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authorDaniel Baumann <daniel.baumann@progress-linux.org>2024-05-04 12:17:33 +0000
committerDaniel Baumann <daniel.baumann@progress-linux.org>2024-05-04 12:17:33 +0000
commit5e45211a64149b3c659b90ff2de6fa982a5a93ed (patch)
tree739caf8c461053357daa9f162bef34516c7bf452 /src/backend/optimizer/path/costsize.c
parentInitial commit. (diff)
downloadpostgresql-15-5e45211a64149b3c659b90ff2de6fa982a5a93ed.tar.xz
postgresql-15-5e45211a64149b3c659b90ff2de6fa982a5a93ed.zip
Adding upstream version 15.5.upstream/15.5
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
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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
+ * parallel_tuple_cost Cost of CPU time to pass a tuple from worker to leader backend
+ * parallel_setup_cost Cost of setting up shared memory for parallelism
+ *
+ * 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. (Note: while path->rows is never zero
+ * for ordinary relations, it is zero for paths for provably-empty relations,
+ * so beware of division-by-zero.) 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) 1996-2022, PostgreSQL Global Development Group
+ * Portions Copyright (c) 1994, Regents of the University of California
+ *
+ * IDENTIFICATION
+ * src/backend/optimizer/path/costsize.c
+ *
+ *-------------------------------------------------------------------------
+ */
+
+#include "postgres.h"
+
+#include <limits.h>
+#include <math.h>
+
+#include "access/amapi.h"
+#include "access/htup_details.h"
+#include "access/tsmapi.h"
+#include "executor/executor.h"
+#include "executor/nodeAgg.h"
+#include "executor/nodeHash.h"
+#include "executor/nodeMemoize.h"
+#include "miscadmin.h"
+#include "nodes/makefuncs.h"
+#include "nodes/nodeFuncs.h"
+#include "optimizer/clauses.h"
+#include "optimizer/cost.h"
+#include "optimizer/optimizer.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)
+
+/*
+ * Append and MergeAppend nodes are less expensive than some other operations
+ * which use cpu_tuple_cost; instead of adding a separate GUC, estimate the
+ * per-tuple cost as cpu_tuple_cost multiplied by this value.
+ */
+#define APPEND_CPU_COST_MULTIPLIER 0.5
+
+/*
+ * Maximum value for row estimates. We cap row estimates to this to help
+ * ensure that costs based on these estimates remain within the range of what
+ * double can represent. add_path() wouldn't act sanely given infinite or NaN
+ * cost values.
+ */
+#define MAXIMUM_ROWCOUNT 1e100
+
+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;
+double parallel_tuple_cost = DEFAULT_PARALLEL_TUPLE_COST;
+double parallel_setup_cost = DEFAULT_PARALLEL_SETUP_COST;
+double recursive_worktable_factor = DEFAULT_RECURSIVE_WORKTABLE_FACTOR;
+
+int effective_cache_size = DEFAULT_EFFECTIVE_CACHE_SIZE;
+
+Cost disable_cost = 1.0e10;
+
+int max_parallel_workers_per_gather = 2;
+
+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_incremental_sort = true;
+bool enable_hashagg = true;
+bool enable_nestloop = true;
+bool enable_material = true;
+bool enable_memoize = true;
+bool enable_mergejoin = true;
+bool enable_hashjoin = true;
+bool enable_gathermerge = true;
+bool enable_partitionwise_join = false;
+bool enable_partitionwise_aggregate = false;
+bool enable_parallel_append = true;
+bool enable_parallel_hash = true;
+bool enable_partition_pruning = true;
+bool enable_async_append = true;
+
+typedef struct
+{
+ PlannerInfo *root;
+ QualCost total;
+} cost_qual_eval_context;
+
+static List *extract_nonindex_conditions(List *qual_clauses, List *indexclauses);
+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 *path);
+static double approx_tuple_count(PlannerInfo *root, JoinPath *path,
+ List *quals);
+static double calc_joinrel_size_estimate(PlannerInfo *root,
+ RelOptInfo *joinrel,
+ RelOptInfo *outer_rel,
+ RelOptInfo *inner_rel,
+ double outer_rows,
+ double inner_rows,
+ SpecialJoinInfo *sjinfo,
+ List *restrictlist);
+static Selectivity get_foreign_key_join_selectivity(PlannerInfo *root,
+ Relids outer_relids,
+ Relids inner_relids,
+ SpecialJoinInfo *sjinfo,
+ List **restrictlist);
+static Cost append_nonpartial_cost(List *subpaths, int numpaths,
+ int parallel_workers);
+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);
+static double get_parallel_divisor(Path *path);
+
+
+/*
+ * clamp_row_est
+ * Force a row-count estimate to a sane value.
+ */
+double
+clamp_row_est(double nrows)
+{
+ /*
+ * Avoid infinite and NaN row estimates. Costs derived from such values
+ * are going to be useless. Also force the 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 > MAXIMUM_ROWCOUNT || isnan(nrows))
+ nrows = MAXIMUM_ROWCOUNT;
+ else if (nrows <= 1.0)
+ nrows = 1.0;
+ else
+ nrows = rint(nrows);
+
+ return nrows;
+}
+
+/*
+ * clamp_cardinality_to_long
+ * Cast a Cardinality value to a sane long value.
+ */
+long
+clamp_cardinality_to_long(Cardinality x)
+{
+ /*
+ * Just for paranoia's sake, ensure we do something sane with negative or
+ * NaN values.
+ */
+ if (isnan(x))
+ return LONG_MAX;
+ if (x <= 0)
+ return 0;
+
+ /*
+ * If "long" is 64 bits, then LONG_MAX cannot be represented exactly as a
+ * double. Casting it to double and back may well result in overflow due
+ * to rounding, so avoid doing that. We trust that any double value that
+ * compares strictly less than "(double) LONG_MAX" will cast to a
+ * representable "long" value.
+ */
+ return (x < (double) LONG_MAX) ? (long) x : LONG_MAX;
+}
+
+
+/*
+ * 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 cpu_run_cost;
+ Cost disk_run_cost;
+ 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
+ */
+ disk_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;
+ cpu_run_cost = cpu_per_tuple * baserel->tuples;
+ /* tlist eval costs are paid per output row, not per tuple scanned */
+ startup_cost += path->pathtarget->cost.startup;
+ cpu_run_cost += path->pathtarget->cost.per_tuple * path->rows;
+
+ /* Adjust costing for parallelism, if used. */
+ if (path->parallel_workers > 0)
+ {
+ double parallel_divisor = get_parallel_divisor(path);
+
+ /* The CPU cost is divided among all the workers. */
+ cpu_run_cost /= parallel_divisor;
+
+ /*
+ * It may be possible to amortize some of the I/O cost, but probably
+ * not very much, because most operating systems already do aggressive
+ * prefetching. For now, we assume that the disk run cost can't be
+ * amortized at all.
+ */
+
+ /*
+ * In the case of a parallel plan, the row count needs to represent
+ * the number of tuples processed per worker.
+ */
+ path->rows = clamp_row_est(path->rows / parallel_divisor);
+ }
+
+ path->startup_cost = startup_cost;
+ path->total_cost = startup_cost + cpu_run_cost + disk_run_cost;
+}
+
+/*
+ * cost_samplescan
+ * Determines and returns the cost of scanning a relation using sampling.
+ *
+ * 'baserel' is the relation to be scanned
+ * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
+ */
+void
+cost_samplescan(Path *path, PlannerInfo *root,
+ RelOptInfo *baserel, ParamPathInfo *param_info)
+{
+ Cost startup_cost = 0;
+ Cost run_cost = 0;
+ RangeTblEntry *rte;
+ TableSampleClause *tsc;
+ TsmRoutine *tsm;
+ double spc_seq_page_cost,
+ spc_random_page_cost,
+ spc_page_cost;
+ QualCost qpqual_cost;
+ Cost cpu_per_tuple;
+
+ /* Should only be applied to base relations with tablesample clauses */
+ Assert(baserel->relid > 0);
+ rte = planner_rt_fetch(baserel->relid, root);
+ Assert(rte->rtekind == RTE_RELATION);
+ tsc = rte->tablesample;
+ Assert(tsc != NULL);
+ tsm = GetTsmRoutine(tsc->tsmhandler);
+
+ /* Mark the path with the correct row estimate */
+ if (param_info)
+ path->rows = param_info->ppi_rows;
+ else
+ path->rows = baserel->rows;
+
+ /* fetch estimated page cost for tablespace containing table */
+ get_tablespace_page_costs(baserel->reltablespace,
+ &spc_random_page_cost,
+ &spc_seq_page_cost);
+
+ /* if NextSampleBlock is used, assume random access, else sequential */
+ spc_page_cost = (tsm->NextSampleBlock != NULL) ?
+ spc_random_page_cost : spc_seq_page_cost;
+
+ /*
+ * disk costs (recall that baserel->pages has already been set to the
+ * number of pages the sampling method will visit)
+ */
+ run_cost += spc_page_cost * baserel->pages;
+
+ /*
+ * CPU costs (recall that baserel->tuples has already been set to the
+ * number of tuples the sampling method will select). Note that we ignore
+ * execution cost of the TABLESAMPLE parameter expressions; they will be
+ * evaluated only once per scan, and in most usages they'll likely be
+ * simple constants anyway. We also don't charge anything for the
+ * calculations the sampling method might do internally.
+ */
+ 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;
+ /* tlist eval costs are paid per output row, not per tuple scanned */
+ startup_cost += path->pathtarget->cost.startup;
+ run_cost += path->pathtarget->cost.per_tuple * path->rows;
+
+ path->startup_cost = startup_cost;
+ path->total_cost = startup_cost + run_cost;
+}
+
+/*
+ * cost_gather
+ * Determines and returns the cost of gather path.
+ *
+ * 'rel' is the relation to be operated upon
+ * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
+ * 'rows' may be used to point to a row estimate; if non-NULL, it overrides
+ * both 'rel' and 'param_info'. This is useful when the path doesn't exactly
+ * correspond to any particular RelOptInfo.
+ */
+void
+cost_gather(GatherPath *path, PlannerInfo *root,
+ RelOptInfo *rel, ParamPathInfo *param_info,
+ double *rows)
+{
+ Cost startup_cost = 0;
+ Cost run_cost = 0;
+
+ /* Mark the path with the correct row estimate */
+ if (rows)
+ path->path.rows = *rows;
+ else if (param_info)
+ path->path.rows = param_info->ppi_rows;
+ else
+ path->path.rows = rel->rows;
+
+ startup_cost = path->subpath->startup_cost;
+
+ run_cost = path->subpath->total_cost - path->subpath->startup_cost;
+
+ /* Parallel setup and communication cost. */
+ startup_cost += parallel_setup_cost;
+ run_cost += parallel_tuple_cost * path->path.rows;
+
+ path->path.startup_cost = startup_cost;
+ path->path.total_cost = (startup_cost + run_cost);
+}
+
+/*
+ * cost_gather_merge
+ * Determines and returns the cost of gather merge path.
+ *
+ * GatherMerge 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
+ * replace the top heap entry with the next tuple from the same stream.
+ */
+void
+cost_gather_merge(GatherMergePath *path, PlannerInfo *root,
+ RelOptInfo *rel, ParamPathInfo *param_info,
+ Cost input_startup_cost, Cost input_total_cost,
+ double *rows)
+{
+ Cost startup_cost = 0;
+ Cost run_cost = 0;
+ Cost comparison_cost;
+ double N;
+ double logN;
+
+ /* Mark the path with the correct row estimate */
+ if (rows)
+ path->path.rows = *rows;
+ else if (param_info)
+ path->path.rows = param_info->ppi_rows;
+ else
+ path->path.rows = rel->rows;
+
+ if (!enable_gathermerge)
+ startup_cost += disable_cost;
+
+ /*
+ * Add one to the number of workers to account for the leader. This might
+ * be overgenerous since the leader will do less work than other workers
+ * in typical cases, but we'll go with it for now.
+ */
+ Assert(path->num_workers > 0);
+ N = (double) path->num_workers + 1;
+ 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 += path->path.rows * comparison_cost * logN;
+
+ /* small cost for heap management, like cost_merge_append */
+ run_cost += cpu_operator_cost * path->path.rows;
+
+ /*
+ * Parallel setup and communication cost. Since Gather Merge, unlike
+ * Gather, requires us to block until a tuple is available from every
+ * worker, we bump the IPC cost up a little bit as compared with Gather.
+ * For lack of a better idea, charge an extra 5%.
