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Diffstat (limited to 'src/backend/optimizer/path/costsize.c')
| -rw-r--r-- | src/backend/optimizer/path/costsize.c | 6176 |
1 files changed, 6176 insertions, 0 deletions
diff --git a/src/backend/optimizer/path/costsize.c b/src/backend/optimizer/path/costsize.c new file mode 100644 index 0000000..006f91f --- /dev/null +++ b/src/backend/optimizer/path/costsize.c @@ -0,0 +1,6176 @@ +/*------------------------------------------------------------------------- + * + * costsize.c + * Routines to compute (and set) relation sizes and path costs + * + * Path costs are measured in arbitrary units established by these basic + * parameters: + * + * seq_page_cost Cost of a sequential page fetch + * random_page_cost Cost of a non-sequential page fetch + * cpu_tuple_cost Cost of typical CPU time to process a tuple + * cpu_index_tuple_cost Cost of typical CPU time to process an index tuple + * cpu_operator_cost Cost of CPU time to execute an operator or function + * parallel_tuple_cost Cost of CPU time to pass a tuple from worker to leader backend + * parallel_setup_cost Cost of setting up shared memory for parallelism + * + * We expect that the kernel will typically do some amount of read-ahead + * optimization; this in conjunction with seek costs means that seq_page_cost + * is normally considerably less than random_page_cost. (However, if the + * database is fully cached in RAM, it is reasonable to set them equal.) + * + * We also use a rough estimate "effective_cache_size" of the number of + * disk pages in Postgres + OS-level disk cache. (We can't simply use + * NBuffers for this purpose because that would ignore the effects of + * the kernel's disk cache.) + * + * Obviously, taking constants for these values is an oversimplification, + * but it's tough enough to get any useful estimates even at this level of + * detail. Note that all of these parameters are user-settable, in case + * the default values are drastically off for a particular platform. + * + * seq_page_cost and random_page_cost can also be overridden for an individual + * tablespace, in case some data is on a fast disk and other data is on a slow + * disk. Per-tablespace overrides never apply to temporary work files such as + * an external sort or a materialize node that overflows work_mem. + * + * We compute two separate costs for each path: + * total_cost: total estimated cost to fetch all tuples + * startup_cost: cost that is expended before first tuple is fetched + * In some scenarios, such as when there is a LIMIT or we are implementing + * an EXISTS(...) sub-select, it is not necessary to fetch all tuples of the + * path's result. A caller can estimate the cost of fetching a partial + * result by interpolating between startup_cost and total_cost. In detail: + * actual_cost = startup_cost + + * (total_cost - startup_cost) * tuples_to_fetch / path->rows; + * Note that a base relation's rows count (and, by extension, plan_rows for + * plan nodes below the LIMIT node) are set without regard to any LIMIT, so + * that this equation works properly. (Note: while path->rows is never zero + * for ordinary relations, it is zero for paths for provably-empty relations, + * so beware of division-by-zero.) The LIMIT is applied as a top-level + * plan node. + * + * For largely historical reasons, most of the routines in this module use + * the passed result Path only to store their results (rows, startup_cost and + * total_cost) into. All the input data they need is passed as separate + * parameters, even though much of it could be extracted from the Path. + * An exception is made for the cost_XXXjoin() routines, which expect all + * the other fields of the passed XXXPath to be filled in, and similarly + * cost_index() assumes the passed IndexPath is valid except for its output + * values. + * + * + * Portions Copyright (c) 1996-2021, 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 <math.h> + +#include "access/amapi.h" +#include "access/htup_details.h" +#include "access/tsmapi.h" +#include "executor/executor.h" +#include "executor/nodeAgg.h" +#include "executor/nodeHash.h" +#include "executor/nodeMemoize.h" +#include "miscadmin.h" +#include "nodes/makefuncs.h" +#include "nodes/nodeFuncs.h" +#include "optimizer/clauses.h" +#include "optimizer/cost.h" +#include "optimizer/optimizer.h" +#include "optimizer/pathnode.h" +#include "optimizer/paths.h" +#include "optimizer/placeholder.h" +#include "optimizer/plancat.h" +#include "optimizer/planmain.h" +#include "optimizer/restrictinfo.h" +#include "parser/parsetree.h" +#include "utils/lsyscache.h" +#include "utils/selfuncs.h" +#include "utils/spccache.h" +#include "utils/tuplesort.h" + + +#define LOG2(x) (log(x) / 0.693147180559945) + +/* + * Append and MergeAppend nodes are less expensive than some other operations + * which use cpu_tuple_cost; instead of adding a separate GUC, estimate the + * per-tuple cost as cpu_tuple_cost multiplied by this value. + */ +#define APPEND_CPU_COST_MULTIPLIER 0.5 + +/* + * Maximum value for row estimates. We cap row estimates to this to help + * ensure that costs based on these estimates remain within the range of what + * double can represent. add_path() wouldn't act sanely given infinite or NaN + * cost values. + */ +#define MAXIMUM_ROWCOUNT 1e100 + +double seq_page_cost = DEFAULT_SEQ_PAGE_COST; +double random_page_cost = DEFAULT_RANDOM_PAGE_COST; +double cpu_tuple_cost = DEFAULT_CPU_TUPLE_COST; +double cpu_index_tuple_cost = DEFAULT_CPU_INDEX_TUPLE_COST; +double cpu_operator_cost = DEFAULT_CPU_OPERATOR_COST; +double parallel_tuple_cost = DEFAULT_PARALLEL_TUPLE_COST; +double parallel_setup_cost = DEFAULT_PARALLEL_SETUP_COST; + +int effective_cache_size = DEFAULT_EFFECTIVE_CACHE_SIZE; + +Cost disable_cost = 1.0e10; + +int max_parallel_workers_per_gather = 2; + +bool enable_seqscan = true; +bool enable_indexscan = true; +bool enable_indexonlyscan = true; +bool enable_bitmapscan = true; +bool enable_tidscan = true; +bool enable_sort = true; +bool enable_incremental_sort = true; +bool enable_hashagg = true; +bool enable_nestloop = true; +bool enable_material = true; +bool enable_memoize = true; +bool enable_mergejoin = true; +bool enable_hashjoin = true; +bool enable_gathermerge = true; +bool enable_partitionwise_join = false; +bool enable_partitionwise_aggregate = false; +bool enable_parallel_append = true; +bool enable_parallel_hash = true; +bool enable_partition_pruning = true; +bool enable_async_append = true; + +typedef struct +{ + PlannerInfo *root; + QualCost total; +} cost_qual_eval_context; + +static List *extract_nonindex_conditions(List *qual_clauses, List *indexclauses); +static MergeScanSelCache *cached_scansel(PlannerInfo *root, + RestrictInfo *rinfo, + PathKey *pathkey); +static void cost_rescan(PlannerInfo *root, Path *path, + Cost *rescan_startup_cost, Cost *rescan_total_cost); +static bool cost_qual_eval_walker(Node *node, cost_qual_eval_context *context); +static void get_restriction_qual_cost(PlannerInfo *root, RelOptInfo *baserel, + ParamPathInfo *param_info, + QualCost *qpqual_cost); +static bool has_indexed_join_quals(NestPath *joinpath); +static double approx_tuple_count(PlannerInfo *root, JoinPath *path, + List *quals); +static double calc_joinrel_size_estimate(PlannerInfo *root, + RelOptInfo *joinrel, + RelOptInfo *outer_rel, + RelOptInfo *inner_rel, + double outer_rows, + double inner_rows, + SpecialJoinInfo *sjinfo, + List *restrictlist); +static Selectivity get_foreign_key_join_selectivity(PlannerInfo *root, + Relids outer_relids, + Relids inner_relids, + SpecialJoinInfo *sjinfo, + List **restrictlist); +static Cost append_nonpartial_cost(List *subpaths, int numpaths, + int parallel_workers); +static void set_rel_width(PlannerInfo *root, RelOptInfo *rel); +static double relation_byte_size(double tuples, int width); +static double page_size(double tuples, int width); +static double get_parallel_divisor(Path *path); + + +/* + * clamp_row_est + * Force a row-count estimate to a sane value. + */ +double +clamp_row_est(double nrows) +{ + /* + * Avoid infinite and NaN row estimates. Costs derived from such values + * are going to be useless. Also force the estimate to be at least one + * row, to make explain output look better and to avoid possible + * divide-by-zero when interpolating costs. Make it an integer, too. + */ + if (nrows > MAXIMUM_ROWCOUNT || isnan(nrows)) + nrows = MAXIMUM_ROWCOUNT; + else if (nrows <= 1.0) + nrows = 1.0; + else + nrows = rint(nrows); + + return nrows; +} + + +/* + * cost_seqscan + * Determines and returns the cost of scanning a relation sequentially. + * + * 'baserel' is the relation to be scanned + * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL + */ +void +cost_seqscan(Path *path, PlannerInfo *root, + RelOptInfo *baserel, ParamPathInfo *param_info) +{ + Cost startup_cost = 0; + Cost cpu_run_cost; + Cost disk_run_cost; + double spc_seq_page_cost; + QualCost qpqual_cost; + Cost cpu_per_tuple; + + /* Should only be applied to base relations */ + Assert(baserel->relid > 0); + Assert(baserel->rtekind == RTE_RELATION); + + /* Mark the path with the correct row estimate */ + if (param_info) + path->rows = param_info->ppi_rows; + else + path->rows = baserel->rows; + + if (!enable_seqscan) + startup_cost += disable_cost; + + /* fetch estimated page cost for tablespace containing table */ + get_tablespace_page_costs(baserel->reltablespace, + NULL, + &spc_seq_page_cost); + + /* + * disk costs + */ + disk_run_cost = spc_seq_page_cost * baserel->pages; + + /* CPU costs */ + get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost); + + startup_cost += qpqual_cost.startup; + cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple; + cpu_run_cost = cpu_per_tuple * baserel->tuples; + /* tlist eval costs are paid per output row, not per tuple scanned */ + startup_cost += path->pathtarget->cost.startup; + cpu_run_cost += path->pathtarget->cost.per_tuple * path->rows; + + /* Adjust costing for parallelism, if used. */ + if (path->parallel_workers > 0) + { + double parallel_divisor = get_parallel_divisor(path); + + /* The CPU cost is divided among all the workers. */ + cpu_run_cost /= parallel_divisor; + + /* + * It may be possible to amortize some of the I/O cost, but probably + * not very much, because most operating systems already do aggressive + * prefetching. For now, we assume that the disk run cost can't be + * amortized at all. + */ + + /* + * In the case of a parallel plan, the row count needs to represent + * the number of tuples processed per worker. + */ + path->rows = clamp_row_est(path->rows / parallel_divisor); + } + + path->startup_cost = startup_cost; + path->total_cost = startup_cost + cpu_run_cost + disk_run_cost; +} + +/* + * cost_samplescan + * Determines and returns the cost of scanning a relation using sampling. + * + * 'baserel' is the relation to be scanned + * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL + */ +void +cost_samplescan(Path *path, PlannerInfo *root, + RelOptInfo *baserel, ParamPathInfo *param_info) +{ + Cost startup_cost = 0; + Cost run_cost = 0; + RangeTblEntry *rte; + TableSampleClause *tsc; + TsmRoutine *tsm; + double spc_seq_page_cost, + spc_random_page_cost, + spc_page_cost; + QualCost qpqual_cost; + Cost cpu_per_tuple; + + /* Should only be applied to base relations with tablesample clauses */ + Assert(baserel->relid > 0); + rte = planner_rt_fetch(baserel->relid, root); + Assert(rte->rtekind == RTE_RELATION); + tsc = rte->tablesample; + Assert(tsc != NULL); + tsm = GetTsmRoutine(tsc->tsmhandler); + + /* Mark the path with the correct row estimate */ + if (param_info) + path->rows = param_info->ppi_rows; + else + path->rows = baserel->rows; + + /* fetch estimated page cost for tablespace containing table */ + get_tablespace_page_costs(baserel->reltablespace, + &spc_random_page_cost, + &spc_seq_page_cost); + + /* if NextSampleBlock is used, assume random access, else sequential */ + spc_page_cost = (tsm->NextSampleBlock != NULL) ? + spc_random_page_cost : spc_seq_page_cost; + + /* + * disk costs (recall that baserel->pages has already been set to the + * number of pages the sampling method will visit) + */ + run_cost += spc_page_cost * baserel->pages; + + /* + * CPU costs (recall that baserel->tuples has already been set to the + * number of tuples the sampling method will select). Note that we ignore + * execution cost of the TABLESAMPLE parameter expressions; they will be + * evaluated only once per scan, and in most usages they'll likely be + * simple constants anyway. We also don't charge anything for the + * calculations the sampling method might do internally. + */ + get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost); + + startup_cost += qpqual_cost.startup; + cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple; + run_cost += cpu_per_tuple * baserel->tuples; + /* tlist eval costs are paid per output row, not per tuple scanned */ + startup_cost += path->pathtarget->cost.startup; + run_cost += path->pathtarget->cost.per_tuple * path->rows; + + path->startup_cost = startup_cost; + path->total_cost = startup_cost + run_cost; +} + +/* + * cost_gather + * Determines and returns the cost of gather path. + * + * 'rel' is the relation to be operated upon + * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL + * 'rows' may be used to point to a row estimate; if non-NULL, it overrides + * both 'rel' and 'param_info'. This is useful when the path doesn't exactly + * correspond to any particular RelOptInfo. + */ +void +cost_gather(GatherPath *path, PlannerInfo *root, + RelOptInfo *rel, ParamPathInfo *param_info, + double *rows) +{ + Cost startup_cost = 0; + Cost run_cost = 0; + + /* Mark the path with the correct row estimate */ + if (rows) + path->path.rows = *rows; + else if (param_info) + path->path.rows = param_info->ppi_rows; + else + path->path.rows = rel->rows; + + startup_cost = path->subpath->startup_cost; + + run_cost = path->subpath->total_cost - path->subpath->startup_cost; + + /* Parallel setup and communication cost. */ + startup_cost += parallel_setup_cost; + run_cost += parallel_tuple_cost * path->path.rows; + + path->path.startup_cost = startup_cost; + path->path.total_cost = (startup_cost + run_cost); +} + +/* + * cost_gather_merge + * Determines and returns the cost of gather merge path. + * + * GatherMerge merges several pre-sorted input streams, using a heap that at + * any given instant holds the next tuple from each stream. If there are N + * streams, we need about N*log2(N) tuple comparisons to construct the heap at + * startup, and then for each output tuple, about log2(N) comparisons to + * replace the top heap entry with the next tuple from the same stream. + */ +void +cost_gather_merge(GatherMergePath *path, PlannerInfo *root, + RelOptInfo *rel, ParamPathInfo *param_info, + Cost input_startup_cost, Cost input_total_cost, + double *rows) +{ + Cost startup_cost = 0; + Cost run_cost = 0; + Cost comparison_cost; + double N; + double logN; + + /* Mark the path with the correct row estimate */ + if (rows) + path->path.rows = *rows; + else if (param_info) + path->path.rows = param_info->ppi_rows; + else + path->path.rows = rel->rows; + + if (!enable_gathermerge) + startup_cost += disable_cost; + + /* + * Add one to the number of workers to account for the leader. This might + * be overgenerous since the leader will do less work than other workers + * in typical cases, but we'll go with it for now. + */ + Assert(path->num_workers > 0); + N = (double) path->num_workers + 1; + logN = LOG2(N); + + /* Assumed cost per tuple comparison */ + comparison_cost = 2.0 * cpu_operator_cost; + + /* Heap creation cost */ + startup_cost += comparison_cost * N * logN; + + /* Per-tuple heap maintenance cost */ + run_cost += path->path.rows * comparison_cost * logN; + + /* small cost for heap management, like cost_merge_append */ + run_cost += cpu_operator_cost * path->path.rows; + + /* + * Parallel setup and communication cost. Since Gather Merge, unlike + * Gather, requires us to block until a tuple is available from every + * worker, we bump the IPC cost up a little bit as compared with Gather. + * For lack of a better idea, charge an extra 5%. + */ + startup_cost += parallel_setup_cost; + run_cost += parallel_tuple_cost * path->path.rows * 1.05; + + path->path.startup_cost = startup_cost + input_startup_cost; + path->path.total_cost = (startup_cost + run_cost + input_total_cost); +} + +/* + * cost_index + * Determines and returns the cost of scanning a relation using an index. + * + * 'path' describes the indexscan under consideration, and is complete + * except for the fields to be set by this routine + * 'loop_count' is the number of repetitions of the indexscan to factor into + * estimates of caching behavior + * + * In addition to rows, startup_cost and total_cost, cost_index() sets the + * path's indextotalcost and indexselectivity fields. These values will be + * needed if the IndexPath is used in a BitmapIndexScan. + * + * NOTE: path->indexquals must contain only clauses usable as index + * restrictions. Any additional quals evaluated as qpquals may reduce the + * number of returned tuples, but they won't reduce the number of tuples + * we have to fetch from the table, so they don't reduce the scan cost. + */ +void +cost_index(IndexPath *path, PlannerInfo *root, double loop_count, + bool partial_path) +{ + IndexOptInfo *index = path->indexinfo; + RelOptInfo *baserel = index->rel; + bool indexonly = (path->path.pathtype == T_IndexOnlyScan); + amcostestimate_function amcostestimate; + List *qpquals; + Cost startup_cost = 0; + Cost run_cost = 0; + Cost cpu_run_cost = 0; + Cost indexStartupCost; + Cost indexTotalCost; + Selectivity indexSelectivity; + double indexCorrelation, + csquared; + double spc_seq_page_cost, + spc_random_page_cost; + Cost min_IO_cost, + max_IO_cost; + QualCost qpqual_cost; + Cost cpu_per_tuple; + double tuples_fetched; + double pages_fetched; + double rand_heap_pages; + double index_pages; + + /* Should only be applied to base relations */ + Assert(IsA(baserel, RelOptInfo) && + IsA(index, IndexOptInfo)); + Assert(baserel->relid > 0); + Assert(baserel->rtekind == RTE_RELATION); + + /* + * Mark the path with the correct row estimate, and identify which quals + * will need to be enforced as qpquals. We need not check any quals that + * are implied by the index's predicate, so we can use indrestrictinfo not + * baserestrictinfo as the list of relevant restriction clauses for the + * rel. + */ + if (path->path.param_info) + { + path->path.rows = path->path.param_info->ppi_rows; + /* qpquals come from the rel's restriction clauses and ppi_clauses */ + qpquals = list_concat(extract_nonindex_conditions(path->indexinfo->indrestrictinfo, + path->indexclauses), + extract_nonindex_conditions(path->path.param_info->ppi_clauses, + path->indexclauses)); + } + else + { + path->path.rows = baserel->rows; + /* qpquals come from just the rel's restriction clauses */ + qpquals = extract_nonindex_conditions(path->indexinfo->indrestrictinfo, + path->indexclauses); + } + + if (!enable_indexscan) + startup_cost += disable_cost; + /* we don't need to check enable_indexonlyscan; indxpath.c does that */ + + /* + * Call index-access-method-specific code to estimate the processing cost + * for scanning the index, as well as the selectivity of the index (ie, + * the fraction of main-table tuples we will have to retrieve) and its + * correlation to the main-table tuple order. We need a cast here because + * pathnodes.h uses a weak function type to avoid including amapi.h. + */ + amcostestimate = (amcostestimate_function) index->amcostestimate; + amcostestimate(root, path, loop_count, + &indexStartupCost, &indexTotalCost, + &indexSelectivity, &indexCorrelation, + &index_pages); + + /* + * Save amcostestimate's results for possible use in bitmap scan planning. + * We don't bother to save indexStartupCost or indexCorrelation, because a + * bitmap scan doesn't care about either. + */ + path->indextotalcost = indexTotalCost; + path->indexselectivity = indexSelectivity; + + /* all costs for touching index itself included here */ + startup_cost += indexStartupCost; + run_cost += indexTotalCost - indexStartupCost; + + /* estimate number of main-table tuples fetched */ + tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples); + + /* fetch estimated page costs for tablespace containing table */ + get_tablespace_page_costs(baserel->reltablespace, + &spc_random_page_cost, + &spc_seq_page_cost); + + /*---------- + * Estimate number of main-table pages fetched, and compute I/O cost. + * + * When the index ordering is uncorrelated with the table ordering, + * we use an approximation proposed by Mackert and Lohman (see + * index_pages_fetched() for details) to compute the number of pages + * fetched, and then charge spc_random_page_cost per page fetched. + * + * When the index ordering is exactly correlated with the table ordering + * (just after a CLUSTER, for example), the number of pages fetched should + * be exactly selectivity * table_size. What's more, all but the first + * will be sequential fetches, not the random fetches that occur in the + * uncorrelated case. So if the number of pages is more than 1, we + * ought to charge + * spc_random_page_cost + (pages_fetched - 1) * spc_seq_page_cost + * For partially-correlated indexes, we ought to charge somewhere between + * these two estimates. We currently interpolate linearly between the + * estimates based on the correlation squared (XXX is that appropriate?). + * + * If it's an index-only scan, then we will not need to fetch any heap + * pages for which the visibility map shows all tuples are visible. + * Hence, reduce the estimated number of heap fetches accordingly. + * We use the measured fraction of the entire heap that is all-visible, + * which might not be particularly relevant to the subset of the heap + * that this query will fetch; but it's not clear how to do better. + *---------- + */ + if (loop_count > 1) + { + /* + * For repeated indexscans, the appropriate estimate for the + * uncorrelated case is to scale up the number of tuples fetched in + * the Mackert and Lohman formula by the number of scans, so that we + * estimate the number of pages fetched by all the scans; then + * pro-rate the costs for one scan. In this case we assume all the + * fetches are random accesses. + */ + pages_fetched = index_pages_fetched(tuples_fetched * loop_count, + baserel->pages, + (double) index->pages, + root); + + if (indexonly) + pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac)); + + rand_heap_pages = pages_fetched; + + max_IO_cost = (pages_fetched * spc_random_page_cost) / loop_count; + + /* + * In the perfectly correlated case, the number of pages touched by + * each scan is selectivity * table_size, and we can use the Mackert + * and Lohman formula at the page level to estimate how much work is + * saved by caching across scans. We still assume all the fetches are + * random, though, which is an overestimate that's hard to correct for + * without double-counting the cache effects. (But in most cases + * where such a plan is actually interesting, only one page would get + * fetched per scan anyway, so it shouldn't matter much.) + */ + pages_fetched = ceil(indexSelectivity * (double) baserel->pages); + + pages_fetched = index_pages_fetched(pages_fetched * loop_count, + baserel->pages, + (double) index->pages, + root); + + if (indexonly) + pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac)); + + min_IO_cost = (pages_fetched * spc_random_page_cost) / loop_count; + } + else + { + /* + * Normal case: apply the Mackert and Lohman formula, and then + * interpolate between that and the correlation-derived result. + */ + pages_fetched = index_pages_fetched(tuples_fetched, + baserel->pages, + (double) index->pages, + root); + + if (indexonly) + pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac)); + + rand_heap_pages = pages_fetched; + + /* max_IO_cost is for the perfectly uncorrelated case (csquared=0) */ + max_IO_cost = pages_fetched * spc_random_page_cost; + + /* min_IO_cost is for the perfectly correlated case (csquared=1) */ + pages_fetched = ceil(indexSelectivity * (double) baserel->pages); + + if (indexonly) + pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac)); + + if (pages_fetched > 0) + { + min_IO_cost = spc_random_page_cost; + if (pages_fetched > 1) + min_IO_cost += (pages_fetched - 1) * spc_seq_page_cost; + } + else + min_IO_cost = 0; + } + + if (partial_path) + { + /* + * For index only scans compute workers based on number of index pages + * fetched; the number of heap pages we fetch might be so small as to + * effectively rule out parallelism, which we don't want to do. + */ + if (indexonly) + rand_heap_pages = -1; + + /* + * Estimate the number of parallel workers required to scan index. Use + * the number of heap pages computed considering heap fetches won't be + * sequential as for parallel scans the pages are accessed in random + * order. + */ + path->path.parallel_workers = compute_parallel_worker(baserel, + rand_heap_pages, + index_pages, + max_parallel_workers_per_gather); + + /* + * Fall out if workers can't be assigned for parallel scan, because in + * such a case this path will be rejected. So there is no benefit in + * doing extra computation. + */ + if (path->path.parallel_workers <= 0) + return; + + path->path.parallel_aware = true; + } + + /* + * Now interpolate based on estimated index order correlation to get total + * disk I/O cost for main table accesses. + */ + csquared = indexCorrelation * indexCorrelation; + + run_cost += max_IO_cost + csquared * (min_IO_cost - max_IO_cost); + + /* + * Estimate CPU costs per tuple. + * + * What we want here is cpu_tuple_cost plus the evaluation costs of any + * qual clauses that we have to evaluate as qpquals. + */ + cost_qual_eval(&qpqual_cost, qpquals, root); + + startup_cost += qpqual_cost.startup; + cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple; + + cpu_run_cost += cpu_per_tuple * tuples_fetched; + + /* tlist eval costs are paid per output row, not per tuple scanned */ + startup_cost += path->path.pathtarget->cost.startup; + cpu_run_cost += path->path.pathtarget->cost.per_tuple * path->path.rows; + + /* Adjust costing for parallelism, if used. */ + if (path->path.parallel_workers > 0) + { + double parallel_divisor = get_parallel_divisor(&path->path); + + path->path.rows = clamp_row_est(path->path.rows / parallel_divisor); + + /* The CPU cost is divided among all the workers. */ + cpu_run_cost /= parallel_divisor; + } + + run_cost += cpu_run_cost; + + path->path.startup_cost = startup_cost; + path->path.total_cost = startup_cost + run_cost; +} + +/* + * extract_nonindex_conditions + * + * Given a list of quals to be enforced in an indexscan, extract the ones that + * will have to be applied as qpquals (ie, the index machinery won't handle + * them). Here we detect only whether a qual clause is directly redundant + * with some indexclause. If the index path is chosen for use, createplan.c + * will try a bit harder to get rid of redundant qual conditions; specifically + * it will see if quals can be proven to be implied by the indexquals. But + * it does not seem worth the cycles to try to factor that in at this stage, + * since we're only trying to estimate qual eval costs. Otherwise this must + * match the logic in create_indexscan_plan(). + * + * qual_clauses, and the result, are lists of RestrictInfos. + * indexclauses is a list of IndexClauses. + */ +static List * +extract_nonindex_conditions(List *qual_clauses, List *indexclauses) +{ + List *result = NIL; + ListCell *lc; + + foreach(lc, qual_clauses) + { + RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc); + + if (rinfo->pseudoconstant) + continue; /* we may drop pseudoconstants here */ + if (is_redundant_with_indexclauses(rinfo, indexclauses)) + continue; /* dup or derived from same EquivalenceClass */ + /* ... skip the predicate proof attempt createplan.c will try ... */ + result = lappend(result, rinfo); + } + return result; +} + +/* + * index_pages_fetched + * Estimate the number of pages actually fetched after accounting for + * cache effects. + * + * We use an approximation proposed by Mackert and Lohman, "Index Scans + * Using a Finite LRU Buffer: A Validated I/O Model", ACM Transactions + * on Database Systems, Vol. 14, No. 3, September 1989, Pages 401-424. + * The Mackert and Lohman approximation is that the number of pages + * fetched is + * PF = + * min(2TNs/(2T+Ns), T) when T <= b + * 2TNs/(2T+Ns) when T > b and Ns <= 2Tb/(2T-b) + * b + (Ns - 2Tb/(2T-b))*(T-b)/T when T > b and Ns > 2Tb/(2T-b) + * where + * T = # pages in table + * N = # tuples in table + * s = selectivity = fraction of table to be scanned + * b = # buffer pages available (we include kernel space here) + * + * We assume that effective_cache_size is the total number of buffer pages + * available for the whole query, and pro-rate that space across all the + * tables in the query and the index currently under consideration. (This + * ignores space needed for other indexes used by the query, but since we + * don't know which indexes will get used, we can't estimate that very well; + * and in any case counting all the tables may well be an overestimate, since + * depending on the join plan not all the tables may be scanned concurrently.) + * + * The product Ns is the number of tuples fetched; we pass in that + * product rather than calculating it here. "pages" is the number of pages + * in the object under consideration (either an index or a table). + * "index_pages" is the amount to add to the total table space, which was + * computed for us by make_one_rel. + * + * Caller is expected to have ensured that tuples_fetched is greater than zero + * and rounded to integer (see clamp_row_est). The result will likewise be + * greater than zero and integral. + */ +double +index_pages_fetched(double tuples_fetched, BlockNumber pages, + double index_pages, PlannerInfo *root) +{ + double pages_fetched; + double total_pages; + double T, + b; + + /* T is # pages in table, but don't allow it to be zero */ + T = (pages > 1) ? (double) pages : 1.0; + + /* Compute number of pages assumed to be competing for cache space */ + total_pages = root->total_table_pages + index_pages; + total_pages = Max(total_pages, 1.0); + Assert(T <= total_pages); + + /* b is pro-rated share of effective_cache_size */ + b = (double) effective_cache_size * T / total_pages; + + /* force it positive and integral */ + if (b <= 1.0) + b = 1.0; + else + b = ceil(b); + + /* This part is the Mackert and Lohman formula */ + if (T <= b) + { + pages_fetched = + (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched); + if (pages_fetched >= T) + pages_fetched = T; + else + pages_fetched = ceil(pages_fetched); + } + else + { + double lim; + + lim = (2.0 * T * b) / (2.0 * T - b); + if (tuples_fetched <= lim) + { + pages_fetched = + (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched); + } + else + { + pages_fetched = + b + (tuples_fetched - lim) * (T - b) / T; + } + pages_fetched = ceil(pages_fetched); + } + return pages_fetched; +} + +/* + * get_indexpath_pages + * Determine the total size of the indexes used in a bitmap index path. + * + * Note: if the same index is used more than once in a bitmap tree, we will + * count it multiple times, which perhaps is the wrong thing ... but it's + * not completely clear, and detecting duplicates is difficult, so ignore it + * for now. + */ +static double +get_indexpath_pages(Path *bitmapqual) +{ + double result = 0; + ListCell *l; + + if (IsA(bitmapqual, BitmapAndPath)) + { + BitmapAndPath *apath = (BitmapAndPath *) bitmapqual; + + foreach(l, apath->bitmapquals) + { + result += get_indexpath_pages((Path *) lfirst(l)); + } + } + else if (IsA(bitmapqual, BitmapOrPath)) + { + BitmapOrPath *opath = (BitmapOrPath *) bitmapqual; + + foreach(l, opath->bitmapquals) + { + result += get_indexpath_pages((Path *) lfirst(l)); + } + } + else if (IsA(bitmapqual, IndexPath)) + { + IndexPath *ipath = (IndexPath *) bitmapqual; + + result = (double) ipath->indexinfo->pages; + } + else + elog(ERROR, "unrecognized node type: %d", nodeTag(bitmapqual)); + + return result; +} + +/* + * cost_bitmap_heap_scan + * Determines and returns the cost of scanning a relation using a bitmap + * index-then-heap plan. + * + * 'baserel' is the relation to be scanned + * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL + * 'bitmapqual' is a tree of IndexPaths, BitmapAndPaths, and BitmapOrPaths + * 'loop_count' is the number of repetitions of the indexscan to factor into + * estimates of caching behavior + * + * Note: the component IndexPaths in bitmapqual should have been costed + * using the same loop_count. + */ +void +cost_bitmap_heap_scan(Path *path, PlannerInfo *root, RelOptInfo *baserel, + ParamPathInfo *param_info, + Path *bitmapqual, double loop_count) +{ + Cost startup_cost = 0; + Cost run_cost = 0; + Cost indexTotalCost; + QualCost qpqual_cost; + Cost cpu_per_tuple; + Cost cost_per_page; + Cost cpu_run_cost; + double tuples_fetched; + double pages_fetched; + double spc_seq_page_cost, + spc_random_page_cost; + double T; + + /* Should only be applied to base relations */ + Assert(IsA(baserel, RelOptInfo)); + Assert(baserel->relid > 0); + Assert(baserel->rtekind == RTE_RELATION); + + /* Mark the path with the correct row estimate */ + if (param_info) + path->rows = param_info->ppi_rows; + else + path->rows = baserel->rows; + + if (!enable_bitmapscan) + startup_cost += disable_cost; + + pages_fetched = compute_bitmap_pages(root, baserel, bitmapqual, + loop_count, &indexTotalCost, + &tuples_fetched); + + startup_cost += indexTotalCost; + T = (baserel->pages > 1) ? (double) baserel->pages : 1.0; + + /* Fetch estimated page costs for tablespace containing table. */ + get_tablespace_page_costs(baserel->reltablespace, + &spc_random_page_cost, + &spc_seq_page_cost); + + /* + * For small numbers of pages we should charge spc_random_page_cost + * apiece, while if nearly all the table's pages are being read, it's more + * appropriate to charge spc_seq_page_cost apiece. The effect is + * nonlinear, too. For lack of a better idea, interpolate like this to + * determine the cost per page. + */ + if (pages_fetched >= 2.0) + cost_per_page = spc_random_page_cost - + (spc_random_page_cost - spc_seq_page_cost) + * sqrt(pages_fetched / T); + else + cost_per_page = spc_random_page_cost; + + run_cost += pages_fetched * cost_per_page; + + /* + * Estimate CPU costs per tuple. + * + * Often the indexquals don't need to be rechecked at each tuple ... but + * not always, especially not if there are enough tuples involved that the + * bitmaps become lossy. For the moment, just assume they will be + * rechecked always. This means we charge the full freight for all the + * scan clauses. + */ + get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost); + + startup_cost += qpqual_cost.startup; + cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple; + cpu_run_cost = cpu_per_tuple * tuples_fetched; + + /* Adjust costing for parallelism, if used. */ + if (path->parallel_workers > 0) + { + double parallel_divisor = get_parallel_divisor(path); + + /* The CPU cost is divided among all the workers. */ + cpu_run_cost /= parallel_divisor; + + path->rows = clamp_row_est(path->rows / parallel_divisor); + } + + + run_cost += cpu_run_cost; + + /* tlist eval costs are paid per output row, not per tuple scanned */ + startup_cost += path->pathtarget->cost.startup; + run_cost += path->pathtarget->cost.per_tuple * path->rows; + + path->startup_cost = startup_cost; + path->total_cost = startup_cost + run_cost; +} + +/* + * cost_bitmap_tree_node + * Extract cost and selectivity from a bitmap tree node (index/and/or) + */ +void +cost_bitmap_tree_node(Path *path, Cost *cost, Selectivity *selec) +{ + if (IsA(path, IndexPath)) + { + *cost = ((IndexPath *) path)->indextotalcost; + *selec = ((IndexPath *) path)->indexselectivity; + + /* + * Charge a small amount per retrieved tuple to reflect the costs of + * manipulating the bitmap. This is mostly to make sure that a bitmap + * scan doesn't look to be the same cost as an indexscan to retrieve a + * single tuple. + */ + *cost += 0.1 * cpu_operator_cost * path->rows; + } + else if (IsA(path, BitmapAndPath)) + { + *cost = path->total_cost; + *selec = ((BitmapAndPath *) path)->bitmapselectivity; + } + else if (IsA(path, BitmapOrPath)) + { + *cost = path->total_cost; + *selec = ((BitmapOrPath *) path)->bitmapselectivity; + } + else + { + elog(ERROR, "unrecognized node type: %d", nodeTag(path)); + *cost = *selec = 0; /* keep compiler quiet */ + } +} + +/* + * cost_bitmap_and_node + * Estimate the cost of a BitmapAnd node + * + * Note that this considers only the costs of index scanning and bitmap + * creation, not the eventual heap access. In that sense the object isn't + * truly a Path, but it has enough path-like properties (costs in particular) + * to warrant treating it as one. We don't bother to set the path rows field, + * however. + */ +void +cost_bitmap_and_node(BitmapAndPath *path, PlannerInfo *root) +{ + Cost totalCost; + Selectivity selec; + ListCell *l; + + /* + * We estimate AND selectivity on the assumption that the inputs are + * independent. This is probably often wrong, but we don't have the info + * to do better. + * + * The runtime cost of the BitmapAnd itself is estimated at 100x + * cpu_operator_cost for each tbm_intersect needed. Probably too small, + * definitely too simplistic? + */ + totalCost = 0.0; + selec = 1.0; + foreach(l, path->bitmapquals) + { + Path *subpath = (Path *) lfirst(l); + Cost subCost; + Selectivity subselec; + + cost_bitmap_tree_node(subpath, &subCost, &subselec); + + selec *= subselec; + + totalCost += subCost; + if (l != list_head(path->bitmapquals)) + totalCost += 100.0 * cpu_operator_cost; + } + path->bitmapselectivity = selec; + path->path.rows = 0; /* per above, not used */ + path->path.startup_cost = totalCost; + path->path.total_cost = totalCost; +} + +/* + * cost_bitmap_or_node + * Estimate the cost of a