costsize.c

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/*-------------------------------------------------------------------------
 *
 * costsize.c
 *    Routines to compute (and set) relation sizes and path costs
 *
 * Path costs are measured in arbitrary units established by these basic
 * parameters:
 *
 *  seq_page_cost       Cost of a sequential page fetch
 *  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 execute an operator or function
 *
 * We expect that the kernel will typically do some amount of read-ahead
 * optimization; this in conjunction with seek costs means that seq_page_cost
 * is normally considerably less than random_page_cost.  (However, if the
 * database is fully cached in RAM, it is reasonable to set them equal.)
 *
 * 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.
 *
 * seq_page_cost and random_page_cost can also be overridden for an individual
 * tablespace, in case some data is on a fast disk and other data is on a slow
 * disk.  Per-tablespace overrides never apply to temporary work files such as
 * an external sort or a materialize node that overflows work_mem.
 *
 * 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->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.
 *
 * For largely historical reasons, most of the routines in this module use
 * the passed result Path only to store their results (rows, startup_cost and
 * total_cost) into.  All the input data they need is passed as separate
 * parameters, even though much of it could be extracted from the Path.
 * An exception is made for the cost_XXXjoin() routines, which expect all
 * the other fields of the passed XXXPath to be filled in, and similarly
 * cost_index() assumes the passed IndexPath is valid except for its output
 * values.
 *
 *
 * Portions Copyright (c) 1996-2013, PostgreSQL Global Development Group
 * Portions Copyright (c) 1994, Regents of the University of California
 *
 * IDENTIFICATION
 *    src/backend/optimizer/path/costsize.c
 *
 *-------------------------------------------------------------------------
 */

#include "postgres.h"

#ifdef _MSC_VER
#include <float.h> /* for _isnan */
#endif
#include <math.h>

#include "access/htup_details.h"
#include "executor/executor.h"
#include "executor/nodeHash.h"
#include "miscadmin.h"
#include "nodes/nodeFuncs.h"
#include "optimizer/clauses.h"
#include "optimizer/cost.h"
#include "optimizer/pathnode.h"
#include "optimizer/paths.h"
#include "optimizer/placeholder.h"
#include "optimizer/plancat.h"
#include "optimizer/planmain.h"
#include "optimizer/restrictinfo.h"
#include "parser/parsetree.h"
#include "utils/lsyscache.h"
#include "utils/selfuncs.h"
#include "utils/spccache.h"
#include "utils/tuplesort.h"


#define LOG2(x)  (log(x) / 0.693147180559945)


double      seq_page_cost = DEFAULT_SEQ_PAGE_COST;
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;

int         effective_cache_size = DEFAULT_EFFECTIVE_CACHE_SIZE;

Cost        disable_cost = 1.0e10;

bool        enable_seqscan = true;
bool        enable_indexscan = true;
bool        enable_indexonlyscan = true;
bool        enable_bitmapscan = true;
bool        enable_tidscan = true;
bool        enable_sort = true;
bool        enable_hashagg = true;
bool        enable_nestloop = true;
bool        enable_material = true;
bool        enable_mergejoin = true;
bool        enable_hashjoin = true;

typedef struct
{
    PlannerInfo *root;
    QualCost    total;
} cost_qual_eval_context;

static MergeScanSelCache *cached_scansel(PlannerInfo *root,
               RestrictInfo *rinfo,
               PathKey *pathkey);
static void cost_rescan(PlannerInfo *root, Path *path,
            Cost *rescan_startup_cost, Cost *rescan_total_cost);
static bool cost_qual_eval_walker(Node *node, cost_qual_eval_context *context);
static void get_restriction_qual_cost(PlannerInfo *root, RelOptInfo *baserel,
                          ParamPathInfo *param_info,
                          QualCost *qpqual_cost);
static bool has_indexed_join_quals(NestPath *joinpath);
static double approx_tuple_count(PlannerInfo *root, JoinPath *path,
                   List *quals);
static double calc_joinrel_size_estimate(PlannerInfo *root,
                           double outer_rows,
                           double inner_rows,
                           SpecialJoinInfo *sjinfo,
                           List *restrictlist);
static void set_rel_width(PlannerInfo *root, RelOptInfo *rel);
static double relation_byte_size(double tuples, int width);
static double page_size(double tuples, int width);


/*
 * clamp_row_est
 *      Force a row-count estimate to a sane value.
 */
double
clamp_row_est(double nrows)
{
    /*
     * Force estimate to be at least one row, to make explain output look
     * better and to avoid possible divide-by-zero when interpolating costs.
     * Make it an integer, too.
     */
    if (nrows <= 1.0)
        nrows = 1.0;
    else
        nrows = rint(nrows);

    return nrows;
}


/*
 * cost_seqscan
 *    Determines and returns the cost of scanning a relation sequentially.
 *
 * 'baserel' is the relation to be scanned
 * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
 */
void
cost_seqscan(Path *path, PlannerInfo *root,
             RelOptInfo *baserel, ParamPathInfo *param_info)
{
    Cost        startup_cost = 0;
    Cost        run_cost = 0;
    double      spc_seq_page_cost;
    QualCost    qpqual_cost;
    Cost        cpu_per_tuple;

    /* Should only be applied to base relations */
    Assert(baserel->relid > 0);
    Assert(baserel->rtekind == RTE_RELATION);

    /* Mark the path with the correct row estimate */
    if (param_info)
        path->rows = param_info->ppi_rows;
    else
        path->rows = baserel->rows;

    if (!enable_seqscan)
        startup_cost += disable_cost;

    /* fetch estimated page cost for tablespace containing table */
    get_tablespace_page_costs(baserel->reltablespace,
                              NULL,
                              &spc_seq_page_cost);

    /*
     * disk costs
     */
    run_cost += spc_seq_page_cost * baserel->pages;

    /* CPU costs */
    get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);

    startup_cost += qpqual_cost.startup;
    cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
    run_cost += cpu_per_tuple * baserel->tuples;

    path->startup_cost = startup_cost;
    path->total_cost = startup_cost + run_cost;
}

/*
 * cost_index
 *    Determines and returns the cost of scanning a relation using an index.
 *
 * 'path' describes the indexscan under consideration, and is complete
 *      except for the fields to be set by this routine
 * 'loop_count' is the number of repetitions of the indexscan to factor into
 *      estimates of caching behavior
 *
 * In addition to rows, startup_cost and total_cost, cost_index() sets the
 * path's indextotalcost and indexselectivity fields.  These values will be
 * needed if the IndexPath is used in a BitmapIndexScan.
 *
 * NOTE: path->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(IndexPath *path, PlannerInfo *root, double loop_count)
{
    IndexOptInfo *index = path->indexinfo;
    RelOptInfo *baserel = index->rel;
    bool        indexonly = (path->path.pathtype == T_IndexOnlyScan);
    List       *allclauses;
    Cost        startup_cost = 0;
    Cost        run_cost = 0;
    Cost        indexStartupCost;
    Cost        indexTotalCost;
    Selectivity indexSelectivity;
    double      indexCorrelation,
                csquared;
    double      spc_seq_page_cost,
                spc_random_page_cost;
    Cost        min_IO_cost,
                max_IO_cost;
    QualCost    qpqual_cost;
    Cost        cpu_per_tuple;
    double      tuples_fetched;
    double      pages_fetched;

    /* Should only be applied to base relations */
    Assert(IsA(baserel, RelOptInfo) &&
           IsA(index, IndexOptInfo));
    Assert(baserel->relid > 0);
    Assert(baserel->rtekind == RTE_RELATION);

    /* Mark the path with the correct row estimate */
    if (path->path.param_info)
    {
        path->path.rows = path->path.param_info->ppi_rows;
        /* also get the set of clauses that should be enforced by the scan */
        allclauses = list_concat(list_copy(path->path.param_info->ppi_clauses),
                                 baserel->baserestrictinfo);
    }
    else
    {
        path->path.rows = baserel->rows;
        /* allclauses should just be the rel's restriction clauses */
        allclauses = baserel->baserestrictinfo;
    }

    if (!enable_indexscan)
        startup_cost += disable_cost;
    /* we don't need to check enable_indexonlyscan; indxpath.c does that */

    /*
     * Call index-access-method-specific code to estimate the processing cost
     * for scanning the index, as well as the selectivity of the index (ie,
     * the fraction of main-table tuples we will have to retrieve) and its
     * correlation to the main-table tuple order.
     */
    OidFunctionCall7(index->amcostestimate,
                     PointerGetDatum(root),
                     PointerGetDatum(path),
                     Float8GetDatum(loop_count),
                     PointerGetDatum(&indexStartupCost),
                     PointerGetDatum(&indexTotalCost),
                     PointerGetDatum(&indexSelectivity),
                     PointerGetDatum(&indexCorrelation));

    /*
     * Save amcostestimate's results for possible use in bitmap scan planning.
     * We don't bother to save indexStartupCost or indexCorrelation, because a
     * bitmap scan doesn't care about either.
     */
    path->indextotalcost = indexTotalCost;
    path->indexselectivity = indexSelectivity;

    /* all costs for touching index itself included here */
    startup_cost += indexStartupCost;
    run_cost += indexTotalCost - indexStartupCost;

    /* estimate number of main-table tuples fetched */
    tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);

    /* fetch estimated page costs for tablespace containing table */
    get_tablespace_page_costs(baserel->reltablespace,
                              &spc_random_page_cost,
                              &spc_seq_page_cost);

    /*----------
     * Estimate number of main-table pages fetched, and compute I/O cost.
     *
     * When the index ordering is uncorrelated with the table ordering,
     * we use an approximation proposed by Mackert and Lohman (see
     * index_pages_fetched() for details) to compute the number of pages
     * fetched, and then charge spc_random_page_cost per page fetched.
     *
     * When the index ordering is exactly correlated with the table ordering
     * (just after a CLUSTER, for example), the number of pages fetched should
     * be exactly selectivity * table_size.  What's more, all but the first
     * will be sequential fetches, not the random fetches that occur in the
     * uncorrelated case.  So if the number of pages is more than 1, we
     * ought to charge
     *      spc_random_page_cost + (pages_fetched - 1) * spc_seq_page_cost
     * For partially-correlated indexes, we ought to charge somewhere between
     * these two estimates.  We currently interpolate linearly between the
     * estimates based on the correlation squared (XXX is that appropriate?).
     *
     * If it's an index-only scan, then we will not need to fetch any heap
     * pages for which the visibility map shows all tuples are visible.
     * Hence, reduce the estimated number of heap fetches accordingly.
     * We use the measured fraction of the entire heap that is all-visible,
     * which might not be particularly relevant to the subset of the heap
     * that this query will fetch; but it's not clear how to do better.
     *----------
     */
    if (loop_count > 1)
    {
        /*
         * For repeated indexscans, the appropriate estimate for the
         * uncorrelated case is to scale up the number of tuples fetched in
         * the Mackert and Lohman formula by the number of scans, so that we
         * estimate the number of pages fetched by all the scans; then
         * pro-rate the costs for one scan.  In this case we assume all the
         * fetches are random accesses.
         */
        pages_fetched = index_pages_fetched(tuples_fetched * loop_count,
                                            baserel->pages,
                                            (double) index->pages,
                                            root);

        if (indexonly)
            pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));

        max_IO_cost = (pages_fetched * spc_random_page_cost) / loop_count;

        /*
         * In the perfectly correlated case, the number of pages touched by
         * each scan is selectivity * table_size, and we can use the Mackert
         * and Lohman formula at the page level to estimate how much work is
         * saved by caching across scans.  We still assume all the fetches are
         * random, though, which is an overestimate that's hard to correct for
         * without double-counting the cache effects.  (But in most cases
         * where such a plan is actually interesting, only one page would get
         * fetched per scan anyway, so it shouldn't matter much.)
         */
        pages_fetched = ceil(indexSelectivity * (double) baserel->pages);

        pages_fetched = index_pages_fetched(pages_fetched * loop_count,
                                            baserel->pages,
                                            (double) index->pages,
                                            root);

        if (indexonly)
            pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));

        min_IO_cost = (pages_fetched * spc_random_page_cost) / loop_count;
    }
    else
    {
        /*
         * Normal case: apply the Mackert and Lohman formula, and then
         * interpolate between that and the correlation-derived result.
         */
        pages_fetched = index_pages_fetched(tuples_fetched,
                                            baserel->pages,
                                            (double) index->pages,
                                            root);

        if (indexonly)
            pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));

        /* max_IO_cost is for the perfectly uncorrelated case (csquared=0) */
        max_IO_cost = pages_fetched * spc_random_page_cost;

        /* min_IO_cost is for the perfectly correlated case (csquared=1) */
        pages_fetched = ceil(indexSelectivity * (double) baserel->pages);

        if (indexonly)
            pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));

        if (pages_fetched > 0)
        {
            min_IO_cost = spc_random_page_cost;
            if (pages_fetched > 1)
                min_IO_cost += (pages_fetched - 1) * spc_seq_page_cost;
        }
        else
            min_IO_cost = 0;
    }

    /*
     * Now interpolate based on estimated index order correlation to get total
     * disk I/O cost for main table accesses.
     */
    csquared = indexCorrelation * indexCorrelation;

    run_cost += max_IO_cost + csquared * (min_IO_cost - max_IO_cost);

    /*
     * Estimate CPU costs per tuple.
     *
     * What we want here is cpu_tuple_cost plus the evaluation costs of any
     * qual clauses that we have to evaluate as qpquals.  We approximate that
     * list as allclauses minus any clauses appearing in indexquals.  (We
     * assume that pointer equality is enough to recognize duplicate
     * RestrictInfos.)  This method neglects some considerations such as
     * clauses that needn't be checked because they are implied by a partial
     * index's predicate.  It does not seem worth the cycles to try to factor
     * those things in at this stage, even though createplan.c will take pains
     * to remove such unnecessary clauses from the qpquals list if this path
     * is selected for use.
     */
    cost_qual_eval(&qpqual_cost,
                   list_difference_ptr(allclauses, path->indexquals),
                   root);

    startup_cost += qpqual_cost.startup;
    cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;

    run_cost += cpu_per_tuple * tuples_fetched;

    path->path.startup_cost = startup_cost;
    path->path.total_cost = startup_cost + run_cost;
}

/*
 * index_pages_fetched
 *    Estimate the number of pages actually fetched after accounting for
 *    cache effects.
 *
 * 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)
 *
 * We assume that effective_cache_size is the total number of buffer pages
 * available for the whole query, and pro-rate that space across all the
 * tables in the query and the index currently under consideration.  (This
 * ignores space needed for other indexes used by the query, but since we
 * don't know which indexes will get used, we can't estimate that very well;
 * and in any case counting all the tables may well be an overestimate, since
 * depending on the join plan not all the tables may be scanned concurrently.)
 *
 * The product Ns is the number of tuples fetched; we pass in that
 * product rather than calculating it here.  "pages" is the number of pages
 * in the object under consideration (either an index or a table).
 * "index_pages" is the amount to add to the total table space, which was
 * computed for us by query_planner.
 *
 * Caller is expected to have ensured that tuples_fetched is greater than zero
 * and rounded to integer (see clamp_row_est).  The result will likewise be
 * greater than zero and integral.
 */
double
index_pages_fetched(double tuples_fetched, BlockNumber pages,
                    double index_pages, PlannerInfo *root)
{
    double      pages_fetched;
    double      total_pages;
    double      T,
                b;

    /* T is # pages in table, but don't allow it to be zero */
    T = (pages > 1) ? (double) pages : 1.0;

    /* Compute number of pages assumed to be competing for cache space */
    total_pages = root->total_table_pages + index_pages;
    total_pages = Max(total_pages, 1.0);
    Assert(T <= total_pages);

    /* b is pro-rated share of effective_cache_size */
    b = (double) effective_cache_size *T / total_pages;

    /* force it positive and integral */
    if (b <= 1.0)
        b = 1.0;
    else
        b = ceil(b);

    /* This part is the Mackert and Lohman formula */
    if (T <= b)
    {
        pages_fetched =
            (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
        if (pages_fetched >= T)
            pages_fetched = T;
        else
            pages_fetched = ceil(pages_fetched);
    }
    else
    {
        double      lim;

        lim = (2.0 * T * b) / (2.0 * T - b);
        if (tuples_fetched <= lim)
        {
            pages_fetched =
                (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
        }
        else
        {
            pages_fetched =
                b + (tuples_fetched - lim) * (T - b) / T;
        }
        pages_fetched = ceil(pages_fetched);
    }
    return pages_fetched;
}

