/*------------------------------------------------------------------------- * * 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. * * Each path stores the total number of disabled nodes that exist at or * below that point in the plan tree. This is regarded as a component of * the cost, and paths with fewer disabled nodes should be regarded as * cheaper than those with more. Disabled nodes occur when the user sets * a GUC like enable_seqscan=false. We can't necessarily respect such a * setting in every part of the plan tree, but we want to respect in as many * parts of the plan tree as possible. Simpler schemes like storing a Boolean * here rather than a count fail to do that. We used to disable nodes by * adding a large constant to the startup cost, but that distorted planning * in other ways. * * 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) 1996-2025, 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 #include #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/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_presorted_aggregate = 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 int32 get_expr_width(PlannerInfo *root, const Node *expr); 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_width_est * Force a tuple-width estimate to a sane value. * * The planner represents datatype width and tuple width estimates as int32. * When summing column width estimates to create a tuple width estimate, * it's possible to reach integer overflow in edge cases. To ensure sane * behavior, we form such sums in int64 arithmetic and then apply this routine * to clamp to int32 range. */ int32 clamp_width_est(int64 tuple_width) { /* * Anything more than MaxAllocSize is clearly bogus, since we could not * create a tuple that large. */ if (tuple_width > MaxAllocSize) return (int32) MaxAllocSize; /* * Unlike clamp_row_est, we just Assert that the value isn't negative, * rather than masking such errors. */ Assert(tuple_width >= 0); return (int32) tuple_width; } /* * 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; /* 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->disabled_nodes = enable_seqscan ? 0 : 1; 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->disabled_nodes = 0; 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.disabled_nodes = path->subpath->disabled_nodes; 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, int input_disabled_nodes, 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; /* * 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.disabled_nodes = input_disabled_nodes + (enable_gathermerge ? 0 : 1); 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); } /* we don't need to check enable_indexonlyscan; indxpath.c does that */ path->path.disabled_nodes = enable_indexscan ? 0 : 1; /* * 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; 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->disabled_nodes = enable_bitmapscan ? 0 : 1; 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.disabled_nodes = 0; 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; QualCost qpqual_cost; Cost cpu_per_tuple; QualCost tid_qual_cost; double ntuples; ListCell *l; double spc_random_page_cost; /* Should only be applied to base relations */ Assert(baserel->relid > 0); Assert(baserel->rtekind == RTE_RELATION); Assert(tidquals != NIL); /* 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; /* * We must use a TID scan for CurrentOfExpr; in any other case, we * should be generating a TID scan only if enable_tidscan=true. Also, * if CurrentOfExpr is the qual, there should be only one. */ Assert(enable_tidscan || IsA(qual, CurrentOfExpr)); Assert(list_length(tidquals) == 1 || !IsA(qual, CurrentOfExpr)); 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(root, arraynode); } else if (IsA(qual, CurrentOfExpr)) { /* CURRENT OF yields 1 tuple */ ntuples++; } else { /* It's just CTID = something, count 1 tuple */ ntuples++; } } /* * 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; /* * There are assertions above verifying that we only reach this function * either when enable_tidscan=true or when the TID scan is the only legal * path, so it's safe to set disabled_nodes to zero here. */ path->disabled_nodes = 0; 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; /* * 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; /* we should not generate this path type when enable_tidscan=false */ Assert(enable_tidscan); path->disabled_nodes = 0; 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 * 'trivial_pathtarget' is true if the pathtarget is believed to be trivial. */ void cost_subqueryscan(SubqueryScanPath *path, PlannerInfo *root, RelOptInfo *baserel, ParamPathInfo *param_info, bool trivial_pathtarget) { 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.disabled_nodes = path->subpath->disabled_nodes; path->path.startup_cost = path->subpath->startup_cost; path->path.total_cost = path->subpath->total_cost; /* * However, if there are no relevant restriction clauses and the * pathtarget is trivial, then we expect that setrefs.c will optimize away * the SubqueryScan plan node altogether, so we should just make its cost * and rowcount equal to the input path's. * * Note: there are some edge cases where createplan.c will apply a * different targetlist to the SubqueryScan node, thus