BitmapOr node + * + * See comments for cost_bitmap_and_node. + */ +void +cost_bitmap_or_node(BitmapOrPath *path, PlannerInfo *root) +{ + Cost totalCost; + Selectivity selec; + ListCell *l; + + /* + * We estimate OR selectivity on the assumption that the inputs are + * non-overlapping, since that's often the case in "x IN (list)" type + * situations. Of course, we clamp to 1.0 at the end. + * + * The runtime cost of the BitmapOr itself is estimated at 100x + * cpu_operator_cost for each tbm_union needed. Probably too small, + * definitely too simplistic? We are aware that the tbm_unions are + * optimized out when the inputs are BitmapIndexScans. + */ + totalCost = 0.0; + selec = 0.0; + foreach(l, path->bitmapquals) + { + Path *subpath = (Path *) lfirst(l); + Cost subCost; + Selectivity subselec; + + cost_bitmap_tree_node(subpath, &subCost, &subselec); + + selec += subselec; + + totalCost += subCost; + if (l != list_head(path->bitmapquals) && + !IsA(subpath, IndexPath)) + totalCost += 100.0 * cpu_operator_cost; + } + path->bitmapselectivity = Min(selec, 1.0); + path->path.rows = 0; /* per above, not used */ + path->path.startup_cost = totalCost; + path->path.total_cost = totalCost; +} + +/* + * cost_tidscan + * Determines and returns the cost of scanning a relation using TIDs. + * + * 'baserel' is the relation to be scanned + * 'tidquals' is the list of TID-checkable quals + * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL + */ +void +cost_tidscan(Path *path, PlannerInfo *root, + RelOptInfo *baserel, List *tidquals, ParamPathInfo *param_info) +{ + Cost startup_cost = 0; + Cost run_cost = 0; + bool isCurrentOf = false; + QualCost qpqual_cost; + Cost cpu_per_tuple; + QualCost tid_qual_cost; + int ntuples; + ListCell *l; + double spc_random_page_cost; + + /* Should only be applied to base relations */ + Assert(baserel->relid > 0); + Assert(baserel->rtekind == RTE_RELATION); + + /* Mark the path with the correct row estimate */ + if (param_info) + path->rows = param_info->ppi_rows; + else + path->rows = baserel->rows; + + /* Count how many tuples we expect to retrieve */ + ntuples = 0; + foreach(l, tidquals) + { + RestrictInfo *rinfo = lfirst_node(RestrictInfo, l); + Expr *qual = rinfo->clause; + + if (IsA(qual, ScalarArrayOpExpr)) + { + /* Each element of the array yields 1 tuple */ + ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) qual; + Node *arraynode = (Node *) lsecond(saop->args); + + ntuples += estimate_array_length(arraynode); + } + else if (IsA(qual, CurrentOfExpr)) + { + /* CURRENT OF yields 1 tuple */ + isCurrentOf = true; + ntuples++; + } + else + { + /* It's just CTID = something, count 1 tuple */ + ntuples++; + } + } + + /* + * We must force TID scan for WHERE CURRENT OF, because only nodeTidscan.c + * understands how to do it correctly. Therefore, honor enable_tidscan + * only when CURRENT OF isn't present. Also note that cost_qual_eval + * counts a CurrentOfExpr as having startup cost disable_cost, which we + * subtract off here; that's to prevent other plan types such as seqscan + * from winning. + */ + if (isCurrentOf) + { + Assert(baserel->baserestrictcost.startup >= disable_cost); + startup_cost -= disable_cost; + } + else if (!enable_tidscan) + startup_cost += disable_cost; + + /* + * The TID qual expressions will be computed once, any other baserestrict + * quals once per retrieved tuple. + */ + cost_qual_eval(&tid_qual_cost, tidquals, root); + + /* fetch estimated page cost for tablespace containing table */ + get_tablespace_page_costs(baserel->reltablespace, + &spc_random_page_cost, + NULL); + + /* disk costs --- assume each tuple on a different page */ + run_cost += spc_random_page_cost * ntuples; + + /* Add scanning CPU costs */ + get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost); + + /* XXX currently we assume TID quals are a subset of qpquals */ + startup_cost += qpqual_cost.startup + tid_qual_cost.per_tuple; + cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple - + tid_qual_cost.per_tuple; + run_cost += cpu_per_tuple * ntuples; + + /* tlist eval costs are paid per output row, not per tuple scanned */ + startup_cost += path->pathtarget->cost.startup; + run_cost += path->pathtarget->cost.per_tuple * path->rows; + + path->startup_cost = startup_cost; + path->total_cost = startup_cost + run_cost; +} + +/* + * cost_tidrangescan + * Determines and sets the costs of scanning a relation using a range of + * TIDs for 'path' + * + * 'baserel' is the relation to be scanned + * 'tidrangequals' is the list of TID-checkable range quals + * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL + */ +void +cost_tidrangescan(Path *path, PlannerInfo *root, + RelOptInfo *baserel, List *tidrangequals, + ParamPathInfo *param_info) +{ + Selectivity selectivity; + double pages; + Cost startup_cost = 0; + Cost run_cost = 0; + QualCost qpqual_cost; + Cost cpu_per_tuple; + QualCost tid_qual_cost; + double ntuples; + double nseqpages; + double spc_random_page_cost; + double spc_seq_page_cost; + + /* Should only be applied to base relations */ + Assert(baserel->relid > 0); + Assert(baserel->rtekind == RTE_RELATION); + + /* Mark the path with the correct row estimate */ + if (param_info) + path->rows = param_info->ppi_rows; + else + path->rows = baserel->rows; + + /* Count how many tuples and pages we expect to scan */ + selectivity = clauselist_selectivity(root, tidrangequals, baserel->relid, + JOIN_INNER, NULL); + pages = ceil(selectivity * baserel->pages); + + if (pages <= 0.0) + pages = 1.0; + + /* + * The first page in a range requires a random seek, but each subsequent + * page is just a normal sequential page read. NOTE: it's desirable for + * TID Range Scans to cost more than the equivalent Sequential Scans, + * because Seq Scans have some performance advantages such as scan + * synchronization and parallelizability, and we'd prefer one of them to + * be picked unless a TID Range Scan really is better. + */ + ntuples = selectivity * baserel->tuples; + nseqpages = pages - 1.0; + + if (!enable_tidscan) + startup_cost += disable_cost; + + /* + * The TID qual expressions will be computed once, any other baserestrict + * quals once per retrieved tuple. + */ + cost_qual_eval(&tid_qual_cost, tidrangequals, root); + + /* fetch estimated page cost for tablespace containing table */ + get_tablespace_page_costs(baserel->reltablespace, + &spc_random_page_cost, + &spc_seq_page_cost); + + /* disk costs; 1 random page and the remainder as seq pages */ + run_cost += spc_random_page_cost + spc_seq_page_cost * nseqpages; + + /* Add scanning CPU costs */ + get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost); + + /* + * XXX currently we assume TID quals are a subset of qpquals at this + * point; they will be removed (if possible) when we create the plan, so + * we subtract their cost from the total qpqual cost. (If the TID quals + * can't be removed, this is a mistake and we're going to underestimate + * the CPU cost a bit.) + */ + startup_cost += qpqual_cost.startup + tid_qual_cost.per_tuple; + cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple - + tid_qual_cost.per_tuple; + run_cost += cpu_per_tuple * ntuples; + + /* tlist eval costs are paid per output row, not per tuple scanned */ + startup_cost += path->pathtarget->cost.startup; + run_cost += path->pathtarget->cost.per_tuple * path->rows; + + path->startup_cost = startup_cost; + path->total_cost = startup_cost + run_cost; +} + +/* + * cost_subqueryscan + * Determines and returns the cost of scanning a subquery RTE. + * + * 'baserel' is the relation to be scanned + * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL + */ +void +cost_subqueryscan(SubqueryScanPath *path, PlannerInfo *root, + RelOptInfo *baserel, ParamPathInfo *param_info) +{ + Cost startup_cost; + Cost run_cost; + QualCost qpqual_cost; + Cost cpu_per_tuple; + + /* Should only be applied to base relations that are subqueries */ + Assert(baserel->relid > 0); + Assert(baserel->rtekind == RTE_SUBQUERY); + + /* Mark the path with the correct row estimate */ + if (param_info) + path->path.rows = param_info->ppi_rows; + else + path->path.rows = baserel->rows; + + /* + * Cost of path is cost of evaluating the subplan, plus cost of evaluating + * any restriction clauses and tlist that will be attached to the + * SubqueryScan node, plus cpu_tuple_cost to account for selection and + * projection overhead. + */ + path->path.startup_cost = path->subpath->startup_cost; + path->path.total_cost = path->subpath->total_cost; + + get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost); + + startup_cost = qpqual_cost.startup; + cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple; + run_cost = cpu_per_tuple * baserel->tuples; + + /* tlist eval costs are paid per output row, not per tuple scanned */ + startup_cost += path->path.pathtarget->cost.startup; + run_cost += path->path.pathtarget->cost.per_tuple * path->path.rows; + + path->path.startup_cost += startup_cost; + path->path.total_cost += startup_cost + run_cost; +} + +/* + * cost_functionscan + * Determines and returns the cost of scanning a function RTE. + * + * 'baserel' is the relation to be scanned + * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL + */ +void +cost_functionscan(Path *path, PlannerInfo *root, + RelOptInfo *baserel, ParamPathInfo *param_info) +{ + Cost startup_cost = 0; + Cost run_cost = 0; + QualCost qpqual_cost; + Cost cpu_per_tuple; + RangeTblEntry *rte; + QualCost exprcost; + + /* Should only be applied to base relations that are functions */ + Assert(baserel->relid > 0); + rte = planner_rt_fetch(baserel->relid, root); + Assert(rte->rtekind == RTE_FUNCTION); + + /* Mark the path with the correct row estimate */ + if (param_info) + path->rows = param_info->ppi_rows; + else + path->rows = baserel->rows; + + /* + * Estimate costs of executing the function expression(s). + * + * Currently, nodeFunctionscan.c always executes the functions to + * completion before returning any rows, and caches the results in a + * tuplestore. So the function eval cost is all startup cost, and per-row + * costs are minimal. + * + * XXX in principle we ought to charge tuplestore spill costs if the + * number of rows is large. However, given how phony our rowcount + * estimates for functions tend to be, there's not a lot of point in that + * refinement right now. + */ + cost_qual_eval_node(&exprcost, (Node *) rte->functions, root); + + startup_cost += exprcost.startup + exprcost.per_tuple; + + /* Add scanning CPU costs */ + get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost); + + startup_cost += qpqual_cost.startup; + cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple; + run_cost += cpu_per_tuple * baserel->tuples; + + /* tlist eval costs are paid per output row, not per tuple scanned */ + startup_cost += path->pathtarget->cost.startup; + run_cost += path->pathtarget->cost.per_tuple * path->rows; + + path->startup_cost = startup_cost; + path->total_cost = startup_cost + run_cost; +} + +/* + * cost_tablefuncscan + * Determines and returns the cost of scanning a table function. + * + * 'baserel' is the relation to be scanned + * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL + */ +void +cost_tablefuncscan(Path *path, PlannerInfo *root, + RelOptInfo *baserel, ParamPathInfo *param_info) +{ + Cost startup_cost = 0; + Cost run_cost = 0; + QualCost qpqual_cost; + Cost cpu_per_tuple; + RangeTblEntry *rte; + QualCost exprcost; + + /* Should only be applied to base relations that are functions */ + Assert(baserel->relid > 0); + rte = planner_rt_fetch(baserel->relid, root); + Assert(rte->rtekind == RTE_TABLEFUNC); + + /* Mark the path with the correct row estimate */ + if (param_info) + path->rows = param_info->ppi_rows; + else + path->rows = baserel->rows; + + /* + * Estimate costs of executing the table func expression(s). + * + * XXX in principle we ought to charge tuplestore spill costs if the + * number of rows is large. However, given how phony our rowcount + * estimates for tablefuncs tend to be, there's not a lot of point in that + * refinement right now. + */ + cost_qual_eval_node(&exprcost, (Node *) rte->tablefunc, root); + + startup_cost += exprcost.startup + exprcost.per_tuple; + + /* Add scanning CPU costs */ + get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost); + + startup_cost += qpqual_cost.startup; + cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple; + run_cost += cpu_per_tuple * baserel->tuples; + + /* tlist eval costs are paid per output row, not per tuple scanned */ + startup_cost += path->pathtarget->cost.startup; + run_cost += path->pathtarget->cost.per_tuple * path->rows; + + path->startup_cost = startup_cost; + path->total_cost = startup_cost + run_cost; +} + +/* + * cost_valuesscan + * Determines and returns the cost of scanning a VALUES RTE. + * + * 'baserel' is the relation to be scanned + * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL + */ +void +cost_valuesscan(Path *path, PlannerInfo *root, + RelOptInfo *baserel, ParamPathInfo *param_info) +{ + Cost startup_cost = 0; + Cost run_cost = 0; + QualCost qpqual_cost; + Cost cpu_per_tuple; + + /* Should only be applied to base relations that are values lists */ + Assert(baserel->relid > 0); + Assert(baserel->rtekind == RTE_VALUES); + + /* Mark the path with the correct row estimate */ + if (param_info) + path->rows = param_info->ppi_rows; + else + path->rows = baserel->rows; + + /* + * For now, estimate list evaluation cost at one operator eval per list + * (probably pretty bogus, but is it worth being smarter?) + */ + cpu_per_tuple = cpu_operator_cost; + + /* Add scanning CPU costs */ + get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost); + + startup_cost += qpqual_cost.startup; + cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple; + run_cost += cpu_per_tuple * baserel->tuples; + + /* tlist eval costs are paid per output row, not per tuple scanned */ + startup_cost += path->pathtarget->cost.startup; + run_cost += path->pathtarget->cost.per_tuple * path->rows; + + path->startup_cost = startup_cost; + path->total_cost = startup_cost + run_cost; +} + +/* + * cost_ctescan + * Determines and returns the cost of scanning a CTE RTE. + * + * Note: this is used for both self-reference and regular CTEs; the + * possible cost differences are below the threshold of what we could + * estimate accurately anyway. Note that the costs of evaluating the + * referenced CTE query are added into the final plan as initplan costs, + * and should NOT be counted here. + */ +void +cost_ctescan(Path *path, PlannerInfo *root, + RelOptInfo *baserel, ParamPathInfo *param_info) +{ + Cost startup_cost = 0; + Cost run_cost = 0; + QualCost qpqual_cost; + Cost cpu_per_tuple; + + /* Should only be applied to base relations that are CTEs */ + Assert(baserel->relid > 0); + Assert(baserel->rtekind == RTE_CTE); + + /* Mark the path with the correct row estimate */ + if (param_info) + path->rows = param_info->ppi_rows; + else + path->rows = baserel->rows; + + /* Charge one CPU tuple cost per row for tuplestore manipulation */ + cpu_per_tuple = cpu_tuple_cost; + + /* Add scanning CPU costs */ + get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost); + + startup_cost += qpqual_cost.startup; + cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple; + run_cost += cpu_per_tuple * baserel->tuples; + + /* tlist eval costs are paid per output row, not per tuple scanned */ + startup_cost += path->pathtarget->cost.startup; + run_cost += path->pathtarget->cost.per_tuple * path->rows; + + path->startup_cost = startup_cost; + path->total_cost = startup_cost + run_cost; +} + +/* + * cost_namedtuplestorescan + * Determines and returns the cost of scanning a named tuplestore. + */ +void +cost_namedtuplestorescan(Path *path, PlannerInfo *root, + RelOptInfo *baserel, ParamPathInfo *param_info) +{ + Cost startup_cost = 0; + Cost run_cost = 0; + QualCost qpqual_cost; + Cost cpu_per_tuple; + + /* Should only be applied to base relations that are Tuplestores */ + Assert(baserel->relid > 0); + Assert(baserel->rtekind == RTE_NAMEDTUPLESTORE); + + /* Mark the path with the correct row estimate */ + if (param_info) + path->rows = param_info->ppi_rows; + else + path->rows = baserel->rows; + + /* Charge one CPU tuple cost per row for tuplestore manipulation */ + cpu_per_tuple = cpu_tuple_cost; + + /* Add scanning CPU costs */ + get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost); + + startup_cost += qpqual_cost.startup; + cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple; + run_cost += cpu_per_tuple * baserel->tuples; + + path->startup_cost = startup_cost; + path->total_cost = startup_cost + run_cost; +} + +/* + * cost_resultscan + * Determines and returns the cost of scanning an RTE_RESULT relation. + */ +void +cost_resultscan(Path *path, PlannerInfo *root, + RelOptInfo *baserel, ParamPathInfo *param_info) +{ + Cost startup_cost = 0; + Cost run_cost = 0; + QualCost qpqual_cost; + Cost cpu_per_tuple; + + /* Should only be applied to RTE_RESULT base relations */ + Assert(baserel->relid > 0); + Assert(baserel->rtekind == RTE_RESULT); + + /* Mark the path with the correct row estimate */ + if (param_info) + path->rows = param_info->ppi_rows; + else + path->rows = baserel->rows; + + /* We charge qual cost plus cpu_tuple_cost */ + get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost); + + startup_cost += qpqual_cost.startup; + cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple; + run_cost += cpu_per_tuple * baserel->tuples; + + path->startup_cost = startup_cost; + path->total_cost = startup_cost + run_cost; +} + +/* + * cost_recursive_union + * Determines and returns the cost of performing a recursive union, + * and also the estimated output size. + * + * We are given Paths for the nonrecursive and recursive terms. + */ +void +cost_recursive_union(Path *runion, Path *nrterm, Path *rterm) +{ + Cost startup_cost; + Cost total_cost; + double total_rows; + + /* We probably have decent estimates for the non-recursive term */ + startup_cost = nrterm->startup_cost; + total_cost = nrterm->total_cost; + total_rows = nrterm->rows; + + /* + * We arbitrarily assume that about 10 recursive iterations will be + * needed, and that we've managed to get a good fix on the cost and output + * size of each one of them. These are mighty shaky assumptions but it's + * hard to see how to do better. + */ + total_cost += 10 * rterm->total_cost; + total_rows += 10 * rterm->rows; + + /* + * Also charge cpu_tuple_cost per row to account for the costs of + * manipulating the tuplestores. (We don't worry about possible + * spill-to-disk costs.) + */ + total_cost += cpu_tuple_cost * total_rows; + + runion->startup_cost = startup_cost; + runion->total_cost = total_cost; + runion->rows = total_rows; + runion->pathtarget->width = Max(nrterm->pathtarget->width, + rterm->pathtarget->width); +} + +/* + * cost_tuplesort + * Determines and returns the cost of sorting a relation using tuplesort, + * not including the cost of reading the input data. + * + * If the total volume of data to sort is less than sort_mem, we will do + * an in-memory sort, which requires no I/O and about t*log2(t) tuple + * comparisons for t tuples. + * + * If the total volume exceeds sort_mem, we switch to a tape-style merge + * algorithm. There will still be about t*log2(t) tuple comparisons in + * total, but we will also need to write and read each tuple once per + * merge pass. We expect about ceil(logM(r)) merge passes where r is the + * number of initial runs formed and M is the merge order used by tuplesort.c. + * Since the average initial run should be about sort_mem, we have + * disk traffic = 2 * relsize * ceil(logM(p / sort_mem)) + * cpu = comparison_cost * t * log2(t) + * + * If the sort is bounded (i.e., only the first k result tuples are needed) + * and k tuples can fit into sort_mem, we use a heap method that keeps only + * k tuples in the heap; this will require about t*log2(k) tuple comparisons. + * + * The disk traffic is assumed to be 3/4ths sequential and 1/4th random + * accesses (XXX can't we refine that guess?) + * + * By default, we charge two operator evals per tuple comparison, which should + * be in the right ballpark in most cases. The caller can tweak this by + * specifying nonzero comparison_cost; typically that's used for any extra + * work that has to be done to prepare the inputs to the comparison operators. + * + * 'tuples' is the number of tuples in the relation + * 'width' is the average tuple width in bytes + * 'comparison_cost' is the extra cost per comparison, if any + * 'sort_mem' is the number of kilobytes of work memory allowed for the sort + * 'limit_tuples' is the bound on the number of output tuples; -1 if no bound + */ +static void +cost_tuplesort(Cost *startup_cost, Cost *run_cost, + double tuples, int width, + Cost comparison_cost, int sort_mem, + double limit_tuples) +{ + double input_bytes = relation_byte_size(tuples, width); + double output_bytes; + double output_tuples; + long sort_mem_bytes = sort_mem * 1024L; + + /* + * We want to be sure the cost of a sort is never estimated as zero, even + * if passed-in tuple count is zero. Besides, mustn't do log(0)... + */ + if (tuples < 2.0) + tuples = 2.0; + + /* Include the default cost-per-comparison */ + comparison_cost += 2.0 * cpu_operator_cost; + + /* Do we have a useful LIMIT? */ + if (limit_tuples > 0 && limit_tuples < tuples) + { + output_tuples = limit_tuples; + output_bytes = relation_byte_size(output_tuples, width); + } + else + { + output_tuples = tuples; + output_bytes = input_bytes; + } + + if (output_bytes > sort_mem_bytes) + { + /* + * We'll have to use a disk-based sort of all the tuples + */ + double npages = ceil(input_bytes / BLCKSZ); + double nruns = input_bytes / sort_mem_bytes; + double mergeorder = tuplesort_merge_order(sort_mem_bytes); + double log_runs; + double npageaccesses; + + /* + * CPU costs + * + * Assume about N log2 N comparisons + */ + *startup_cost = comparison_cost * tuples * LOG2(tuples); + + /* Disk costs */ + + /* Compute logM(r) as log(r) / log(M) */ + if (nruns > mergeorder) + log_runs = ceil(log(nruns) / log(mergeorder)); + else + log_runs = 1.0; + npageaccesses = 2.0 * npages * log_runs; + /* Assume 3/4ths of accesses are sequential, 1/4th are not */ + *startup_cost += npageaccesses * + (seq_page_cost * 0.75 + random_page_cost * 0.25); + } + else if (tuples > 2 * output_tuples || input_bytes > sort_mem_bytes) + { + /* + * We'll use a bounded heap-sort keeping just K tuples in memory, for + * a total number of tuple comparisons of N log2 K; but the constant + * factor is a bit higher than for quicksort. Tweak it so that the + * cost curve is continuous at the crossover point. + */ + *startup_cost = comparison_cost * tuples * LOG2(2.0 * output_tuples); + } + else + { + /* We'll use plain quicksort on all the input tuples */ + *startup_cost = comparison_cost * tuples * LOG2(tuples); + } + + /* + * Also charge a small amount (arbitrarily set equal to operator cost) per + * extracted tuple. We don't charge cpu_tuple_cost because a Sort node + * doesn't do qual-checking or projection, so it has less overhead than + * most plan nodes. Note it's correct to use tuples not output_tuples + * here --- the upper LIMIT will pro-rate the run cost so we'd be double + * counting the LIMIT otherwise. + */ + *run_cost = cpu_operator_cost * tuples; +} + +/* + * cost_incremental_sort + * Determines and returns the cost of sorting a relation incrementally, when + * the input path is presorted by a prefix of the pathkeys. + * + * 'presorted_keys' is the number of leading pathkeys by which the input path + * is sorted. + * + * We estimate the number of groups into which the relation is divided by the + * leading pathkeys, and then calculate the cost of sorting a single group + * with tuplesort using cost_tuplesort(). + */ +void +cost_incremental_sort(Path *path, + PlannerInfo *root, List *pathkeys, int presorted_keys, + Cost input_startup_cost, Cost input_total_cost, + double input_tuples, int width, Cost comparison_cost, int sort_mem, + double limit_tuples) +{ + Cost startup_cost = 0, + run_cost = 0, + input_run_cost = input_total_cost - input_startup_cost; + double group_tuples, + input_groups; + Cost group_startup_cost, + group_run_cost, + group_input_run_cost; + List *presortedExprs = NIL; + ListCell *l; + int i = 0; + bool unknown_varno = false; + + Assert(presorted_keys != 0); + + /* + * We want to be sure the cost of a sort is never estimated as zero, even + * if passed-in tuple count is zero. Besides, mustn't do log(0)... + */ + if (input_tuples < 2.0) + input_tuples = 2.0; + + /* Default estimate of number of groups, capped to one group per row. */ + input_groups = Min(input_tuples, DEFAULT_NUM_DISTINCT); + + /* + * Extract presorted keys as list of expressions. + * + * We need to be careful about Vars containing "varno 0" which might have + * been introduced by generate_append_tlist, which would confuse + * estimate_num_groups (in fact it'd fail for such expressions). See + * recurse_set_operations which has to deal with the same issue. + * + * Unlike recurse_set_operations we can't access the original target list + * here, and even if we could it's not very clear how useful would that be + * for a set operation combining multiple tables. So we simply detect if + * there are any expressions with "varno 0" and use the default + * DEFAULT_NUM_DISTINCT