This commit was manufactured by cvs2git to create branch 'ecpg_big_bison'.

Sprout from master 2002-06-20 20:29:54 UTC Bruce Momjian <bruce@momjian.us> 'Update copyright to 2002.'
Delete:
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diff --git a/src/backend/optimizer/path/costsize.c b/src/backend/optimizer/path/costsize.c
deleted file mode 100644
index 3bf6d2f4dc8..00000000000
--- a/src/backend/optimizer/path/costsize.c
+++ /dev/null
@@ -1,1458 +0,0 @@
-/*-------------------------------------------------------------------------
- *
- * costsize.c
- *	  Routines to compute (and set) relation sizes and path costs
- *
- * Path costs are measured in units of disk accesses: one sequential page
- * fetch has cost 1.  All else is scaled relative to a page fetch, using
- * the scaling parameters
- *
- *	random_page_cost	Cost of a non-sequential page fetch
- *	cpu_tuple_cost		Cost of typical CPU time to process a tuple
- *	cpu_index_tuple_cost  Cost of typical CPU time to process an index tuple
- *	cpu_operator_cost	Cost of CPU time to process a typical WHERE operator
- *
- * We also use a rough estimate "effective_cache_size" of the number of
- * disk pages in Postgres + OS-level disk cache.  (We can't simply use
- * NBuffers for this purpose because that would ignore the effects of
- * the kernel's disk cache.)
- *
- * Obviously, taking constants for these values is an oversimplification,
- * but it's tough enough to get any useful estimates even at this level of
- * detail.	Note that all of these parameters are user-settable, in case
- * the default values are drastically off for a particular platform.
- *
- * We compute two separate costs for each path:
- *		total_cost: total estimated cost to fetch all tuples
- *		startup_cost: cost that is expended before first tuple is fetched
- * In some scenarios, such as when there is a LIMIT or we are implementing
- * an EXISTS(...) sub-select, it is not necessary to fetch all tuples of the
- * path's result.  A caller can estimate the cost of fetching a partial
- * result by interpolating between startup_cost and total_cost.  In detail:
- *		actual_cost = startup_cost +
- *			(total_cost - startup_cost) * tuples_to_fetch / path->parent->rows;
- * Note that a base relation's rows count (and, by extension, plan_rows for
- * plan nodes below the LIMIT node) are set without regard to any LIMIT, so
- * that this equation works properly.  (Also, these routines guarantee not to
- * set the rows count to zero, so there will be no zero divide.)  The LIMIT is
- * applied as a top-level plan node.
- *
- *
- * Portions Copyright (c) 1996-2002, PostgreSQL Global Development Group
- * Portions Copyright (c) 1994, Regents of the University of California
- *
- * IDENTIFICATION
- *	  $Header: /cvsroot/pgsql/src/backend/optimizer/path/costsize.c,v 1.85 2002/06/20 20:29:29 momjian Exp $
- *
- *-------------------------------------------------------------------------
- */
-
-#include "postgres.h"
-
-#include <math.h>
-
-#include "catalog/pg_statistic.h"
-#include "executor/nodeHash.h"
-#include "miscadmin.h"
-#include "optimizer/clauses.h"
-#include "optimizer/cost.h"
-#include "optimizer/pathnode.h"
-#include "parser/parsetree.h"
-#include "utils/selfuncs.h"
-#include "utils/lsyscache.h"
-#include "utils/syscache.h"
-
-
-#define LOG2(x)  (log(x) / 0.693147180559945)
-#define LOG6(x)  (log(x) / 1.79175946922805)
-
-
-double		effective_cache_size = DEFAULT_EFFECTIVE_CACHE_SIZE;
-double		random_page_cost = DEFAULT_RANDOM_PAGE_COST;
-double		cpu_tuple_cost = DEFAULT_CPU_TUPLE_COST;
-double		cpu_index_tuple_cost = DEFAULT_CPU_INDEX_TUPLE_COST;
-double		cpu_operator_cost = DEFAULT_CPU_OPERATOR_COST;
-
-Cost		disable_cost = 100000000.0;
-
-bool		enable_seqscan = true;
-bool		enable_indexscan = true;
-bool		enable_tidscan = true;
-bool		enable_sort = true;
-bool		enable_nestloop = true;
-bool		enable_mergejoin = true;
-bool		enable_hashjoin = true;
-
-
-static Selectivity estimate_hash_bucketsize(Query *root, Var *var);
-static bool cost_qual_eval_walker(Node *node, Cost *total);
-static Selectivity approx_selectivity(Query *root, List *quals);
-static void set_rel_width(Query *root, RelOptInfo *rel);
-static double relation_byte_size(double tuples, int width);
-static double page_size(double tuples, int width);
-
-
-/*
- * cost_seqscan
- *	  Determines and returns the cost of scanning a relation sequentially.