/*
 * get_indexpath_pages
 *      Determine the total size of the indexes used in a bitmap index path.
 *
 * Note: if the same index is used more than once in a bitmap tree, we will
 * count it multiple times, which perhaps is the wrong thing ... but it's
 * not completely clear, and detecting duplicates is difficult, so ignore it
 * for now.
 */
static double
get_indexpath_pages(Path *bitmapqual)
{
    double      result = 0;
    ListCell   *l;

    if (IsA(bitmapqual, BitmapAndPath))
    {
        BitmapAndPath *apath = (BitmapAndPath *) bitmapqual;

        foreach(l, apath->bitmapquals)
        {
            result += get_indexpath_pages((Path *) lfirst(l));
        }
    }
    else if (IsA(bitmapqual, BitmapOrPath))
    {
        BitmapOrPath *opath = (BitmapOrPath *) bitmapqual;

        foreach(l, opath->bitmapquals)
        {
            result += get_indexpath_pages((Path *) lfirst(l));
        }
    }
    else if (IsA(bitmapqual, IndexPath))
    {
        IndexPath  *ipath = (IndexPath *) bitmapqual;

        result = (double) ipath->indexinfo->pages;
    }
    else
        elog(ERROR, "unrecognized node type: %d", nodeTag(bitmapqual));

    return result;
}

/*
 * cost_bitmap_heap_scan
 *    Determines and returns the cost of scanning a relation using a bitmap
 *    index-then-heap plan.
 *
 * 'baserel' is the relation to be scanned
 * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
 * 'bitmapqual' is a tree of IndexPaths, BitmapAndPaths, and BitmapOrPaths
 * 'loop_count' is the number of repetitions of the indexscan to factor into
 *      estimates of caching behavior
 *
 * Note: the component IndexPaths in bitmapqual should have been costed
 * using the same loop_count.
 */
void
cost_bitmap_heap_scan(Path *path, PlannerInfo *root, RelOptInfo *baserel,
                      ParamPathInfo *param_info,
                      Path *bitmapqual, double loop_count)
{
    Cost        startup_cost = 0;
    Cost        run_cost = 0;
    Cost        indexTotalCost;
    Selectivity indexSelectivity;
    QualCost    qpqual_cost;
    Cost        cpu_per_tuple;
    Cost        cost_per_page;
    double      tuples_fetched;
    double      pages_fetched;
    double      spc_seq_page_cost,
                spc_random_page_cost;
    double      T;

    /* Should only be applied to base relations */
    Assert(IsA(baserel, RelOptInfo));
    Assert(baserel->relid > 0);
    Assert(baserel->rtekind == RTE_RELATION);

    /* Mark the path with the correct row estimate */
    if (param_info)
        path->rows = param_info->ppi_rows;
    else
        path->rows = baserel->rows;

    if (!enable_bitmapscan)
        startup_cost += disable_cost;

    /*
     * Fetch total cost of obtaining the bitmap, as well as its total
     * selectivity.
     */
    cost_bitmap_tree_node(bitmapqual, &indexTotalCost, &indexSelectivity);

    startup_cost += indexTotalCost;

    /* Fetch estimated page costs for tablespace containing table. */
    get_tablespace_page_costs(baserel->reltablespace,
                              &spc_random_page_cost,
                              &spc_seq_page_cost);

    /*
     * Estimate number of main-table pages fetched.
     */
    tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);

    T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;

    if (loop_count > 1)
    {
        /*
         * For repeated bitmap scans, scale up the number of tuples fetched in
         * the Mackert and Lohman formula by the number of scans, so that we
         * estimate the number of pages fetched by all the scans. Then
         * pro-rate for one scan.
         */
        pages_fetched = index_pages_fetched(tuples_fetched * loop_count,
                                            baserel->pages,
                                            get_indexpath_pages(bitmapqual),
                                            root);
        pages_fetched /= loop_count;
    }
    else
    {
        /*
         * For a single scan, the number of heap pages that need to be fetched
         * is the same as the Mackert and Lohman formula for the case T <= b
         * (ie, no re-reads needed).
         */
        pages_fetched = (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
    }
    if (pages_fetched >= T)
        pages_fetched = T;
    else
        pages_fetched = ceil(pages_fetched);

    /*
     * For small numbers of pages we should charge spc_random_page_cost
     * apiece, while if nearly all the table's pages are being read, it's more
     * appropriate to charge spc_seq_page_cost apiece.  The effect is
     * nonlinear, too. For lack of a better idea, interpolate like this to
     * determine the cost per page.
     */
    if (pages_fetched >= 2.0)
        cost_per_page = spc_random_page_cost -
            (spc_random_page_cost - spc_seq_page_cost)
            * sqrt(pages_fetched / T);
    else
        cost_per_page = spc_random_page_cost;

    run_cost += pages_fetched * cost_per_page;

    /*
     * Estimate CPU costs per tuple.
     *
     * Often the indexquals don't need to be rechecked at each tuple ... but
     * not always, especially not if there are enough tuples involved that the
     * bitmaps become lossy.  For the moment, just assume they will be
     * rechecked always.  This means we charge the full freight for all the
     * scan clauses.
     */
    get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);

    startup_cost += qpqual_cost.startup;
    cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;

    run_cost += cpu_per_tuple * tuples_fetched;

    path->startup_cost = startup_cost;
    path->total_cost = startup_cost + run_cost;
}

/*
 * cost_bitmap_tree_node
 *      Extract cost and selectivity from a bitmap tree node (index/and/or)
 */
void
cost_bitmap_tree_node(Path *path, Cost *cost, Selectivity *selec)
{
    if (IsA(path, IndexPath))
    {
        *cost = ((IndexPath *) path)->indextotalcost;
        *selec = ((IndexPath *) path)->indexselectivity;

        /*
         * Charge a small amount per retrieved tuple to reflect the costs of
         * manipulating the bitmap.  This is mostly to make sure that a bitmap
         * scan doesn't look to be the same cost as an indexscan to retrieve a
         * single tuple.
         */
        *cost += 0.1 * cpu_operator_cost * path->rows;
    }
    else if (IsA(path, BitmapAndPath))
    {
        *cost = path->total_cost;
        *selec = ((BitmapAndPath *) path)->bitmapselectivity;
    }
    else if (IsA(path, BitmapOrPath))
    {
        *cost = path->total_cost;
        *selec = ((BitmapOrPath *) path)->bitmapselectivity;
    }
    else
    {
        elog(ERROR, "unrecognized node type: %d", nodeTag(path));
        *cost = *selec = 0;     /* keep compiler quiet */
    }
}

/*
 * cost_bitmap_and_node
 *      Estimate the cost of a BitmapAnd node
 *
 * Note that this considers only the costs of index scanning and bitmap
 * creation, not the eventual heap access.  In that sense the object isn't
 * truly a Path, but it has enough path-like properties (costs in particular)
 * to warrant treating it as one.  We don't bother to set the path rows field,
 * however.
 */
void
cost_bitmap_and_node(BitmapAndPath *path, PlannerInfo *root)
{
    Cost        totalCost;
    Selectivity selec;
    ListCell   *l;

    /*
     * We estimate AND selectivity on the assumption that the inputs are
     * independent.  This is probably often wrong, but we don't have the info
     * to do better.
     *
     * The runtime cost of the BitmapAnd itself is estimated at 100x
     * cpu_operator_cost for each tbm_intersect needed.  Probably too small,
     * definitely too simplistic?
     */
    totalCost = 0.0;
    selec = 1.0;
    foreach(l, path->bitmapquals)
    {
        Path       *subpath = (Path *) lfirst(l);
        Cost        subCost;
        Selectivity subselec;

        cost_bitmap_tree_node(subpath, &subCost, &subselec);

        selec *= subselec;

        totalCost += subCost;
        if (l != list_head(path->bitmapquals))
            totalCost += 100.0 * cpu_operator_cost;
    }
    path->bitmapselectivity = selec;
    path->path.rows = 0;        /* per above, not used */
    path->path.startup_cost = totalCost;
    path->path.total_cost = totalCost;
}

/*
 * cost_bitmap_or_node
 *      Estimate the cost of a BitmapOr node
 *
 * See comments for cost_bitmap_and_node.
 */
void
cost_bitmap_or_node(BitmapOrPath *path, PlannerInfo *root)
{
    Cost        totalCost;
    Selectivity selec;
    ListCell   *l;

    /*
     * We estimate OR selectivity on the assumption that the inputs are
     * non-overlapping, since that's often the case in "x IN (list)" type
     * situations.  Of course, we clamp to 1.0 at the end.
     *
     * The runtime cost of the BitmapOr itself is estimated at 100x
     * cpu_operator_cost for each tbm_union needed.  Probably too small,
     * definitely too simplistic?  We are aware that the tbm_unions are
     * optimized out when the inputs are BitmapIndexScans.
     */
    totalCost = 0.0;
    selec = 0.0;
    foreach(l, path->bitmapquals)
    {
        Path       *subpath = (Path *) lfirst(l);
        Cost        subCost;
        Selectivity subselec;

        cost_bitmap_tree_node(subpath, &subCost, &subselec);

        selec += subselec;

        totalCost += subCost;
        if (l != list_head(path->bitmapquals) &&
            !IsA(subpath, IndexPath))
            totalCost += 100.0 * cpu_operator_cost;
    }
    path->bitmapselectivity = Min(selec, 1.0);
    path->path.rows = 0;        /* per above, not used */
    path->path.startup_cost = totalCost;
    path->path.total_cost = totalCost;
}

/*
 * cost_tidscan
 *    Determines and returns the cost of scanning a relation using TIDs.
 *
 * 'baserel' is the relation to be scanned
 * 'tidquals' is the list of TID-checkable quals
 * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
 */
void
cost_tidscan(Path *path, PlannerInfo *root,
             RelOptInfo *baserel, List *tidquals, ParamPathInfo *param_info)
{
    Cost        startup_cost = 0;
    Cost        run_cost = 0;
    bool        isCurrentOf = false;
    QualCost    qpqual_cost;
    Cost        cpu_per_tuple;
    QualCost    tid_qual_cost;
    int         ntuples;
    ListCell   *l;
    double      spc_random_page_cost;

    /* Should only be applied to base relations */
    Assert(baserel->relid > 0);
    Assert(baserel->rtekind == RTE_RELATION);

    /* Mark the path with the correct row estimate */
    if (param_info)
        path->rows = param_info->ppi_rows;
    else
        path->rows = baserel->rows;

    /* Count how many tuples we expect to retrieve */
    ntuples = 0;
    foreach(l, tidquals)
    {
        if (IsA(lfirst(l), ScalarArrayOpExpr))
        {
            /* Each element of the array yields 1 tuple */
            ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) lfirst(l);
            Node       *arraynode = (Node *) lsecond(saop->args);

            ntuples += estimate_array_length(arraynode);
        }
        else if (IsA(lfirst(l), CurrentOfExpr))
        {
            /* CURRENT OF yields 1 tuple */
            isCurrentOf = true;
            ntuples++;
        }
        else
        {
            /* It's just CTID = something, count 1 tuple */
            ntuples++;
        }
    }

    /*
     * We must force TID scan for WHERE CURRENT OF, because only nodeTidscan.c
     * understands how to do it correctly.  Therefore, honor enable_tidscan
     * only when CURRENT OF isn't present.  Also note that cost_qual_eval
     * counts a CurrentOfExpr as having startup cost disable_cost, which we
     * subtract off here; that's to prevent other plan types such as seqscan
     * from winning.
     */
    if (isCurrentOf)
    {
        Assert(baserel->baserestrictcost.startup >= disable_cost);
        startup_cost -= disable_cost;
    }
    else if (!enable_tidscan)
        startup_cost += disable_cost;

    /*
     * The TID qual expressions will be computed once, any other baserestrict
     * quals once per retrived tuple.
     */
    cost_qual_eval(&tid_qual_cost, tidquals, root);

    /* fetch estimated page cost for tablespace containing table */
    get_tablespace_page_costs(baserel->reltablespace,
                              &spc_random_page_cost,
                              NULL);

    /* disk costs --- assume each tuple on a different page */
    run_cost += spc_random_page_cost * ntuples;

    /* Add scanning CPU costs */
    get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);

    /* XXX currently we assume TID quals are a subset of qpquals */
    startup_cost += qpqual_cost.startup + tid_qual_cost.per_tuple;
    cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple -
        tid_qual_cost.per_tuple;
    run_cost += cpu_per_tuple * ntuples;

    path->startup_cost = startup_cost;
    path->total_cost = startup_cost + run_cost;
}

/*
 * cost_subqueryscan
 *    Determines and returns the cost of scanning a subquery RTE.
 *
 * 'baserel' is the relation to be scanned
 * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
 */
void
cost_subqueryscan(Path *path, PlannerInfo *root,
                  RelOptInfo *baserel, ParamPathInfo *param_info)
{
    Cost        startup_cost;
    Cost        run_cost;
    QualCost    qpqual_cost;
    Cost        cpu_per_tuple;

    /* Should only be applied to base relations that are subqueries */
    Assert(baserel->relid > 0);
    Assert(baserel->rtekind == RTE_SUBQUERY);

    /* Mark the path with the correct row estimate */
    if (param_info)
        path->rows = param_info->ppi_rows;
    else
        path->rows = baserel->rows;

    /*
     * Cost of path is cost of evaluating the subplan, plus cost of evaluating
     * any restriction clauses that will be attached to the SubqueryScan node,
     * plus cpu_tuple_cost to account for selection and projection overhead.
     */
    path->startup_cost = baserel->subplan->startup_cost;
    path->total_cost = baserel->subplan->total_cost;

    get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);

    startup_cost = qpqual_cost.startup;
    cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
    run_cost = cpu_per_tuple * baserel->tuples;

    path->startup_cost += startup_cost;
    path->total_cost += startup_cost + run_cost;
}

/*
 * cost_functionscan
 *    Determines and returns the cost of scanning a function RTE.
 *
 * 'baserel' is the relation to be scanned
 * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
 */
void
cost_functionscan(Path *path, PlannerInfo *root,
                  RelOptInfo *baserel, ParamPathInfo *param_info)
{
    Cost        startup_cost = 0;
    Cost        run_cost = 0;
    QualCost    qpqual_cost;
    Cost        cpu_per_tuple;
    RangeTblEntry *rte;
    QualCost    exprcost;

    /* Should only be applied to base relations that are functions */
    Assert(baserel->relid > 0);
    rte = planner_rt_fetch(baserel->relid, root);
    Assert(rte->rtekind == RTE_FUNCTION);

    /* Mark the path with the correct row estimate */
    if (param_info)
        path->rows = param_info->ppi_rows;
    else
        path->rows = baserel->rows;

    /*
     * Estimate costs of executing the function expression.
     *
     * Currently, nodeFunctionscan.c always executes the function to
     * completion before returning any rows, and caches the results in a
     * tuplestore.  So the function eval cost is all startup cost, and per-row
     * costs are minimal.
     *
     * XXX in principle we ought to charge tuplestore spill costs if the
     * number of rows is large.  However, given how phony our rowcount
     * estimates for functions tend to be, there's not a lot of point in that
     * refinement right now.
     */
    cost_qual_eval_node(&exprcost, rte->funcexpr, root);

    startup_cost += exprcost.startup + exprcost.per_tuple;

    /* Add scanning CPU costs */
    get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);

    startup_cost += qpqual_cost.startup;
    cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
    run_cost += cpu_per_tuple * baserel->tuples;

    path->startup_cost = startup_cost;
    path->total_cost = startup_cost + run_cost;
}

/*
 * cost_valuesscan
 *    Determines and returns the cost of scanning a VALUES RTE.
 *
 * 'baserel' is the relation to be scanned
 * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
 */
void
cost_valuesscan(Path *path, PlannerInfo *root,
                RelOptInfo *baserel, ParamPathInfo *param_info)
{
    Cost        startup_cost = 0;
    Cost        run_cost = 0;
    QualCost    qpqual_cost;
    Cost        cpu_per_tuple;

    /* Should only be applied to base relations that are values lists */
    Assert(baserel->relid > 0);
    Assert(baserel->rtekind == RTE_VALUES);

    /* Mark the path with the correct row estimate */
    if (param_info)
        path->rows = param_info->ppi_rows;
    else
        path->rows = baserel->rows;

    /*
     * For now, estimate list evaluation cost at one operator eval per list
     * (probably pretty bogus, but is it worth being smarter?)
     */
    cpu_per_tuple = cpu_operator_cost;

    /* Add scanning CPU costs */
    get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);

    startup_cost += qpqual_cost.startup;
    cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
    run_cost += cpu_per_tuple * baserel->tuples;

    path->startup_cost = startup_cost;
    path->total_cost = startup_cost + run_cost;
}

/*
 * cost_ctescan
 *    Determines and returns the cost of scanning a CTE RTE.
 *
 * Note: this is used for both self-reference and regular CTEs; the
 * possible cost differences are below the threshold of what we could
 * estimate accurately anyway.  Note that the costs of evaluating the
 * referenced CTE query are added into the final plan as initplan costs,
 * and should NOT be counted here.
 */
void
cost_ctescan(Path *path, PlannerInfo *root,
             RelOptInfo *baserel, ParamPathInfo *param_info)
{
    Cost        startup_cost = 0;
    Cost        run_cost = 0;
    QualCost    qpqual_cost;
    Cost        cpu_per_tuple;