in that case. + * + * We might also use either 1.0 (a single group) or input_tuples (each row + * being a separate group), pretty much the worst and best case for + * incremental sort. But those are extreme cases and using something in + * between seems reasonable. Furthermore, generate_append_tlist is used + * for set operations, which are likely to produce mostly unique output + * anyway - from that standpoint the DEFAULT_NUM_DISTINCT is defensive + * while maintaining lower startup cost. + */ + foreach(l, pathkeys) + { + PathKey *key = (PathKey *) lfirst(l); + EquivalenceMember *member = (EquivalenceMember *) + linitial(key->pk_eclass->ec_members); + + /* + * Check if the expression contains Var with "varno 0" so that we + * don't call estimate_num_groups in that case. + */ + if (bms_is_member(0, pull_varnos(root, (Node *) member->em_expr))) + { + unknown_varno = true; + break; + } + + /* expression not containing any Vars with "varno 0" */ + presortedExprs = lappend(presortedExprs, member->em_expr); + + i++; + if (i >= presorted_keys) + break; + } + + /* Estimate number of groups with equal presorted keys. */ + if (!unknown_varno) + input_groups = estimate_num_groups(root, presortedExprs, input_tuples, + NULL, NULL); + + group_tuples = input_tuples / input_groups; + group_input_run_cost = input_run_cost / input_groups; + + /* + * Estimate average cost of sorting of one group where presorted keys are + * equal. Incremental sort is sensitive to distribution of tuples to the + * groups, where we're relying on quite rough assumptions. Thus, we're + * pessimistic about incremental sort performance and increase its average + * group size by half. + */ + cost_tuplesort(&group_startup_cost, &group_run_cost, + 1.5 * group_tuples, width, comparison_cost, sort_mem, + limit_tuples); + + /* + * Startup cost of incremental sort is the startup cost of its first group + * plus the cost of its input. + */ + startup_cost += group_startup_cost + + input_startup_cost + group_input_run_cost; + + /* + * After we started producing tuples from the first group, the cost of + * producing all the tuples is given by the cost to finish processing this + * group, plus the total cost to process the remaining groups, plus the + * remaining cost of input. + */ + run_cost += group_run_cost + + (group_run_cost + group_startup_cost) * (input_groups - 1) + + group_input_run_cost * (input_groups - 1); + + /* + * Incremental sort adds some overhead by itself. Firstly, it has to + * detect the sort groups. This is roughly equal to one extra copy and + * comparison per tuple. Secondly, it has to reset the tuplesort context + * for every group. + */ + run_cost += (cpu_tuple_cost + comparison_cost) * input_tuples; + run_cost += 2.0 * cpu_tuple_cost * input_groups; + + path->rows = input_tuples; + path->startup_cost = startup_cost; + path->total_cost = startup_cost + run_cost; +} + +/* + * cost_sort + * Determines and returns the cost of sorting a relation, including + * the cost of reading the input data. + * + * NOTE: some callers currently pass NIL for pathkeys because they + * can't conveniently supply the sort keys. Since this routine doesn't + * currently do anything with pathkeys anyway, that doesn't matter... + * but if it ever does, it should react gracefully to lack of key data. + * (Actually, the thing we'd most likely be interested in is just the number + * of sort keys, which all callers *could* supply.) + */ +void +cost_sort(Path *path, PlannerInfo *root, + List *pathkeys, Cost input_cost, double tuples, int width, + Cost comparison_cost, int sort_mem, + double limit_tuples) + +{ + Cost startup_cost; + Cost run_cost; + + cost_tuplesort(&startup_cost, &run_cost, + tuples, width, + comparison_cost, sort_mem, + limit_tuples); + + if (!enable_sort) + startup_cost += disable_cost; + + startup_cost += input_cost; + + path->rows = tuples; + path->startup_cost = startup_cost; + path->total_cost = startup_cost + run_cost; +} + +/* + * append_nonpartial_cost + * Estimate the cost of the non-partial paths in a Parallel Append. + * The non-partial paths are assumed to be the first "numpaths" paths + * from the subpaths list, and to be in order of decreasing cost. + */ +static Cost +append_nonpartial_cost(List *subpaths, int numpaths, int parallel_workers) +{ + Cost *costarr; + int arrlen; + ListCell *l; + ListCell *cell; + int i; + int path_index; + int min_index; + int max_index; + + if (numpaths == 0) + return 0; + + /* + * Array length is number of workers or number of relevant paths, + * whichever is less. + */ + arrlen = Min(parallel_workers, numpaths); + costarr = (Cost *) palloc(sizeof(Cost) * arrlen); + + /* The first few paths will each be claimed by a different worker. */ + path_index = 0; + foreach(cell, subpaths) + { + Path *subpath = (Path *) lfirst(cell); + + if (path_index == arrlen) + break; + costarr[path_index++] = subpath->total_cost; + } + + /* + * Since subpaths are sorted by decreasing cost, the last one will have + * the minimum cost. + */ + min_index = arrlen - 1; + + /* + * For each of the remaining subpaths, add its cost to the array element + * with minimum cost. + */ + for_each_cell(l, subpaths, cell) + { + Path *subpath = (Path *) lfirst(l); + int i; + + /* Consider only the non-partial paths */ + if (path_index++ == numpaths) + break; + + costarr[min_index] += subpath->total_cost; + + /* Update the new min cost array index */ + for (min_index = i = 0; i < arrlen; i++) + { + if (costarr[i] < costarr[min_index]) + min_index = i; + } + } + + /* Return the highest cost from the array */ + for (max_index = i = 0; i < arrlen; i++) + { + if (costarr[i] > costarr[max_index]) + max_index = i; + } + + return costarr[max_index]; +} + +/* + * cost_append + * Determines and returns the cost of an Append node. + */ +void +cost_append(AppendPath *apath) +{ + ListCell *l; + + apath->path.startup_cost = 0; + apath->path.total_cost = 0; + apath->path.rows = 0; + + if (apath->subpaths == NIL) + return; + + if (!apath->path.parallel_aware) + { + List *pathkeys = apath->path.pathkeys; + + if (pathkeys == NIL) + { + Path *subpath = (Path *) linitial(apath->subpaths); + + /* + * For an unordered, non-parallel-aware Append we take the startup + * cost as the startup cost of the first subpath. + */ + apath->path.startup_cost = subpath->startup_cost; + + /* Compute rows and costs as sums of subplan rows and costs. */ + foreach(l, apath->subpaths) + { + Path *subpath = (Path *) lfirst(l); + + apath->path.rows += subpath->rows; + apath->path.total_cost += subpath->total_cost; + } + } + else + { + /* + * For an ordered, non-parallel-aware Append we take the startup + * cost as the sum of the subpath startup costs. This ensures + * that we don't underestimate the startup cost when a query's + * LIMIT is such that several of the children have to be run to + * satisfy it. This might be overkill --- another plausible hack + * would be to take the Append's startup cost as the maximum of + * the child startup costs. But we don't want to risk believing + * that an ORDER BY LIMIT query can be satisfied at small cost + * when the first child has small startup cost but later ones + * don't. (If we had the ability to deal with nonlinear cost + * interpolation for partial retrievals, we would not need to be + * so conservative about this.) + * + * This case is also different from the above in that we have to + * account for possibly injecting sorts into subpaths that aren't + * natively ordered. + */ + foreach(l, apath->subpaths) + { + Path *subpath = (Path *) lfirst(l); + Path sort_path; /* dummy for result of cost_sort */ + + if (!pathkeys_contained_in(pathkeys, subpath->pathkeys)) + { + /* + * We'll need to insert a Sort node, so include costs for + * that. We can use the parent's LIMIT if any, since we + * certainly won't pull more than that many tuples from + * any child. + */ + cost_sort(&sort_path, + NULL, /* doesn't currently need root */ + pathkeys, + subpath->total_cost, + subpath->rows, + subpath->pathtarget->width, + 0.0, + work_mem, + apath->limit_tuples); + subpath = &sort_path; + } + + apath->path.rows += subpath->rows; + apath->path.startup_cost += subpath->startup_cost; + apath->path.total_cost += subpath->total_cost; + } + } + } + else /* parallel-aware */ + { + int i = 0; + double parallel_divisor = get_parallel_divisor(&apath->path); + + /* Parallel-aware Append never produces ordered output. */ + Assert(apath->path.pathkeys == NIL); + + /* Calculate startup cost. */ + foreach(l, apath->subpaths) + { + Path *subpath = (Path *) lfirst(l); + + /* + * Append will start returning tuples when the child node having + * lowest startup cost is done setting up. We consider only the + * first few subplans that immediately get a worker assigned. + */ + if (i == 0) + apath->path.startup_cost = subpath->startup_cost; + else if (i < apath->path.parallel_workers) + apath->path.startup_cost = Min(apath->path.startup_cost, + subpath->startup_cost); + + /* + * Apply parallel divisor to subpaths. Scale the number of rows + * for each partial subpath based on the ratio of the parallel + * divisor originally used for the subpath to the one we adopted. + * Also add the cost of partial paths to the total cost, but + * ignore non-partial paths for now. + */ + if (i < apath->first_partial_path) + apath->path.rows += subpath->rows / parallel_divisor; + else + { + double subpath_parallel_divisor; + + subpath_parallel_divisor = get_parallel_divisor(subpath); + apath->path.rows += subpath->rows * (subpath_parallel_divisor / + parallel_divisor); + apath->path.total_cost += subpath->total_cost; + } + + apath->path.rows = clamp_row_est(apath->path.rows); + + i++; + } + + /* Add cost for non-partial subpaths. */ + apath->path.total_cost += + append_nonpartial_cost(apath->subpaths, + apath->first_partial_path, + apath->path.parallel_workers); + } + + /* + * Although Append does not do any selection or projection, it's not free; + * add a small per-tuple overhead. + */ + apath->path.total_cost += + cpu_tuple_cost * APPEND_CPU_COST_MULTIPLIER * apath->path.rows; +} + +/* + * cost_merge_append + * Determines and returns the cost of a MergeAppend node. + * + * MergeAppend merges several pre-sorted input streams, using a heap that + * at any given instant holds the next tuple from each stream. If there + * are N streams, we need about N*log2(N) tuple comparisons to construct + * the heap at startup, and then for each output tuple, about log2(N) + * comparisons to replace the top entry. + * + * (The effective value of N will drop once some of the input streams are + * exhausted, but it seems unlikely to be worth trying to account for that.) + * + * The heap is never spilled to disk, since we assume N is not very large. + * So this is much simpler than cost_sort. + * + * As in cost_sort, we charge two operator evals per tuple comparison. + * + * 'pathkeys' is a list of sort keys + * 'n_streams' is the number of input streams + * 'input_startup_cost' is the sum of the input streams' startup costs + * 'input_total_cost' is the sum of the input streams' total costs + * 'tuples' is the number of tuples in all the streams + */ +void +cost_merge_append(Path *path, PlannerInfo *root, + List *pathkeys, int n_streams, + Cost input_startup_cost, Cost input_total_cost, + double tuples) +{ + Cost startup_cost = 0; + Cost run_cost = 0; + Cost comparison_cost; + double N; + double logN; + + /* + * Avoid log(0)... + */ + N = (n_streams < 2) ? 