- *
- * Note: for historical reasons, this routine and the others in this module
- * use the passed result Path only to store their startup_cost and total_cost
- * results into.  All the input data they need is passed as separate
- * parameters, even though much of it could be extracted from the Path.
- */
-void
-cost_seqscan(Path *path, Query *root,
-			 RelOptInfo *baserel)
-{
-	Cost		startup_cost = 0;
-	Cost		run_cost = 0;
-	Cost		cpu_per_tuple;
-
-	/* Should only be applied to base relations */
-	Assert(length(baserel->relids) == 1);
-	Assert(baserel->rtekind == RTE_RELATION);
-
-	if (!enable_seqscan)
-		startup_cost += disable_cost;
-
-	/*
-	 * disk costs
-	 *
-	 * The cost of reading a page sequentially is 1.0, by definition. Note
-	 * that the Unix kernel will typically do some amount of read-ahead
-	 * optimization, so that this cost is less than the true cost of
-	 * reading a page from disk.  We ignore that issue here, but must take
-	 * it into account when estimating the cost of non-sequential
-	 * accesses!
-	 */
-	run_cost += baserel->pages; /* sequential fetches with cost 1.0 */
-
-	/* CPU costs */
-	cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost;
-	run_cost += cpu_per_tuple * baserel->tuples;
-
-	path->startup_cost = startup_cost;
-	path->total_cost = startup_cost + run_cost;
-}
-
-/*
- * cost_nonsequential_access
- *	  Estimate the cost of accessing one page at random from a relation
- *	  (or sort temp file) of the given size in pages.
- *
- * The simplistic model that the cost is random_page_cost is what we want
- * to use for large relations; but for small ones that is a serious
- * overestimate because of the effects of caching.	This routine tries to
- * account for that.
- *
- * Unfortunately we don't have any good way of estimating the effective cache
- * size we are working with --- we know that Postgres itself has NBuffers
- * internal buffers, but the size of the kernel's disk cache is uncertain,
- * and how much of it we get to use is even less certain.  We punt the problem
- * for now by assuming we are given an effective_cache_size parameter.
- *
- * Given a guesstimated cache size, we estimate the actual I/O cost per page
- * with the entirely ad-hoc equations:
- *	for rel_size <= effective_cache_size:
- *		1 + (random_page_cost/2-1) * (rel_size/effective_cache_size) ** 2
- *	for rel_size >= effective_cache_size:
- *		random_page_cost * (1 - (effective_cache_size/rel_size)/2)
- * These give the right asymptotic behavior (=> 1.0 as rel_size becomes
- * small, => random_page_cost as it becomes large) and meet in the middle
- * with the estimate that the cache is about 50% effective for a relation
- * of the same size as effective_cache_size.  (XXX this is probably all
- * wrong, but I haven't been able to find any theory about how effective
- * a disk cache should be presumed to be.)