    /* Should only be applied to base relations that are CTEs */
    Assert(baserel->relid > 0);
    Assert(baserel->rtekind == RTE_CTE);

    /* Mark the path with the correct row estimate */
    if (param_info)
        path->rows = param_info->ppi_rows;
    else
        path->rows = baserel->rows;

    /* Charge one CPU tuple cost per row for tuplestore manipulation */
    cpu_per_tuple = cpu_tuple_cost;

    /* Add scanning CPU costs */
    get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);

    startup_cost += qpqual_cost.startup;
    cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
    run_cost += cpu_per_tuple * baserel->tuples;

    path->startup_cost = startup_cost;
    path->total_cost = startup_cost + run_cost;
}

/*
 * cost_recursive_union
 *    Determines and returns the cost of performing a recursive union,
 *    and also the estimated output size.
 *
 * We are given Plans for the nonrecursive and recursive terms.
 *
 * Note that the arguments and output are Plans, not Paths as in most of
 * the rest of this module.  That's because we don't bother setting up a
 * Path representation for recursive union --- we have only one way to do it.
 */
void
cost_recursive_union(Plan *runion, Plan *nrterm, Plan *rterm)
{
    Cost        startup_cost;
    Cost        total_cost;
    double      total_rows;

    /* We probably have decent estimates for the non-recursive term */
    startup_cost = nrterm->startup_cost;
    total_cost = nrterm->total_cost;
    total_rows = nrterm->plan_rows;

    /*
     * We arbitrarily assume that about 10 recursive iterations will be
     * needed, and that we've managed to get a good fix on the cost and output
     * size of each one of them.  These are mighty shaky assumptions but it's
     * hard to see how to do better.
     */
    total_cost += 10 * rterm->total_cost;
    total_rows += 10 * rterm->plan_rows;

    /*
     * Also charge cpu_tuple_cost per row to account for the costs of
     * manipulating the tuplestores.  (We don't worry about possible
     * spill-to-disk costs.)
     */
    total_cost += cpu_tuple_cost * total_rows;

    runion->startup_cost = startup_cost;
    runion->total_cost = total_cost;
    runion->plan_rows = total_rows;
    runion->plan_width = Max(nrterm->plan_width, rterm->plan_width);
}

/*
 * cost_sort
 *    Determines and returns the cost of sorting a relation, including
 *    the cost of reading the input data.
 *
 * If the total volume of data to sort is less than sort_mem, we will do
 * an in-memory sort, which requires no I/O and about t*log2(t) tuple
 * comparisons for t tuples.
 *
 * If the total volume exceeds sort_mem, we switch to a tape-style merge
 * algorithm.  There will still be about t*log2(t) tuple comparisons in
 * total, but we will also need to write and read each tuple once per
 * merge pass.  We expect about ceil(logM(r)) merge passes where r is the
 * number of initial runs formed and M is the merge order used by tuplesort.c.
 * Since the average initial run should be about twice sort_mem, we have
 *      disk traffic = 2 * relsize * ceil(logM(p / (2*sort_mem)))
 *      cpu = comparison_cost * t * log2(t)
 *
 * If the sort is bounded (i.e., only the first k result tuples are needed)
 * and k tuples can fit into sort_mem, we use a heap method that keeps only
 * k tuples in the heap; this will require about t*log2(k) tuple comparisons.
 *
 * The disk traffic is assumed to be 3/4ths sequential and 1/4th random
 * accesses (XXX can't we refine that guess?)
 *
 * By default, we charge two operator evals per tuple comparison, which should
 * be in the right ballpark in most cases.  The caller can tweak this by
 * specifying nonzero comparison_cost; typically that's used for any extra
 * work that has to be done to prepare the inputs to the comparison operators.
 *
 * 'pathkeys' is a list of sort keys
 * 'input_cost' is the total cost for reading the input data
 * 'tuples' is the number of tuples in the relation
 * 'width' is the average tuple width in bytes
 * 'comparison_cost' is the extra cost per comparison, if any
 * 'sort_mem' is the number of kilobytes of work memory allowed for the sort
 * 'limit_tuples' is the bound on the number of output tuples; -1 if no bound
 *
 * NOTE: some callers currently pass NIL for pathkeys because they
 * can't conveniently supply the sort keys.  Since this routine doesn't
 * currently do anything with pathkeys anyway, that doesn't matter...
 * but if it ever does, it should react gracefully to lack of key data.
 * (Actually, the thing we'd most likely be interested in is just the number
 * of sort keys, which all callers *could* supply.)
 */
void
cost_sort(Path *path, PlannerInfo *root,
          List *pathkeys, Cost input_cost, double tuples, int width,
          Cost comparison_cost, int sort_mem,
          double limit_tuples)
{
    Cost        startup_cost = input_cost;
    Cost        run_cost = 0;
    double      input_bytes = relation_byte_size(tuples, width);
    double      output_bytes;
    double      output_tuples;
    long        sort_mem_bytes = sort_mem * 1024L;

    if (!enable_sort)
        startup_cost += disable_cost;

    path->rows = tuples;

    /*
     * We want to be sure the cost of a sort is never estimated as zero, even
     * if passed-in tuple count is zero.  Besides, mustn't do log(0)...
     */
    if (tuples < 2.0)
        tuples = 2.0;

    /* Include the default cost-per-comparison */
    comparison_cost += 2.0 * cpu_operator_cost;

    /* Do we have a useful LIMIT? */
    if (limit_tuples > 0 && limit_tuples < tuples)
    {
        output_tuples = limit_tuples;
        output_bytes = relation_byte_size(output_tuples, width);
    }
    else
    {
        output_tuples = tuples;
        output_bytes = input_bytes;
    }

    if (output_bytes > sort_mem_bytes)
    {
        /*
         * We'll have to use a disk-based sort of all the tuples
         */
        double      npages = ceil(input_bytes / BLCKSZ);
        double      nruns = (input_bytes / sort_mem_bytes) * 0.5;
        double      mergeorder = tuplesort_merge_order(sort_mem_bytes);
        double      log_runs;
        double      npageaccesses;

        /*
         * CPU costs
         *
         * Assume about N log2 N comparisons
         */
        startup_cost += comparison_cost * tuples * LOG2(tuples);

        /* Disk costs */

        /* Compute logM(r) as log(r) / log(M) */
        if (nruns > mergeorder)
            log_runs = ceil(log(nruns) / log(mergeorder));
        else
            log_runs = 1.0;
        npageaccesses = 2.0 * npages * log_runs;
        /* Assume 3/4ths of accesses are sequential, 1/4th are not */
        startup_cost += npageaccesses *
            (seq_page_cost * 0.75 + random_page_cost * 0.25);
    }
    else if (tuples > 2 * output_tuples || input_bytes > sort_mem_bytes)
    {
        /*
         * We'll use a bounded heap-sort keeping just K tuples in memory, for
         * a total number of tuple comparisons of N log2 K; but the constant
         * factor is a bit higher than for quicksort.  Tweak it so that the
         * cost curve is continuous at the crossover point.
         */
        startup_cost += comparison_cost * tuples * LOG2(2.0 * output_tuples);
    }
    else
    {
        /* We'll use plain quicksort on all the input tuples */
        startup_cost += comparison_cost * tuples * LOG2(tuples);
    }

    /*
     * Also charge a small amount (arbitrarily set equal to operator cost) per
     * extracted tuple.  We don't charge cpu_tuple_cost because a Sort node
     * doesn't do qual-checking or projection, so it has less overhead than
     * most plan nodes.  Note it's correct to use tuples not output_tuples
     * here --- the upper LIMIT will pro-rate the run cost so we'd be double
     * counting the LIMIT otherwise.
     */
    run_cost += cpu_operator_cost * tuples;

    path->startup_cost = startup_cost;
    path->total_cost = startup_cost + run_cost;
}

/*
 * cost_merge_append
 *    Determines and returns the cost of a MergeAppend node.
 *
 * MergeAppend merges several pre-sorted input streams, using a heap that
 * at any given instant holds the next tuple from each stream.  If there
 * are N streams, we need about N*log2(N) tuple comparisons to construct
 * the heap at startup, and then for each output tuple, about log2(N)
 * comparisons to delete the top heap entry and another log2(N) comparisons
 * to insert its successor from the same stream.
 *
 * (The effective value of N will drop once some of the input streams are
 * exhausted, but it seems unlikely to be worth trying to account for that.)
 *
 * The heap is never spilled to disk, since we assume N is not very large.
 * So this is much simpler than cost_sort.
 *
 * As in cost_sort, we charge two operator evals per tuple comparison.
 *
 * 'pathkeys' is a list of sort keys
 * 'n_streams' is the number of input streams
 * 'input_startup_cost' is the sum of the input streams' startup costs
 * 'input_total_cost' is the sum of the input streams' total costs
 * 'tuples' is the number of tuples in all the streams
 */
void
cost_merge_append(Path *path, PlannerInfo *root,
                  List *pathkeys, int n_streams,
                  Cost input_startup_cost, Cost input_total_cost,
                  double tuples)
{
    Cost        startup_cost = 0;
    Cost        run_cost = 0;
    Cost        comparison_cost;
    double      N;
    double      logN;

    /*
     * Avoid log(0)...
     */
    N = (n_streams < 2) ? 2.0 : (double) n_streams;
    logN = LOG2(N);

    /* Assumed cost per tuple comparison */
    comparison_cost = 2.0 * cpu_operator_cost;

    /* Heap creation cost */
    startup_cost += comparison_cost * N * logN;

    /* Per-tuple heap maintenance cost */
    run_cost += tuples * comparison_cost * 2.0 * logN;

    /*
     * Also charge a small amount (arbitrarily set equal to operator cost) per
     * extracted tuple.  We don't charge cpu_tuple_cost because a MergeAppend
     * node doesn't do qual-checking or projection, so it has less overhead
     * than most plan nodes.
     */
    run_cost += cpu_operator_cost * tuples;

    path->startup_cost = startup_cost + input_startup_cost;
    path->total_cost = startup_cost + run_cost + input_total_cost;
}

/*
 * cost_material
 *    Determines and returns the cost of materializing a relation, including
 *    the cost of reading the input data.
 *
 * If the total volume of data to materialize exceeds work_mem, we will need
 * to write it to disk, so the cost is much higher in that case.
 *
 * Note that here we are estimating the costs for the first scan of the
 * relation, so the materialization is all overhead --- any savings will
 * occur only on rescan, which is estimated in cost_rescan.
 */
void
cost_material(Path *path,
              Cost input_startup_cost, Cost input_total_cost,
              double tuples, int width)
{
    Cost        startup_cost = input_startup_cost;
    Cost        run_cost = input_total_cost - input_startup_cost;
    double      nbytes = relation_byte_size(tuples, width);
    long        work_mem_bytes = work_mem * 1024L;

    path->rows = tuples;

    /*
     * Whether spilling or not, charge 2x cpu_operator_cost per tuple to
     * reflect bookkeeping overhead.  (This rate must be more than what
     * cost_rescan charges for materialize, ie, cpu_operator_cost per tuple;
     * if it is exactly the same then there will be a cost tie between
     * nestloop with A outer, materialized B inner and nestloop with B outer,
     * materialized A inner.  The extra cost ensures we'll prefer
     * materializing the smaller rel.)  Note that this is normally a good deal
     * less than cpu_tuple_cost; which is OK because a Material plan node
     * doesn't do qual-checking or projection, so it's got less overhead than
     * most plan nodes.
     */
    run_cost += 2 * cpu_operator_cost * tuples;

    /*
     * If we will spill to disk, charge at the rate of seq_page_cost per page.
     * This cost is assumed to be evenly spread through the plan run phase,
     * which isn't exactly accurate but our cost model doesn't allow for
     * nonuniform costs within the run phase.
     */
    if (nbytes > work_mem_bytes)
    {
        double      npages = ceil(nbytes / BLCKSZ);

        run_cost += seq_page_cost * npages;
    }

    path->startup_cost = startup_cost;
    path->total_cost = startup_cost + run_cost;
}

/*
 * cost_agg
 *      Determines and returns the cost of performing an Agg plan node,
 *      including the cost of its input.
 *
 * aggcosts can be NULL when there are no actual aggregate functions (i.e.,
 * we are using a hashed Agg node just to do grouping).
 *
 * Note: when aggstrategy == AGG_SORTED, caller must ensure that input costs
 * are for appropriately-sorted input.
 */
void
cost_agg(Path *path, PlannerInfo *root,
         AggStrategy aggstrategy, const AggClauseCosts *aggcosts,
         int numGroupCols, double numGroups,
         Cost input_startup_cost, Cost input_total_cost,
         double input_tuples)
{
    double      output_tuples;
    Cost        startup_cost;
    Cost        total_cost;
    AggClauseCosts dummy_aggcosts;

    /* Use all-zero per-aggregate costs if NULL is passed */
    if (aggcosts == NULL)
    {
        Assert(aggstrategy == AGG_HASHED);
        MemSet(&dummy_aggcosts, 0, sizeof(AggClauseCosts));
        aggcosts = &dummy_aggcosts;
    }

    /*
     * The transCost.per_tuple component of aggcosts should be charged once
     * per input tuple, corresponding to the costs of evaluating the aggregate
     * transfns and their input expressions (with any startup cost of course
     * charged but once).  The finalCost component is charged once per output
     * tuple, corresponding to the costs of evaluating the finalfns.
     *
     * If we are grouping, we charge an additional cpu_operator_cost per
     * grouping column per input tuple for grouping comparisons.
     *
     * We will produce a single output tuple if not grouping, and a tuple per
     * group otherwise.  We charge cpu_tuple_cost for each output tuple.
     *
     * Note: in this cost model, AGG_SORTED and AGG_HASHED have exactly the
     * same total CPU cost, but AGG_SORTED has lower startup cost.  If the
     * input path is already sorted appropriately, AGG_SORTED should be
     * preferred (since it has no risk of memory overflow).  This will happen
     * as long as the computed total costs are indeed exactly equal --- but if
     * there's roundoff error we might do the wrong thing.  So be sure that
     * the computations below form the same intermediate values in the same
     * order.
     */
    if (aggstrategy == AGG_PLAIN)
    {
        startup_cost = input_total_cost;
        startup_cost += aggcosts->transCost.startup;
        startup_cost += aggcosts->transCost.per_tuple * input_tuples;
        startup_cost += aggcosts->finalCost;
        /* we aren't grouping */
        total_cost = startup_cost + cpu_tuple_cost;
        output_tuples = 1;
    }
    else if (aggstrategy == AGG_SORTED)
    {
        /* Here we are able to deliver output on-the-fly */
        startup_cost = input_startup_cost;
        total_cost = input_total_cost;
        /* calcs phrased this way to match HASHED case, see note above */
        total_cost += aggcosts->transCost.startup;
        total_cost += aggcosts->transCost.per_tuple * input_tuples;
        total_cost += (cpu_operator_cost * numGroupCols) * input_tuples;
        total_cost += aggcosts->finalCost * numGroups;
        total_cost += cpu_tuple_cost * numGroups;
        output_tuples = numGroups;
    }
    else
    {
        /* must be AGG_HASHED */
        startup_cost = input_total_cost;
        startup_cost += aggcosts->transCost.startup;
        startup_cost += aggcosts->transCost.per_tuple * input_tuples;
        startup_cost += (cpu_operator_cost * numGroupCols) * input_tuples;
        total_cost = startup_cost;
        total_cost += aggcosts->finalCost * numGroups;
        total_cost += cpu_tuple_cost * numGroups;
        output_tuples = numGroups;
    }

    path->rows = output_tuples;
    path->startup_cost = startup_cost;
    path->total_cost = total_cost;
}

/*
 * cost_windowagg
 *      Determines and returns the cost of performing a WindowAgg plan node,
 *      including the cost of its input.
 *
 * Input is assumed already properly sorted.
 */
void
cost_windowagg(Path *path, PlannerInfo *root,
               List *windowFuncs, int numPartCols, int numOrderCols,
               Cost input_startup_cost, Cost input_total_cost,
               double input_tuples)
{
    Cost        startup_cost;
    Cost        total_cost;
    ListCell   *lc;

    startup_cost = input_startup_cost;
    total_cost = input_total_cost;

    /*
     * Window functions are assumed to cost their stated execution cost, plus
     * the cost of evaluating their input expressions, per tuple.  Since they
     * may in fact evaluate their inputs at multiple rows during each cycle,
     * this could be a drastic underestimate; but without a way to know how
     * many rows the window function will fetch, it's hard to do better.  In
     * any case, it's a good estimate for all the built-in window functions,
     * so we'll just do this for now.
     */
    foreach(lc, windowFuncs)
    {
        WindowFunc *wfunc = (WindowFunc *) lfirst(lc);
        Cost        wfunccost;
        QualCost    argcosts;

        Assert(IsA(wfunc, WindowFunc));

        wfunccost = get_func_cost(wfunc->winfnoid) * cpu_operator_cost;