+ */
+ startup_cost += parallel_setup_cost;
+ run_cost += parallel_tuple_cost * path->path.rows * 1.05;
+
+ path->path.startup_cost = startup_cost + input_startup_cost;
+ path->path.total_cost = (startup_cost + run_cost + input_total_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,
+ bool partial_path)
+{
+ IndexOptInfo *index = path->indexinfo;
+ RelOptInfo *baserel = index->rel;
+ bool indexonly = (path->path.pathtype == T_IndexOnlyScan);
+ amcostestimate_function amcostestimate;
+ List *qpquals;
+ Cost startup_cost = 0;
+ Cost run_cost = 0;
+ Cost cpu_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;
+ double rand_heap_pages;
+ double index_pages;
+
+ /* 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, and identify which quals
+ * will need to be enforced as qpquals. We need not check any quals that
+ * are implied by the index's predicate, so we can use indrestrictinfo not
+ * baserestrictinfo as the list of relevant restriction clauses for the
+ * rel.
+ */
+ if (path->path.param_info)
+ {
+ path->path.rows = path->path.param_info->ppi_rows;
+ /* qpquals come from the rel's restriction clauses and ppi_clauses */
+ qpquals = list_concat(extract_nonindex_conditions(path->indexinfo->indrestrictinfo,
+ path->indexclauses),
+ extract_nonindex_conditions(path->path.param_info->ppi_clauses,
+ path->indexclauses));
+ }
+ else
+ {
+ path->path.rows = baserel->rows;
+ /* qpquals come from just the rel's restriction clauses */
+ qpquals = extract_nonindex_conditions(path->indexinfo->indrestrictinfo,
+ path->indexclauses);
+ }
+
+ 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. We need a cast here because
+ * pathnodes.h uses a weak function type to avoid including amapi.h.
+ */
+ amcostestimate = (amcostestimate_function) index->amcostestimate;
+ amcostestimate(root, path, loop_count,
+ &indexStartupCost, &indexTotalCost,
+ &indexSelectivity, &indexCorrelation,
+ &index_pages);
+
+ /*
+ * 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));
+
+ rand_heap_pages = pages_fetched;
+
+ 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));
+
+ rand_heap_pages = pages_fetched;
+
+ /* 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;
+ }
+
+ if (partial_path)
+ {
+ /*
+ * For index only scans compute workers based on number of index pages
+ * fetched; the number of heap pages we fetch might be so small as to
+ * effectively rule out parallelism, which we don't want to do.
+ */
+ if (indexonly)
+ rand_heap_pages = -1;
+
+ /*
+ * Estimate the number of parallel workers required to scan index. Use
+ * the number of heap pages computed considering heap fetches won't be
+ * sequential as for parallel scans the pages are accessed in random
+ * order.
+ */
+ path->path.parallel_workers = compute_parallel_worker(baserel,
+ rand_heap_pages,
+ index_pages,
+ max_parallel_workers_per_gather);
+
+ /*
+ * Fall out if workers can't be assigned for parallel scan, because in
+ * such a case this path will be rejected. So there is no benefit in
+ * doing extra computation.
+ */
+ if (path->path.parallel_workers <= 0)
+ return;
+
+ path->path.parallel_aware = true;
+ }
+
+ /*
+ * 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.
+ */
+ cost_qual_eval(&qpqual_cost, qpquals, root);
+
+ startup_cost += qpqual_cost.startup;
+ cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
+
+ cpu_run_cost += cpu_per_tuple * tuples_fetched;
+
+ /* tlist eval costs are paid per output row, not per tuple scanned */
+ startup_cost += path->path.pathtarget->cost.startup;
+ cpu_run_cost += path->path.pathtarget->cost.per_tuple * path->path.rows;
+
+ /* Adjust costing for parallelism, if used. */
+ if (path->path.parallel_workers > 0)
+ {
+ double parallel_divisor = get_parallel_divisor(&path->path);
+
+ path->path.rows = clamp_row_est(path->path.rows / parallel_divisor);
+
+ /* The CPU cost is divided among all the workers. */
+ cpu_run_cost /= parallel_divisor;
+ }
+
+ run_cost += cpu_run_cost;
+
+ path->path.startup_cost = startup_cost;
+ path->path.total_cost = startup_cost + run_cost;
+}
+
+/*
+ * extract_nonindex_conditions
+ *
+ * Given a list of quals to be enforced in an indexscan, extract the ones that
+ * will have to be applied as qpquals (ie, the index machinery won't handle
+ * them). Here we detect only whether a qual clause is directly redundant
+ * with some indexclause. If the index path is chosen for use, createplan.c
+ * will try a bit harder to get rid of redundant qual conditions; specifically
+ * it will see if quals can be proven to be implied by the indexquals. But
+ * it does not seem worth the cycles to try to factor that in at this stage,
+ * since we're only trying to estimate qual eval costs. Otherwise this must
+ * match the logic in create_indexscan_plan().
+ *
+ * qual_clauses, and the result, are lists of RestrictInfos.
+ * indexclauses is a list of IndexClauses.
+ */
+static List *
+extract_nonindex_conditions(List *qual_clauses, List *indexclauses)
+{
+ List *result = NIL;
+ ListCell *lc;
+
+ foreach(lc, qual_clauses)
+ {
+ RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc);
+
+ if (rinfo->pseudoconstant)
+ continue; /* we may drop pseudoconstants here */
+ if (is_redundant_with_indexclauses(rinfo, indexclauses))
+ continue; /* dup or derived from same EquivalenceClass */
+ /* ... skip the predicate proof attempt createplan.c will try ... */
+ result = lappend(result, rinfo);
+ }
+ return result;
+}
+
+/*
+ * 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 make_one_rel.
+ *
+ * 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;
+ QualCost qpqual_cost;
+ Cost cpu_per_tuple;
+ Cost cost_per_page;
+ Cost cpu_run_cost;
+ 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;
+
+ pages_fetched = compute_bitmap_pages(root, baserel, bitmapqual,
+ loop_count, &indexTotalCost,
+ &tuples_fetched);
+
+ startup_cost += indexTotalCost;
+ T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
+
+ /* Fetch estimated page costs for tablespace containing table. */
+ get_tablespace_page_costs(baserel->reltablespace,
+ &spc_random_page_cost,
+ &spc_seq_page_cost);
+
+ /*
+ * 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;
+ cpu_run_cost = cpu_per_tuple * tuples_fetched;
+
+ /* Adjust costing for parallelism, if used. */
+ if (path->parallel_workers > 0)
+ {
+ double parallel_divisor = get_parallel_divisor(path);
+
+ /* The CPU cost is divided among all the workers. */
+ cpu_run_cost /= parallel_divisor;
+
+ path->rows = clamp_row_est(path->rows / parallel_divisor);
+ }
+
+
+ run_cost += cpu_run_cost;
+
+ /* tlist eval costs are paid per output row, not per tuple scanned */
+ startup_cost += path->pathtarget->cost.startup;
+ run_cost += path->pathtarget->cost.per_tuple * path->rows;
+
+ 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)
+ {
+ RestrictInfo *rinfo = lfirst_node(RestrictInfo, l);
+ Expr *qual = rinfo->clause;
+
+ if (IsA(qual, ScalarArrayOpExpr))
+ {
+ /* Each element of the array yields 1 tuple */
+ ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) qual;
+ Node *arraynode = (Node *) lsecond(saop->args);
+
+ ntuples += estimate_array_length(arraynode);
+ }
+ else if (IsA(qual, 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 retrieved 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;
+
+ /* tlist eval costs are paid per output row, not per tuple scanned */
+ startup_cost += path->pathtarget->cost.startup;
+ run_cost += path->pathtarget->cost.per_tuple * path->rows;
+
+ path->startup_cost = startup_cost;
+ path->total_cost = startup_cost + run_cost;
+}
+
+/*
+ * cost_tidrangescan
+ * Determines and sets the costs of scanning a relation using a range of
+ * TIDs for 'path'
+ *
+ * 'baserel' is the relation to be scanned
+ * 'tidrangequals' is the list of TID-checkable range quals
+ * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
+ */
+void
+cost_tidrangescan(Path *path, PlannerInfo *root,
+ RelOptInfo *baserel, List *tidrangequals,
+ ParamPathInfo *param_info)
+{
+ Selectivity selectivity;
+ double pages;
+ Cost startup_cost = 0;
+ Cost run_cost = 0;
+ QualCost qpqual_cost;
+ Cost cpu_per_tuple;
+ QualCost tid_qual_cost;
+ double ntuples;
+ double nseqpages;
+ double spc_random_page_cost;
+ double spc_seq_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 and pages we expect to scan */
+ selectivity = clauselist_selectivity(root, tidrangequals, baserel->relid,
+ JOIN_INNER, NULL);
+ pages = ceil(selectivity * baserel->pages);
+
+ if (pages <= 0.0)
+ pages = 1.0;
+
+ /*
+ * The first page in a range requires a random seek, but each subsequent
+ * page is just a normal sequential page read. NOTE: it's desirable for
+ * TID Range Scans to cost more than the equivalent Sequential Scans,
+ * because Seq Scans have some performance advantages such as scan
+ * synchronization and parallelizability, and we'd prefer one of them to
+ * be picked unless a TID Range Scan really is better.
+ */
+ ntuples = selectivity * baserel->tuples;
+ nseqpages = pages - 1.0;
+
+ if (!enable_tidscan)
+ startup_cost += disable_cost;
+
+ /*
+ * The TID qual expressions will be computed once, any other baserestrict
+ * quals once per retrieved tuple.
+ */
+ cost_qual_eval(&tid_qual_cost, tidrangequals, root);
+
+ /* fetch estimated page cost for tablespace containing table */
+ get_tablespace_page_costs(baserel->reltablespace,
+ &spc_random_page_cost,
+ &spc_seq_page_cost);
+
+ /* disk costs; 1 random page and the remainder as seq pages */
+ run_cost += spc_random_page_cost + spc_seq_page_cost * nseqpages;
+
+ /* 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 at this
+ * point; they will be removed (if possible) when we create the plan, so
+ * we subtract their cost from the total qpqual cost. (If the TID quals
+ * can't be removed, this is a mistake and we're going to underestimate
+ * the CPU cost a bit.)
+ */
+ 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;
+
+ /* tlist eval costs are paid per output row, not per tuple scanned */
+ startup_cost += path->pathtarget->cost.startup;
+ run_cost += path->pathtarget->cost.per_tuple * path->rows;
+
+ 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(SubqueryScanPath *path, PlannerInfo *root,
+ RelOptInfo *baserel, ParamPathInfo *param_info)
+{
+ Cost startup_cost;
+ Cost run_cost;
+ List *qpquals;
+ 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);
+
+ /*
+ * We compute the rowcount estimate as the subplan's estimate times the
+ * selectivity of relevant restriction clauses. In simple cases this will
+ * come out the same as baserel->rows; but when dealing with parallelized
+ * paths we must do it like this to get the right answer.
+ */
+ if (param_info)
+ qpquals = list_concat_copy(param_info->ppi_clauses,
+ baserel->baserestrictinfo);
+ else
+ qpquals = baserel->baserestrictinfo;
+
+ path->path.rows = clamp_row_est(path->subpath->rows *
+ clauselist_selectivity(root,
+ qpquals,
+ 0,
+ JOIN_INNER,
+ NULL));
+
+ /*
+ * Cost of path is cost of evaluating the subplan, plus cost of evaluating
+ * any restriction clauses and tlist that will be attached to the
+ * SubqueryScan node, plus cpu_tuple_cost to account for selection and
+ * projection overhead.
+ */
+ path->path.startup_cost = path->subpath->startup_cost;
+ path->path.total_cost = path->subpath->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 * path->subpath->rows;
+
+ /* tlist eval costs are paid per output row, not per tuple scanned */
+ startup_cost += path->path.pathtarget->cost.startup;
+ run_cost += path->path.pathtarget->cost.per_tuple * path->path.rows;
+
+ path->path.startup_cost += startup_cost;
+ path->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(s).
+ *
+ * Currently, nodeFunctionscan.c always executes the functions 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, (Node *) rte->functions, 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;
+
+ /* tlist eval costs are paid per output row, not per tuple scanned */
+ startup_cost += path->pathtarget->cost.startup;
+ run_cost += path->pathtarget->cost.per_tuple * path->rows;
+
+ path->startup_cost = startup_cost;
+ path->total_cost = startup_cost + run_cost;
+}
+
+/*
+ * cost_tablefuncscan
+ * Determines and returns the cost of scanning a table function.
+ *
+ * 'baserel' is the relation to be scanned
+ * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
+ */
+void
+cost_tablefuncscan(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_TABLEFUNC);
+
+ /* 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 table func expression(s).