falsifying our * current estimate of whether the target is trivial, and making the cost * estimate (though not the rowcount) wrong. It does not seem worth the * extra complication to try to account for that exactly, especially since * that behavior falsifies other cost estimates as well. */ if (qpquals == NIL && trivial_pathtarget) return; 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->disabled_nodes = 0; 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->disabled_nodes = 0; 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->disabled_nodes = 0; 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->disabled_nodes = 0; 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->disabled_nodes = 0; 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->disabled_nodes = 0; 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->disabled_nodes = nrterm->disabled_nodes + rterm->disabled_nodes; 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; int64 sort_mem_bytes = sort_mem * (int64) 1024; /* * 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, int input_disabled_nodes, 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, run_cost, 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; bool unknown_varno = false; Assert(presorted_keys > 0 && presorted_keys < list_length(pathkeys)); /* * 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); if (foreach_current_index(l) + 1 >= presorted_keys) break; } /* Estimate the 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 the average cost of sorting of one group where presorted keys * are equal. */ cost_tuplesort(&group_startup_cost, &group_run_cost, 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. */ run_cost += (cpu_tuple_cost + comparison_cost) * input_tuples; /* * Additionally, we charge double cpu_tuple_cost for each input group to * account for the tuplesort_reset that's performed after each group. */ run_cost += 2.0 * cpu_tuple_cost * input_groups; path->rows = input_tuples; /* should not generate these paths when enable_incremental_sort=false */ Assert(enable_incremental_sort); path->disabled_nodes = input_disabled_nodes; 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, int input_disabled_nodes, 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); startup_cost += input_cost; path->rows = tuples; path->disabled_nodes = input_disabled_nodes + (enable_sort ? 0 : 1); 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 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); /* Consider only the non-partial paths */ if (path_index++ == numpaths) break; costarr[min_index] += subpath->total_cost; /* Update the new min cost array index */ min_index = 0; for (int i = 0; i < arrlen; i++) { if (costarr[i] < costarr[min_index]) min_index = i; } } /* Return the highest cost from the array */ max_index = 0; for (int 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.disabled_nodes = 0; 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 *firstsubpath = (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 = firstsubpath->startup_cost; /* * Compute rows, number of disabled nodes, and total cost as sums * of underlying subplan values. */ foreach(l, apath->subpaths) { Path *subpath = (Path *) lfirst(l); apath->path.rows += subpath->rows; apath->path.disabled_nodes += subpath->disabled_nodes; 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->disabled_nodes, 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.disabled_nodes += subpath->disabled_nodes; 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.disabled_nodes += subpath->disabled_nodes; 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_disabled_nodes' is the sum of the input streams' disabled node counts * '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, int input_disabled_nodes, 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->disabled_nodes = input_disabled_nodes; 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, int input_disabled_nodes, 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); double work_mem_bytes = work_mem * (Size) 1024; 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->disabled_nodes = input_disabled_nodes + (enable_material ? 0 : 1); 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; ListCell *lc; 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. */ est_entry_bytes = relation_byte_size(tuples, width) + ExecEstimateCacheEntryOverheadBytes(tuples); /* include the estimated width for the cache keys */ foreach(lc, mpath->param_exprs) est_entry_bytes += get_expr_width(root, (Node *) lfirst(lc)); /* 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 = ((calls - ndistinct) / calls) * (est_cache_entries / Max(ndistinct, est_cache_entries)); Assert(hit_ratio >= 0 && hit_ratio <= 1.0); /* * 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, int disabled_nodes, Cost input_startup_cost, Cost input_total_cost, double input_tuples, double input_width) { double output_tuples; Cost startup_cost; Cost total_cost; const AggClauseCosts dummy_aggcosts = {0}; /* Use all-zero per-aggregate costs if NULL is passed */ if (aggcosts == NULL) { Assert(aggstrategy == AGG_HASHED); 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) ++disabled_nodes; /* 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) ++disabled_nodes; 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->disabled_nodes = disabled_nodes; path->startup_cost = startup_cost; path->total_cost = total_cost; } /* * get_windowclause_startup_tuples * Estimate how many tuples we'll need to fetch from a WindowAgg's * subnode before we can output the first WindowAgg tuple. * * How many tuples need to be read depends on the WindowClause. For example, * a WindowClause with no PARTITION BY and no ORDER BY requires that all * subnode tuples are read and aggregated before the WindowAgg can output * anything. If there's a PARTITION BY, then we only need to look at tuples * in the first partition. Here we attempt to estimate just how many * 'input_tuples' the WindowAgg will need to read for the given WindowClause * before the first tuple can be output. */ static double get_windowclause_startup_tuples(PlannerInfo *root, WindowClause *wc, double input_tuples) { int frameOptions = wc->frameOptions; double partition_tuples; double return_tuples; double peer_tuples; /* * First, figure out how many partitions there are likely to be and set * partition_tuples according to that estimate. */ if (wc->partitionClause != NIL) { double num_partitions; List *partexprs = get_sortgrouplist_exprs(wc->partitionClause, root->parse->targetList); num_partitions = estimate_num_groups(root, partexprs, input_tuples, NULL, NULL); list_free(partexprs); partition_tuples = input_tuples / num_partitions; } else { /* all tuples belong to the same partition */ partition_tuples = input_tuples; } /* estimate the number of tuples in each peer group */ if (wc->orderClause != NIL) { double num_groups; List *orderexprs; orderexprs = get_sortgrouplist_exprs(wc->orderClause, root->parse->targetList); /* estimate out how many peer groups there are in the partition */ num_groups = estimate_num_groups(root, orderexprs, partition_tuples, NULL, NULL); list_free(orderexprs); peer_tuples = partition_tuples / num_groups; } else { /* no ORDER BY so only 1 tuple belongs in each peer group */ peer_tuples = 1.0; } if (frameOptions & FRAMEOPTION_END_UNBOUNDED_FOLLOWING) { /* include all partition rows */ return_tuples = partition_tuples; } else if (frameOptions & FRAMEOPTION_END_CURRENT_ROW) { if (frameOptions & FRAMEOPTION_ROWS) { /* just count the current row */ return_tuples = 1.0; } else if (frameOptions & (FRAMEOPTION_RANGE | FRAMEOPTION_GROUPS)) { /* * When in RANGE/GROUPS mode, it's more complex. If there's no * ORDER BY, then all rows in the partition are peers, otherwise * we'll need to read the first group of peers. */ if (wc->orderClause == NIL) return_tuples = partition_tuples; else return_tuples = peer_tuples; } else { /* * Something new we don't support yet? This needs attention. * We'll just return 1.0 in the meantime. */ Assert(false); return_tuples = 1.0; } } else if (frameOptions & FRAMEOPTION_END_OFFSET_PRECEDING) { /* * BETWEEN ... AND N PRECEDING will only need to read the WindowAgg's * subnode after N ROWS/RANGES/GROUPS. N can be 0, but not negative, * so we'll just assume only the current row needs to be read to fetch * the first WindowAgg row. */ return_tuples = 1.0; } else if (frameOptions & FRAMEOPTION_END_OFFSET_FOLLOWING) { Const *endOffset = (Const *) wc->endOffset; double end_offset_value; /* try and figure out the value specified in the endOffset. */ if (IsA(endOffset, Const)) { if (endOffset->constisnull) { /* * NULLs are not allowed, but currently, there's no code to * error out if there's a NULL Const. We'll only discover * this during execution. For now, just pretend everything is * fine and assume that just the first row/range/group will be * needed. */ end_offset_value = 1.0; } else { switch (endOffset->consttype) { case INT2OID: end_offset_value = (double) DatumGetInt16(endOffset->constvalue); break; case INT4OID: end_offset_value = (double) DatumGetInt32(endOffset->constvalue); break; case INT8OID: end_offset_value = (double) DatumGetInt64(endOffset->constvalue); break; default: end_offset_value = partition_tuples / peer_tuples * DEFAULT_INEQ_SEL; break; } } } else { /* * When the end bound is not a Const, we'll just need to guess. We * just make use of DEFAULT_INEQ_SEL. */ end_offset_value = partition_tuples / peer_tuples * DEFAULT_INEQ_SEL; } if (frameOptions & FRAMEOPTION_ROWS) { /* include the N FOLLOWING and the current row */ return_tuples = end_offset_value + 1.0; } else if (frameOptions & (FRAMEOPTION_RANGE | FRAMEOPTION_GROUPS)) { /* include N FOLLOWING ranges/group and the initial range/group */ return_tuples = peer_tuples * (end_offset_value + 1.0); } else { /* * Something new we don't support yet? This needs attention. * We'll just return 1.0 in the meantime. */ Assert(false); return_tuples = 1.0; } } else { /* * Something new we don't support yet? This needs attention. We'll * just return 1.0 in the meantime. */ Assert(false); return_tuples = 1.0; } if (wc->partitionClause != NIL || wc->orderClause != NIL) { /* * Cap the return value to the estimated partition tuples and account * for the extra tuple WindowAgg will need to read to confirm the next * tuple does not belong to the same partition or peer group. */ return_tuples = Min(return_tuples + 1.0, partition_tuples); } else { /* * Cap the return value so it's never higher than the expected tuples * in the partition. */ return_tuples = Min(return_tuples, partition_tuples); } /* * We needn't