        /* also add the input expressions' cost to per-input-row costs */
        cost_qual_eval_node(&argcosts, (Node *) wfunc->args, root);
        startup_cost += argcosts.startup;
        wfunccost += argcosts.per_tuple;

        total_cost += wfunccost * input_tuples;
    }

    /*
     * We also charge cpu_operator_cost per grouping column per tuple for
     * grouping comparisons, plus cpu_tuple_cost per tuple for general
     * overhead.
     *
     * XXX this neglects costs of spooling the data to disk when it overflows
     * work_mem.  Sooner or later that should get accounted for.
     */
    total_cost += cpu_operator_cost * (numPartCols + numOrderCols) * input_tuples;
    total_cost += cpu_tuple_cost * input_tuples;

    path->rows = input_tuples;
    path->startup_cost = startup_cost;
    path->total_cost = total_cost;
}

/*
 * cost_group
 *      Determines and returns the cost of performing a Group plan node,
 *      including the cost of its input.
 *
 * Note: caller must ensure that input costs are for appropriately-sorted
 * input.
 */
void
cost_group(Path *path, PlannerInfo *root,
           int numGroupCols, double numGroups,
           Cost input_startup_cost, Cost input_total_cost,
           double input_tuples)
{
    Cost        startup_cost;
    Cost        total_cost;

    startup_cost = input_startup_cost;
    total_cost = input_total_cost;

    /*
     * Charge one cpu_operator_cost per comparison per input tuple. We assume
     * all columns get compared at most of the tuples.
     */
    total_cost += cpu_operator_cost * input_tuples * numGroupCols;

    path->rows = numGroups;
    path->startup_cost = startup_cost;
    path->total_cost = total_cost;
}

/*
 * initial_cost_nestloop
 *    Preliminary estimate of the cost of a nestloop join path.
 *
 * This must quickly produce lower-bound estimates of the path's startup and
 * total costs.  If we are unable to eliminate the proposed path from
 * consideration using the lower bounds, final_cost_nestloop will be called
 * to obtain the final estimates.
 *
 * The exact division of labor between this function and final_cost_nestloop
 * is private to them, and represents a tradeoff between speed of the initial
 * estimate and getting a tight lower bound.  We choose to not examine the
 * join quals here, since that's by far the most expensive part of the
 * calculations.  The end result is that CPU-cost considerations must be
 * left for the second phase.
 *
 * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
 *      other data to be used by final_cost_nestloop
 * 'jointype' is the type of join to be performed
 * 'outer_path' is the outer input to the join
 * 'inner_path' is the inner input to the join
 * 'sjinfo' is extra info about the join for selectivity estimation
 * 'semifactors' contains valid data if jointype is SEMI or ANTI
 */
void
initial_cost_nestloop(PlannerInfo *root, JoinCostWorkspace *workspace,
                      JoinType jointype,
                      Path *outer_path, Path *inner_path,
                      SpecialJoinInfo *sjinfo,
                      SemiAntiJoinFactors *semifactors)
{
    Cost        startup_cost = 0;
    Cost        run_cost = 0;
    double      outer_path_rows = outer_path->rows;
    Cost        inner_rescan_start_cost;
    Cost        inner_rescan_total_cost;
    Cost        inner_run_cost;
    Cost        inner_rescan_run_cost;

    /* estimate costs to rescan the inner relation */
    cost_rescan(root, inner_path,
                &inner_rescan_start_cost,
                &inner_rescan_total_cost);

    /* cost of source data */

    /*
     * NOTE: clearly, we must pay both outer and inner paths' startup_cost
     * before we can start returning tuples, so the join's startup cost is
     * their sum.  We'll also pay the inner path's rescan startup cost
     * multiple times.
     */
    startup_cost += outer_path->startup_cost + inner_path->startup_cost;
    run_cost += outer_path->total_cost - outer_path->startup_cost;
    if (outer_path_rows > 1)
        run_cost += (outer_path_rows - 1) * inner_rescan_start_cost;

    inner_run_cost = inner_path->total_cost - inner_path->startup_cost;
    inner_rescan_run_cost = inner_rescan_total_cost - inner_rescan_start_cost;

    if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
    {
        double      outer_matched_rows;
        Selectivity inner_scan_frac;

        /*
         * SEMI or ANTI join: executor will stop after first match.
         *
         * For an outer-rel row that has at least one match, we can expect the
         * inner scan to stop after a fraction 1/(match_count+1) of the inner
         * rows, if the matches are evenly distributed.  Since they probably
         * aren't quite evenly distributed, we apply a fuzz factor of 2.0 to
         * that fraction.  (If we used a larger fuzz factor, we'd have to
         * clamp inner_scan_frac to at most 1.0; but since match_count is at
         * least 1, no such clamp is needed now.)
         *
         * A complicating factor is that rescans may be cheaper than first
         * scans.  If we never scan all the way to the end of the inner rel,
         * it might be (depending on the plan type) that we'd never pay the
         * whole inner first-scan run cost.  However it is difficult to
         * estimate whether that will happen, so be conservative and always
         * charge the whole first-scan cost once.
         */
        run_cost += inner_run_cost;

        outer_matched_rows = rint(outer_path_rows * semifactors->outer_match_frac);
        inner_scan_frac = 2.0 / (semifactors->match_count + 1.0);

        /* Add inner run cost for additional outer tuples having matches */
        if (outer_matched_rows > 1)
            run_cost += (outer_matched_rows - 1) * inner_rescan_run_cost * inner_scan_frac;

        /*
         * The cost of processing unmatched rows varies depending on the
         * details of the joinclauses, so we leave that part for later.
         */

        /* Save private data for final_cost_nestloop */
        workspace->outer_matched_rows = outer_matched_rows;
        workspace->inner_scan_frac = inner_scan_frac;
    }
    else
    {
        /* Normal case; we'll scan whole input rel for each outer row */
        run_cost += inner_run_cost;
        if (outer_path_rows > 1)
            run_cost += (outer_path_rows - 1) * inner_rescan_run_cost;
    }

    /* CPU costs left for later */

    /* Public result fields */
    workspace->startup_cost = startup_cost;
    workspace->total_cost = startup_cost + run_cost;
    /* Save private data for final_cost_nestloop */
    workspace->run_cost = run_cost;
    workspace->inner_rescan_run_cost = inner_rescan_run_cost;
}

/*
 * final_cost_nestloop
 *    Final estimate of the cost and result size of a nestloop join path.
 *
 * 'path' is already filled in except for the rows and cost fields
 * 'workspace' is the result from initial_cost_nestloop
 * 'sjinfo' is extra info about the join for selectivity estimation
 * 'semifactors' contains valid data if path->jointype is SEMI or ANTI
 */
void
final_cost_nestloop(PlannerInfo *root, NestPath *path,
                    JoinCostWorkspace *workspace,
                    SpecialJoinInfo *sjinfo,
                    SemiAntiJoinFactors *semifactors)
{
    Path       *outer_path = path->outerjoinpath;
    Path       *inner_path = path->innerjoinpath;
    double      outer_path_rows = outer_path->rows;
    double      inner_path_rows = inner_path->rows;
    Cost        startup_cost = workspace->startup_cost;
    Cost        run_cost = workspace->run_cost;
    Cost        inner_rescan_run_cost = workspace->inner_rescan_run_cost;
    Cost        cpu_per_tuple;
    QualCost    restrict_qual_cost;
    double      ntuples;

    /* Mark the path with the correct row estimate */
    if (path->path.param_info)
        path->path.rows = path->path.param_info->ppi_rows;
    else
        path->path.rows = path->path.parent->rows;

    /*
     * We could include disable_cost in the preliminary estimate, but that
     * would amount to optimizing for the case where the join method is
     * disabled, which doesn't seem like the way to bet.
     */
    if (!enable_nestloop)
        startup_cost += disable_cost;

    /* cost of source data */

    if (path->jointype == JOIN_SEMI || path->jointype == JOIN_ANTI)
    {
        double      outer_matched_rows = workspace->outer_matched_rows;
        Selectivity inner_scan_frac = workspace->inner_scan_frac;

        /*
         * SEMI or ANTI join: executor will stop after first match.
         */

        /* Compute number of tuples processed (not number emitted!) */
        ntuples = outer_matched_rows * inner_path_rows * inner_scan_frac;

        /*
         * For unmatched outer-rel rows, there are two cases.  If the inner
         * path is an indexscan using all the joinquals as indexquals, then an
         * unmatched row results in an indexscan returning no rows, which is
         * probably quite cheap.  We estimate this case as the same cost to
         * return the first tuple of a nonempty scan.  Otherwise, the executor
         * will have to scan the whole inner rel; not so cheap.
         */
        if (has_indexed_join_quals(path))
        {
            run_cost += (outer_path_rows - outer_matched_rows) *
                inner_rescan_run_cost / inner_path_rows;

            /*
             * We won't be evaluating any quals at all for these rows, so
             * don't add them to ntuples.
             */
        }
        else
        {
            run_cost += (outer_path_rows - outer_matched_rows) *
                inner_rescan_run_cost;
            ntuples += (outer_path_rows - outer_matched_rows) *
                inner_path_rows;
        }
    }
    else
    {
        /* Normal-case source costs were included in preliminary estimate */

        /* Compute number of tuples processed (not number emitted!) */
        ntuples = outer_path_rows * inner_path_rows;
    }

    /* CPU costs */
    cost_qual_eval(&restrict_qual_cost, path->joinrestrictinfo, root);
    startup_cost += restrict_qual_cost.startup;
    cpu_per_tuple = cpu_tuple_cost + restrict_qual_cost.per_tuple;
    run_cost += cpu_per_tuple * ntuples;

    path->path.startup_cost = startup_cost;
    path->path.total_cost = startup_cost + run_cost;
}

/*
 * initial_cost_mergejoin
 *    Preliminary estimate of the cost of a mergejoin path.
 *
 * This must quickly produce lower-bound estimates of the path's startup and
 * total costs.  If we are unable to eliminate the proposed path from
 * consideration using the lower bounds, final_cost_mergejoin will be called
 * to obtain the final estimates.
 *
 * The exact division of labor between this function and final_cost_mergejoin
 * is private to them, and represents a tradeoff between speed of the initial
 * estimate and getting a tight lower bound.  We choose to not examine the
 * join quals here, except for obtaining the scan selectivity estimate which
 * is really essential (but fortunately, use of caching keeps the cost of
 * getting that down to something reasonable).
 * We also assume that cost_sort is cheap enough to use here.
 *
 * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
 *      other data to be used by final_cost_mergejoin
 * 'jointype' is the type of join to be performed
 * 'mergeclauses' is the list of joinclauses to be used as merge clauses
 * 'outer_path' is the outer input to the join
 * 'inner_path' is the inner input to the join
 * 'outersortkeys' is the list of sort keys for the outer path
 * 'innersortkeys' is the list of sort keys for the inner path
 * 'sjinfo' is extra info about the join for selectivity estimation
 *
 * Note: outersortkeys and innersortkeys should be NIL if no explicit
 * sort is needed because the respective source path is already ordered.
 */
void
initial_cost_mergejoin(PlannerInfo *root, JoinCostWorkspace *workspace,
                       JoinType jointype,
                       List *mergeclauses,
                       Path *outer_path, Path *inner_path,
                       List *outersortkeys, List *innersortkeys,
                       SpecialJoinInfo *sjinfo)
{
    Cost        startup_cost = 0;
    Cost        run_cost = 0;
    double      outer_path_rows = outer_path->rows;
    double      inner_path_rows = inner_path->rows;
    Cost        inner_run_cost;
    double      outer_rows,
                inner_rows,
                outer_skip_rows,
                inner_skip_rows;
    Selectivity outerstartsel,
                outerendsel,
                innerstartsel,
                innerendsel;
    Path        sort_path;      /* dummy for result of cost_sort */

    /* Protect some assumptions below that rowcounts aren't zero or NaN */
    if (outer_path_rows <= 0 || isnan(outer_path_rows))
        outer_path_rows = 1;
    if (inner_path_rows <= 0 || isnan(inner_path_rows))
        inner_path_rows = 1;

    /*
     * A merge join will stop as soon as it exhausts either input stream
     * (unless it's an outer join, in which case the outer side has to be
     * scanned all the way anyway).  Estimate fraction of the left and right
     * inputs that will actually need to be scanned.  Likewise, we can
     * estimate the number of rows that will be skipped before the first join
     * pair is found, which should be factored into startup cost. We use only
     * the first (most significant) merge clause for this purpose. Since
     * mergejoinscansel() is a fairly expensive computation, we cache the
     * results in the merge clause RestrictInfo.
     */
    if (mergeclauses && jointype != JOIN_FULL)
    {
        RestrictInfo *firstclause = (RestrictInfo *) linitial(mergeclauses);
        List       *opathkeys;
        List       *ipathkeys;
        PathKey    *opathkey;
        PathKey    *ipathkey;
        MergeScanSelCache *cache;

        /* Get the input pathkeys to determine the sort-order details */
        opathkeys = outersortkeys ? outersortkeys : outer_path->pathkeys;
        ipathkeys = innersortkeys ? innersortkeys : inner_path->pathkeys;
        Assert(opathkeys);
        Assert(ipathkeys);
        opathkey = (PathKey *) linitial(opathkeys);
        ipathkey = (PathKey *) linitial(ipathkeys);
        /* debugging check */
        if (opathkey->pk_opfamily != ipathkey->pk_opfamily ||
            opathkey->pk_eclass->ec_collation != ipathkey->pk_eclass->ec_collation ||
            opathkey->pk_strategy != ipathkey->pk_strategy ||
            opathkey->pk_nulls_first != ipathkey->pk_nulls_first)
            elog(ERROR, "left and right pathkeys do not match in mergejoin");

        /* Get the selectivity with caching */
        cache = cached_scansel(root, firstclause, opathkey);

        if (bms_is_subset(firstclause->left_relids,
                          outer_path->parent->relids))
        {
            /* left side of clause is outer */
            outerstartsel = cache->leftstartsel;
            outerendsel = cache->leftendsel;
            innerstartsel = cache->rightstartsel;
            innerendsel = cache->rightendsel;
        }
        else
        {
            /* left side of clause is inner */
            outerstartsel = cache->rightstartsel;
            outerendsel = cache->rightendsel;
            innerstartsel = cache->leftstartsel;
            innerendsel = cache->leftendsel;
        }
        if (jointype == JOIN_LEFT ||
            jointype == JOIN_ANTI)
        {
            outerstartsel = 0.0;
            outerendsel = 1.0;
        }
        else if (jointype == JOIN_RIGHT)
        {
            innerstartsel = 0.0;
            innerendsel = 1.0;
        }
    }
    else
    {
        /* cope with clauseless or full mergejoin */
        outerstartsel = innerstartsel = 0.0;
        outerendsel = innerendsel = 1.0;
    }

    /*
     * Convert selectivities to row counts.  We force outer_rows and
     * inner_rows to be at least 1, but the skip_rows estimates can be zero.
     */
    outer_skip_rows = rint(outer_path_rows * outerstartsel);
    inner_skip_rows = rint(inner_path_rows * innerstartsel);
    outer_rows = clamp_row_est(outer_path_rows * outerendsel);
    inner_rows = clamp_row_est(inner_path_rows * innerendsel);

    Assert(outer_skip_rows <= outer_rows);
    Assert(inner_skip_rows <= inner_rows);

    /*
     * Readjust scan selectivities to account for above rounding.  This is
     * normally an insignificant effect, but when there are only a few rows in
     * the inputs, failing to do this makes for a large percentage error.
     */
    outerstartsel = outer_skip_rows / outer_path_rows;
    innerstartsel = inner_skip_rows / inner_path_rows;
    outerendsel = outer_rows / outer_path_rows;
    innerendsel = inner_rows / inner_path_rows;

    Assert(outerstartsel <= outerendsel);
    Assert(innerstartsel <= innerendsel);

    /* cost of source data */

    if (outersortkeys)          /* do we need to sort outer? */
    {
        cost_sort(&sort_path,
                  root,
                  outersortkeys,
                  outer_path->total_cost,
                  outer_path_rows,
                  outer_path->parent->width,
                  0.0,
                  work_mem,
                  -1.0);
        startup_cost += sort_path.startup_cost;
        startup_cost += (sort_path.total_cost - sort_path.startup_cost)
            * outerstartsel;
        run_cost += (sort_path.total_cost - sort_path.startup_cost)
            * (outerendsel - outerstartsel);
    }
    else
    {
        startup_cost += outer_path->startup_cost;
        startup_cost += (outer_path->total_cost - outer_path->startup_cost)
            * outerstartsel;
        run_cost += (outer_path->total_cost - outer_path->startup_cost)
            * (outerendsel - outerstartsel);
    }

    if (innersortkeys)          /* do we need to sort inner? */
    {
        cost_sort(&sort_path,
                  root,
                  innersortkeys,
                  inner_path->total_cost,
                  inner_path_rows,
                  inner_path->parent->width,
                  0.0,
                  work_mem,
                  -1.0);
        startup_cost += sort_path.startup_cost;
        startup_cost += (sort_path.total_cost - sort_path.startup_cost)
            * innerstartsel;
        inner_run_cost = (sort_path.total_cost - sort_path.startup_cost)
            * (innerendsel - innerstartsel);
    }
    else
    {
        startup_cost += inner_path->startup_cost;
        startup_cost += (inner_path->total_cost - inner_path->startup_cost)
            * innerstartsel;
        inner_run_cost = (inner_path->total_cost - inner_path->startup_cost)
            * (innerendsel - innerstartsel);
    }