+ *
+ * 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 tablefuncs tend to be, there's not a lot of point in that
+ * refinement right now.
+ */
+ cost_qual_eval_node(&exprcost, (Node *) rte->tablefunc, 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;
+
+ /* tlist eval costs are paid per output row, not per tuple scanned */
+ startup_cost += path->pathtarget->cost.startup;
+ run_cost += path->pathtarget->cost.per_tuple * path->rows;
+
+ 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;
+
+ /* tlist eval costs are paid per output row, not per tuple scanned */
+ startup_cost += path->pathtarget->cost.startup;
+ run_cost += path->pathtarget->cost.per_tuple * path->rows;
+
+ 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;
+
+ /* tlist eval costs are paid per output row, not per tuple scanned */
+ startup_cost += path->pathtarget->cost.startup;
+ run_cost += path->pathtarget->cost.per_tuple * path->rows;
+
+ path->startup_cost = startup_cost;
+ path->total_cost = startup_cost + run_cost;
+}
+
+/*
+ * cost_namedtuplestorescan
+ * Determines and returns the cost of scanning a named tuplestore.
+ */
+void
+cost_namedtuplestorescan(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 Tuplestores */
+ Assert(baserel->relid > 0);
+ Assert(baserel->rtekind == RTE_NAMEDTUPLESTORE);
+
+ /* 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_resultscan
+ * Determines and returns the cost of scanning an RTE_RESULT relation.
+ */
+void
+cost_resultscan(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 RTE_RESULT base relations */
+ Assert(baserel->relid > 0);
+ Assert(baserel->rtekind == RTE_RESULT);
+
+ /* Mark the path with the correct row estimate */
+ if (param_info)
+ path->rows = param_info->ppi_rows;
+ else
+ path->rows = baserel->rows;
+
+ /* We charge qual cost plus cpu_tuple_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_recursive_union
+ * Determines and returns the cost of performing a recursive union,
+ * and also the estimated output size.
+ *
+ * We are given Paths for the nonrecursive and recursive terms.
+ */
+void
+cost_recursive_union(Path *runion, Path *nrterm, Path *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->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->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->rows = total_rows;
+ runion->pathtarget->width = Max(nrterm->pathtarget->width,
+ rterm->pathtarget->width);
+}
+
+/*
+ * cost_tuplesort
+ * Determines and returns the cost of sorting a relation using tuplesort,
+ * not 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 sort_mem, we have
+ * disk traffic = 2 * relsize * ceil(logM(p / 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.
+ *
+ * '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
+ */
+static void
+cost_tuplesort(Cost *startup_cost, Cost *run_cost,
+ double tuples, int width,
+ Cost comparison_cost, int sort_mem,
+ double limit_tuples)
+{
+ double input_bytes = relation_byte_size(tuples, width);
+ double output_bytes;
+ double output_tuples;
+ long sort_mem_bytes = sort_mem * 1024L;
+
+ /*
+ * 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;
+ 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;
+}
+
+/*
+ * cost_incremental_sort
+ * Determines and returns the cost of sorting a relation incrementally, when
+ * the input path is presorted by a prefix of the pathkeys.
+ *
+ * 'presorted_keys' is the number of leading pathkeys by which the input path
+ * is sorted.
+ *
+ * We estimate the number of groups into which the relation is divided by the
+ * leading pathkeys, and then calculate the cost of sorting a single group
+ * with tuplesort using cost_tuplesort().
+ */
+void
+cost_incremental_sort(Path *path,
+ PlannerInfo *root, List *pathkeys, int presorted_keys,
+ Cost input_startup_cost, Cost input_total_cost,
+ double input_tuples, int width, Cost comparison_cost, int sort_mem,
+ double limit_tuples)
+{
+ Cost startup_cost = 0,
+ run_cost = 0,
+ input_run_cost = input_total_cost - input_startup_cost;
+ double group_tuples,
+ input_groups;
+ Cost group_startup_cost,
+ group_run_cost,
+ group_input_run_cost;
+ List *presortedExprs = NIL;
+ ListCell *l;
+ int i = 0;
+ bool unknown_varno = false;
+
+ Assert(presorted_keys != 0);
+
+ /*
+ * 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 (input_tuples < 2.0)
+ input_tuples = 2.0;
+
+ /* Default estimate of number of groups, capped to one group per row. */
+ input_groups = Min(input_tuples, DEFAULT_NUM_DISTINCT);
+
+ /*
+ * Extract presorted keys as list of expressions.
+ *
+ * We need to be careful about Vars containing "varno 0" which might have
+ * been introduced by generate_append_tlist, which would confuse
+ * estimate_num_groups (in fact it'd fail for such expressions). See
+ * recurse_set_operations which has to deal with the same issue.
+ *
+ * Unlike recurse_set_operations we can't access the original target list
+ * here, and even if we could it's not very clear how useful would that be
+ * for a set operation combining multiple tables. So we simply detect if
+ * there are any expressions with "varno 0" and use the default
+ * DEFAULT_NUM_DISTINCT in that case.
+ *
+ * We might also use either 1.0 (a single group) or input_tuples (each row
+ * being a separate group), pretty much the worst and best case for
+ * incremental sort. But those are extreme cases and using something in
+ * between seems reasonable. Furthermore, generate_append_tlist is used
+ * for set operations, which are likely to produce mostly unique output
+ * anyway - from that standpoint the DEFAULT_NUM_DISTINCT is defensive
+ * while maintaining lower startup cost.
+ */
+ foreach(l, pathkeys)
+ {
+ PathKey *key = (PathKey *) lfirst(l);
+ EquivalenceMember *member = (EquivalenceMember *)
+ linitial(key->pk_eclass->ec_members);
+
+ /*
+ * Check if the expression contains Var with "varno 0" so that we
+ * don't call estimate_num_groups in that case.
+ */
+ if (bms_is_member(0, pull_varnos(root, (Node *) member->em_expr)))
+ {
+ unknown_varno = true;
+ break;
+ }
+
+ /* expression not containing any Vars with "varno 0" */
+ presortedExprs = lappend(presortedExprs, member->em_expr);
+
+ i++;
+ if (i >= presorted_keys)
+ break;
+ }
+
+ /* Estimate number of groups with equal presorted keys. */
+ if (!unknown_varno)
+ input_groups = estimate_num_groups(root, presortedExprs, input_tuples,
+ NULL, NULL);
+
+ group_tuples = input_tuples / input_groups;
+ group_input_run_cost = input_run_cost / input_groups;
+
+ /*
+ * Estimate average cost of sorting of one group where presorted keys are
+ * equal. Incremental sort is sensitive to distribution of tuples to the
+ * groups, where we're relying on quite rough assumptions. Thus, we're
+ * pessimistic about incremental sort performance and increase its average
+ * group size by half.
+ */
+ cost_tuplesort(&group_startup_cost, &group_run_cost,
+ 1.5 * group_tuples, width, comparison_cost, sort_mem,
+ limit_tuples);
+
+ /*
+ * Startup cost of incremental sort is the startup cost of its first group
+ * plus the cost of its input.
+ */
+ startup_cost += group_startup_cost
+ + input_startup_cost + group_input_run_cost;
+
+ /*
+ * After we started producing tuples from the first group, the cost of
+ * producing all the tuples is given by the cost to finish processing this
+ * group, plus the total cost to process the remaining groups, plus the
+ * remaining cost of input.
+ */
+ run_cost += group_run_cost
+ + (group_run_cost + group_startup_cost) * (input_groups - 1)
+ + group_input_run_cost * (input_groups - 1);
+
+ /*
+ * Incremental sort adds some overhead by itself. Firstly, it has to
+ * detect the sort groups. This is roughly equal to one extra copy and
+ * comparison per tuple. Secondly, it has to reset the tuplesort context
+ * for every group.
+ */
+ run_cost += (cpu_tuple_cost + comparison_cost) * input_tuples;
+ run_cost += 2.0 * cpu_tuple_cost * input_groups;
+
+ path->rows = input_tuples;
+ path->startup_cost = startup_cost;
+ path->total_cost = startup_cost + run_cost;
+}
+
+/*
+ * cost_sort
+ * Determines and returns the cost of sorting a relation, including
+ * the cost of reading the input data.
+ *
+ * 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;
+ Cost run_cost;
+
+ cost_tuplesort(&startup_cost, &run_cost,
+ tuples, width,
+ comparison_cost, sort_mem,
+ limit_tuples);
+
+ if (!enable_sort)
+ startup_cost += disable_cost;
+
+ startup_cost += input_cost;
+
+ path->rows = tuples;
+ path->startup_cost = startup_cost;
+ path->total_cost = startup_cost + run_cost;
+}
+
+/*
+ * append_nonpartial_cost
+ * Estimate the cost of the non-partial paths in a Parallel Append.
+ * The non-partial paths are assumed to be the first "numpaths" paths
+ * from the subpaths list, and to be in order of decreasing cost.
+ */
+static Cost
+append_nonpartial_cost(List *subpaths, int numpaths, int parallel_workers)
+{
+ Cost *costarr;
+ int arrlen;
+ ListCell *l;
+ ListCell *cell;
+ int i;
+ int path_index;
+ int min_index;
+ int max_index;
+
+ if (numpaths == 0)
+ return 0;
+
+ /*
+ * Array length is number of workers or number of relevant paths,
+ * whichever is less.
+ */
+ arrlen = Min(parallel_workers, numpaths);
+ costarr = (Cost *) palloc(sizeof(Cost) * arrlen);
+
+ /* The first few paths will each be claimed by a different worker. */
+ path_index = 0;
+ foreach(cell, subpaths)
+ {
+ Path *subpath = (Path *) lfirst(cell);
+
+ if (path_index == arrlen)
+ break;
+ costarr[path_index++] = subpath->total_cost;
+ }
+
+ /*
+ * Since subpaths are sorted by decreasing cost, the last one will have
+ * the minimum cost.
+ */
+ min_index = arrlen - 1;
+
+ /*
+ * For each of the remaining subpaths, add its cost to the array element
+ * with minimum cost.
+ */
+ for_each_cell(l, subpaths, cell)
+ {
+ Path *subpath = (Path *) lfirst(l);
+ int i;
+
+ /* Consider only the non-partial paths */
+ if (path_index++ == numpaths)
+ break;
+
+ costarr[min_index] += subpath->total_cost;
+
+ /* Update the new min cost array index */
+ for (min_index = i = 0; i < arrlen; i++)
+ {
+ if (costarr[i] < costarr[min_index])
+ min_index = i;
+ }
+ }
+
+ /* Return the highest cost from the array */
+ for (max_index = i = 0; i < arrlen; i++)
+ {
+ if (costarr[i] > costarr[max_index])
+ max_index = i;
+ }
+
+ return costarr[max_index];
+}
+
+/*
+ * cost_append
+ * Determines and returns the cost of an Append node.
+ */
+void
+cost_append(AppendPath *apath)
+{
+ ListCell *l;
+
+ apath->path.startup_cost = 0;
+ apath->path.total_cost = 0;
+ apath->path.rows = 0;
+
+ if (apath->subpaths == NIL)
+ return;
+
+ if (!apath->path.parallel_aware)
+ {
+ List *pathkeys = apath->path.pathkeys;
+
+ if (pathkeys == NIL)
+ {
+ Path *subpath = (Path *) linitial(apath->subpaths);
+
+ /*
+ * For an unordered, non-parallel-aware Append we take the startup
+ * cost as the startup cost of the first subpath.
+ */
+ apath->path.startup_cost = subpath->startup_cost;
+
+ /* Compute rows and costs as sums of subplan rows and costs. */
+ foreach(l, apath->subpaths)
+ {
+ Path *subpath = (Path *) lfirst(l);
+
+ apath->path.rows += subpath->rows;
+ apath->path.total_cost += subpath->total_cost;
+ }
+ }
+ else
+ {
+ /*
+ * For an ordered, non-parallel-aware Append we take the startup
+ * cost as the sum of the subpath startup costs. This ensures
+ * that we don't underestimate the startup cost when a query's
+ * LIMIT is such that several of the children have to be run to
+ * satisfy it. This might be overkill --- another plausible hack
+ * would be to take the Append's startup cost as the maximum of
+ * the child startup costs. But we don't want to risk believing
+ * that an ORDER BY LIMIT query can be satisfied at small cost
+ * when the first child has small startup cost but later ones
+ * don't. (If we had the ability to deal with nonlinear cost
+ * interpolation for partial retrievals, we would not need to be
+ * so conservative about this.)
