
|
|
|
|
/*
|
|
* Look up the frequency of the most common value, if available.
|
|
*/
|
|
mcvfreq = 0.0;
|
|
|
|
if (get_attstatsslot(tuple, var->vartype, var->vartypmod,
|
|
STATISTIC_KIND_MCV, InvalidOid,
|
|
NULL, NULL, &numbers, &nnumbers))
|
|
{
|
|
/*
|
|
* The first MCV stat is for the most common value.
|
|
*/
|
|
if (nnumbers > 0)
|
|
mcvfreq = numbers[0];
|
|
free_attstatsslot(var->vartype, NULL, 0,
|
|
numbers, nnumbers);
|
|
}
|
|
|
|
/*
|
|
* Adjust estimated bucketsize upward to account for skewed
|
|
* distribution.
|
|
*/
|
|
if (avgfreq > 0.0 && mcvfreq > avgfreq)
|
|
estfract *= mcvfreq / avgfreq;
|
|
|
|
ReleaseSysCache(tuple);
|
|
|
|
return (Selectivity) estfract;
|
|
}
|
|
|
|
|
|
/*
|
|
* cost_qual_eval
|
|
* Estimate the CPU cost of evaluating a WHERE clause (once).
|
|
* The input can be either an implicitly-ANDed list of boolean
|
|
* expressions, or a list of RestrictInfo nodes.
|
|
*/
|
|
Cost
|
|
cost_qual_eval(List *quals)
|
|
{
|
|
Cost total = 0;
|
|
List *l;
|
|
|
|
/* 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 will contain -1.
|
|
*/
|
|
if (qual && IsA(qual, RestrictInfo))
|
|
{
|
|
RestrictInfo *restrictinfo = (RestrictInfo *) qual;
|
|
|
|
if (restrictinfo->eval_cost < 0)
|
|
{
|
|
restrictinfo->eval_cost = 0;
|
|
cost_qual_eval_walker((Node *) restrictinfo->clause,
|
|
&restrictinfo->eval_cost);
|
|
}
|
|
total += restrictinfo->eval_cost;
|
|
}
|
|
else
|
|
{
|
|
/* If it's a bare expression, must always do it the hard way */
|
|
cost_qual_eval_walker(qual, &total);
|
|
}
|
|
}
|
|
return total;
|
|
}
|
|
|
|
static bool
|
|
cost_qual_eval_walker(Node *node, Cost *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, Expr))
|
|
{
|
|
Expr *expr = (Expr *) node;
|
|
|
|
switch (expr->opType)
|
|
{
|
|
case OP_EXPR:
|
|
case FUNC_EXPR:
|
|
*total += cpu_operator_cost;
|
|
break;
|
|
case OR_EXPR:
|
|
case AND_EXPR:
|
|
case NOT_EXPR:
|
|
break;
|
|
case SUBPLAN_EXPR:
|
|
|
|
/*
|
|
* A subplan node in an expression indicates that the
|
|
* subplan will be executed on each evaluation, so charge
|
|
* accordingly. (We assume that sub-selects that can be
|
|
* executed as InitPlans have already been removed from
|
|
* the expression.)
|
|
*
|
|
* NOTE: this logic should agree with the estimates used by
|
|
* make_subplan() in plan/subselect.c.
|
|
*/
|
|
{
|
|
SubPlan *subplan = (SubPlan *) expr->oper;
|
|
Plan *plan = subplan->plan;
|
|
Cost subcost;
|
|
|
|
if (subplan->sublink->subLinkType == EXISTS_SUBLINK)
|
|
{
|
|
/* we only need to fetch 1 tuple */
|
|
subcost = plan->startup_cost +
|
|
(plan->total_cost - plan->startup_cost) / plan->plan_rows;
|
|
}
|
|
else if (subplan->sublink->subLinkType == ALL_SUBLINK ||
|
|
subplan->sublink->subLinkType == ANY_SUBLINK)
|
|
{
|
|
/* assume we need 50% of the tuples */
|
|
subcost = plan->startup_cost +
|
|
0.50 * (plan->total_cost - plan->startup_cost);
|
|
/* XXX what if subplan has been materialized? */
|
|
}
|
|
else
|
|
{
|
|
/* assume we need all tuples */
|
|
subcost = plan->total_cost;
|
|
}
|
|
*total += subcost;
|
|
}
|
|
break;
|
|
}
|
|
/* fall through to examine args of Expr node */
|
|
}
|
|
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).
|
|
*
|
|
* The "quick" part comes from caching the selectivity estimates so we can
|
|
* avoid recomputing them later. (Since the same clauses are typically
|
|
* examined over and over in different possible join trees, this makes a
|
|
* big difference.)
|
|
*
|
|
* The "dirty" part comes from the fact that the selectivities of multiple
|
|
* clauses are estimated independently and multiplied together. Now
|
|
* clauselist_selectivity often can't do any better than that anyhow, but
|
|
* for some situations (such as range constraints) it is smarter.
|
|
*
|
|
* 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(Query *root, List *quals)
|
|
{
|
|
Selectivity total = 1.0;
|
|
List *l;
|
|
|
|
foreach(l, quals)
|
|
{
|
|
Node *qual = (Node *) lfirst(l);
|
|
Selectivity selec;
|
|
|
|
/*
|
|
* RestrictInfo nodes contain a this_selec field reserved for this
|
|
* routine's use, so that it's not necessary to evaluate the qual
|
|
* clause's selectivity more than once. If the clause's
|
|
* selectivity hasn't been computed yet, the field will contain
|
|
* -1.