    /*
     * We can't yet determine whether rescanning occurs, or whether
     * materialization of the inner input should be done.  The minimum
     * possible inner input cost, regardless of rescan and materialization
     * considerations, is inner_run_cost.  We include that in
     * workspace->total_cost, but not yet in run_cost.
     */

    /* CPU costs left for later */

    /* Public result fields */
    workspace->startup_cost = startup_cost;
    workspace->total_cost = startup_cost + run_cost + inner_run_cost;
    /* Save private data for final_cost_mergejoin */
    workspace->run_cost = run_cost;
    workspace->inner_run_cost = inner_run_cost;
    workspace->outer_rows = outer_rows;
    workspace->inner_rows = inner_rows;
    workspace->outer_skip_rows = outer_skip_rows;
    workspace->inner_skip_rows = inner_skip_rows;
}

/*
 * final_cost_mergejoin
 *    Final estimate of the cost and result size of a mergejoin path.
 *
 * Unlike other costsize functions, this routine makes one actual decision:
 * whether we should materialize the inner path.  We do that either because
 * the inner path can't support mark/restore, or because it's cheaper to
 * use an interposed Material node to handle mark/restore.  When the decision
 * is cost-based it would be logically cleaner to build and cost two separate
 * paths with and without that flag set; but that would require repeating most
 * of the cost calculations, which are not all that cheap.  Since the choice
 * will not affect output pathkeys or startup cost, only total cost, there is
 * no possibility of wanting to keep both paths.  So it seems best to make
 * the decision here and record it in the path's materialize_inner field.
 *
 * 'path' is already filled in except for the rows and cost fields and
 *      materialize_inner
 * 'workspace' is the result from initial_cost_mergejoin
 * 'sjinfo' is extra info about the join for selectivity estimation
 */
void
final_cost_mergejoin(PlannerInfo *root, MergePath *path,
                     JoinCostWorkspace *workspace,
                     SpecialJoinInfo *sjinfo)
{
    Path       *outer_path = path->jpath.outerjoinpath;
    Path       *inner_path = path->jpath.innerjoinpath;
    double      inner_path_rows = inner_path->rows;
    List       *mergeclauses = path->path_mergeclauses;
    List       *innersortkeys = path->innersortkeys;
    Cost        startup_cost = workspace->startup_cost;
    Cost        run_cost = workspace->run_cost;
    Cost        inner_run_cost = workspace->inner_run_cost;
    double      outer_rows = workspace->outer_rows;
    double      inner_rows = workspace->inner_rows;
    double      outer_skip_rows = workspace->outer_skip_rows;
    double      inner_skip_rows = workspace->inner_skip_rows;
    Cost        cpu_per_tuple,
                bare_inner_cost,
                mat_inner_cost;
    QualCost    merge_qual_cost;
    QualCost    qp_qual_cost;
    double      mergejointuples,
                rescannedtuples;
    double      rescanratio;

    /* Protect some assumptions below that rowcounts aren't zero or NaN */
    if (inner_path_rows <= 0 || isnan(inner_path_rows))
        inner_path_rows = 1;

    /* Mark the path with the correct row estimate */
    if (path->jpath.path.param_info)
        path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
    else
        path->jpath.path.rows = path->jpath.path.parent->rows;

    /*
     * We could include disable_cost in the preliminary estimate, but that
     * would amount to optimizing for the case where the join method is
     * disabled, which doesn't seem like the way to bet.
     */
    if (!enable_mergejoin)
        startup_cost += disable_cost;

    /*
     * Compute cost of the mergequals and qpquals (other restriction clauses)
     * separately.
     */
    cost_qual_eval(&merge_qual_cost, mergeclauses, root);
    cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo, root);
    qp_qual_cost.startup -= merge_qual_cost.startup;
    qp_qual_cost.per_tuple -= merge_qual_cost.per_tuple;

    /*
     * Get approx # tuples passing the mergequals.  We use approx_tuple_count
     * here because we need an estimate done with JOIN_INNER semantics.
     */
    mergejointuples = approx_tuple_count(root, &path->jpath, mergeclauses);

    /*
     * When there are equal merge keys in the outer relation, the mergejoin
     * must rescan any matching tuples in the inner relation. This means
     * re-fetching inner tuples; we have to estimate how often that happens.
     *
     * For regular inner and outer joins, the number of re-fetches can be
     * estimated approximately as size of merge join output minus size of
     * inner relation. Assume that the distinct key values are 1, 2, ..., and
     * denote the number of values of each key in the outer relation as m1,
     * m2, ...; in the inner relation, n1, n2, ...  Then we have
     *
     * size of join = m1 * n1 + m2 * n2 + ...
     *
     * number of rescanned tuples = (m1 - 1) * n1 + (m2 - 1) * n2 + ... = m1 *
     * n1 + m2 * n2 + ... - (n1 + n2 + ...) = size of join - size of inner
     * relation
     *
     * This equation works correctly for outer tuples having no inner match
     * (nk = 0), but not for inner tuples having no outer match (mk = 0); we
     * are effectively subtracting those from the number of rescanned tuples,
     * when we should not.  Can we do better without expensive selectivity
     * computations?
     *
     * The whole issue is moot if we are working from a unique-ified outer
     * input.
     */
    if (IsA(outer_path, UniquePath))
        rescannedtuples = 0;
    else
    {
        rescannedtuples = mergejointuples - inner_path_rows;
        /* Must clamp because of possible underestimate */
        if (rescannedtuples < 0)
            rescannedtuples = 0;
    }
    /* We'll inflate various costs this much to account for rescanning */
    rescanratio = 1.0 + (rescannedtuples / inner_path_rows);

    /*
     * Decide whether we want to materialize the inner input to shield it from
     * mark/restore and performing re-fetches.  Our cost model for regular
     * re-fetches is that a re-fetch costs the same as an original fetch,
     * which is probably an overestimate; but on the other hand we ignore the
     * bookkeeping costs of mark/restore.  Not clear if it's worth developing
     * a more refined model.  So we just need to inflate the inner run cost by
     * rescanratio.
     */
    bare_inner_cost = inner_run_cost * rescanratio;

    /*
     * When we interpose a Material node the re-fetch cost is assumed to be
     * just cpu_operator_cost per tuple, independently of the underlying
     * plan's cost; and we charge an extra cpu_operator_cost per original
     * fetch as well.  Note that we're assuming the materialize node will
     * never spill to disk, since it only has to remember tuples back to the
     * last mark.  (If there are a huge number of duplicates, our other cost
     * factors will make the path so expensive that it probably won't get
     * chosen anyway.)  So we don't use cost_rescan here.
     *
     * Note: keep this estimate in sync with create_mergejoin_plan's labeling
     * of the generated Material node.
     */
    mat_inner_cost = inner_run_cost +
        cpu_operator_cost * inner_path_rows * rescanratio;

    /*
     * Prefer materializing if it looks cheaper, unless the user has asked to
     * suppress materialization.
     */
    if (enable_material && mat_inner_cost < bare_inner_cost)
        path->materialize_inner = true;

    /*
     * Even if materializing doesn't look cheaper, we *must* do it if the
     * inner path is to be used directly (without sorting) and it doesn't
     * support mark/restore.
     *
     * Since the inner side must be ordered, and only Sorts and IndexScans can
     * create order to begin with, and they both support mark/restore, you
     * might think there's no problem --- but you'd be wrong.  Nestloop and
     * merge joins can *preserve* the order of their inputs, so they can be
     * selected as the input of a mergejoin, and they don't support
     * mark/restore at present.
     *
     * We don't test the value of enable_material here, because
     * materialization is required for correctness in this case, and turning
     * it off does not entitle us to deliver an invalid plan.
     */
    else if (innersortkeys == NIL &&
             !ExecSupportsMarkRestore(inner_path->pathtype))
        path->materialize_inner = true;

    /*
     * Also, force materializing if the inner path is to be sorted and the
     * sort is expected to spill to disk.  This is because the final merge
     * pass can be done on-the-fly if it doesn't have to support mark/restore.
     * We don't try to adjust the cost estimates for this consideration,
     * though.
     *
     * Since materialization is a performance optimization in this case,
     * rather than necessary for correctness, we skip it if enable_material is
     * off.
     */
    else if (enable_material && innersortkeys != NIL &&
             relation_byte_size(inner_path_rows, inner_path->parent->width) >
             (work_mem * 1024L))
        path->materialize_inner = true;
    else
        path->materialize_inner = false;

    /* Charge the right incremental cost for the chosen case */
    if (path->materialize_inner)
        run_cost += mat_inner_cost;
    else
        run_cost += bare_inner_cost;

    /* CPU costs */

    /*
     * The number of tuple comparisons needed is approximately number of outer
     * rows plus number of inner rows plus number of rescanned tuples (can we
     * refine this?).  At each one, we need to evaluate the mergejoin quals.
     */
    startup_cost += merge_qual_cost.startup;
    startup_cost += merge_qual_cost.per_tuple *
        (outer_skip_rows + inner_skip_rows * rescanratio);
    run_cost += merge_qual_cost.per_tuple *
        ((outer_rows - outer_skip_rows) +
         (inner_rows - inner_skip_rows) * rescanratio);

    /*
     * For each tuple that gets through the mergejoin proper, we charge
     * cpu_tuple_cost plus the cost of evaluating additional restriction
     * clauses that are to be applied at the join.  (This is pessimistic since
     * not all of the quals may get evaluated at each tuple.)
     *
     * Note: we could adjust for SEMI/ANTI joins skipping some qual
     * evaluations here, but it's probably not worth the trouble.
     */
    startup_cost += qp_qual_cost.startup;
    cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
    run_cost += cpu_per_tuple * mergejointuples;

    path->jpath.path.startup_cost = startup_cost;
    path->jpath.path.total_cost = startup_cost + run_cost;
}

/*
 * run mergejoinscansel() with caching
 */
static MergeScanSelCache *
cached_scansel(PlannerInfo *root, RestrictInfo *rinfo, PathKey *pathkey)
{
    MergeScanSelCache *cache;
    ListCell   *lc;
    Selectivity leftstartsel,
                leftendsel,
                rightstartsel,
                rightendsel;
    MemoryContext oldcontext;

    /* Do we have this result already? */
    foreach(lc, rinfo->scansel_cache)
    {
        cache = (MergeScanSelCache *) lfirst(lc);
        if (cache->opfamily == pathkey->pk_opfamily &&
            cache->collation == pathkey->pk_eclass->ec_collation &&
            cache->strategy == pathkey->pk_strategy &&
            cache->nulls_first == pathkey->pk_nulls_first)
            return cache;
    }

    /* Nope, do the computation */
    mergejoinscansel(root,
                     (Node *) rinfo->clause,
                     pathkey->pk_opfamily,
                     pathkey->pk_strategy,
                     pathkey->pk_nulls_first,
                     &leftstartsel,
                     &leftendsel,
                     &rightstartsel,
                     &rightendsel);

    /* Cache the result in suitably long-lived workspace */
    oldcontext = MemoryContextSwitchTo(root->planner_cxt);

    cache = (MergeScanSelCache *) palloc(sizeof(MergeScanSelCache));
    cache->opfamily = pathkey->pk_opfamily;
    cache->collation = pathkey->pk_eclass->ec_collation;
    cache->strategy = pathkey->pk_strategy;
    cache->nulls_first = pathkey->pk_nulls_first;
    cache->leftstartsel = leftstartsel;
    cache->leftendsel = leftendsel;
    cache->rightstartsel = rightstartsel;
    cache->rightendsel = rightendsel;

    rinfo->scansel_cache = lappend(rinfo->scansel_cache, cache);

    MemoryContextSwitchTo(oldcontext);

    return cache;
}

/*
 * initial_cost_hashjoin
 *    Preliminary estimate of the cost of a hashjoin path.
 *
 * This must quickly produce lower-bound estimates of the path's startup and
 * total costs.  If we are unable to eliminate the proposed path from
 * consideration using the lower bounds, final_cost_hashjoin will be called
 * to obtain the final estimates.
 *
 * The exact division of labor between this function and final_cost_hashjoin
 * is private to them, and represents a tradeoff between speed of the initial
 * estimate and getting a tight lower bound.  We choose to not examine the
 * join quals here (other than by counting the number of hash clauses),
 * so we can't do much with CPU costs.  We do assume that
 * ExecChooseHashTableSize is cheap enough to use here.
 *
 * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
 *      other data to be used by final_cost_hashjoin
 * 'jointype' is the type of join to be performed
 * 'hashclauses' is the list of joinclauses to be used as hash clauses
 * 'outer_path' is the outer input to the join
 * 'inner_path' is the inner input to the join
 * 'sjinfo' is extra info about the join for selectivity estimation
 * 'semifactors' contains valid data if jointype is SEMI or ANTI
 */
void
initial_cost_hashjoin(PlannerInfo *root, JoinCostWorkspace *workspace,
                      JoinType jointype,
                      List *hashclauses,
                      Path *outer_path, Path *inner_path,
                      SpecialJoinInfo *sjinfo,
                      SemiAntiJoinFactors *semifactors)
{
    Cost        startup_cost = 0;
    Cost        run_cost = 0;
    double      outer_path_rows = outer_path->rows;
    double      inner_path_rows = inner_path->rows;
    int         num_hashclauses = list_length(hashclauses);
    int         numbuckets;
    int         numbatches;
    int         num_skew_mcvs;

    /* cost of source data */
    startup_cost += outer_path->startup_cost;
    run_cost += outer_path->total_cost - outer_path->startup_cost;
    startup_cost += inner_path->total_cost;

    /*
     * Cost of computing hash function: must do it once per input tuple. We
     * charge one cpu_operator_cost for each column's hash function.  Also,
     * tack on one cpu_tuple_cost per inner row, to model the costs of
     * inserting the row into the hashtable.
     *
     * XXX when a hashclause is more complex than a single operator, we really
     * should charge the extra eval costs of the left or right side, as
     * appropriate, here.  This seems more work than it's worth at the moment.
     */
    startup_cost += (cpu_operator_cost * num_hashclauses + cpu_tuple_cost)
        * inner_path_rows;
    run_cost += cpu_operator_cost * num_hashclauses * outer_path_rows;

    /*
     * Get hash table size that executor would use for inner relation.
     *
     * XXX for the moment, always assume that skew optimization will be
     * performed.  As long as SKEW_WORK_MEM_PERCENT is small, it's not worth
     * trying to determine that for sure.
     *
     * XXX at some point it might be interesting to try to account for skew
     * optimization in the cost estimate, but for now, we don't.
     */
    ExecChooseHashTableSize(inner_path_rows,
                            inner_path->parent->width,
                            true,       /* useskew */
                            &numbuckets,
                            &numbatches,
                            &num_skew_mcvs);

    /*
     * If inner relation is too big then we will need to "batch" the join,
     * which implies writing and reading most of the tuples to disk an extra
     * time.  Charge seq_page_cost per page, since the I/O should be nice and
     * sequential.  Writing the inner rel counts as startup cost, all the rest
     * as run cost.
     */
    if (numbatches > 1)
    {
        double      outerpages = page_size(outer_path_rows,
                                           outer_path->parent->width);
        double      innerpages = page_size(inner_path_rows,
                                           inner_path->parent->width);

        startup_cost += seq_page_cost * innerpages;
        run_cost += seq_page_cost * (innerpages + 2 * outerpages);
    }

    /* CPU costs left for later */

    /* Public result fields */
    workspace->startup_cost = startup_cost;
    workspace->total_cost = startup_cost + run_cost;
    /* Save private data for final_cost_hashjoin */
    workspace->run_cost = run_cost;
    workspace->numbuckets = numbuckets;
    workspace->numbatches = numbatches;
}