+ *
+ * This case is also different from the above in that we have to
+ * account for possibly injecting sorts into subpaths that aren't
+ * natively ordered.
+ */
+ foreach(l, apath->subpaths)
+ {
+ Path *subpath = (Path *) lfirst(l);
+ Path sort_path; /* dummy for result of cost_sort */
+
+ if (!pathkeys_contained_in(pathkeys, subpath->pathkeys))
+ {
+ /*
+ * We'll need to insert a Sort node, so include costs for
+ * that. We can use the parent's LIMIT if any, since we
+ * certainly won't pull more than that many tuples from
+ * any child.
+ */
+ cost_sort(&sort_path,
+ NULL, /* doesn't currently need root */
+ pathkeys,
+ subpath->total_cost,
+ subpath->rows,
+ subpath->pathtarget->width,
+ 0.0,
+ work_mem,
+ apath->limit_tuples);
+ subpath = &sort_path;
+ }
+
+ apath->path.rows += subpath->rows;
+ apath->path.startup_cost += subpath->startup_cost;
+ apath->path.total_cost += subpath->total_cost;
+ }
+ }
+ }
+ else /* parallel-aware */
+ {
+ int i = 0;
+ double parallel_divisor = get_parallel_divisor(&apath->path);
+
+ /* Parallel-aware Append never produces ordered output. */
+ Assert(apath->path.pathkeys == NIL);
+
+ /* Calculate startup cost. */
+ foreach(l, apath->subpaths)
+ {
+ Path *subpath = (Path *) lfirst(l);
+
+ /*
+ * Append will start returning tuples when the child node having
+ * lowest startup cost is done setting up. We consider only the
+ * first few subplans that immediately get a worker assigned.
+ */
+ if (i == 0)
+ apath->path.startup_cost = subpath->startup_cost;
+ else if (i < apath->path.parallel_workers)
+ apath->path.startup_cost = Min(apath->path.startup_cost,
+ subpath->startup_cost);
+
+ /*
+ * Apply parallel divisor to subpaths. Scale the number of rows
+ * for each partial subpath based on the ratio of the parallel
+ * divisor originally used for the subpath to the one we adopted.
+ * Also add the cost of partial paths to the total cost, but
+ * ignore non-partial paths for now.
+ */
+ if (i < apath->first_partial_path)
+ apath->path.rows += subpath->rows / parallel_divisor;
+ else
+ {
+ double subpath_parallel_divisor;
+
+ subpath_parallel_divisor = get_parallel_divisor(subpath);
+ apath->path.rows += subpath->rows * (subpath_parallel_divisor /
+ parallel_divisor);
+ apath->path.total_cost += subpath->total_cost;
+ }
+
+ apath->path.rows = clamp_row_est(apath->path.rows);
+
+ i++;
+ }
+
+ /* Add cost for non-partial subpaths. */
+ apath->path.total_cost +=
+ append_nonpartial_cost(apath->subpaths,
+ apath->first_partial_path,
+ apath->path.parallel_workers);
+ }
+
+ /*
+ * Although Append does not do any selection or projection, it's not free;
+ * add a small per-tuple overhead.
+ */
+ apath->path.total_cost +=
+ cpu_tuple_cost * APPEND_CPU_COST_MULTIPLIER * apath->path.rows;
+}
+
+/*
+ * 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 replace the top entry.
+ *
+ * (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 * logN;
+
+ /*
+ * Although MergeAppend does not do any selection or projection, it's not
+ * free; add a small per-tuple overhead.
+ */
+ run_cost += cpu_tuple_cost * APPEND_CPU_COST_MULTIPLIER * 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_memoize_rescan
+ * Determines the estimated cost of rescanning a Memoize node.
+ *
+ * In order to estimate this, we must gain knowledge of how often we expect to
+ * be called and how many distinct sets of parameters we are likely to be
+ * called with. If we expect a good cache hit ratio, then we can set our
+ * costs to account for that hit ratio, plus a little bit of cost for the
+ * caching itself. Caching will not work out well if we expect to be called
+ * with too many distinct parameter values. The worst-case here is that we
+ * never see any parameter value twice, in which case we'd never get a cache
+ * hit and caching would be a complete waste of effort.
+ */
+static void
+cost_memoize_rescan(PlannerInfo *root, MemoizePath *mpath,
+ Cost *rescan_startup_cost, Cost *rescan_total_cost)
+{
+ EstimationInfo estinfo;
+ Cost input_startup_cost = mpath->subpath->startup_cost;
+ Cost input_total_cost = mpath->subpath->total_cost;
+ double tuples = mpath->subpath->rows;
+ double calls = mpath->calls;
+ int width = mpath->subpath->pathtarget->width;
+
+ double hash_mem_bytes;
+ double est_entry_bytes;
+ double est_cache_entries;
+ double ndistinct;
+ double evict_ratio;
+ double hit_ratio;
+ Cost startup_cost;
+ Cost total_cost;
+
+ /* available cache space */
+ hash_mem_bytes = get_hash_memory_limit();
+
+ /*
+ * Set the number of bytes each cache entry should consume in the cache.
+ * To provide us with better estimations on how many cache entries we can
+ * store at once, we make a call to the executor here to ask it what
+ * memory overheads there are for a single cache entry.
+ *
+ * XXX we also store the cache key, but that's not accounted for here.
+ */
+ est_entry_bytes = relation_byte_size(tuples, width) +
+ ExecEstimateCacheEntryOverheadBytes(tuples);
+
+ /* estimate on the upper limit of cache entries we can hold at once */
+ est_cache_entries = floor(hash_mem_bytes / est_entry_bytes);
+
+ /* estimate on the distinct number of parameter values */
+ ndistinct = estimate_num_groups(root, mpath->param_exprs, calls, NULL,
+ &estinfo);
+
+ /*
+ * When the estimation fell back on using a default value, it's a bit too
+ * risky to assume that it's ok to use a Memoize node. The use of a
+ * default could cause us to use a Memoize node when it's really
+ * inappropriate to do so. If we see that this has been done, then we'll
+ * assume that every call will have unique parameters, which will almost
+ * certainly mean a MemoizePath will never survive add_path().
+ */
+ if ((estinfo.flags & SELFLAG_USED_DEFAULT) != 0)
+ ndistinct = calls;
+
+ /*
+ * Since we've already estimated the maximum number of entries we can
+ * store at once and know the estimated number of distinct values we'll be
+ * called with, we'll take this opportunity to set the path's est_entries.
+ * This will ultimately determine the hash table size that the executor
+ * will use. If we leave this at zero, the executor will just choose the
+ * size itself. Really this is not the right place to do this, but it's
+ * convenient since everything is already calculated.
+ */
+ mpath->est_entries = Min(Min(ndistinct, est_cache_entries),
+ PG_UINT32_MAX);
+
+ /*
+ * When the number of distinct parameter values is above the amount we can
+ * store in the cache, then we'll have to evict some entries from the
+ * cache. This is not free. Here we estimate how often we'll incur the
+ * cost of that eviction.
+ */
+ evict_ratio = 1.0 - Min(est_cache_entries, ndistinct) / ndistinct;
+
+ /*
+ * In order to estimate how costly a single scan will be, we need to
+ * attempt to estimate what the cache hit ratio will be. To do that we
+ * must look at how many scans are estimated in total for this node and
+ * how many of those scans we expect to get a cache hit.
+ */
+ hit_ratio = 1.0 / ndistinct * Min(est_cache_entries, ndistinct) -
+ (ndistinct / calls);
+
+ /* Ensure we don't go negative */
+ hit_ratio = Max(hit_ratio, 0.0);
+
+ /*
+ * Set the total_cost accounting for the expected cache hit ratio. We
+ * also add on a cpu_operator_cost to account for a cache lookup. This
+ * will happen regardless of whether it's a cache hit or not.
+ */
+ total_cost = input_total_cost * (1.0 - hit_ratio) + cpu_operator_cost;
+
+ /* Now adjust the total cost to account for cache evictions */
+
+ /* Charge a cpu_tuple_cost for evicting the actual cache entry */
+ total_cost += cpu_tuple_cost * evict_ratio;
+
+ /*
+ * Charge a 10th of cpu_operator_cost to evict every tuple in that entry.
+ * The per-tuple eviction is really just a pfree, so charging a whole
+ * cpu_operator_cost seems a little excessive.
+ */
+ total_cost += cpu_operator_cost / 10.0 * evict_ratio * tuples;
+
+ /*
+ * Now adjust for storing things in the cache, since that's not free
+ * either. Everything must go in the cache. We don't proportion this
+ * over any ratio, just apply it once for the scan. We charge a
+ * cpu_tuple_cost for the creation of the cache entry and also a
+ * cpu_operator_cost for each tuple we expect to cache.
+ */
+ total_cost += cpu_tuple_cost + cpu_operator_cost * tuples;
+
+ /*
+ * Getting the first row must be also be proportioned according to the
+ * expected cache hit ratio.
+ */
+ startup_cost = input_startup_cost * (1.0 - hit_ratio);
+
+ /*
+ * Additionally we charge a cpu_tuple_cost to account for cache lookups,
+ * which we'll do regardless of whether it was a cache hit or not.
+ */
+ startup_cost += cpu_tuple_cost;
+
+ *rescan_startup_cost = startup_cost;
+ *rescan_total_cost = total_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,
+ List *quals,
+ Cost input_startup_cost, Cost input_total_cost,
+ double input_tuples, double input_width)
+{
+ 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. The finalCost.per_tuple component
+ * is charged once per output tuple, corresponding to the costs of
+ * evaluating the finalfns. Startup costs are of course charged but once.
+ *
+ * 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.startup;
+ startup_cost += aggcosts->finalCost.per_tuple;
+ /* we aren't grouping */
+ total_cost = startup_cost + cpu_tuple_cost;
+ output_tuples = 1;
+ }
+ else if (aggstrategy == AGG_SORTED || aggstrategy == AGG_MIXED)
+ {
+ /* Here we are able to deliver output on-the-fly */
+ startup_cost = input_startup_cost;
+ total_cost = input_total_cost;
+ if (aggstrategy == AGG_MIXED && !enable_hashagg)
+ {
+ startup_cost += disable_cost;
+ total_cost += disable_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.startup;
+ total_cost += aggcosts->finalCost.per_tuple * numGroups;
+ total_cost += cpu_tuple_cost * numGroups;
+ output_tuples = numGroups;
+ }
+ else
+ {
+ /* must be AGG_HASHED */
+ startup_cost = input_total_cost;
+ if (!enable_hashagg)
+ startup_cost += disable_cost;
+ startup_cost += aggcosts->transCost.startup;
+ startup_cost += aggcosts->transCost.per_tuple * input_tuples;
+ /* cost of computing hash value */
+ startup_cost += (cpu_operator_cost * numGroupCols) * input_tuples;
+ startup_cost += aggcosts->finalCost.startup;
+
+ total_cost = startup_cost;
+ total_cost += aggcosts->finalCost.per_tuple * numGroups;
+ /* cost of retrieving from hash table */
+ total_cost += cpu_tuple_cost * numGroups;
+ output_tuples = numGroups;
+ }
+
+ /*
+ * Add the disk costs of hash aggregation that spills to disk.
+ *
+ * Groups that go into the hash table stay in memory until finalized, so
+ * spilling and reprocessing tuples doesn't incur additional invocations
+ * of transCost or finalCost. Furthermore, the computed hash value is
+ * stored with the spilled tuples, so we don't incur extra invocations of
+ * the hash function.
+ *
+ * Hash Agg begins returning tuples after the first batch is complete.
+ * Accrue writes (spilled tuples) to startup_cost and to total_cost;
+ * accrue reads only to total_cost.
+ */
+ if (aggstrategy == AGG_HASHED || aggstrategy == AGG_MIXED)
+ {
+ double pages;
+ double pages_written = 0.0;
+ double pages_read = 0.0;
+ double spill_cost;
+ double hashentrysize;
+ double nbatches;
+ Size mem_limit;
+ uint64 ngroups_limit;
+ int num_partitions;
+ int depth;
+
+ /*
+ * Estimate number of batches based on the computed limits. If less
+ * than or equal to one, all groups are expected to fit in memory;
+ * otherwise we expect to spill.
+ */
+ hashentrysize = hash_agg_entry_size(list_length(root->aggtransinfos),
+ input_width,
+ aggcosts->transitionSpace);
+ hash_agg_set_limits(hashentrysize, numGroups, 0, &mem_limit,
+ &ngroups_limit, &num_partitions);
+
+ nbatches = Max((numGroups * hashentrysize) / mem_limit,
+ numGroups / ngroups_limit);
+
+ nbatches = Max(ceil(nbatches), 1.0);
+ num_partitions = Max(num_partitions, 2);
+
+ /*
+ * The number of partitions can change at different levels of
+ * recursion; but for the purposes of this calculation assume it stays
+ * constant.