2.0 : (double) n_streams; + logN = LOG2(N); + + /* Assumed cost per tuple comparison */ + comparison_cost = 2.0 * cpu_operator_cost; + + /* Heap creation cost */ + startup_cost += comparison_cost * N * logN; + + /* Per-tuple heap maintenance cost */ + run_cost += tuples * comparison_cost * logN; + + /* + * Although MergeAppend does not do any selection or projection, it's not + * free; add a small per-tuple overhead. + */ + run_cost += cpu_tuple_cost * APPEND_CPU_COST_MULTIPLIER * tuples; + + path->startup_cost = startup_cost + input_startup_cost; + path->total_cost = startup_cost + run_cost + input_total_cost; +} + +/* + * cost_material + * Determines and returns the cost of materializing a relation, including + * the cost of reading the input data. + * + * If the total volume of data to materialize exceeds work_mem, we will need + * to write it to disk, so the cost is much higher in that case. + * + * Note that here we are estimating the costs for the first scan of the + * relation, so the materialization is all overhead --- any savings will + * occur only on rescan, which is estimated in cost_rescan. + */ +void +cost_material(Path *path, + Cost input_startup_cost, Cost input_total_cost, + double tuples, int width) +{ + Cost startup_cost = input_startup_cost; + Cost run_cost = input_total_cost - input_startup_cost; + double nbytes = relation_byte_size(tuples, width); + long work_mem_bytes = work_mem * 1024L; + + path->rows = tuples; + + /* + * Whether spilling or not, charge 2x cpu_operator_cost per tuple to + * reflect bookkeeping overhead. (This rate must be more than what + * cost_rescan charges for materialize, ie, cpu_operator_cost per tuple; + * if it is exactly the same then there will be a cost tie between + * nestloop with A outer, materialized B inner and nestloop with B outer, + * materialized A inner. The extra cost ensures we'll prefer + * materializing the smaller rel.) Note that this is normally a good deal + * less than cpu_tuple_cost; which is OK because a Material plan node + * doesn't do qual-checking or projection, so it's got less overhead than + * most plan nodes. + */ + run_cost += 2 * cpu_operator_cost * tuples; + + /* + * If we will spill to disk, charge at the rate of seq_page_cost per page. + * This cost is assumed to be evenly spread through the plan run phase, + * which isn't exactly accurate but our cost model doesn't allow for + * nonuniform costs within the run phase. + */ + if (nbytes > work_mem_bytes) + { + double npages = ceil(nbytes / BLCKSZ); + + run_cost += seq_page_cost * npages; + } + + path->startup_cost = startup_cost; + path->total_cost = startup_cost + run_cost; +} + +/* + * cost_memoize_rescan + * Determines the estimated cost of rescanning a Memoize node. + * + * In order to estimate this, we must gain knowledge of how often we expect to + * be called and how many distinct sets of parameters we are likely to be + * called with. If we expect a good cache hit ratio, then we can set our + * costs to account for that hit ratio, plus a little bit of cost for the + * caching itself. Caching will not work out well if we expect to be called + * with too many distinct parameter values. The worst-case here is that we + * never see any parameter value twice, in which case we'd never get a cache + * hit and caching would be a complete waste of effort. + */ +static void +cost_memoize_rescan(PlannerInfo *root, MemoizePath *mpath, + Cost *rescan_startup_cost, Cost *rescan_total_cost) +{ + EstimationInfo estinfo; + Cost input_startup_cost = mpath->subpath->startup_cost; + Cost input_total_cost = mpath->subpath->total_cost; + double tuples = mpath->subpath->rows; + double calls = mpath->calls; + int width = mpath->subpath->pathtarget->width; + + double hash_mem_bytes; + double est_entry_bytes; + double est_cache_entries; + double ndistinct; + double evict_ratio; + double hit_ratio; + Cost startup_cost; + Cost total_cost; + + /* available cache space */ + hash_mem_bytes = get_hash_memory_limit(); + + /* + * Set the number of bytes each cache entry should consume in the cache. + * To provide us with better estimations on how many cache entries we can + * store at once, we make a call to the executor here to ask it what + * memory overheads there are for a single cache entry. + * + * XXX we also store the cache key, but that's not accounted for here. + */ + est_entry_bytes = relation_byte_size(tuples, width) + + ExecEstimateCacheEntryOverheadBytes(tuples); + + /* estimate on the upper limit of cache entries we can hold at once */ + est_cache_entries = floor(hash_mem_bytes / est_entry_bytes); + + /* estimate on the distinct number of parameter values */ + ndistinct = estimate_num_groups(root, mpath->param_exprs, calls, NULL, + &estinfo); + + /* + * When the estimation fell back on using a default value, it's a bit too + * risky to assume that it's ok to use a Memoize node. The use of a + * default could cause us to use a Memoize node when it's really + * inappropriate to do so. If we see that this has been done, then we'll + * assume that every call will have unique parameters, which will almost + * certainly mean a MemoizePath will never survive add_path(). + */ + if ((estinfo.flags & SELFLAG_USED_DEFAULT) != 0) + ndistinct = calls; + + /* + * Since we've already estimated the maximum number of entries we can + * store at once and know the estimated number of distinct values we'll be + * called with, we'll take this opportunity to set the path's est_entries. + * This will ultimately determine the hash table size that the executor + * will use. If we leave this at zero, the executor will just choose the + * size itself. Really this is not the right place to do this, but it's + * convenient since everything is already calculated. + */ + mpath->est_entries = Min(Min(ndistinct, est_cache_entries), + PG_UINT32_MAX); + + /* + * When the number of distinct parameter values is above the amount we can + * store in the cache, then we'll have to evict some entries from the + * cache. This is not free. Here we estimate how often we'll incur the + * cost of that eviction. + */ + evict_ratio = 1.0 - Min(est_cache_entries, ndistinct) / ndistinct; + + /* + * In order to estimate how costly a single scan will be, we need to + * attempt to estimate what the cache hit ratio will be. To do that we + * must look at how many scans are estimated in total for this node and + * how many of those scans we expect to get a cache hit. + */ + hit_ratio = 1.0 / ndistinct * Min(est_cache_entries, ndistinct) - + (ndistinct / calls); + + /* Ensure we don't go negative */ + hit_ratio = Max(hit_ratio, 0.0); + + /* + * Set the total_cost accounting for the expected cache hit ratio. We + * also add on a cpu_operator_cost to account for a cache lookup. This + * will happen regardless of whether it's a cache hit or not. + */ + total_cost = input_total_cost * (1.0 - hit_ratio) + cpu_operator_cost; + + /* Now adjust the total cost to account for cache evictions */ + + /* Charge a cpu_tuple_cost for evicting the actual cache entry */ + total_cost += cpu_tuple_cost * evict_ratio; + + /* + * Charge a 10th of cpu_operator_cost to evict every tuple in that entry. + * The per-tuple eviction is really just a pfree, so charging a whole + * cpu_operator_cost seems a little excessive. + */ + total_cost += cpu_operator_cost / 10.0 * evict_ratio * tuples; + + /* + * Now adjust for storing things in the cache, since that's not free + * either. Everything must go in the cache. We don't proportion this + * over any ratio, just apply it once for the scan. We charge a + * cpu_tuple_cost for the creation of the cache entry and also a + * cpu_operator_cost for each tuple we expect to cache. + */ + total_cost += cpu_tuple_cost + cpu_operator_cost * tuples; + + /* + * Getting the first row must be also be proportioned according to the + * expected cache hit ratio. + */ + startup_cost = input_startup_cost * (1.0 - hit_ratio); + + /* + * Additionally we charge a cpu_tuple_cost to account for cache lookups, + * which we'll do regardless of whether it was a cache hit or not. + */ + startup_cost += cpu_tuple_cost; + + *rescan_startup_cost = startup_cost; + *rescan_total_cost = total_cost; +} + +/* + * cost_agg + * Determines and returns the cost of performing an Agg plan node, + * including the cost of its input. + * + * aggcosts can be NULL when there are no actual aggregate functions (i.e., + * we are using a hashed Agg node just to do grouping). + * + * Note: when aggstrategy == AGG_SORTED, caller must ensure that input costs + * are for appropriately-sorted input. + */ +void +cost_agg(Path *path, PlannerInfo *root, + AggStrategy aggstrategy, const AggClauseCosts *aggcosts, + int numGroupCols, double numGroups, + List *quals, + Cost input_startup_cost, Cost input_total_cost, + double input_tuples, double input_width) +{ + double output_tuples; + Cost startup_cost; + Cost total_cost; + AggClauseCosts dummy_aggcosts; + + /* Use all-zero per-aggregate costs if NULL is passed */ + if (aggcosts == NULL) + { + Assert(aggstrategy == AGG_HASHED); + MemSet(&dummy_aggcosts, 0, sizeof(AggClauseCosts)); + aggcosts = &dummy_aggcosts; + } + + /* + * The transCost.per_tuple component of aggcosts should be charged once + * per input tuple, corresponding to the costs of evaluating the aggregate + * transfns and their input expressions. The finalCost.per_tuple component + * is charged once per output tuple, corresponding to the costs of + * evaluating the finalfns. Startup costs are of course charged but once. + * + * If we are grouping, we charge an additional cpu_operator_cost per + * grouping column per input tuple for grouping comparisons. + * + * We will produce a single output tuple if not grouping, and a tuple per + * group otherwise. We charge cpu_tuple_cost for each output tuple. + * + * Note: in this cost model, AGG_SORTED and AGG_HASHED have exactly the + * same total CPU cost, but AGG_SORTED has lower startup cost. If the + * input path is already sorted appropriately, AGG_SORTED should be + * preferred (since it has no risk of memory overflow). This will happen + * as long as the computed total costs are indeed exactly equal --- but if + * there's roundoff error we might do the wrong thing. So be sure that + * the computations below form the same intermediate values in the same + * order. + */ + if (aggstrategy == AGG_PLAIN) + { + startup_cost = input_total_cost; + startup_cost += aggcosts->transCost.startup; + startup_cost += aggcosts->transCost.per_tuple * input_tuples; + startup_cost += aggcosts->finalCost.startup; + startup_cost += aggcosts->finalCost.per_tuple; + /* we aren't grouping */ + total_cost = startup_cost + cpu_tuple_cost; + output_tuples = 1; + } + else if (aggstrategy == AGG_SORTED || aggstrategy == AGG_MIXED) + { + /* Here we are able to deliver output on-the-fly */ + startup_cost = input_startup_cost; + total_cost = input_total_cost; + if (aggstrategy == AGG_MIXED && !enable_hashagg) + { + startup_cost += disable_cost; + total_cost += disable_cost; + } + /* calcs phrased this way to match HASHED case, see note above */ + total_cost += aggcosts->transCost.startup; + total_cost += aggcosts->transCost.per_tuple * input_tuples; + total_cost += (cpu_operator_cost * numGroupCols) * input_tuples; + total_cost += aggcosts->finalCost.startup; + total_cost += aggcosts->finalCost.per_tuple * numGroups; + total_cost += cpu_tuple_cost * numGroups; + output_tuples = numGroups; + } + else + { + /* must be AGG_HASHED */ + startup_cost = input_total_cost; + if (!enable_hashagg) + startup_cost += disable_cost; + startup_cost += aggcosts->transCost.startup; + startup_cost += aggcosts->transCost.per_tuple * input_tuples; + /* cost of computing hash value */ + startup_cost += (cpu_operator_cost * numGroupCols) * input_tuples; + startup_cost += aggcosts->finalCost.startup; + + total_cost = startup_cost; + total_cost += aggcosts->finalCost.per_tuple * numGroups; + /* cost of retrieving from hash table */ + total_cost += cpu_tuple_cost * numGroups; + output_tuples = numGroups; + } + + /* + * Add the disk costs of hash aggregation that spills to disk. + * + * Groups that go into the hash table stay in memory until finalized, so + * spilling and reprocessing tuples doesn't incur additional invocations + * of transCost or finalCost. Furthermore, the computed hash value is + * stored with the spilled tuples, so we don't incur extra invocations of + * the hash function. + * + * Hash Agg begins returning tuples after the first batch is complete. + * Accrue writes (spilled tuples) to startup_cost and to total_cost; + * accrue reads only to total_cost. + */ + if (aggstrategy == AGG_HASHED || aggstrategy == AGG_MIXED) + { + double pages; + double pages_written = 0.0; + double pages_read = 0.0; + double spill_cost; + double hashentrysize; + double nbatches; + Size mem_limit; + uint64 ngroups_limit; + int num_partitions; + int depth; + + /* + * Estimate number of batches based on the computed limits. If less + * than or equal to one, all groups are expected to fit in memory; + * otherwise we expect to spill. + */ + hashentrysize = hash_agg_entry_size(list_length(root->aggtransinfos), + input_width, + aggcosts->transitionSpace); + hash_agg_set_limits(hashentrysize, numGroups, 0, &mem_limit, + &ngroups_limit, &num_partitions); + + nbatches = Max((numGroups * hashentrysize) / mem_limit, + numGroups / ngroups_limit); + + nbatches = Max(ceil(nbatches), 1.0); + num_partitions = Max(num_partitions, 2); + + /* + * The number of partitions can change at different levels of + * recursion; but for the purposes of this calculation assume it stays + * constant. + */ + depth = ceil(log(nbatches) / log(num_partitions)); + + /* + * Estimate number of pages read and written. For each level of + * recursion, a tuple must be written and then later read. + */ + pages = relation_byte_size(input_tuples, input_width) / BLCKSZ; + pages_written = pages_read = pages * depth; + + /* + * HashAgg has somewhat worse IO behavior