- */
-static Cost
-cost_nonsequential_access(double relpages)
-{
-	double		relsize;
-
-	/* don't crash on bad input data */
-	if (relpages <= 0.0 || effective_cache_size <= 0.0)
-		return random_page_cost;
-
-	relsize = relpages / effective_cache_size;
-
-	if (relsize >= 1.0)
-		return random_page_cost * (1.0 - 0.5 / relsize);
-	else
-		return 1.0 + (random_page_cost * 0.5 - 1.0) * relsize * relsize;
-}
-
-/*
- * cost_index
- *	  Determines and returns the cost of scanning a relation using an index.
- *
- *	  NOTE: an indexscan plan node can actually represent several passes,
- *	  but here we consider the cost of just one pass.
- *
- * 'root' is the query root
- * 'baserel' is the base relation the index is for
- * 'index' is the index to be used
- * 'indexQuals' is the list of applicable qual clauses (implicit AND semantics)
- * 'is_injoin' is T if we are considering using the index scan as the inside
- *		of a nestloop join (hence, some of the indexQuals are join clauses)
- *
- * NOTE: 'indexQuals' must contain only clauses usable as index restrictions.
- * Any additional quals evaluated as qpquals may reduce the number of returned
- * tuples, but they won't reduce the number of tuples we have to fetch from
- * the table, so they don't reduce the scan cost.
- */
-void
-cost_index(Path *path, Query *root,
-		   RelOptInfo *baserel,
-		   IndexOptInfo *index,
-		   List *indexQuals,
-		   bool is_injoin)
-{
-	Cost		startup_cost = 0;
-	Cost		run_cost = 0;
-	Cost		indexStartupCost;
-	Cost		indexTotalCost;
-	Selectivity indexSelectivity;
-	double		indexCorrelation,
-				csquared;
-	Cost		min_IO_cost,
-				max_IO_cost;
-	Cost		cpu_per_tuple;
-	double		tuples_fetched;
-	double		pages_fetched;
-	double		T,
-				b;
-
-	/* Should only be applied to base relations */
-	Assert(IsA(baserel, RelOptInfo) &&
-		   IsA(index, IndexOptInfo));
-	Assert(length(baserel->relids) == 1);
-	Assert(baserel->rtekind == RTE_RELATION);
-
-	if (!enable_indexscan && !is_injoin)
-		startup_cost += disable_cost;
-
-	/*
-	 * Call index-access-method-specific code to estimate the processing
-	 * cost for scanning the index, as well as the selectivity of the
-	 * index (ie, the fraction of main-table tuples we will have to
-	 * retrieve) and its correlation to the main-table tuple order.
-	 */
-	OidFunctionCall8(index->amcostestimate,
-					 PointerGetDatum(root),
-					 PointerGetDatum(baserel),
-					 PointerGetDatum(index),
-					 PointerGetDatum(indexQuals),
-					 PointerGetDatum(&indexStartupCost),
-					 PointerGetDatum(&indexTotalCost),
-					 PointerGetDatum(&indexSelectivity),
-					 PointerGetDatum(&indexCorrelation));
-
-	/* all costs for touching index itself included here */
-	startup_cost += indexStartupCost;
-	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
-	 * random fetches that occur in the uncorrelated case.	So, depending on
-	 * 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
-	 * correlation squared (XXX is that appropriate?).
-	 *
-	 * In any case the number of tuples fetched is Ns.
-	 *----------
-	 */
-
-	tuples_fetched = indexSelectivity * baserel->tuples;
-	/* Don't believe estimates less than 1... */
-	if (tuples_fetched < 1.0)
-		tuples_fetched = 1.0;
-
-	/* This part is the Mackert and Lohman formula */
-
-	T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
-	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);
-		if (pages_fetched > T)
-			pages_fetched = T;
-	}
-	else
-	{
-		double		lim;
-
-		lim = (2.0 * T * b) / (2.0 * T - b);
-		if (tuples_fetched <= lim)
-		{
-			pages_fetched =
-				(2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
-		}
-		else
-		{
-			pages_fetched =
-				b + (tuples_fetched - lim) * (T - b) / T;
-		}
-	}
-
-	/*
-	 * 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.  XXX For a lossy index, not all
-	 * the quals will be removed and so we really shouldn't subtract their
-	 * costs; but detecting that seems more expensive than it's worth.