+ */
+ depth = ceil(log(nbatches) / log(num_partitions));
+
+ /*
+ * Estimate number of pages read and written. For each level of
+ * recursion, a tuple must be written and then later read.
+ */
+ pages = relation_byte_size(input_tuples, input_width) / BLCKSZ;
+ pages_written = pages_read = pages * depth;
+
+ /*
+ * HashAgg has somewhat worse IO behavior than Sort on typical
+ * hardware/OS combinations. Account for this with a generic penalty.
+ */
+ pages_read *= 2.0;
+ pages_written *= 2.0;
+
+ startup_cost += pages_written * random_page_cost;
+ total_cost += pages_written * random_page_cost;
+ total_cost += pages_read * seq_page_cost;
+
+ /* account for CPU cost of spilling a tuple and reading it back */
+ spill_cost = depth * input_tuples * 2.0 * cpu_tuple_cost;
+ startup_cost += spill_cost;
+ total_cost += spill_cost;
+ }
+
+ /*
+ * If there are quals (HAVING quals), account for their cost and
+ * selectivity.
+ */
+ if (quals)
+ {
+ QualCost qual_cost;
+
+ cost_qual_eval(&qual_cost, quals, root);
+ startup_cost += qual_cost.startup;
+ total_cost += qual_cost.startup + output_tuples * qual_cost.per_tuple;
+
+ output_tuples = clamp_row_est(output_tuples *
+ clauselist_selectivity(root,
+ quals,
+ 0,
+ JOIN_INNER,
+ NULL));
+ }
+
+ 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 = lfirst_node(WindowFunc, lc);
+ Cost wfunccost;
+ QualCost argcosts;
+
+ argcosts.startup = argcosts.per_tuple = 0;
+ add_function_cost(root, wfunc->winfnoid, (Node *) wfunc,
+ &argcosts);
+ startup_cost += argcosts.startup;
+ wfunccost = argcosts.per_tuple;
+
+ /* 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;
+
+ /*
+ * Add the filter's cost to per-input-row costs. XXX We should reduce
+ * input expression costs according to filter selectivity.
+ */
+ cost_qual_eval_node(&argcosts, (Node *) wfunc->aggfilter, 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,
+ List *quals,
+ Cost input_startup_cost, Cost input_total_cost,
+ double input_tuples)
+{
+ double output_tuples;
+ Cost startup_cost;
+ Cost total_cost;
+
+ output_tuples = numGroups;
+ 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;
+
+ /*
+ * If there are quals (HAVING quals), account for their cost and
+ * selectivity.
+ */
+ if (quals)
+ {
+ QualCost qual_cost;
+
+ cost_qual_eval(&qual_cost, quals, root);
+ startup_cost += qual_cost.startup;
+ total_cost += qual_cost.startup + output_tuples * qual_cost.per_tuple;
+
+ output_tuples = clamp_row_est(output_tuples *
+ clauselist_selectivity(root,
+ quals,
+ 0,
+ JOIN_INNER,
+ NULL));
+ }
+
+ path->rows = output_tuples;
+ 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; and for SEMI/ANTI joins, we must also postpone
+ * incorporation of the inner path's run cost.
+ *
+ * '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
+ * 'extra' contains miscellaneous information about the join
+ */
+void
+initial_cost_nestloop(PlannerInfo *root, JoinCostWorkspace *workspace,
+ JoinType jointype,
+ Path *outer_path, Path *inner_path,
+ JoinPathExtraData *extra)
+{
+ 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 ||
+ extra->inner_unique)
+ {
+ /*
+ * With a SEMI or ANTI join, or if the innerrel is known unique, the
+ * executor will stop after the first match.
+ *
+ * Getting decent estimates requires inspection of the join quals,
+ * which we choose to postpone to final_cost_nestloop.
+ */
+
+ /* Save private data for final_cost_nestloop */
+ workspace->inner_run_cost = inner_run_cost;
+ workspace->inner_rescan_run_cost = inner_rescan_run_cost;
+ }
+ 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;
+}
+
+/*
+ * 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
+ * 'extra' contains miscellaneous information about the join
+ */
+void
+final_cost_nestloop(PlannerInfo *root, NestPath *path,
+ JoinCostWorkspace *workspace,
+ JoinPathExtraData *extra)
+{
+ 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;
+ Cost startup_cost = workspace->startup_cost;
+ Cost run_cost = workspace->run_cost;
+ Cost cpu_per_tuple;
+ QualCost restrict_qual_cost;
+ double ntuples;
+
+ /* Protect some assumptions below that rowcounts aren't zero */
+ if (outer_path_rows <= 0)
+ outer_path_rows = 1;
+ if (inner_path_rows <= 0)
+ 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;
+
+ /* For partial paths, scale row estimate. */
+ if (path->jpath.path.parallel_workers > 0)
+ {
+ double parallel_divisor = get_parallel_divisor(&path->jpath.path);
+
+ path->jpath.path.rows =
+ clamp_row_est(path->jpath.path.rows / parallel_divisor);
+ }
+
+ /*
+ * 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 inner-relation source data (we already dealt with outer rel) */
+
+ if (path->jpath.jointype == JOIN_SEMI || path->jpath.jointype == JOIN_ANTI ||
+ extra->inner_unique)
+ {
+ /*
+ * With a SEMI or ANTI join, or if the innerrel is known unique, the
+ * executor will stop after the first match.
+ */
+ Cost inner_run_cost = workspace->inner_run_cost;
+ Cost inner_rescan_run_cost = workspace->inner_rescan_run_cost;
+ double outer_matched_rows;
+ double outer_unmatched_rows;
+ Selectivity inner_scan_frac;
+
+ /*
+ * 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.)
+ */
+ outer_matched_rows = rint(outer_path_rows * extra->semifactors.outer_match_frac);
+ outer_unmatched_rows = outer_path_rows - outer_matched_rows;
+ inner_scan_frac = 2.0 / (extra->semifactors.match_count + 1.0);
+
+ /*
+ * Compute number of tuples processed (not number emitted!). First,
+ * account for successfully-matched outer rows.
+ */
+ ntuples = outer_matched_rows * inner_path_rows * inner_scan_frac;
+
+ /*
+ * Now we need to estimate the actual costs of scanning the inner
+ * relation, which may be quite a bit less than N times inner_run_cost
+ * due to early scan stops. We consider two cases. If the inner path
+ * is an indexscan using all the joinquals as indexquals, then an
+ * unmatched outer row results in an indexscan returning no rows,
+ * which is probably quite cheap. Otherwise, the executor will have
+ * to scan the whole inner rel for an unmatched row; not so cheap.
+ */
+ if (has_indexed_join_quals(path))
+ {
+ /*
+ * Successfully-matched outer rows will only require scanning
+ * inner_scan_frac of the inner relation. In this case, we don't
+ * need to charge the full inner_run_cost even when that's more
+ * than inner_rescan_run_cost, because we can assume that none of
+ * the inner scans ever scan the whole inner relation. So it's
+ * okay to assume that all the inner scan executions can be
+ * fractions of the full cost, even if materialization is reducing
+ * the rescan cost. At this writing, it's impossible to get here
+ * for a materialized inner scan, so inner_run_cost and
+ * inner_rescan_run_cost will be the same anyway; but just in
+ * case, use inner_run_cost for the first matched tuple and
+ * inner_rescan_run_cost for additional ones.
+ */
+ run_cost += inner_run_cost * inner_scan_frac;
+ if (outer_matched_rows > 1)
+ run_cost += (outer_matched_rows - 1) * inner_rescan_run_cost * inner_scan_frac;
+
+ /*
+ * Add the cost of inner-scan executions for unmatched outer rows.
+ * We estimate this as the same cost as returning the first tuple
+ * of a nonempty scan. We consider that these are all rescans,
+ * since we used inner_run_cost once already.
+ */
+ run_cost += outer_unmatched_rows *
+ inner_rescan_run_cost / inner_path_rows;
+
+ /*
+ * We won't be evaluating any quals at all for unmatched rows, so
+ * don't add them to ntuples.
+ */
+ }
+ else
+ {
+ /*
+ * Here, 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 (and it could
+ * not happen if there are any unmatched outer rows!), so be
+ * conservative and always charge the whole first-scan cost once.
+ * We consider this charge to correspond to the first unmatched
+ * outer row, unless there isn't one in our estimate, in which
+ * case blame it on the first matched row.
+ */
+
+ /* First, count all unmatched join tuples as being processed */
+ ntuples += outer_unmatched_rows * inner_path_rows;
+
+ /* Now add the forced full scan, and decrement appropriate count */
+ run_cost += inner_run_cost;
+ if (outer_unmatched_rows >= 1)
+ outer_unmatched_rows -= 1;
+ else
+ outer_matched_rows -= 1;
+
+ /* Add inner run cost for additional outer tuples having matches */
+ if (outer_matched_rows > 0)
+ run_cost += outer_matched_rows * inner_rescan_run_cost * inner_scan_frac;
+
+ /* Add inner run cost for additional unmatched outer tuples */
+ if (outer_unmatched_rows > 0)
+ run_cost += outer_unmatched_rows * inner_rescan_run_cost;
+ }
+ }
+ 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->jpath.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;
+
+ /* tlist eval costs are paid per output row, not per tuple scanned */
+ startup_cost += path->jpath.path.pathtarget->cost.startup;
+ run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows;
+
+ path->jpath.path.startup_cost = startup_cost;
+ path->jpath.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
+ * 'extra' contains miscellaneous information about the join
+ *
+ * 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,
+ JoinPathExtraData *extra)
+{
+ 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 */
+ if (outer_path_rows <= 0)
+ outer_path_rows = 1;
+ if (inner_path_rows <= 0)
+ 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->pathtarget->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->pathtarget->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 two actual decisions:
+ * whether the executor will need to do mark/restore, and whether we should
+ * materialize the inner path. It would be logically cleaner to build
+ * separate paths testing these alternatives, 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 more than one path. So it seems
+ * best to make the decisions here and record them in the path's
+ * skip_mark_restore and materialize_inner fields.
+ *
+ * Mark/restore overhead is usually required, but can be skipped if we know
+ * that the executor need find only one match per outer tuple, and that the
+ * mergeclauses are sufficient to identify a match.
+ *
+ * We materialize the inner path if we need mark/restore and either the inner
+ * path can't support mark/restore, or it's cheaper to use an interposed
+ * Material node to handle mark/restore.
+ *
+ * 'path' is already filled in except for the rows and cost fields and
+ * skip_mark_restore and materialize_inner
+ * 'workspace' is the result from initial_cost_mergejoin
+ * 'extra' contains miscellaneous information about the join
+ */
+void
+final_cost_mergejoin(PlannerInfo *root, MergePath *path,
+ JoinCostWorkspace *workspace,
+ JoinPathExtraData *extra)
+{
+ 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 */
+ if (inner_path_rows <= 0)
+ 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;
+
+ /* For partial paths, scale row estimate. */
+ if (path->jpath.path.parallel_workers > 0)
+ {
+ double parallel_divisor = get_parallel_divisor(&path->jpath.path);
+
+ path->jpath.path.rows =
+ clamp_row_est(path->jpath.path.rows / parallel_divisor);
+ }
+
+ /*
+ * 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;
+
+ /*
+ * With a SEMI or ANTI join, or if the innerrel is known unique, the
+ * executor will stop scanning for matches after the first match. When
+ * all the joinclauses are merge clauses, this means we don't ever need to
+ * back up the merge, and so we can skip mark/restore overhead.
+ */
+ if ((path->jpath.jointype == JOIN_SEMI ||
+ path->jpath.jointype == JOIN_ANTI ||
+ extra->inner_unique) &&
+ (list_length(path->jpath.joinrestrictinfo) ==
+ list_length(path->path_mergeclauses)))
+ path->skip_mark_restore = true;
+ else
+ path->skip_mark_restore = false;
+
+ /*
+ * 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, or if we know we don't need to mark/restore at all.
+ */
+ if (IsA(outer_path, UniquePath) || path->skip_mark_restore)
+ 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. Note
+ * that this is to be multiplied by something involving inner_rows, or
+ * another number related to the portion of the inner rel we'll scan.
+ */
+ rescanratio = 1.0 + (rescannedtuples / inner_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_rows * rescanratio;
+
+ /*
+ * If we don't need mark/restore at all, we don't need materialization.