|
|
*/
|
|
if (qual && IsA(qual, RestrictInfo))
|
|
{
|
|
RestrictInfo *restrictinfo = (RestrictInfo *) qual;
|
|
|
|
if (restrictinfo->this_selec < 0)
|
|
restrictinfo->this_selec =
|
|
clause_selectivity(root,
|
|
(Node *) restrictinfo->clause,
|
|
0);
|
|
selec = restrictinfo->this_selec;
|
|
}
|
|
else
|
|
{
|
|
/* If it's a bare expression, must always do it the hard way */
|
|
selec = clause_selectivity(root, qual, 0);
|
|
}
|
|
total *= selec;
|
|
}
|
|
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(Query *root, RelOptInfo *rel)
|
|
{
|
|
/* Should only be applied to base relations */
|
|
Assert(length(rel->relids) == 1);
|
|
|
|
rel->rows = rel->tuples *
|
|
restrictlist_selectivity(root,
|
|
rel->baserestrictinfo,
|
|
lfirsti(rel->relids));
|
|
|
|
/*
|
|
* Force estimate to be at least one row, to make explain output look
|
|
* better and to avoid possible divide-by-zero when interpolating
|
|
* cost.
|
|
*/
|
|
if (rel->rows < 1.0)
|
|
rel->rows = 1.0;
|
|
|
|
rel->baserestrictcost = cost_qual_eval(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.)
|
|
*
|
|
* We set the same relnode fields as set_baserel_size_estimates() does.
|
|
*/
|
|
void
|
|
set_joinrel_size_estimates(Query *root, RelOptInfo *rel,
|
|
RelOptInfo *outer_rel,
|
|
RelOptInfo *inner_rel,
|
|
JoinType jointype,
|
|
List *restrictlist)
|
|
{
|
|
double temp;
|
|
|
|
/* Start with the Cartesian product */
|
|
temp = outer_rel->rows * inner_rel->rows;
|
|
|
|
/*
|
|
* Apply join restrictivity. 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.
|
|
*/
|
|
temp *= restrictlist_selectivity(root,
|
|
restrictlist,
|
|
0);
|
|
|
|
/*
|
|
* 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?)
|
|
*/
|
|
switch (jointype)
|
|
{
|
|
case JOIN_INNER:
|
|
break;
|
|
case JOIN_LEFT:
|
|
if (temp < outer_rel->rows)
|
|
temp = outer_rel->rows;
|
|
break;
|
|
case JOIN_RIGHT:
|
|
if (temp < inner_rel->rows)
|
|
temp = inner_rel->rows;
|
|
break;
|
|
case JOIN_FULL:
|
|
if (temp < outer_rel->rows)
|
|
temp = outer_rel->rows;
|
|
if (temp < inner_rel->rows)
|
|
temp = inner_rel->rows;
|
|
break;
|
|
default:
|
|
elog(ERROR, "set_joinrel_size_estimates: unsupported join type %d",
|
|
(int) jointype);
|
|
break;
|
|
}
|
|
|
|
/*
|
|
* Force estimate to be at least one row, to make explain output look
|
|
* better and to avoid possible divide-by-zero when interpolating
|
|
* cost.
|
|
*/
|
|
if (temp < 1.0)
|
|
temp = 1.0;
|
|
|
|
rel->rows = temp;
|
|
|
|
/*
|
|
* We could apply set_rel_width() to compute the output tuple width
|
|
* from scratch, but at present it's always just the sum of the input
|
|
* widths, so why work harder than necessary? If relnode.c is ever
|
|
* taught to remove unneeded columns from join targetlists, go back to
|
|
* using set_rel_width here.
|
|
*/
|
|
rel->width = outer_rel->width + inner_rel->width;
|
|
}
|
|
|
|
/*
|
|
* set_rel_width
|
|
* Set the estimated output width of the relation.
|
|
*
|
|
* NB: this works best on base 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?
|
|
*/
|
|
static void
|
|
set_rel_width(Query *root, RelOptInfo *rel)
|
|
{
|
|
int32 tuple_width = 0;
|
|
List *tllist;
|
|
|
|
foreach(tllist, rel->targetlist)
|
|
{
|
|
TargetEntry *tle = (TargetEntry *) lfirst(tllist);
|
|
int32 item_width;
|
|
|
|
/*
|
|
* If it's a Var, try to get statistical info from pg_statistic.
|
|
*/
|
|
if (tle->expr && IsA(tle->expr, Var))
|
|
{
|
|
Var *var = (Var *) tle->expr;
|
|
Oid relid;
|
|
|
|
relid = getrelid(var->varno, root->rtable);
|
|
if (relid != InvalidOid)
|
|
{
|
|
item_width = get_attavgwidth(relid, var->varattno);
|
|
if (item_width > 0)
|
|
{
|
|
tuple_width += item_width;
|
|
continue;
|
|
}
|
|
}
|
|
}
|
|
|
|
/*
|
|
* Not a Var, or can't find statistics for it. Estimate using
|
|
* just the type info.
|
|
*/
|
|
item_width = get_typavgwidth(tle->resdom->restype,
|
|
tle->resdom->restypmod);
|
|
Assert(item_width > 0);
|
|
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 * ((double) MAXALIGN(width + sizeof(HeapTupleData)));
|
|
}
|
|
|
|
/*
|
|
* 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);
|
|
}
|