/*
 * final_cost_hashjoin
 *    Final estimate of the cost and result size of a hashjoin path.
 *
 * Note: the numbatches estimate is also saved into 'path' for use later
 *
 * 'path' is already filled in except for the rows and cost fields and
 *      num_batches
 * 'workspace' is the result from initial_cost_hashjoin
 * 'sjinfo' is extra info about the join for selectivity estimation
 * 'semifactors' contains valid data if path->jointype is SEMI or ANTI
 */
void
final_cost_hashjoin(PlannerInfo *root, HashPath *path,
                    JoinCostWorkspace *workspace,
                    SpecialJoinInfo *sjinfo,
                    SemiAntiJoinFactors *semifactors)
{
    Path       *outer_path = path->jpath.outerjoinpath;
    Path       *inner_path = path->jpath.innerjoinpath;
    double      outer_path_rows = outer_path->rows;
    double      inner_path_rows = inner_path->rows;
    List       *hashclauses = path->path_hashclauses;
    Cost        startup_cost = workspace->startup_cost;
    Cost        run_cost = workspace->run_cost;
    int         numbuckets = workspace->numbuckets;
    int         numbatches = workspace->numbatches;
    Cost        cpu_per_tuple;
    QualCost    hash_qual_cost;
    QualCost    qp_qual_cost;
    double      hashjointuples;
    double      virtualbuckets;
    Selectivity innerbucketsize;
    ListCell   *hcl;

    /* Mark the path with the correct row estimate */
    if (path->jpath.path.param_info)
        path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
    else
        path->jpath.path.rows = path->jpath.path.parent->rows;

    /*
     * We could include disable_cost in the preliminary estimate, but that
     * would amount to optimizing for the case where the join method is
     * disabled, which doesn't seem like the way to bet.
     */
    if (!enable_hashjoin)
        startup_cost += disable_cost;

    /* mark the path with estimated # of batches */
    path->num_batches = numbatches;

    /* and compute the number of "virtual" buckets in the whole join */
    virtualbuckets = (double) numbuckets *(double) numbatches;

    /*
     * Determine bucketsize fraction for inner relation.  We use the smallest
     * bucketsize estimated for any individual hashclause; this is undoubtedly
     * conservative.
     *
     * BUT: if inner relation has been unique-ified, we can assume it's good
     * for hashing.  This is important both because it's the right answer, and
     * because we avoid contaminating the cache with a value that's wrong for
     * non-unique-ified paths.
     */
    if (IsA(inner_path, UniquePath))
        innerbucketsize = 1.0 / virtualbuckets;
    else
    {
        innerbucketsize = 1.0;
        foreach(hcl, hashclauses)
        {
            RestrictInfo *restrictinfo = (RestrictInfo *) lfirst(hcl);
            Selectivity thisbucketsize;

            Assert(IsA(restrictinfo, RestrictInfo));

            /*
             * First we have to figure out which side of the hashjoin clause
             * is the inner side.
             *
             * Since we tend to visit the same clauses over and over when
             * planning a large query, we cache the bucketsize estimate in the
             * RestrictInfo node to avoid repeated lookups of statistics.
             */
            if (bms_is_subset(restrictinfo->right_relids,
                              inner_path->parent->relids))
            {
                /* righthand side is inner */
                thisbucketsize = restrictinfo->right_bucketsize;
                if (thisbucketsize < 0)
                {
                    /* not cached yet */
                    thisbucketsize =
                        estimate_hash_bucketsize(root,
                                           get_rightop(restrictinfo->clause),
                                                 virtualbuckets);
                    restrictinfo->right_bucketsize = thisbucketsize;
                }
            }
            else
            {
                Assert(bms_is_subset(restrictinfo->left_relids,
                                     inner_path->parent->relids));
                /* lefthand side is inner */
                thisbucketsize = restrictinfo->left_bucketsize;
                if (thisbucketsize < 0)
                {
                    /* not cached yet */
                    thisbucketsize =
                        estimate_hash_bucketsize(root,
                                            get_leftop(restrictinfo->clause),
                                                 virtualbuckets);
                    restrictinfo->left_bucketsize = thisbucketsize;
                }
            }

            if (innerbucketsize > thisbucketsize)
                innerbucketsize = thisbucketsize;
        }
    }

    /*
     * Compute cost of the hashquals and qpquals (other restriction clauses)
     * separately.
     */
    cost_qual_eval(&hash_qual_cost, hashclauses, root);
    cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo, root);
    qp_qual_cost.startup -= hash_qual_cost.startup;
    qp_qual_cost.per_tuple -= hash_qual_cost.per_tuple;

    /* CPU costs */

    if (path->jpath.jointype == JOIN_SEMI || path->jpath.jointype == JOIN_ANTI)
    {
        double      outer_matched_rows;
        Selectivity inner_scan_frac;

        /*
         * SEMI or ANTI join: executor will stop after first match.
         *
         * For an outer-rel row that has at least one match, we can expect the
         * bucket scan to stop after a fraction 1/(match_count+1) of the
         * bucket's rows, if the matches are evenly distributed.  Since they
         * probably aren't quite evenly distributed, we apply a fuzz factor of
         * 2.0 to that fraction.  (If we used a larger fuzz factor, we'd have
         * to clamp inner_scan_frac to at most 1.0; but since match_count is
         * at least 1, no such clamp is needed now.)
         */
        outer_matched_rows = rint(outer_path_rows * semifactors->outer_match_frac);
        inner_scan_frac = 2.0 / (semifactors->match_count + 1.0);

        startup_cost += hash_qual_cost.startup;
        run_cost += hash_qual_cost.per_tuple * outer_matched_rows *
            clamp_row_est(inner_path_rows * innerbucketsize * inner_scan_frac) * 0.5;

        /*
         * For unmatched outer-rel rows, the picture is quite a lot different.
         * In the first place, there is no reason to assume that these rows
         * preferentially hit heavily-populated buckets; instead assume they
         * are uncorrelated with the inner distribution and so they see an
         * average bucket size of inner_path_rows / virtualbuckets.  In the
         * second place, it seems likely that they will have few if any exact
         * hash-code matches and so very few of the tuples in the bucket will
         * actually require eval of the hash quals.  We don't have any good
         * way to estimate how many will, but for the moment assume that the
         * effective cost per bucket entry is one-tenth what it is for
         * matchable tuples.
         */
        run_cost += hash_qual_cost.per_tuple *
            (outer_path_rows - outer_matched_rows) *
            clamp_row_est(inner_path_rows / virtualbuckets) * 0.05;

        /* Get # of tuples that will pass the basic join */
        if (path->jpath.jointype == JOIN_SEMI)
            hashjointuples = outer_matched_rows;
        else
            hashjointuples = outer_path_rows - outer_matched_rows;
    }
    else
    {
        /*
         * The number of tuple comparisons needed is the number of outer
         * tuples times the typical number of tuples in a hash bucket, which
         * is the inner relation size times its bucketsize fraction.  At each
         * one, we need to evaluate the hashjoin quals.  But actually,
         * charging the full qual eval cost at each tuple is pessimistic,
         * since we don't evaluate the quals unless the hash values match
         * exactly.  For lack of a better idea, halve the cost estimate to
         * allow for that.
         */
        startup_cost += hash_qual_cost.startup;
        run_cost += hash_qual_cost.per_tuple * outer_path_rows *
            clamp_row_est(inner_path_rows * innerbucketsize) * 0.5;

        /*
         * Get approx # tuples passing the hashquals.  We use
         * approx_tuple_count here because we need an estimate done with
         * JOIN_INNER semantics.
         */
        hashjointuples = approx_tuple_count(root, &path->jpath, hashclauses);
    }

    /*
     * For each tuple that gets through the hashjoin proper, we charge
     * cpu_tuple_cost plus the cost of evaluating additional restriction
     * clauses that are to be applied at the join.  (This is pessimistic since
     * not all of the quals may get evaluated at each tuple.)
     */
    startup_cost += qp_qual_cost.startup;
    cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
    run_cost += cpu_per_tuple * hashjointuples;

    path->jpath.path.startup_cost = startup_cost;
    path->jpath.path.total_cost = startup_cost + run_cost;
}


/*
 * cost_subplan
 *      Figure the costs for a SubPlan (or initplan).
 *
 * Note: we could dig the subplan's Plan out of the root list, but in practice
 * all callers have it handy already, so we make them pass it.
 */
void
cost_subplan(PlannerInfo *root, SubPlan *subplan, Plan *plan)
{
    QualCost    sp_cost;

    /* Figure any cost for evaluating the testexpr */
    cost_qual_eval(&sp_cost,
                   make_ands_implicit((Expr *) subplan->testexpr),
                   root);

    if (subplan->useHashTable)
    {
        /*
         * If we are using a hash table for the subquery outputs, then the
         * cost of evaluating the query is a one-time cost.  We charge one
         * cpu_operator_cost per tuple for the work of loading the hashtable,
         * too.
         */
        sp_cost.startup += plan->total_cost +
            cpu_operator_cost * plan->plan_rows;

        /*
         * The per-tuple costs include the cost of evaluating the lefthand
         * expressions, plus the cost of probing the hashtable.  We already
         * accounted for the lefthand expressions as part of the testexpr, and
         * will also have counted one cpu_operator_cost for each comparison
         * operator.  That is probably too low for the probing cost, but it's
         * hard to make a better estimate, so live with it for now.
         */
    }
    else
    {
        /*
         * Otherwise we will be rescanning the subplan output on each
         * evaluation.  We need to estimate how much of the output we will
         * actually need to scan.  NOTE: this logic should agree with the
         * tuple_fraction estimates used by make_subplan() in
         * plan/subselect.c.
         */
        Cost        plan_run_cost = plan->total_cost - plan->startup_cost;

        if (subplan->subLinkType == EXISTS_SUBLINK)
        {
            /* we only need to fetch 1 tuple */
            sp_cost.per_tuple += plan_run_cost / plan->plan_rows;
        }
        else if (subplan->subLinkType == ALL_SUBLINK ||
                 subplan->subLinkType == ANY_SUBLINK)
        {
            /* assume we need 50% of the tuples */
            sp_cost.per_tuple += 0.50 * plan_run_cost;
            /* also charge a cpu_operator_cost per row examined */
            sp_cost.per_tuple += 0.50 * plan->plan_rows * cpu_operator_cost;
        }
        else
        {
            /* assume we need all tuples */
            sp_cost.per_tuple += plan_run_cost;
        }

        /*
         * Also account for subplan's startup cost. If the subplan is
         * uncorrelated or undirect correlated, AND its topmost node is one
         * that materializes its output, assume that we'll only need to pay
         * its startup cost once; otherwise assume we pay the startup cost
         * every time.
         */
        if (subplan->parParam == NIL &&
            ExecMaterializesOutput(nodeTag(plan)))
            sp_cost.startup += plan->startup_cost;
        else
            sp_cost.per_tuple += plan->startup_cost;
    }

    subplan->startup_cost = sp_cost.startup;
    subplan->per_call_cost = sp_cost.per_tuple;
}


/*
 * cost_rescan
 *      Given a finished Path, estimate the costs of rescanning it after
 *      having done so the first time.  For some Path types a rescan is
 *      cheaper than an original scan (if no parameters change), and this
 *      function embodies knowledge about that.  The default is to return
 *      the same costs stored in the Path.  (Note that the cost estimates
 *      actually stored in Paths are always for first scans.)
 *
 * This function is not currently intended to model effects such as rescans
 * being cheaper due to disk block caching; what we are concerned with is
 * plan types wherein the executor caches results explicitly, or doesn't
 * redo startup calculations, etc.
 */
static void
cost_rescan(PlannerInfo *root, Path *path,
            Cost *rescan_startup_cost,  /* output parameters */
            Cost *rescan_total_cost)
{
    switch (path->pathtype)
    {
        case T_FunctionScan:

            /*
             * Currently, nodeFunctionscan.c always executes the function to
             * completion before returning any rows, and caches the results in
             * a tuplestore.  So the function eval cost is all startup cost
             * and isn't paid over again on rescans. However, all run costs
             * will be paid over again.
             */
            *rescan_startup_cost = 0;
            *rescan_total_cost = path->total_cost - path->startup_cost;
            break;
        case T_HashJoin:

            /*
             * Assume that all of the startup cost represents hash table
             * building, which we won't have to do over.
             */
            *rescan_startup_cost = 0;
            *rescan_total_cost = path->total_cost - path->startup_cost;
            break;
        case T_CteScan:
        case T_WorkTableScan:
            {
                /*
                 * These plan types materialize their final result in a
                 * tuplestore or tuplesort object.  So the rescan cost is only
                 * cpu_tuple_cost per tuple, unless the result is large enough
                 * to spill to disk.
                 */
                Cost        run_cost = cpu_tuple_cost * path->rows;
                double      nbytes = relation_byte_size(path->rows,
                                                        path->parent->width);
                long        work_mem_bytes = work_mem * 1024L;

                if (nbytes > work_mem_bytes)
                {
                    /* It will spill, so account for re-read cost */
                    double      npages = ceil(nbytes / BLCKSZ);

                    run_cost += seq_page_cost * npages;
                }
                *rescan_startup_cost = 0;
                *rescan_total_cost = run_cost;
            }
            break;
        case T_Material:
        case T_Sort:
            {
                /*
                 * These plan types not only materialize their results, but do
                 * not implement qual filtering or projection.  So they are
                 * even cheaper to rescan than the ones above.  We charge only
                 * cpu_operator_cost per tuple.  (Note: keep that in sync with
                 * the run_cost charge in cost_sort, and also see comments in
                 * cost_material before you change it.)
                 */
                Cost        run_cost = cpu_operator_cost * path->rows;
                double      nbytes = relation_byte_size(path->rows,
                                                        path->parent->width);
                long        work_mem_bytes = work_mem * 1024L;

                if (nbytes > work_mem_bytes)
                {
                    /* It will spill, so account for re-read cost */
                    double      npages = ceil(nbytes / BLCKSZ);

                    run_cost += seq_page_cost * npages;
                }
                *rescan_startup_cost = 0;
                *rescan_total_cost = run_cost;
            }
            break;
        default:
            *rescan_startup_cost = path->startup_cost;
            *rescan_total_cost = path->total_cost;
            break;
    }
}


/*
 * cost_qual_eval
 *      Estimate the CPU costs of evaluating a WHERE clause.
 *      The input can be either an implicitly-ANDed list of boolean
 *      expressions, or a list of RestrictInfo nodes.  (The latter is
 *      preferred since it allows caching of the results.)
 *      The result includes both a one-time (startup) component,
 *      and a per-evaluation component.
 */
void
cost_qual_eval(QualCost *cost, List *quals, PlannerInfo *root)
{
    cost_qual_eval_context context;
    ListCell   *l;

    context.root = root;
    context.total.startup = 0;
    context.total.per_tuple = 0;

    /* We don't charge any cost for the implicit ANDing at top level ... */

    foreach(l, quals)
    {
        Node       *qual = (Node *) lfirst(l);

        cost_qual_eval_walker(qual, &context);
    }

    *cost = context.total;
}

/*
 * cost_qual_eval_node
 *      As above, for a single RestrictInfo or expression.
 */
void
cost_qual_eval_node(QualCost *cost, Node *qual, PlannerInfo *root)
{
    cost_qual_eval_context context;

    context.root = root;
    context.total.startup = 0;
    context.total.per_tuple = 0;

    cost_qual_eval_walker(qual, &context);

    *cost = context.total;
}

static bool
cost_qual_eval_walker(Node *node, cost_qual_eval_context *context)
{
    if (node == NULL)
        return false;

    /*
     * RestrictInfo nodes contain an eval_cost field reserved for this
     * routine's use, so that it's not necessary to evaluate the qual clause's
     * cost more than once.  If the clause's cost hasn't been computed yet,
     * the field's startup value will contain -1.
     */
    if (IsA(node, RestrictInfo))
    {
        RestrictInfo *rinfo = (RestrictInfo *) node;

        if (rinfo->eval_cost.startup < 0)
        {
            cost_qual_eval_context locContext;

            locContext.root = context->root;
            locContext.total.startup = 0;
            locContext.total.per_tuple = 0;

            /*
             * For an OR clause, recurse into the marked-up tree so that we
             * set the eval_cost for contained RestrictInfos too.
             */
            if (rinfo->orclause)
                cost_qual_eval_walker((Node *) rinfo->orclause, &locContext);
            else
                cost_qual_eval_walker((Node *) rinfo->clause, &locContext);

            /*
             * If the RestrictInfo is marked pseudoconstant, it will be tested
             * only once, so treat its cost as all startup cost.
             */
            if (rinfo->pseudoconstant)
            {
                /* count one execution during startup */
                locContext.total.startup += locContext.total.per_tuple;
                locContext.total.per_tuple = 0;
            }
            rinfo->eval_cost = locContext.total;
        }
        context->total.startup += rinfo->eval_cost.startup;
        context->total.per_tuple += rinfo->eval_cost.per_tuple;
        /* do NOT recurse into children */
        return false;
    }