+ */
+ if (path->skip_mark_restore)
+ path->materialize_inner = false;
+
+ /*
+ * Prefer materializing if it looks cheaper, unless the user has asked to
+ * suppress materialization.
+ */
+ else 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))
+ 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->pathtarget->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;
+
+ /* tlist eval costs are paid per output row, not per tuple scanned */
+ startup_cost += path->jpath.path.pathtarget->cost.startup;
+ run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows;
+
+ 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
+ * 'extra' contains miscellaneous information about the join
+ * 'parallel_hash' indicates that inner_path is partial and that a shared
+ * hash table will be built in parallel
+ */
+void
+initial_cost_hashjoin(PlannerInfo *root, JoinCostWorkspace *workspace,
+ JoinType jointype,
+ List *hashclauses,
+ Path *outer_path, Path *inner_path,
+ JoinPathExtraData *extra,
+ bool parallel_hash)
+{
+ Cost startup_cost = 0;
+ Cost run_cost = 0;
+ double outer_path_rows = outer_path->rows;
+ double inner_path_rows = inner_path->rows;
+ double inner_path_rows_total = inner_path_rows;
+ int num_hashclauses = list_length(hashclauses);
+ int numbuckets;
+ int numbatches;
+ int num_skew_mcvs;
+ size_t space_allowed; /* unused */
+
+ /* 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;
+
+ /*
+ * If this is a parallel hash build, then the value we have for
+ * inner_rows_total currently refers only to the rows returned by each
+ * participant. For shared hash table size estimation, we need the total
+ * number, so we need to undo the division.
+ */
+ if (parallel_hash)
+ inner_path_rows_total *= get_parallel_divisor(inner_path);
+
+ /*
+ * 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_HASH_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_total,
+ inner_path->pathtarget->width,
+ true, /* useskew */
+ parallel_hash, /* try_combined_hash_mem */
+ outer_path->parallel_workers,
+ &space_allowed,
+ &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->pathtarget->width);
+ double innerpages = page_size(inner_path_rows,
+ inner_path->pathtarget->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;
+ workspace->inner_rows_total = inner_path_rows_total;
+}
+
+/*
+ * 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
+ * 'extra' contains miscellaneous information about the join
+ */
+void
+final_cost_hashjoin(PlannerInfo *root, HashPath *path,
+ JoinCostWorkspace *workspace,
+ JoinPathExtraData *extra)
+{
+ 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;
+ double inner_path_rows_total = workspace->inner_rows_total;
+ 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;
+ Selectivity innermcvfreq;
+ 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;
+
+ /* For partial paths, scale row estimate. */
+ if (path->jpath.path.parallel_workers > 0)
+ {
+ double parallel_divisor = get_parallel_divisor(&path->jpath.path);
+
+ path->jpath.path.rows =
+ clamp_row_est(path->jpath.path.rows / parallel_divisor);
+ }
+
+ /*
+ * 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;
+
+ /* store the total number of tuples (sum of partial row estimates) */
+ path->inner_rows_total = inner_path_rows_total;
+
+ /* and compute the number of "virtual" buckets in the whole join */
+ virtualbuckets = (double) numbuckets * (double) numbatches;
+
+ /*
+ * Determine bucketsize fraction and MCV frequency for the inner relation.
+ * We use the smallest bucketsize or MCV frequency 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;
+ innermcvfreq = 0.0;
+ }
+ else
+ {
+ innerbucketsize = 1.0;
+ innermcvfreq = 1.0;
+ foreach(hcl, hashclauses)
+ {
+ RestrictInfo *restrictinfo = lfirst_node(RestrictInfo, hcl);
+ Selectivity thisbucketsize;
+ Selectivity thismcvfreq;
+
+ /*
+ * 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 bucket stats estimates 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 */
+ estimate_hash_bucket_stats(root,
+ get_rightop(restrictinfo->clause),
+ virtualbuckets,
+ &restrictinfo->right_mcvfreq,
+ &restrictinfo->right_bucketsize);
+ thisbucketsize = restrictinfo->right_bucketsize;
+ }
+ thismcvfreq = restrictinfo->right_mcvfreq;
+ }
+ 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 */
+ estimate_hash_bucket_stats(root,
+ get_leftop(restrictinfo->clause),
+ virtualbuckets,
+ &restrictinfo->left_mcvfreq,
+ &restrictinfo->left_bucketsize);
+ thisbucketsize = restrictinfo->left_bucketsize;
+ }
+ thismcvfreq = restrictinfo->left_mcvfreq;
+ }
+
+ if (innerbucketsize > thisbucketsize)
+ innerbucketsize = thisbucketsize;
+ if (innermcvfreq > thismcvfreq)
+ innermcvfreq = thismcvfreq;
+ }
+ }
+
+ /*
+ * If the bucket holding the inner MCV would exceed hash_mem, we don't
+ * want to hash unless there is really no other alternative, so apply
+ * disable_cost. (The executor normally copes with excessive memory usage
+ * by splitting batches, but obviously it cannot separate equal values
+ * that way, so it will be unable to drive the batch size below hash_mem
+ * when this is true.)
+ */
+ if (relation_byte_size(clamp_row_est(inner_path_rows * innermcvfreq),
+ inner_path->pathtarget->width) > get_hash_memory_limit())
+ startup_cost += disable_cost;
+
+ /*
+ * 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 ||
+ extra->inner_unique)
+ {
+ double outer_matched_rows;
+ Selectivity inner_scan_frac;
+
+ /*
+ * With a SEMI or ANTI join, or if the innerrel is known unique, the
+ * executor will stop after the 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 * extra->semifactors.outer_match_frac);
+ inner_scan_frac = 2.0 / (extra->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_ANTI)
+ hashjointuples = outer_path_rows - outer_matched_rows;
+ else
+ hashjointuples = 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;
+
+ /* tlist eval costs are paid per output row, not per tuple scanned */
+ startup_cost += path->jpath.path.pathtarget->cost.startup;
+ run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows;
+
+ 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; clamp to avoid zero divide */
+ sp_cost.per_tuple += plan_run_cost / clamp_row_est(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:
+
+ /*
+ * If it's a single-batch join, we don't need to rebuild the hash
+ * table during a rescan.
+ */
+ if (((HashPath *) path)->num_batches == 1)
+ {
+ /* Startup cost is exactly the cost of hash table building */
+ *rescan_startup_cost = 0;
+ *rescan_total_cost = path->total_cost - path->startup_cost;
+ }
+ else
+ {
+ /* Otherwise, no special treatment */
+ *rescan_startup_cost = path->startup_cost;
+ *rescan_total_cost = path->total_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->pathtarget->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->pathtarget->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_Memoize:
+ /* All the hard work is done by cost_memoize_rescan */
+ cost_memoize_rescan(root, (MemoizePath *) path,
+ rescan_startup_cost, rescan_total_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))
+ {
+ add_function_cost(context->root, ((FuncExpr *) node)->funcid, node,
+ &context->total);
+ }
+ else if (IsA(node, OpExpr) ||
+ IsA(node, DistinctExpr) ||
+ IsA(node, NullIfExpr))
+ {
+ /* rely on struct equivalence to treat these all alike */
+ set_opfuncid((OpExpr *) node);
+ add_function_cost(context->root, ((OpExpr *) node)->opfuncid, node,
+ &context->total);
+ }
+ else if (IsA(node, ScalarArrayOpExpr))
+ {
+ ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) node;
+ Node *arraynode = (Node *) lsecond(saop->args);
+ QualCost sacosts;
+ QualCost hcosts;
+ int estarraylen = estimate_array_length(arraynode);
+
+ set_sa_opfuncid(saop);
+ sacosts.startup = sacosts.per_tuple = 0;
+ add_function_cost(context->root, saop->opfuncid, NULL,
+ &sacosts);
+
+ if (OidIsValid(saop->hashfuncid))
+ {
+ /* Handle costs for hashed ScalarArrayOpExpr */
+ hcosts.startup = hcosts.per_tuple = 0;
+
+ add_function_cost(context->root, saop->hashfuncid, NULL, &hcosts);
+ context->total.startup += sacosts.startup + hcosts.startup;
+
+ /* Estimate the cost of building the hashtable. */
+ context->total.startup += estarraylen * hcosts.per_tuple;
+
+ /*
+ * XXX should we charge a little bit for sacosts.per_tuple when
+ * building the table, or is it ok to assume there will be zero
+ * hash collision?
+ */
+
+ /*
+ * Charge for hashtable lookups. Charge a single hash and a
+ * single comparison.
+ */
+ context->total.per_tuple += hcosts.per_tuple + sacosts.per_tuple;
+ }
+ else
+ {
+ /*
+ * Estimate that the operator will be applied to about half of the
+ * array elements before the answer is determined.
+ */
+ context->total.startup += sacosts.startup;
+ context->total.per_tuple += sacosts.per_tuple *
+ 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 at execution. 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, GroupingFunc))
+ {
+ /* Treat this as having cost 1 */
+ context->total.per_tuple += cpu_operator_cost;
+ 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);
+ add_function_cost(context->root, iofunc, NULL,
+ &context->total);
+ /* check the input type's output function */
+ getTypeOutputInfo(exprType((Node *) iocoerce->arg),
+ &iofunc, &typisvarlena);
+ add_function_cost(context->root, iofunc, NULL,
+ &context->total);
+ }
+ else if (IsA(node, ArrayCoerceExpr))
+ {
+ ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node;
+ QualCost perelemcost;
+
+ cost_qual_eval_node(&perelemcost, (Node *) acoerce->elemexpr,
+ context->root);
+ context->total.startup += perelemcost.startup;
+ if (perelemcost.per_tuple > 0)
+ context->total.per_tuple += perelemcost.per_tuple *
+ estimate_array_length((Node *) acoerce->arg);
+ }
+ 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);
+
+ add_function_cost(context->root, get_opcode(opid), NULL,
+ &context->total);
+ }
+ }
+ else if (IsA(node, MinMaxExpr) ||
+ IsA(node, SQLValueFunction) ||
+ IsA(node, XmlExpr) ||
+ IsA(node, CoerceToDomain) ||
+ IsA(node, NextValueExpr))
+ {
+ /* Treat all these as having cost 1 */
+ context->total.per_tuple += 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);
+ }
+ else if (IsA(node, PlaceHolderVar))
+ {
+ /*
+ * A PlaceHolderVar should be given cost zero when considering general
+ * expression evaluation costs. The expense of doing the contained
+ * expression is charged as part of the tlist eval costs of the scan
+ * or join where the PHV is first computed (see set_rel_width and
+ * add_placeholders_to_joinrel). If we charged it again here, we'd be
+ * double-counting the cost for each level of plan that the PHV
+ * bubbles up through. Hence, return without recursing into the
+ * phexpr.
+ */
+ return false;
+ }
+
+ /* 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, ANTI, or inner_unique 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.
+ * The same happens if we have detected the inner rel is unique.
+ * 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:
+ * joinrel: join relation under consideration
+ * outerrel: outer relation under consideration
+ * innerrel: inner relation under consideration
+ * jointype: if not JOIN_SEMI or JOIN_ANTI, we assume it's inner_unique
+ * sjinfo: SpecialJoinInfo relevant to this join
+ * restrictlist: join quals
+ * Output parameters:
+ * *semifactors is filled in (see pathnodes.h for field definitions)
+ */
+void
+compute_semi_anti_join_factors(PlannerInfo *root,
+ RelOptInfo *joinrel,
+ 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;
+
+ /*
+ * 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. For other outer join types, we should consider only
+ * non-pushed-down quals, so that this devolves to an IS_OUTER_JOIN check.
+ */
+ if (IS_OUTER_JOIN(jointype))
+ {
+ joinquals = NIL;
+ foreach(l, restrictlist)
+ {
+ RestrictInfo *rinfo = lfirst_node(RestrictInfo, l);
+
+ if (!RINFO_IS_PUSHED_DOWN(rinfo, joinrel->relids))
+ 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 == JOIN_ANTI) ? JOIN_ANTI : JOIN_SEMI,
+ 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.semi_can_btree = false;
+ norm_sjinfo.semi_can_hash = false;
+ norm_sjinfo.semi_operators = NIL;
+ norm_sjinfo.semi_rhs_exprs = NIL;
+
+ nselec = clauselist_selectivity(root,
+ joinquals,
+ 0,
+ JOIN_INNER,
+ &norm_sjinfo);
+
+ /* Avoid leaking a lot of ListCells */
+ if (IS_OUTER_JOIN(jointype))
+ 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 *path)
+{
+ JoinPath *joinpath = &path->jpath;
+ 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 (!is_redundant_with_indexclauses(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.semi_can_btree = false;
+ sjinfo.semi_can_hash = false;
+ sjinfo.semi_operators = NIL;
+ sjinfo.semi_rhs_exprs = 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_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 reltarget 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,
+ rel,
+ outer_rel,
+ inner_rel,
+ 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_path', 'inner_path' are (probably also parameterized) Paths that
+ * produce the relations being joined.