-	 * Also, 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.
-	 */
-	cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost;
-
-	if (!is_injoin)
-		cpu_per_tuple -= cost_qual_eval(indexQuals);
-
-	run_cost += cpu_per_tuple * tuples_fetched;
-
-	path->startup_cost = startup_cost;
-	path->total_cost = startup_cost + run_cost;
-}
-
-/*
- * cost_tidscan
- *	  Determines and returns the cost of scanning a relation using TIDs.
- */
-void
-cost_tidscan(Path *path, Query *root,
-			 RelOptInfo *baserel, List *tideval)
-{
-	Cost		startup_cost = 0;
-	Cost		run_cost = 0;
-	Cost		cpu_per_tuple;
-	int			ntuples = length(tideval);
-
-	/* Should only be applied to base relations */
-	Assert(length(baserel->relids) == 1);
-	Assert(baserel->rtekind == RTE_RELATION);
-
-	if (!enable_tidscan)
-		startup_cost += disable_cost;
-
-	/* disk costs --- assume each tuple on a different page */
-	run_cost += random_page_cost * ntuples;
-
-	/* CPU costs */
-	cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost;
-	run_cost += cpu_per_tuple * ntuples;
-
-	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, Query *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(length(baserel->relids) == 1);
-	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 */
-	cpu_per_tuple += cpu_tuple_cost + baserel->baserestrictcost;
-	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.
- *
- * The cost of supplying the input data is NOT included; the caller should
- * add that cost to both startup and total costs returned from this routine!
- *
- * If the total volume of data to sort is less than SortMem, 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 SortMem, 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 SortMem,
- * we have
- *		disk traffic = 2 * relsize * ceil(log6(p / (2*SortMem)))
- *		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
- * '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.
- */
-void
-cost_sort(Path *path, Query *root,
-		  List *pathkeys, double tuples, int width)
-{
-	Cost		startup_cost = 0;
-	Cost		run_cost = 0;
-	double		nbytes = relation_byte_size(tuples, width);
-	long		sortmembytes = SortMem * 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 > sortmembytes)
-	{
-		double		npages = ceil(nbytes / BLCKSZ);
-		double		nruns = nbytes / (sortmembytes * 2);
-		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_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;
-	Var		   *leftvar;
-	double		outer_rows,
-				inner_rows;
-	double		ntuples;
-	Selectivity	outerscansel,
-				innerscansel;
-	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);
-
-	leftvar = get_leftop(firstclause->clause);
-	Assert(IsA(leftvar, Var));
-	if (VARISRELMEMBER(leftvar->varno, outer_path->parent))
-	{
-		/* 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;
-	}
-
-	outer_rows = outer_path->parent->rows * outerscansel;
-	inner_rows = inner_path->parent->rows * innerscansel;
-
-	/* 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)
-			* 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? */
-	{
-		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)
-			* innerscansel;
-	}
-	else
-	{
-		startup_cost += inner_path->startup_cost;
-		run_cost += (inner_path->total_cost - inner_path->startup_cost)
-			* innerscansel;
-	}
-
-	/*
-	 * 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 (VARISRELMEMBER(right->varno, inner_path->parent))
-	{
-		/* 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(VARISRELMEMBER(left->varno, inner_path->parent));
-		/* 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_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 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_function_size_estimates(Query *root, RelOptInfo *rel)
-{
-	/* Should only be applied to base relations that are functions */
-	Assert(length(rel->relids) == 1);
-	Assert(rel->rtekind == RTE_FUNCTION);
-
-	/*
-	 * Estimate number of rows the function itself will return.
-	 *
-	 * XXX no idea how to do this yet; but should at least check whether
-	 * function returns set or not...
-	 */
-	rel->tuples = 1000;
-
-	/* Now estimate number of output rows */
-	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_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);
-}