    /*
     * For each operator or function node in the given tree, we charge the
     * estimated execution cost given by pg_proc.procost (remember to multiply
     * this by cpu_operator_cost).
     *
     * 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?  Probably *not*, because that would make the
     * results depend on the clause ordering, and we are not in any position
     * to expect that the current ordering of the clauses is the one that's
     * going to end up being used.  The above per-RestrictInfo caching would
     * not mix well with trying to re-order clauses anyway.
     *
     * Another issue that is entirely ignored here is that if a set-returning
     * function is below top level in the tree, the functions/operators above
     * it will need to be evaluated multiple times.  In practical use, such
     * cases arise so seldom as to not be worth the added complexity needed;
     * moreover, since our rowcount estimates for functions tend to be pretty
     * phony, the results would also be pretty phony.
     */
    if (IsA(node, FuncExpr))
    {
        context->total.per_tuple +=
            get_func_cost(((FuncExpr *) node)->funcid) * cpu_operator_cost;
    }
    else if (IsA(node, OpExpr) ||
             IsA(node, DistinctExpr) ||
             IsA(node, NullIfExpr))
    {
        /* rely on struct equivalence to treat these all alike */
        set_opfuncid((OpExpr *) node);
        context->total.per_tuple +=
            get_func_cost(((OpExpr *) node)->opfuncid) * cpu_operator_cost;
    }
    else if (IsA(node, ScalarArrayOpExpr))
    {
        /*
         * Estimate that the operator will be applied to about half of the
         * array elements before the answer is determined.
         */
        ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) node;
        Node       *arraynode = (Node *) lsecond(saop->args);

        set_sa_opfuncid(saop);
        context->total.per_tuple += get_func_cost(saop->opfuncid) *
            cpu_operator_cost * estimate_array_length(arraynode) * 0.5;
    }
    else if (IsA(node, Aggref) ||
             IsA(node, WindowFunc))
    {
        /*
         * Aggref and WindowFunc nodes are (and should be) treated like Vars,
         * ie, zero execution cost in the current model, because they behave
         * essentially like Vars in execQual.c.  We disregard the costs of
         * their input expressions for the same reason.  The actual execution
         * costs of the aggregate/window functions and their arguments have to
         * be factored into plan-node-specific costing of the Agg or WindowAgg
         * plan node.
         */
        return false;           /* don't recurse into children */
    }
    else if (IsA(node, CoerceViaIO))
    {
        CoerceViaIO *iocoerce = (CoerceViaIO *) node;
        Oid         iofunc;
        Oid         typioparam;
        bool        typisvarlena;

        /* check the result type's input function */
        getTypeInputInfo(iocoerce->resulttype,
                         &iofunc, &typioparam);
        context->total.per_tuple += get_func_cost(iofunc) * cpu_operator_cost;
        /* check the input type's output function */
        getTypeOutputInfo(exprType((Node *) iocoerce->arg),
                          &iofunc, &typisvarlena);
        context->total.per_tuple += get_func_cost(iofunc) * cpu_operator_cost;
    }
    else if (IsA(node, ArrayCoerceExpr))
    {
        ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node;
        Node       *arraynode = (Node *) acoerce->arg;

        if (OidIsValid(acoerce->elemfuncid))
            context->total.per_tuple += get_func_cost(acoerce->elemfuncid) *
                cpu_operator_cost * estimate_array_length(arraynode);
    }
    else if (IsA(node, RowCompareExpr))
    {
        /* Conservatively assume we will check all the columns */
        RowCompareExpr *rcexpr = (RowCompareExpr *) node;
        ListCell   *lc;

        foreach(lc, rcexpr->opnos)
        {
            Oid         opid = lfirst_oid(lc);

            context->total.per_tuple += get_func_cost(get_opcode(opid)) *
                cpu_operator_cost;
        }
    }
    else if (IsA(node, CurrentOfExpr))
    {
        /* Report high cost to prevent selection of anything but TID scan */
        context->total.startup += disable_cost;
    }
    else if (IsA(node, SubLink))
    {
        /* This routine should not be applied to un-planned expressions */
        elog(ERROR, "cannot handle unplanned sub-select");
    }
    else if (IsA(node, SubPlan))
    {
        /*
         * A subplan node in an expression typically indicates that the
         * subplan will be executed on each evaluation, so charge accordingly.
         * (Sub-selects that can be executed as InitPlans have already been
         * removed from the expression.)
         */
        SubPlan    *subplan = (SubPlan *) node;

        context->total.startup += subplan->startup_cost;
        context->total.per_tuple += subplan->per_call_cost;

        /*
         * We don't want to recurse into the testexpr, because it was already
         * counted in the SubPlan node's costs.  So we're done.
         */
        return false;
    }
    else if (IsA(node, AlternativeSubPlan))
    {
        /*
         * Arbitrarily use the first alternative plan for costing.  (We should
         * certainly only include one alternative, and we don't yet have
         * enough information to know which one the executor is most likely to
         * use.)
         */
        AlternativeSubPlan *asplan = (AlternativeSubPlan *) node;

        return cost_qual_eval_walker((Node *) linitial(asplan->subplans),
                                     context);
    }

    /* recurse into children */
    return expression_tree_walker(node, cost_qual_eval_walker,
                                  (void *) context);
}

/*
 * get_restriction_qual_cost
 *    Compute evaluation costs of a baserel's restriction quals, plus any
 *    movable join quals that have been pushed down to the scan.
 *    Results are returned into *qpqual_cost.
 *
 * This is a convenience subroutine that works for seqscans and other cases
 * where all the given quals will be evaluated the hard way.  It's not useful
 * for cost_index(), for example, where the index machinery takes care of
 * some of the quals.  We assume baserestrictcost was previously set by
 * set_baserel_size_estimates().
 */
static void
get_restriction_qual_cost(PlannerInfo *root, RelOptInfo *baserel,
                          ParamPathInfo *param_info,
                          QualCost *qpqual_cost)
{
    if (param_info)
    {
        /* Include costs of pushed-down clauses */
        cost_qual_eval(qpqual_cost, param_info->ppi_clauses, root);

        qpqual_cost->startup += baserel->baserestrictcost.startup;
        qpqual_cost->per_tuple += baserel->baserestrictcost.per_tuple;
    }
    else
        *qpqual_cost = baserel->baserestrictcost;
}


/*
 * compute_semi_anti_join_factors
 *    Estimate how much of the inner input a SEMI or ANTI join
 *    can be expected to scan.
 *
 * In a hash or nestloop SEMI/ANTI join, the executor will stop scanning
 * inner rows as soon as it finds a match to the current outer row.
 * We should therefore adjust some of the cost components for this effect.
 * This function computes some estimates needed for these adjustments.
 * These estimates will be the same regardless of the particular paths used
 * for the outer and inner relation, so we compute these once and then pass
 * them to all the join cost estimation functions.
 *
 * Input parameters:
 *  outerrel: outer relation under consideration
 *  innerrel: inner relation under consideration
 *  jointype: must be JOIN_SEMI or JOIN_ANTI
 *  sjinfo: SpecialJoinInfo relevant to this join
 *  restrictlist: join quals
 * Output parameters:
 *  *semifactors is filled in (see relation.h for field definitions)
 */
void
compute_semi_anti_join_factors(PlannerInfo *root,
                               RelOptInfo *outerrel,
                               RelOptInfo *innerrel,
                               JoinType jointype,
                               SpecialJoinInfo *sjinfo,
                               List *restrictlist,
                               SemiAntiJoinFactors *semifactors)
{
    Selectivity jselec;
    Selectivity nselec;
    Selectivity avgmatch;
    SpecialJoinInfo norm_sjinfo;
    List       *joinquals;
    ListCell   *l;

    /* Should only be called in these cases */
    Assert(jointype == JOIN_SEMI || jointype == JOIN_ANTI);

    /*
     * In an ANTI join, we must ignore clauses that are "pushed down", since
     * those won't affect the match logic.  In a SEMI join, we do not
     * distinguish joinquals from "pushed down" quals, so just use the whole
     * restrictinfo list.
     */
    if (jointype == JOIN_ANTI)
    {
        joinquals = NIL;
        foreach(l, restrictlist)
        {
            RestrictInfo *rinfo = (RestrictInfo *) lfirst(l);

            Assert(IsA(rinfo, RestrictInfo));
            if (!rinfo->is_pushed_down)
                joinquals = lappend(joinquals, rinfo);
        }
    }
    else
        joinquals = restrictlist;

    /*
     * Get the JOIN_SEMI or JOIN_ANTI selectivity of the join clauses.
     */
    jselec = clauselist_selectivity(root,
                                    joinquals,
                                    0,
                                    jointype,
                                    sjinfo);

    /*
     * Also get the normal inner-join selectivity of the join clauses.
     */
    norm_sjinfo.type = T_SpecialJoinInfo;
    norm_sjinfo.min_lefthand = outerrel->relids;
    norm_sjinfo.min_righthand = innerrel->relids;
    norm_sjinfo.syn_lefthand = outerrel->relids;
    norm_sjinfo.syn_righthand = innerrel->relids;
    norm_sjinfo.jointype = JOIN_INNER;
    /* we don't bother trying to make the remaining fields valid */
    norm_sjinfo.lhs_strict = false;
    norm_sjinfo.delay_upper_joins = false;
    norm_sjinfo.join_quals = NIL;

    nselec = clauselist_selectivity(root,
                                    joinquals,
                                    0,
                                    JOIN_INNER,
                                    &norm_sjinfo);

    /* Avoid leaking a lot of ListCells */
    if (jointype == JOIN_ANTI)
        list_free(joinquals);

    /*
     * jselec can be interpreted as the fraction of outer-rel rows that have
     * any matches (this is true for both SEMI and ANTI cases).  And nselec is
     * the fraction of the Cartesian product that matches.  So, the average
     * number of matches for each outer-rel row that has at least one match is
     * nselec * inner_rows / jselec.
     *
     * Note: it is correct to use the inner rel's "rows" count here, even
     * though we might later be considering a parameterized inner path with
     * fewer rows.  This is because we have included all the join clauses in
     * the selectivity estimate.
     */
    if (jselec > 0)             /* protect against zero divide */
    {
        avgmatch = nselec * innerrel->rows / jselec;
        /* Clamp to sane range */
        avgmatch = Max(1.0, avgmatch);
    }
    else
        avgmatch = 1.0;

    semifactors->outer_match_frac = jselec;
    semifactors->match_count = avgmatch;
}

/*
 * has_indexed_join_quals
 *    Check whether all the joinquals of a nestloop join are used as
 *    inner index quals.
 *
 * If the inner path of a SEMI/ANTI join is an indexscan (including bitmap
 * indexscan) that uses all the joinquals as indexquals, we can assume that an
 * unmatched outer tuple is cheap to process, whereas otherwise it's probably
 * expensive.
 */
static bool
has_indexed_join_quals(NestPath *joinpath)
{
    Relids      joinrelids = joinpath->path.parent->relids;
    Path       *innerpath = joinpath->innerjoinpath;
    List       *indexclauses;
    bool        found_one;
    ListCell   *lc;

    /* If join still has quals to evaluate, it's not fast */
    if (joinpath->joinrestrictinfo != NIL)
        return false;
    /* Nor if the inner path isn't parameterized at all */
    if (innerpath->param_info == NULL)
        return false;

    /* Find the indexclauses list for the inner scan */
    switch (innerpath->pathtype)
    {
        case T_IndexScan:
        case T_IndexOnlyScan:
            indexclauses = ((IndexPath *) innerpath)->indexclauses;
            break;
        case T_BitmapHeapScan:
            {
                /* Accept only a simple bitmap scan, not AND/OR cases */
                Path       *bmqual = ((BitmapHeapPath *) innerpath)->bitmapqual;

                if (IsA(bmqual, IndexPath))
                    indexclauses = ((IndexPath *) bmqual)->indexclauses;
                else
                    return false;
                break;
            }
        default:

            /*
             * If it's not a simple indexscan, it probably doesn't run quickly
             * for zero rows out, even if it's a parameterized path using all
             * the joinquals.
             */
            return false;
    }

    /*
     * Examine the inner path's param clauses.  Any that are from the outer
     * path must be found in the indexclauses list, either exactly or in an
     * equivalent form generated by equivclass.c.  Also, we must find at least
     * one such clause, else it's a clauseless join which isn't fast.
     */
    found_one = false;
    foreach(lc, innerpath->param_info->ppi_clauses)
    {
        RestrictInfo *rinfo = (RestrictInfo *) lfirst(lc);

        if (join_clause_is_movable_into(rinfo,
                                        innerpath->parent->relids,
                                        joinrelids))
        {
            if (!(list_member_ptr(indexclauses, rinfo) ||
                  is_redundant_derived_clause(rinfo, indexclauses)))
                return false;
            found_one = true;
        }
    }
    return found_one;
}


/*
 * approx_tuple_count
 *      Quick-and-dirty estimation of the number of join rows passing
 *      a set of qual conditions.
 *
 * The quals can be either an implicitly-ANDed list of boolean expressions,
 * or a list of RestrictInfo nodes (typically the latter).
 *
 * We intentionally compute the selectivity under JOIN_INNER rules, even
 * if it's some type of outer join.  This is appropriate because we are
 * trying to figure out how many tuples pass the initial merge or hash
 * join step.
 *
 * This is quick-and-dirty because we bypass clauselist_selectivity, and
 * simply multiply the independent clause selectivities together.  Now
 * clauselist_selectivity often can't do any better than that anyhow, but
 * for some situations (such as range constraints) it is smarter.  However,
 * we can't effectively cache the results of clauselist_selectivity, whereas
 * the individual clause selectivities can be and are cached.
 *
 * Since we are only using the results to estimate how many potential
 * output tuples are generated and passed through qpqual checking, it
 * seems OK to live with the approximation.
 */
static double
approx_tuple_count(PlannerInfo *root, JoinPath *path, List *quals)
{
    double      tuples;
    double      outer_tuples = path->outerjoinpath->rows;
    double      inner_tuples = path->innerjoinpath->rows;
    SpecialJoinInfo sjinfo;
    Selectivity selec = 1.0;
    ListCell   *l;

    /*
     * Make up a SpecialJoinInfo for JOIN_INNER semantics.
     */
    sjinfo.type = T_SpecialJoinInfo;
    sjinfo.min_lefthand = path->outerjoinpath->parent->relids;
    sjinfo.min_righthand = path->innerjoinpath->parent->relids;
    sjinfo.syn_lefthand = path->outerjoinpath->parent->relids;
    sjinfo.syn_righthand = path->innerjoinpath->parent->relids;
    sjinfo.jointype = JOIN_INNER;
    /* we don't bother trying to make the remaining fields valid */
    sjinfo.lhs_strict = false;
    sjinfo.delay_upper_joins = false;
    sjinfo.join_quals = NIL;

    /* Get the approximate selectivity */
    foreach(l, quals)
    {
        Node       *qual = (Node *) lfirst(l);

        /* Note that clause_selectivity will be able to cache its result */
        selec *= clause_selectivity(root, qual, 0, JOIN_INNER, &sjinfo);
    }

    /* Apply it to the input relation sizes */
    tuples = selec * outer_tuples * inner_tuples;

    return clamp_row_est(tuples);
}


/*
 * 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, and rel->tuples must be set.
 *
 * We set the following fields of the rel node:
 *  rows: the estimated number of output tuples (after applying
 *        restriction clauses).
 *  width: the estimated average output tuple width in bytes.
 *  baserestrictcost: estimated cost of evaluating baserestrictinfo clauses.
 */
void
set_baserel_size_estimates(PlannerInfo *root, RelOptInfo *rel)
{
    double      nrows;

    /* Should only be applied to base relations */
    Assert(rel->relid > 0);

    nrows = rel->tuples *
        clauselist_selectivity(root,
                               rel->baserestrictinfo,
                               0,
                               JOIN_INNER,
                               NULL);

    rel->rows = clamp_row_est(nrows);

    cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo, root);

    set_rel_width(root, rel);
}

/*
 * get_parameterized_baserel_size
 *      Make a size estimate for a parameterized scan of a base relation.
 *
 * 'param_clauses' lists the additional join clauses to be used.
 *
 * set_baserel_size_estimates must have been applied already.
 */
double
get_parameterized_baserel_size(PlannerInfo *root, RelOptInfo *rel,
                               List *param_clauses)
{
    List       *allclauses;
    double      nrows;

    /*
     * Estimate the number of rows returned by the parameterized scan, knowing
     * that it will apply all the extra join clauses as well as the rel's own
     * restriction clauses.  Note that we force the clauses to be treated as
     * non-join clauses during selectivity estimation.
     */
    allclauses = list_concat(list_copy(param_clauses),
                             rel->baserestrictinfo);
    nrows = rel->tuples *
        clauselist_selectivity(root,
                               allclauses,
                               rel->relid,      /* do not use 0! */
                               JOIN_INNER,
                               NULL);
    nrows = clamp_row_est(nrows);
    /* For safety, make sure result is not more than the base estimate */
    if (nrows > rel->rows)
        nrows = rel->rows;
    return nrows;
}