+ * '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,
+ Path *outer_path,
+ Path *inner_path,
+ 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,
+ rel,
+ outer_path->parent,
+ inner_path->parent,
+ outer_path->rows,
+ inner_path->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.
+ *
+ * outer_rel/inner_rel are the relations being joined, but they should be
+ * assumed to have sizes outer_rows/inner_rows; those numbers might be less
+ * than what rel->rows says, when we are considering parameterized paths.
+ */
+static double
+calc_joinrel_size_estimate(PlannerInfo *root,
+ RelOptInfo *joinrel,
+ RelOptInfo *outer_rel,
+ RelOptInfo *inner_rel,
+ double outer_rows,
+ double inner_rows,
+ SpecialJoinInfo *sjinfo,
+ List *restrictlist_in)
+{
+ /* This apparently-useless variable dodges a compiler bug in VS2013: */
+ List *restrictlist = restrictlist_in;
+ JoinType jointype = sjinfo->jointype;
+ Selectivity fkselec;
+ 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.
+ *
+ * First, see whether any of the joinclauses can be matched to known FK
+ * constraints. If so, drop those clauses from the restrictlist, and
+ * instead estimate their selectivity using FK semantics. (We do this
+ * without regard to whether said clauses are local or "pushed down".
+ * Probably, an FK-matching clause could never be seen as pushed down at
+ * an outer join, since it would be strict and hence would be grounds for
+ * join strength reduction.) fkselec gets the net selectivity for
+ * FK-matching clauses, or 1.0 if there are none.
+ */
+ fkselec = get_foreign_key_join_selectivity(root,
+ outer_rel->relids,
+ inner_rel->relids,
+ sjinfo,
+ &restrictlist);
+
+ /*
+ * 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 = lfirst_node(RestrictInfo, l);
+
+ if (RINFO_IS_PUSHED_DOWN(rinfo, joinrel->relids))
+ 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 * fkselec * jselec;
+ /* pselec not used */
+ break;
+ case JOIN_LEFT:
+ nrows = outer_rows * inner_rows * fkselec * jselec;
+ if (nrows < outer_rows)
+ nrows = outer_rows;
+ nrows *= pselec;
+ break;
+ case JOIN_FULL:
+ nrows = outer_rows * inner_rows * fkselec * jselec;
+ if (nrows < outer_rows)
+ nrows = outer_rows;
+ if (nrows < inner_rows)
+ nrows = inner_rows;
+ nrows *= pselec;
+ break;
+ case JOIN_SEMI:
+ nrows = outer_rows * fkselec * jselec;
+ /* pselec not used */
+ break;
+ case JOIN_ANTI:
+ nrows = outer_rows * (1.0 - fkselec * 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);
+}
+
+/*
+ * get_foreign_key_join_selectivity
+ * Estimate join selectivity for foreign-key-related clauses.
+ *
+ * Remove any clauses that can be matched to FK constraints from *restrictlist,
+ * and return a substitute estimate of their selectivity. 1.0 is returned
+ * when there are no such clauses.
+ *
+ * The reason for treating such clauses specially is that we can get better
+ * estimates this way than by relying on clauselist_selectivity(), especially
+ * for multi-column FKs where that function's assumption that the clauses are
+ * independent falls down badly. But even with single-column FKs, we may be
+ * able to get a better answer when the pg_statistic stats are missing or out
+ * of date.
+ */
+static Selectivity
+get_foreign_key_join_selectivity(PlannerInfo *root,
+ Relids outer_relids,
+ Relids inner_relids,
+ SpecialJoinInfo *sjinfo,
+ List **restrictlist)
+{
+ Selectivity fkselec = 1.0;
+ JoinType jointype = sjinfo->jointype;
+ List *worklist = *restrictlist;
+ ListCell *lc;
+
+ /* Consider each FK constraint that is known to match the query */
+ foreach(lc, root->fkey_list)
+ {
+ ForeignKeyOptInfo *fkinfo = (ForeignKeyOptInfo *) lfirst(lc);
+ bool ref_is_outer;
+ List *removedlist;
+ ListCell *cell;
+
+ /*
+ * This FK is not relevant unless it connects a baserel on one side of
+ * this join to a baserel on the other side.
+ */
+ if (bms_is_member(fkinfo->con_relid, outer_relids) &&
+ bms_is_member(fkinfo->ref_relid, inner_relids))
+ ref_is_outer = false;
+ else if (bms_is_member(fkinfo->ref_relid, outer_relids) &&
+ bms_is_member(fkinfo->con_relid, inner_relids))
+ ref_is_outer = true;
+ else
+ continue;
+
+ /*
+ * If we're dealing with a semi/anti join, and the FK's referenced
+ * relation is on the outside, then knowledge of the FK doesn't help
+ * us figure out what we need to know (which is the fraction of outer
+ * rows that have matches). On the other hand, if the referenced rel
+ * is on the inside, then all outer rows must have matches in the
+ * referenced table (ignoring nulls). But any restriction or join
+ * clauses that filter that table will reduce the fraction of matches.
+ * We can account for restriction clauses, but it's too hard to guess
+ * how many table rows would get through a join that's inside the RHS.
+ * Hence, if either case applies, punt and ignore the FK.
+ */
+ if ((jointype == JOIN_SEMI || jointype == JOIN_ANTI) &&
+ (ref_is_outer || bms_membership(inner_relids) != BMS_SINGLETON))
+ continue;
+
+ /*
+ * Modify the restrictlist by removing clauses that match the FK (and
+ * putting them into removedlist instead). It seems unsafe to modify
+ * the originally-passed List structure, so we make a shallow copy the
+ * first time through.
+ */
+ if (worklist == *restrictlist)
+ worklist = list_copy(worklist);
+
+ removedlist = NIL;
+ foreach(cell, worklist)
+ {
+ RestrictInfo *rinfo = (RestrictInfo *) lfirst(cell);
+ bool remove_it = false;
+ int i;
+
+ /* Drop this clause if it matches any column of the FK */
+ for (i = 0; i < fkinfo->nkeys; i++)
+ {
+ if (rinfo->parent_ec)
+ {
+ /*
+ * EC-derived clauses can only match by EC. It is okay to
+ * consider any clause derived from the same EC as
+ * matching the FK: even if equivclass.c chose to generate
+ * a clause equating some other pair of Vars, it could
+ * have generated one equating the FK's Vars. So for
+ * purposes of estimation, we can act as though it did so.
+ *
+ * Note: checking parent_ec is a bit of a cheat because
+ * there are EC-derived clauses that don't have parent_ec
+ * set; but such clauses must compare expressions that
+ * aren't just Vars, so they cannot match the FK anyway.
+ */
+ if (fkinfo->eclass[i] == rinfo->parent_ec)
+ {
+ remove_it = true;
+ break;
+ }
+ }
+ else
+ {
+ /*
+ * Otherwise, see if rinfo was previously matched to FK as
+ * a "loose" clause.
+ */
+ if (list_member_ptr(fkinfo->rinfos[i], rinfo))
+ {
+ remove_it = true;
+ break;
+ }
+ }
+ }
+ if (remove_it)
+ {
+ worklist = foreach_delete_current(worklist, cell);
+ removedlist = lappend(removedlist, rinfo);
+ }
+ }
+
+ /*
+ * If we failed to remove all the matching clauses we expected to
+ * find, chicken out and ignore this FK; applying its selectivity
+ * might result in double-counting. Put any clauses we did manage to
+ * remove back into the worklist.
+ *
+ * Since the matching clauses are known not outerjoin-delayed, they
+ * would normally have appeared in the initial joinclause list. If we
+ * didn't find them, there are two possibilities:
+ *
+ * 1. If the FK match is based on an EC that is ec_has_const, it won't
+ * have generated any join clauses at all. We discount such ECs while
+ * checking to see if we have "all" the clauses. (Below, we'll adjust
+ * the selectivity estimate for this case.)
+ *
+ * 2. The clauses were matched to some other FK in a previous
+ * iteration of this loop, and thus removed from worklist. (A likely
+ * case is that two FKs are matched to the same EC; there will be only
+ * one EC-derived clause in the initial list, so the first FK will
+ * consume it.) Applying both FKs' selectivity independently risks
+ * underestimating the join size; in particular, this would undo one
+ * of the main things that ECs were invented for, namely to avoid
+ * double-counting the selectivity of redundant equality conditions.
+ * Later we might think of a reasonable way to combine the estimates,
+ * but for now, just punt, since this is a fairly uncommon situation.
+ */
+ if (removedlist == NIL ||
+ list_length(removedlist) !=
+ (fkinfo->nmatched_ec - fkinfo->nconst_ec + fkinfo->nmatched_ri))
+ {
+ worklist = list_concat(worklist, removedlist);
+ continue;
+ }
+
+ /*
+ * Finally we get to the payoff: estimate selectivity using the
+ * knowledge that each referencing row will match exactly one row in
+ * the referenced table.
+ *
+ * XXX that's not true in the presence of nulls in the referencing
+ * column(s), so in principle we should derate the estimate for those.
+ * However (1) if there are any strict restriction clauses for the
+ * referencing column(s) elsewhere in the query, derating here would
+ * be double-counting the null fraction, and (2) it's not very clear
+ * how to combine null fractions for multiple referencing columns. So
+ * we do nothing for now about correcting for nulls.
+ *
+ * XXX another point here is that if either side of an FK constraint
+ * is an inheritance parent, we estimate as though the constraint
+ * covers all its children as well. This is not an unreasonable
+ * assumption for a referencing table, ie the user probably applied
+ * identical constraints to all child tables (though perhaps we ought
+ * to check that). But it's not possible to have done that for a
+ * referenced table. Fortunately, precisely because that doesn't
+ * work, it is uncommon in practice to have an FK referencing a parent
+ * table. So, at least for now, disregard inheritance here.
+ */
+ if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
+ {
+ /*
+ * For JOIN_SEMI and JOIN_ANTI, we only get here when the FK's
+ * referenced table is exactly the inside of the join. The join
+ * selectivity is defined as the fraction of LHS rows that have
+ * matches. The FK implies that every LHS row has a match *in the
+ * referenced table*; but any restriction clauses on it will
+ * reduce the number of matches. Hence we take the join
+ * selectivity as equal to the selectivity of the table's
+ * restriction clauses, which is rows / tuples; but we must guard
+ * against tuples == 0.
+ */
+ RelOptInfo *ref_rel = find_base_rel(root, fkinfo->ref_relid);
+ double ref_tuples = Max(ref_rel->tuples, 1.0);
+
+ fkselec *= ref_rel->rows / ref_tuples;
+ }
+ else
+ {
+ /*
+ * Otherwise, selectivity is exactly 1/referenced-table-size; but
+ * guard against tuples == 0. Note we should use the raw table
+ * tuple count, not any estimate of its filtered or joined size.
+ */
+ RelOptInfo *ref_rel = find_base_rel(root, fkinfo->ref_relid);
+ double ref_tuples = Max(ref_rel->tuples, 1.0);
+
+ fkselec *= 1.0 / ref_tuples;
+ }
+
+ /*
+ * If any of the FK columns participated in ec_has_const ECs, then
+ * equivclass.c will have generated "var = const" restrictions for
+ * each side of the join, thus reducing the sizes of both input
+ * relations. Taking the fkselec at face value would amount to
+ * double-counting the selectivity of the constant restriction for the
+ * referencing Var. Hence, look for the restriction clause(s) that
+ * were applied to the referencing Var(s), and divide out their
+ * selectivity to correct for this.
+ */
+ if (fkinfo->nconst_ec > 0)
+ {
+ for (int i = 0; i < fkinfo->nkeys; i++)
+ {
+ EquivalenceClass *ec = fkinfo->eclass[i];
+
+ if (ec && ec->ec_has_const)
+ {
+ EquivalenceMember *em = fkinfo->fk_eclass_member[i];
+ RestrictInfo *rinfo = find_derived_clause_for_ec_member(ec,
+ em);
+
+ if (rinfo)
+ {
+ Selectivity s0;
+
+ s0 = clause_selectivity(root,
+ (Node *) rinfo,
+ 0,
+ jointype,
+ sjinfo);
+ if (s0 > 0)
+ fkselec /= s0;
+ }
+ }
+ }
+ }
+ }
+
+ *restrictlist = worklist;
+ CLAMP_PROBABILITY(fkselec);
+ return fkselec;
+}
+
+/*
+ * 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 Paths for the subquery must have been completed.