worry about any EXCLUDE options as those only exclude rows * from being aggregated, not from being read from the WindowAgg's * subnode. */ return clamp_row_est(return_tuples); } /* * 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, WindowClause *winclause, int input_disabled_nodes, Cost input_startup_cost, Cost input_total_cost, double input_tuples) { Cost startup_cost; Cost total_cost; double startup_tuples; int numPartCols; int numOrderCols; ListCell *lc; numPartCols = list_length(winclause->partitionClause); numOrderCols = list_length(winclause->orderClause); 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->disabled_nodes = input_disabled_nodes; path->startup_cost = startup_cost; path->total_cost = total_cost; /* * Also, take into account how many tuples we need to read from the * subnode in order to produce the first tuple from the WindowAgg. To do * this we proportion the run cost (total cost not including startup cost) * over the estimated startup tuples. We already included the startup * cost of the subnode, so we only need to do this when the estimated * startup tuples is above 1.0. */ startup_tuples = get_windowclause_startup_tuples(root, winclause, input_tuples); if (startup_tuples > 1.0) path->startup_cost += (total_cost - startup_cost) / input_tuples * (startup_tuples - 1.0); } /* * 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, int input_disabled_nodes, 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->disabled_nodes = input_disabled_nodes; 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) { int disabled_nodes; 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; /* Count up disabled nodes. */ disabled_nodes = enable_nestloop ? 0 : 1; disabled_nodes += inner_path->disabled_nodes; disabled_nodes += outer_path->disabled_nodes; /* 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->disabled_nodes = disabled_nodes; 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; /* Set the number of disabled nodes. */ path->jpath.path.disabled_nodes = workspace->disabled_nodes; /* 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); } /* 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/cost_incremental_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 * 'outer_presorted_keys' is the number of presorted keys of the outer 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, int outer_presorted_keys, JoinPathExtraData *extra) { int disabled_nodes; 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/cost_incremental_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_cmptype != ipathkey->pk_cmptype || 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 || jointype == JOIN_RIGHT_ANTI) { 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); disabled_nodes = enable_mergejoin ? 0 : 1; /* cost of source data */ if (outersortkeys) /* do we need to sort outer? */ { /* * We can assert that the outer path is not already ordered * appropriately for the mergejoin; otherwise, outersortkeys would * have been set to NIL. */ Assert(!pathkeys_contained_in(outersortkeys, outer_path->pathkeys)); /* * We choose to use incremental sort if it is enabled and there are * presorted keys; otherwise we use full sort. */ if (enable_incremental_sort && outer_presorted_keys > 0) { cost_incremental_sort(&sort_path, root, outersortkeys, outer_presorted_keys, outer_path->disabled_nodes, outer_path->startup_cost, outer_path->total_cost, outer_path_rows, outer_path->pathtarget->width, 0.0, work_mem, -1.0); } else { cost_sort(&sort_path, root, outersortkeys, outer_path->disabled_nodes, outer_path->total_cost, outer_path_rows, outer_path->pathtarget->width, 0.0, work_mem, -1.0); } disabled_nodes += sort_path.disabled_nodes; 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 { disabled_nodes += outer_path->disabled_nodes; 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? */ { /* * We can assert that the inner path is not already ordered * appropriately for the mergejoin; otherwise, innersortkeys would * have been set to NIL. */ Assert(!pathkeys_contained_in(innersortkeys, inner_path->pathkeys)); /* * We do not consider incremental sort for inner path, because * incremental sort does not support mark/restore. */ cost_sort(&sort_path, root, innersortkeys, inner_path->disabled_nodes, inner_path->total_cost, inner_path_rows, inner_path->pathtarget->width, 0.0, work_mem, -1.0); disabled_nodes += sort_path.disabled_nodes; 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 { disabled_nodes += inner_path->disabled_nodes; 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->disabled_nodes = disabled_nodes; 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; /* Set the number of disabled nodes. */ path->jpath.path.disabled_nodes = workspace->disabled_nodes; /* 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); } /* * 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 * (Size) 1024) 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->cmptype == pathkey->pk_cmptype && cache->nulls_first == pathkey->pk_nulls_first) return cache; } /* Nope, do the computation */ mergejoinscansel(root, (Node *) rinfo->clause, pathkey->pk_opfamily, pathkey->pk_cmptype, 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->cmptype = pathkey->pk_cmptype; 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) { int disabled_nodes; 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 */ /* Count up disabled nodes. */ disabled_nodes = enable_hashjoin ? 