/*
 * 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, and
 * anyway we must keep the rowcount estimate the same for all paths for the
 * joinrel.)
 *
 * We set only the rows field here.  The width field was already set by
 * build_joinrel_tlist, and baserestrictcost is not used for join rels.
 */
void
set_joinrel_size_estimates(PlannerInfo *root, RelOptInfo *rel,
                           RelOptInfo *outer_rel,
                           RelOptInfo *inner_rel,
                           SpecialJoinInfo *sjinfo,
                           List *restrictlist)
{
    rel->rows = calc_joinrel_size_estimate(root,
                                           outer_rel->rows,
                                           inner_rel->rows,
                                           sjinfo,
                                           restrictlist);
}

/*
 * get_parameterized_joinrel_size
 *      Make a size estimate for a parameterized scan of a join relation.
 *
 * 'rel' is the joinrel under consideration.
 * 'outer_rows', 'inner_rows' are the sizes of the (probably also
 *      parameterized) join inputs under consideration.
 * 'sjinfo' is any SpecialJoinInfo relevant to this join.
 * 'restrict_clauses' lists the join clauses that need to be applied at the
 * join node (including any movable clauses that were moved down to this join,
 * and not including any movable clauses that were pushed down into the
 * child paths).
 *
 * set_joinrel_size_estimates must have been applied already.
 */
double
get_parameterized_joinrel_size(PlannerInfo *root, RelOptInfo *rel,
                               double outer_rows,
                               double inner_rows,
                               SpecialJoinInfo *sjinfo,
                               List *restrict_clauses)
{
    double      nrows;

    /*
     * Estimate the number of rows returned by the parameterized join as the
     * sizes of the input paths times the selectivity of the clauses that have
     * ended up at this join node.
     *
     * As with set_joinrel_size_estimates, the rowcount estimate could depend
     * on the pair of input paths provided, though ideally we'd get the same
     * estimate for any pair with the same parameterization.
     */
    nrows = calc_joinrel_size_estimate(root,
                                       outer_rows,
                                       inner_rows,
                                       sjinfo,
                                       restrict_clauses);
    /* For safety, make sure result is not more than the base estimate */
    if (nrows > rel->rows)
        nrows = rel->rows;
    return nrows;
}

/*
 * calc_joinrel_size_estimate
 *      Workhorse for set_joinrel_size_estimates and
 *      get_parameterized_joinrel_size.
 */
static double
calc_joinrel_size_estimate(PlannerInfo *root,
                           double outer_rows,
                           double inner_rows,
                           SpecialJoinInfo *sjinfo,
                           List *restrictlist)
{
    JoinType    jointype = sjinfo->jointype;
    Selectivity jselec;
    Selectivity pselec;
    double      nrows;

    /*
     * Compute joinclause selectivity.  Note that we are only considering
     * clauses that become restriction clauses at this join level; we are not
     * double-counting them because they were not considered in estimating the
     * sizes of the component rels.
     *
     * For an outer join, we have to distinguish the selectivity of the join's
     * own clauses (JOIN/ON conditions) from any clauses that were "pushed
     * down".  For inner joins we just count them all as joinclauses.
     */
    if (IS_OUTER_JOIN(jointype))
    {
        List       *joinquals = NIL;
        List       *pushedquals = NIL;
        ListCell   *l;

        /* Grovel through the clauses to separate into two lists */
        foreach(l, restrictlist)
        {
            RestrictInfo *rinfo = (RestrictInfo *) lfirst(l);

            Assert(IsA(rinfo, RestrictInfo));
            if (rinfo->is_pushed_down)
                pushedquals = lappend(pushedquals, rinfo);
            else
                joinquals = lappend(joinquals, rinfo);
        }

        /* Get the separate selectivities */
        jselec = clauselist_selectivity(root,
                                        joinquals,
                                        0,
                                        jointype,
                                        sjinfo);
        pselec = clauselist_selectivity(root,
                                        pushedquals,
                                        0,
                                        jointype,
                                        sjinfo);

        /* Avoid leaking a lot of ListCells */
        list_free(joinquals);
        list_free(pushedquals);
    }
    else
    {
        jselec = clauselist_selectivity(root,
                                        restrictlist,
                                        0,
                                        jointype,
                                        sjinfo);
        pselec = 0.0;           /* not used, keep compiler quiet */
    }

    /*
     * Basically, we multiply size of Cartesian product by selectivity.
     *
     * If we are doing an outer join, take that into account: the joinqual
     * selectivity has to be clamped using the knowledge that the output must
     * be at least as large as the non-nullable input.  However, any
     * pushed-down quals are applied after the outer join, so their
     * selectivity applies fully.
     *
     * For JOIN_SEMI and JOIN_ANTI, the selectivity is defined as the fraction
     * of LHS rows that have matches, and we apply that straightforwardly.
     */
    switch (jointype)
    {
        case JOIN_INNER:
            nrows = outer_rows * inner_rows * jselec;
            break;
        case JOIN_LEFT:
            nrows = outer_rows * inner_rows * jselec;
            if (nrows < outer_rows)
                nrows = outer_rows;
            nrows *= pselec;
            break;
        case JOIN_FULL:
            nrows = outer_rows * inner_rows * jselec;
            if (nrows < outer_rows)
                nrows = outer_rows;
            if (nrows < inner_rows)
                nrows = inner_rows;
            nrows *= pselec;
            break;
        case JOIN_SEMI:
            nrows = outer_rows * jselec;
            /* pselec not used */
            break;
        case JOIN_ANTI:
            nrows = outer_rows * (1.0 - jselec);
            nrows *= pselec;
            break;
        default:
            /* other values not expected here */
            elog(ERROR, "unrecognized join type: %d", (int) jointype);
            nrows = 0;          /* keep compiler quiet */
            break;
    }

    return clamp_row_est(nrows);
}

/*
 * set_subquery_size_estimates
 *      Set the size estimates for a base relation that is a subquery.
 *
 * The rel's targetlist and restrictinfo list must have been constructed
 * already, and the plan for the subquery must have been completed.
 * We look at the subquery's plan and PlannerInfo to extract data.
 *
 * We set the same fields as set_baserel_size_estimates.
 */
void
set_subquery_size_estimates(PlannerInfo *root, RelOptInfo *rel)
{
    PlannerInfo *subroot = rel->subroot;
    RangeTblEntry *rte PG_USED_FOR_ASSERTS_ONLY;
    ListCell   *lc;

    /* Should only be applied to base relations that are subqueries */
    Assert(rel->relid > 0);
    rte = planner_rt_fetch(rel->relid, root);
    Assert(rte->rtekind == RTE_SUBQUERY);

    /* Copy raw number of output rows from subplan */
    rel->tuples = rel->subplan->plan_rows;

    /*
     * Compute per-output-column width estimates by examining the subquery's
     * targetlist.  For any output that is a plain Var, get the width estimate
     * that was made while planning the subquery.  Otherwise, we leave it to
     * set_rel_width to fill in a datatype-based default estimate.
     */
    foreach(lc, subroot->parse->targetList)
    {
        TargetEntry *te = (TargetEntry *) lfirst(lc);
        Node       *texpr = (Node *) te->expr;
        int32       item_width = 0;

        Assert(IsA(te, TargetEntry));
        /* junk columns aren't visible to upper query */
        if (te->resjunk)
            continue;

        /*
         * The subquery could be an expansion of a view that's had columns
         * added to it since the current query was parsed, so that there are
         * non-junk tlist columns in it that don't correspond to any column
         * visible at our query level.  Ignore such columns.
         */
        if (te->resno < rel->min_attr || te->resno > rel->max_attr)
            continue;

        /*
         * XXX This currently doesn't work for subqueries containing set
         * operations, because the Vars in their tlists are bogus references
         * to the first leaf subquery, which wouldn't give the right answer
         * even if we could still get to its PlannerInfo.
         *
         * Also, the subquery could be an appendrel for which all branches are
         * known empty due to constraint exclusion, in which case
         * set_append_rel_pathlist will have left the attr_widths set to zero.
         *
         * In either case, we just leave the width estimate zero until
         * set_rel_width fixes it.
         */
        if (IsA(texpr, Var) &&
            subroot->parse->setOperations == NULL)
        {
            Var        *var = (Var *) texpr;
            RelOptInfo *subrel = find_base_rel(subroot, var->varno);

            item_width = subrel->attr_widths[var->varattno - subrel->min_attr];
        }
        rel->attr_widths[te->resno - rel->min_attr] = item_width;
    }

    /* Now estimate number of output rows, etc */
    set_baserel_size_estimates(root, rel);
}

/*
 * set_function_size_estimates
 *      Set the size estimates for a base relation that is a function call.
 *
 * The rel's targetlist and restrictinfo list must have been constructed
 * already.
 *
 * We set the same fields as set_baserel_size_estimates.
 */
void
set_function_size_estimates(PlannerInfo *root, RelOptInfo *rel)
{
    RangeTblEntry *rte;

    /* Should only be applied to base relations that are functions */
    Assert(rel->relid > 0);
    rte = planner_rt_fetch(rel->relid, root);
    Assert(rte->rtekind == RTE_FUNCTION);

    /* Estimate number of rows the function itself will return */
    rel->tuples = expression_returns_set_rows(rte->funcexpr);

    /* Now estimate number of output rows, etc */
    set_baserel_size_estimates(root, rel);
}

/*
 * set_values_size_estimates
 *      Set the size estimates for a base relation that is a values list.
 *
 * The rel's targetlist and restrictinfo list must have been constructed
 * already.
 *
 * We set the same fields as set_baserel_size_estimates.
 */
void
set_values_size_estimates(PlannerInfo *root, RelOptInfo *rel)
{
    RangeTblEntry *rte;

    /* Should only be applied to base relations that are values lists */
    Assert(rel->relid > 0);
    rte = planner_rt_fetch(rel->relid, root);
    Assert(rte->rtekind == RTE_VALUES);

    /*
     * Estimate number of rows the values list will return. We know this
     * precisely based on the list length (well, barring set-returning
     * functions in list items, but that's a refinement not catered for
     * anywhere else either).
     */
    rel->tuples = list_length(rte->values_lists);

    /* Now estimate number of output rows, etc */
    set_baserel_size_estimates(root, rel);
}

/*
 * set_cte_size_estimates
 *      Set the size estimates for a base relation that is a CTE reference.
 *
 * The rel's targetlist and restrictinfo list must have been constructed
 * already, and we need the completed plan for the CTE (if a regular CTE)
 * or the non-recursive term (if a self-reference).
 *
 * We set the same fields as set_baserel_size_estimates.
 */
void
set_cte_size_estimates(PlannerInfo *root, RelOptInfo *rel, Plan *cteplan)
{
    RangeTblEntry *rte;

    /* Should only be applied to base relations that are CTE references */
    Assert(rel->relid > 0);
    rte = planner_rt_fetch(rel->relid, root);
    Assert(rte->rtekind == RTE_CTE);

    if (rte->self_reference)
    {
        /*
         * In a self-reference, arbitrarily assume the average worktable size
         * is about 10 times the nonrecursive term's size.
         */
        rel->tuples = 10 * cteplan->plan_rows;
    }
    else
    {
        /* Otherwise just believe the CTE plan's output estimate */
        rel->tuples = cteplan->plan_rows;
    }

    /* Now estimate number of output rows, etc */
    set_baserel_size_estimates(root, rel);
}

/*
 * set_foreign_size_estimates
 *      Set the size estimates for a base relation that is a foreign table.
 *
 * There is not a whole lot that we can do here; the foreign-data wrapper
 * is responsible for producing useful estimates.  We can do a decent job
 * of estimating baserestrictcost, so we set that, and we also set up width
 * using what will be purely datatype-driven estimates from the targetlist.
 * There is no way to do anything sane with the rows value, so we just put
 * a default estimate and hope that the wrapper can improve on it.  The
 * wrapper's GetForeignRelSize function will be called momentarily.
 *
 * The rel's targetlist and restrictinfo list must have been constructed
 * already.
 */
void
set_foreign_size_estimates(PlannerInfo *root, RelOptInfo *rel)
{
    /* Should only be applied to base relations */
    Assert(rel->relid > 0);

    rel->rows = 1000;           /* entirely bogus default estimate */

    cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo, root);

    set_rel_width(root, rel);
}


/*
 * set_rel_width
 *      Set the estimated output width of a base relation.
 *
 * The estimated output width is the sum of the per-attribute width estimates
 * for the actually-referenced columns, plus any PHVs or other expressions
 * that have to be calculated at this relation.  This is the amount of data
 * we'd need to pass upwards in case of a sort, hash, etc.
 *
 * NB: this works best on plain relations because it prefers to look at
 * real Vars.  For subqueries, set_subquery_size_estimates will already have
 * copied up whatever per-column estimates were made within the subquery,
 * and for other types of rels there isn't much we can do anyway.  We fall
 * back on (fairly stupid) datatype-based width estimates if we can't get
 * any better number.
 *
 * The per-attribute width estimates are cached for possible re-use while
 * building join relations.
 */
static void
set_rel_width(PlannerInfo *root, RelOptInfo *rel)
{
    Oid         reloid = planner_rt_fetch(rel->relid, root)->relid;
    int32       tuple_width = 0;
    bool        have_wholerow_var = false;
    ListCell   *lc;

    foreach(lc, rel->reltargetlist)
    {
        Node       *node = (Node *) lfirst(lc);

        /*
         * Ordinarily, a Var in a rel's reltargetlist must belong to that rel;
         * but there are corner cases involving LATERAL references in
         * appendrel members where that isn't so (see set_append_rel_size()).
         * If the Var has the wrong varno, fall through to the generic case
         * (it doesn't seem worth the trouble to be any smarter).
         */
        if (IsA(node, Var) &&
            ((Var *) node)->varno == rel->relid)
        {
            Var        *var = (Var *) node;
            int         ndx;
            int32       item_width;

            Assert(var->varattno >= rel->min_attr);
            Assert(var->varattno <= rel->max_attr);

            ndx = var->varattno - rel->min_attr;

            /*
             * If it's a whole-row Var, we'll deal with it below after we have
             * already cached as many attr widths as possible.
             */
            if (var->varattno == 0)
            {
                have_wholerow_var = true;
                continue;
            }

            /*
             * The width may have been cached already (especially if it's a
             * subquery), so don't duplicate effort.
             */
            if (rel->attr_widths[ndx] > 0)
            {
                tuple_width += rel->attr_widths[ndx];
                continue;
            }

            /* Try to get column width from statistics */
            if (reloid != InvalidOid && var->varattno > 0)
            {
                item_width = get_attavgwidth(reloid, var->varattno);
                if (item_width > 0)
                {
                    rel->attr_widths[ndx] = item_width;
                    tuple_width += item_width;
                    continue;
                }
            }

            /*
             * Not a plain relation, or can't find statistics for it. Estimate
             * using just the type info.
             */
            item_width = get_typavgwidth(var->vartype, var->vartypmod);
            Assert(item_width > 0);
            rel->attr_widths[ndx] = item_width;
            tuple_width += item_width;
        }
        else if (IsA(node, PlaceHolderVar))
        {
            PlaceHolderVar *phv = (PlaceHolderVar *) node;
            PlaceHolderInfo *phinfo = find_placeholder_info(root, phv, false);

            tuple_width += phinfo->ph_width;
        }
        else
        {
            /*
             * We could be looking at an expression pulled up from a subquery,
             * or a ROW() representing a whole-row child Var, etc.  Do what we
             * can using the expression type information.
             */
            int32       item_width;

            item_width = get_typavgwidth(exprType(node), exprTypmod(node));
            Assert(item_width > 0);
            tuple_width += item_width;
        }
    }

    /*
     * If we have a whole-row reference, estimate its width as the sum of
     * per-column widths plus sizeof(HeapTupleHeaderData).
     */
    if (have_wholerow_var)
    {
        int32       wholerow_width = sizeof(HeapTupleHeaderData);

        if (reloid != InvalidOid)
        {
            /* Real relation, so estimate true tuple width */
            wholerow_width += get_relation_data_width(reloid,
                                           rel->attr_widths - rel->min_attr);
        }
        else
        {
            /* Do what we can with info for a phony rel */
            AttrNumber  i;

            for (i = 1; i <= rel->max_attr; i++)
                wholerow_width += rel->attr_widths[i - rel->min_attr];
        }

        rel->attr_widths[0 - rel->min_attr] = wholerow_width;

        /*
         * Include the whole-row Var as part of the output tuple.  Yes, that
         * really is what happens at runtime.
         */
        tuple_width += wholerow_width;
    }

    Assert(tuple_width >= 0);
    rel->width = tuple_width;
}

/*
 * relation_byte_size
 *    Estimate the storage space in bytes for a given number of tuples
 *    of a given width (size in bytes).
 */
static double
relation_byte_size(double tuples, int width)
{
    return tuples * (MAXALIGN(width) + MAXALIGN(sizeof(HeapTupleHeaderData)));
}

/*
 * page_size
 *    Returns an estimate of the number of pages covered by a given
 *    number of tuples of a given width (size in bytes).
 */
static double
page_size(double tuples, int width)
{
    return ceil(relation_byte_size(tuples, width) / BLCKSZ);
}