+ * We look at the subquery's 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;
+ RelOptInfo *sub_final_rel;
+ ListCell *lc;
+
+ /* Should only be applied to base relations that are subqueries */
+ Assert(rel->relid > 0);
+ Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_SUBQUERY);
+
+ /*
+ * Copy raw number of output rows from subquery. All of its paths should
+ * have the same output rowcount, so just look at cheapest-total.
+ */
+ sub_final_rel = fetch_upper_rel(subroot, UPPERREL_FINAL, NULL);
+ rel->tuples = sub_final_rel->cheapest_total_path->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 = lfirst_node(TargetEntry, lc);
+ Node *texpr = (Node *) te->expr;
+ int32 item_width = 0;
+
+ /* 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;
+ ListCell *lc;
+
+ /* 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 functions will return. The rowcount of the
+ * node is that of the largest function result.
+ */
+ rel->tuples = 0;
+ foreach(lc, rte->functions)
+ {
+ RangeTblFunction *rtfunc = (RangeTblFunction *) lfirst(lc);
+ double ntup = expression_returns_set_rows(root, rtfunc->funcexpr);
+
+ if (ntup > rel->tuples)
+ rel->tuples = ntup;
+ }
+
+ /* 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_tablefunc_size_estimates.
+ */
+void
+set_tablefunc_size_estimates(PlannerInfo *root, RelOptInfo *rel)
+{
+ /* Should only be applied to base relations that are functions */
+ Assert(rel->relid > 0);
+ Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_TABLEFUNC);
+
+ rel->tuples = 100;
+
+ /* 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 an estimate of the number of rows returned by 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, double cte_rows)
+{
+ 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, we assume the average worktable size is a
+ * multiple of the nonrecursive term's size. The best multiplier will
+ * vary depending on query "fan-out", so make its value adjustable.
+ */
+ rel->tuples = clamp_row_est(recursive_worktable_factor * cte_rows);
+ }
+ else
+ {
+ /* Otherwise just believe the CTE's rowcount estimate */
+ rel->tuples = cte_rows;
+ }
+
+ /* Now estimate number of output rows, etc */
+ set_baserel_size_estimates(root, rel);
+}
+
+/*
+ * set_namedtuplestore_size_estimates
+ * Set the size estimates for a base relation that is a tuplestore reference.
+ *
+ * The rel's targetlist and restrictinfo list must have been constructed
+ * already.
+ *
+ * We set the same fields as set_baserel_size_estimates.
+ */
+void
+set_namedtuplestore_size_estimates(PlannerInfo *root, RelOptInfo *rel)
+{
+ RangeTblEntry *rte;
+
+ /* Should only be applied to base relations that are tuplestore references */
+ Assert(rel->relid > 0);
+ rte = planner_rt_fetch(rel->relid, root);
+ Assert(rte->rtekind == RTE_NAMEDTUPLESTORE);
+
+ /*
+ * Use the estimate provided by the code which is generating the named
+ * tuplestore. In some cases, the actual number might be available; in
+ * others the same plan will be re-used, so a "typical" value might be
+ * estimated and used.
+ */
+ rel->tuples = rte->enrtuples;
+ if (rel->tuples < 0)
+ rel->tuples = 1000;
+
+ /* Now estimate number of output rows, etc */
+ set_baserel_size_estimates(root, rel);
+}
+
+/*
+ * set_result_size_estimates
+ * Set the size estimates for an RTE_RESULT base relation
+ *
+ * The rel's targetlist and restrictinfo list must have been constructed
+ * already.
+ *
+ * We set the same fields as set_baserel_size_estimates.
+ */
+void
+set_result_size_estimates(PlannerInfo *root, RelOptInfo *rel)
+{
+ /* Should only be applied to RTE_RESULT base relations */
+ Assert(rel->relid > 0);
+ Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_RESULT);
+
+ /* RTE_RESULT always generates a single row, natively */
+ rel->tuples = 1;
+
+ /* 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.
+ *
+ * This function also sets reltarget->cost, so it's a bit misnamed now.
+ *
+ * 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 or post-scan/join pathtargets.
+ */
+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;
+
+ /* Vars are assumed to have cost zero, but other exprs do not */
+ rel->reltarget->cost.startup = 0;
+ rel->reltarget->cost.per_tuple = 0;
+
+ foreach(lc, rel->reltarget->exprs)
+ {
+ Node *node = (Node *) lfirst(lc);
+
+ /*
+ * Ordinarily, a Var in a rel's targetlist must belong to that rel;
+ * but there are corner cases involving LATERAL references where that
+ * isn't so. 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))
+ {
+ /*
+ * We will need to evaluate the PHV's contained expression while
+ * scanning this rel, so be sure to include it in reltarget->cost.
+ */
+ PlaceHolderVar *phv = (PlaceHolderVar *) node;
+ PlaceHolderInfo *phinfo = find_placeholder_info(root, phv, false);
+ QualCost cost;
+
+ tuple_width += phinfo->ph_width;
+ cost_qual_eval_node(&cost, (Node *) phv->phexpr, root);
+ rel->reltarget->cost.startup += cost.startup;
+ rel->reltarget->cost.per_tuple += cost.per_tuple;
+ }
+ 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;
+ QualCost cost;
+
+ item_width = get_typavgwidth(exprType(node), exprTypmod(node));
+ Assert(item_width > 0);
+ tuple_width += item_width;
+ /* Not entirely clear if we need to account for cost, but do so */
+ cost_qual_eval_node(&cost, node, root);
+ rel->reltarget->cost.startup += cost.startup;
+ rel->reltarget->cost.per_tuple += cost.per_tuple;
+ }
+ }
+
+ /*
+ * If we have a whole-row reference, estimate its width as the sum of
+ * per-column widths plus heap tuple header overhead.
+ */
+ if (have_wholerow_var)
+ {
+ int32 wholerow_width = MAXALIGN(SizeofHeapTupleHeader);
+
+ 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->reltarget->width = tuple_width;
+}
+
+/*
+ * set_pathtarget_cost_width
+ * Set the estimated eval cost and output width of a PathTarget tlist.
+ *
+ * As a notational convenience, returns the same PathTarget pointer passed in.
+ *
+ * Most, though not quite all, uses of this function occur after we've run
+ * set_rel_width() for base relations; so we can usually obtain cached width
+ * estimates for Vars. If we can't, fall back on datatype-based width
+ * estimates. Present early-planning uses of PathTargets don't need accurate
+ * widths badly enough to justify going to the catalogs for better data.
+ */
+PathTarget *
+set_pathtarget_cost_width(PlannerInfo *root, PathTarget *target)
+{
+ int32 tuple_width = 0;
+ ListCell *lc;
+
+ /* Vars are assumed to have cost zero, but other exprs do not */
+ target->cost.startup = 0;
+ target->cost.per_tuple = 0;
+
+ foreach(lc, target->exprs)
+ {
+ Node *node = (Node *) lfirst(lc);
+
+ if (IsA(node, Var))
+ {
+ Var *var = (Var *) node;
+ int32 item_width;
+
+ /* We should not see any upper-level Vars here */
+ Assert(var->varlevelsup == 0);
+
+ /* Try to get data from RelOptInfo cache */
+ if (!IS_SPECIAL_VARNO(var->varno) &&
+ var->varno < root->simple_rel_array_size)
+ {
+ RelOptInfo *rel = root->simple_rel_array[var->varno];
+
+ if (rel != NULL &&
+ var->varattno >= rel->min_attr &&
+ var->varattno <= rel->max_attr)
+ {
+ int ndx = var->varattno - rel->min_attr;
+
+ if (rel->attr_widths[ndx] > 0)
+ {
+ tuple_width += rel->attr_widths[ndx];
+ continue;
+ }
+ }
+ }
+
+ /*
+ * No cached data available, so estimate using just the type info.
+ */
+ item_width = get_typavgwidth(var->vartype, var->vartypmod);
+ Assert(item_width > 0);
+ tuple_width += item_width;
+ }
+ else
+ {
+ /*
+ * Handle general expressions using type info.
+ */
+ int32 item_width;
+ QualCost cost;
+
+ item_width = get_typavgwidth(exprType(node), exprTypmod(node));
+ Assert(item_width > 0);
+ tuple_width += item_width;
+
+ /* Account for cost, too */
+ cost_qual_eval_node(&cost, node, root);
+ target->cost.startup += cost.startup;
+ target->cost.per_tuple += cost.per_tuple;
+ }
+ }
+
+ Assert(tuple_width >= 0);
+ target->width = tuple_width;
+
+ return target;
+}
+
+/*
+ * 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(SizeofHeapTupleHeader));
+}
+
+/*
+ * 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);
+}
+
+/*
+ * Estimate the fraction of the work that each worker will do given the
+ * number of workers budgeted for the path.
+ */
+static double
+get_parallel_divisor(Path *path)
+{
+ double parallel_divisor = path->parallel_workers;
+
+ /*
+ * Early experience with parallel query suggests that when there is only
+ * one worker, the leader often makes a very substantial contribution to
+ * executing the parallel portion of the plan, but as more workers are
+ * added, it does less and less, because it's busy reading tuples from the
+ * workers and doing whatever non-parallel post-processing is needed. By
+ * the time we reach 4 workers, the leader no longer makes a meaningful
+ * contribution. Thus, for now, estimate that the leader spends 30% of
+ * its time servicing each worker, and the remainder executing the
+ * parallel plan.
+ */
+ if (parallel_leader_participation)
+ {
+ double leader_contribution;
+
+ leader_contribution = 1.0 - (0.3 * path->parallel_workers);
+ if (leader_contribution > 0)
+ parallel_divisor += leader_contribution;
+ }
+
+ return parallel_divisor;
+}
+
+/*
+ * compute_bitmap_pages
+ *
+ * compute number of pages fetched from heap in bitmap heap scan.
+ */
+double
+compute_bitmap_pages(PlannerInfo *root, RelOptInfo *baserel, Path *bitmapqual,
+ int loop_count, Cost *cost, double *tuple)
+{
+ Cost indexTotalCost;
+ Selectivity indexSelectivity;
+ double T;
+ double pages_fetched;
+ double tuples_fetched;
+ double heap_pages;
+ long maxentries;
+
+ /*
+ * Fetch total cost of obtaining the bitmap, as well as its total
+ * selectivity.
+ */
+ cost_bitmap_tree_node(bitmapqual, &indexTotalCost, &indexSelectivity);
+
+ /*
+ * Estimate number of main-table pages fetched.
+ */
+ tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);
+
+ T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
+
+ /*
+ * 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);
+
+ /*
+ * Calculate the number of pages fetched from the heap. Then based on
+ * current work_mem estimate get the estimated maxentries in the bitmap.
+ * (Note that we always do this calculation based on the number of pages
+ * that would be fetched in a single iteration, even if loop_count > 1.
+ * That's correct, because only that number of entries will be stored in
+ * the bitmap at one time.)
+ */
+ heap_pages = Min(pages_fetched, baserel->pages);
+ maxentries = tbm_calculate_entries(work_mem * 1024L);
+
+ 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;
+ }
+
+ if (pages_fetched >= T)
+ pages_fetched = T;
+ else
+ pages_fetched = ceil(pages_fetched);
+
+ if (maxentries < heap_pages)
+ {
+ double exact_pages;
+ double lossy_pages;
+
+ /*
+ * Crude approximation of the number of lossy pages. Because of the
+ * way tbm_lossify() is coded, the number of lossy pages increases
+ * very sharply as soon as we run short of memory; this formula has
+ * that property and seems to perform adequately in testing, but it's
+ * possible we could do better somehow.
+ */
+ lossy_pages = Max(0, heap_pages - maxentries / 2);
+ exact_pages = heap_pages - lossy_pages;
+
+ /*
+ * If there are lossy pages then recompute the number of tuples
+ * processed by the bitmap heap node. We assume here that the chance
+ * of a given tuple coming from an exact page is the same as the
+ * chance that a given page is exact. This might not be true, but
+ * it's not clear how we can do any better.
+ */
+ if (lossy_pages > 0)
+ tuples_fetched =
+ clamp_row_est(indexSelectivity *
+ (exact_pages / heap_pages) * baserel->tuples +
+ (lossy_pages / heap_pages) * baserel->tuples);
+ }
+
+ if (cost)
+ *cost = indexTotalCost;
+ if (tuple)
+ *tuple = tuples_fetched;
+
+ return pages_fetched;
+}