0 : 1; disabled_nodes += inner_path->disabled_nodes; disabled_nodes += outer_path->disabled_nodes; /* 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->disabled_nodes = disabled_nodes; 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; /* Set the number of disabled nodes. */ path->jpath.path.disabled_nodes = workspace->disabled_nodes; /* 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); } /* 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 { List *otherclauses; innerbucketsize = 1.0; innermcvfreq = 1.0; /* At first, try to estimate bucket size using extended statistics. */ otherclauses = estimate_multivariate_bucketsize(root, inner_path->parent, hashclauses, &innerbucketsize); /* Pass through the remaining clauses */ foreach(hcl, otherclauses) { 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); double work_mem_bytes = work_mem * (Size) 1024; 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); double work_mem_bytes = work_mem * (Size) 1024; 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. * * Note: in some code paths root can be passed as NULL, resulting in * slightly worse estimates. */ 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; double estarraylen = estimate_array_length(context->root, 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(context->root, 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(context->root, (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) || IsA(node, JsonExpr)) { /* Treat all these as having cost 1 */ context->total.per_tuple += cpu_operator_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, 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. */ init_dummy_sjinfo(&norm_sjinfo, outerrel->relids, innerrel->relids); 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. */ init_dummy_sjinfo(&sjinfo, path->outerjoinpath->parent->relids, path->innerjoinpath->parent->relids); /* 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) { 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(root, 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; int64 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); 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) { int64 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] = clamp_width_est(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; } rel->reltarget->width = clamp_width_est(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) { int64 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); tuple_width += get_expr_width(root, node); /* For non-Vars, account for evaluation cost */ if (!IsA(node, Var)) { QualCost cost; cost_qual_eval_node(&cost, node, root); target->cost.startup += cost.startup; target->cost.per_tuple += cost.per_tuple; } } target->width = clamp_width_est(tuple_width); return target; } /* * get_expr_width * Estimate the width of the given expr attempting to use the width * cached in a Var's owning RelOptInfo, else fallback on the type's * average width when unable to or when the given Node is not a Var. */ static int32 get_expr_width(PlannerInfo *root, const Node *expr) { int32 width; if (IsA(expr, Var)) { const Var *var = (const Var *) expr; /* 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) return rel->attr_widths[ndx]; } } /* * No cached data available, so estimate using just the type info. */ width = get_typavgwidth(var->vartype, var->vartypmod); Assert(width > 0); return width; } width = get_typavgwidth(exprType(expr), exprTypmod(expr)); Assert(width > 0); return width; } /* * relation_byte_size * Estimate the storage space in bytes for a given number of tuples * of a given width (size in bytes). */ static double relation_byte_size(double tuples, int width) { return tuples * (MAXALIGN(width) + MAXALIGN(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 * Estimate number of pages fetched from heap in a bitmap heap scan. * * 'baserel' is the relation to be scanned * '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 * * If cost_p isn't NULL, the indexTotalCost estimate is returned in *cost_p. * If tuples_p isn't NULL, the tuples_fetched estimate is returned in *tuples_p. */ double compute_bitmap_pages(PlannerInfo *root, RelOptInfo *baserel, Path *bitmapqual, double loop_count, Cost *cost_p, double *tuples_p) { Cost indexTotalCost; Selectivity indexSelectivity; double T; double pages_fetched; double tuples_fetched; double heap_pages; double 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 * (Size) 1024); 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_p) *cost_p = indexTotalCost; if (tuples_p) *tuples_p = tuples_fetched; return pages_fetched; } /* * compute_gather_rows * Estimate number of rows for gather (merge) nodes. * * In a parallel plan, each worker's row estimate is determined by dividing the * total number of rows by parallel_divisor, which accounts for the leader's * contribution in addition to the number of workers. Accordingly, when * estimating the number of rows for gather (merge) nodes, we multiply the rows * per worker by the same parallel_divisor to undo the division. */ double compute_gather_rows(Path *path) { Assert(path->parallel_workers > 0); return clamp_row_est(path->rows * get_parallel_divisor(path)); }