than Sort on typical + * hardware/OS combinations. Account for this with a generic penalty. + */ + pages_read *= 2.0; + pages_written *= 2.0; + + startup_cost += pages_written * random_page_cost; + total_cost += pages_written * random_page_cost; + total_cost += pages_read * seq_page_cost; + + /* account for CPU cost of spilling a tuple and reading it back */ + spill_cost = depth * input_tuples * 2.0 * cpu_tuple_cost; + startup_cost += spill_cost; + total_cost += spill_cost; + } + + /* + * If there are quals (HAVING quals), account for their cost and + * selectivity. + */ + if (quals) + { + QualCost qual_cost; + + cost_qual_eval(&qual_cost, quals, root); + startup_cost += qual_cost.startup; + total_cost += qual_cost.startup + output_tuples * qual_cost.per_tuple; + + output_tuples = clamp_row_est(output_tuples * + clauselist_selectivity(root, + quals, + 0, + JOIN_INNER, + NULL)); + } + + path->rows = output_tuples; + path->startup_cost = startup_cost; + path->total_cost = total_cost; +} + +/* + * cost_windowagg + * Determines and returns the cost of performing a WindowAgg plan node, + * including the cost of its input. + * + * Input is assumed already properly sorted. + */ +void +cost_windowagg(Path *path, PlannerInfo *root, + List *windowFuncs, int numPartCols, int numOrderCols, + Cost input_startup_cost, Cost input_total_cost, + double input_tuples) +{ + Cost startup_cost; + Cost total_cost; + ListCell *lc; + + startup_cost = input_startup_cost; + total_cost = input_total_cost; + + /* + * Window functions are assumed to cost their stated execution cost, plus + * the cost of evaluating their input expressions, per tuple. Since they + * may in fact evaluate their inputs at multiple rows during each cycle, + * this could be a drastic underestimate; but without a way to know how + * many rows the window function will fetch, it's hard to do better. In + * any case, it's a good estimate for all the built-in window functions, + * so we'll just do this for now. + */ + foreach(lc, windowFuncs) + { + WindowFunc *wfunc = lfirst_node(WindowFunc, lc); + Cost wfunccost; + QualCost argcosts; + + argcosts.startup = argcosts.per_tuple = 0; + add_function_cost(root, wfunc->winfnoid, (Node *) wfunc, + &argcosts); + startup_cost += argcosts.startup; + wfunccost = argcosts.per_tuple; + + /* also add the input expressions' cost to per-input-row costs */ + cost_qual_eval_node(&argcosts, (Node *) wfunc->args, root); + startup_cost += argcosts.startup; + wfunccost += argcosts.per_tuple; + + /* + * Add the filter's cost to per-input-row costs. XXX We should reduce + * input expression costs according to filter selectivity. + */ + cost_qual_eval_node(&argcosts, (Node *) wfunc->aggfilter, root); + startup_cost += argcosts.startup; + wfunccost += argcosts.per_tuple; + + total_cost += wfunccost * input_tuples; + } + + /* + * We also charge cpu_operator_cost per grouping column per tuple for + * grouping comparisons, plus cpu_tuple_cost per tuple for general + * overhead. + * + * XXX this neglects costs of spooling the data to disk when it overflows + * work_mem. Sooner or later that should get accounted for. + */ + total_cost += cpu_operator_cost * (numPartCols + numOrderCols) * input_tuples; + total_cost += cpu_tuple_cost * input_tuples; + + path->rows = input_tuples; + path->startup_cost = startup_cost; + path->total_cost = total_cost; +} + +/* + * cost_group + * Determines and returns the cost of performing a Group plan node, + * including the cost of its input. + * + * Note: caller must ensure that input costs are for appropriately-sorted + * input. + */ +void +cost_group(Path *path, PlannerInfo *root, + int numGroupCols, double numGroups, + List *quals, + Cost input_startup_cost, Cost input_total_cost, + double input_tuples) +{ + double output_tuples; + Cost startup_cost; + Cost total_cost; + + output_tuples = numGroups; + startup_cost = input_startup_cost; + total_cost = input_total_cost; + + /* + * Charge one cpu_operator_cost per comparison per input tuple. We assume + * all columns get compared at most of the tuples. + */ + total_cost += cpu_operator_cost * input_tuples * numGroupCols; + + /* + * If there are quals (HAVING quals), account for their cost and + * selectivity. + */ + if (quals) + { + QualCost qual_cost; + + cost_qual_eval(&qual_cost, quals, root); + startup_cost += qual_cost.startup; + total_cost += qual_cost.startup + output_tuples * qual_cost.per_tuple; + + output_tuples = clamp_row_est(output_tuples * + clauselist_selectivity(root, + quals, + 0, + JOIN_INNER, + NULL)); + } + + path->rows = output_tuples; + path->startup_cost = startup_cost; + path->total_cost = total_cost; +} + +/* + * initial_cost_nestloop + * Preliminary estimate of the cost of a nestloop join path. + * + * This must quickly produce lower-bound estimates of the path's startup and + * total costs. If we are unable to eliminate the proposed path from + * consideration using the lower bounds, final_cost_nestloop will be called + * to obtain the final estimates. + * + * The exact division of labor between this function and final_cost_nestloop + * is private to them, and represents a tradeoff between speed of the initial + * estimate and getting a tight lower bound. We choose to not examine the + * join quals here, since that's by far the most expensive part of the + * calculations. The end result is that CPU-cost considerations must be + * left for the second phase; and for SEMI/ANTI joins, we must also postpone + * incorporation of the inner path's run cost. + * + * 'workspace' is to be filled with startup_cost, total_cost, and perhaps + * other data to be used by final_cost_nestloop + * 'jointype' is the type of join to be performed + * 'outer_path' is the outer input to the join + * 'inner_path' is the inner input to the join + * 'extra' contains miscellaneous information about the join + */ +void +initial_cost_nestloop(PlannerInfo *root, JoinCostWorkspace *workspace, + JoinType jointype, + Path *outer_path, Path *inner_path, + JoinPathExtraData *extra) +{ + Cost startup_cost = 0; + Cost run_cost = 0; + double outer_path_rows = outer_path->rows; + Cost inner_rescan_start_cost; + Cost inner_rescan_total_cost; + Cost inner_run_cost; + Cost inner_rescan_run_cost; + + /* estimate costs to rescan the inner relation */ + cost_rescan(root, inner_path, + &inner_rescan_start_cost, + &inner_rescan_total_cost); + + /* cost of source data */ + + /* + * NOTE: clearly, we must pay both outer and inner paths' startup_cost + * before we can start returning tuples, so the join's startup cost is + * their sum. We'll also pay the inner path's rescan startup cost + * multiple times. + */ + startup_cost += outer_path->startup_cost + inner_path->startup_cost; + run_cost += outer_path->total_cost - outer_path->startup_cost; + if (outer_path_rows > 1) + run_cost += (outer_path_rows - 1) * inner_rescan_start_cost; + + inner_run_cost = inner_path->total_cost - inner_path->startup_cost; + inner_rescan_run_cost = inner_rescan_total_cost - inner_rescan_start_cost; + + if (jointype == JOIN_SEMI || jointype == JOIN_ANTI || + extra->inner_unique) + { + /* + * With a SEMI or ANTI join, or if the innerrel is known unique, the + * executor will stop after the first match. + * + * Getting decent estimates requires inspection of the join quals, + * which we choose to postpone to final_cost_nestloop. + */ + + /* Save private data for final_cost_nestloop */ + workspace->inner_run_cost = inner_run_cost; + workspace->inner_rescan_run_cost = inner_rescan_run_cost; + } + else + { + /* Normal case; we'll scan whole input rel for each outer row */ + run_cost += inner_run_cost; + if (outer_path_rows > 1) + run_cost += (outer_path_rows - 1) * inner_rescan_run_cost; + } + + /* CPU costs left for later */ + + /* Public result fields */ + workspace->startup_cost = startup_cost; + workspace->total_cost = startup_cost + run_cost; + /* Save private data for final_cost_nestloop */ + workspace->run_cost = run_cost; +} + +/* + * final_cost_nestloop + * Final estimate of the cost and result size of a nestloop join path. + * + * 'path' is already filled in except for the rows and cost fields + * 'workspace' is the result from initial_cost_nestloop + * 'extra' contains miscellaneous information about the join + */ +void +final_cost_nestloop(PlannerInfo *root, NestPath *path, + JoinCostWorkspace *workspace, + JoinPathExtraData *extra) +{ + Path *outer_path = path->outerjoinpath; + Path *inner_path = path->innerjoinpath; + double outer_path_rows = outer_path->rows; + double inner_path_rows = inner_path->rows; + Cost startup_cost = workspace->startup_cost; + Cost run_cost = workspace->run_cost; + Cost cpu_per_tuple; + QualCost restrict_qual_cost; + double ntuples; + + /* Protect some assumptions below that rowcounts aren't zero */ + if (outer_path_rows <= 0) + outer_path_rows = 1; + if (inner_path_rows <= 0) + inner_path_rows = 1; + /* Mark the path with the correct row estimate */ + if (path->path.param_info) + path->path.rows = path->path.param_info->ppi_rows; + else + path->path.rows = path->path.parent->rows; + + /* For partial paths, scale row estimate. */ + 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); + } + + /* + * We could include disable_cost in the preliminary estimate, but that + * would amount to optimizing for the case where the join method is + * disabled, which doesn't seem like the way to bet. + */ + if (!enable_nestloop) + startup_cost += disable_cost; + + /* cost of inner-relation source data (we already dealt with outer rel) */ + + if (path->jointype == JOIN_SEMI || path->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->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->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; +} + +/* + * initial_cost_mergejoin + * Preliminary estimate of the cost of a mergejoin path. + * + * This must quickly produce lower-bound estimates of the path's startup and + * total costs. If we are unable to eliminate the proposed path from + * consideration using the lower bounds, final_cost_mergejoin will be called + * to obtain the final estimates. + * + * The exact division of labor between this function and final_cost_mergejoin + * is private to them, and represents a tradeoff between speed of the initial + * estimate and getting a tight lower bound. We choose to not examine the + * join quals here, except for obtaining the scan selectivity estimate which + * is really essential (but fortunately, use of caching keeps the cost of + * getting that down to something reasonable). + * We also assume that cost_sort is cheap enough to use here. + * + * 'workspace' is to be filled with startup_cost, total_cost, and perhaps + * other data to be used by final_cost_mergejoin + * 'jointype' is the type of join to be performed + * 'mergeclauses' is the list of joinclauses to be used as merge clauses + * 'outer_path' is the outer input to the join + * 'inner_path' is the inner input to the join + * 'outersortkeys' is the list of sort keys for the outer path + * 'innersortkeys' is the list of sort keys for the inner path + * 'extra' contains miscellaneous information about the join + * + * Note: outersortkeys and innersortkeys should be NIL if no explicit + * sort is needed because the respective source path is already ordered. + */ +void +initial_cost_mergejoin(PlannerInfo *root, JoinCostWorkspace *workspace, + JoinType jointype, + List *mergeclauses, + Path *outer_path, Path *inner_path, + List *outersortkeys, List *innersortkeys, + JoinPathExtraData *extra) +{ + Cost startup_cost = 0; + Cost run_cost = 0; + double outer_path_rows = outer_path->rows; + double inner_path_rows = inner_path->rows; + Cost inner_run_cost; + double outer_rows, + inner_rows, + outer_skip_rows, + inner_skip_rows; + Selectivity outerstartsel, + outerendsel, + innerstartsel, + innerendsel; + Path sort_path; /* dummy for result of cost_sort */ + + /* Protect some assumptions below that rowcounts aren't zero */ + if (outer_path_rows <= 0) + outer_path_rows = 1; + if (inner_path_rows <= 0) + inner_path_rows = 1; + + /* + * A merge join will stop as soon as it exhausts either input stream + * (unless it's an outer join, in which case the outer side has to be + * scanned all the way anyway). Estimate fraction of the left and right + * inputs that will actually need to be scanned. Likewise, we can + * estimate the number of rows that will be skipped before the first join + * pair is found, which should be factored into startup cost. We use only + * the first (most significant) merge clause for this purpose. Since + * mergejoinscansel() is a fairly expensive computation, we cache the + * results in the merge clause RestrictInfo. + */ + if (mergeclauses && jointype != JOIN_FULL) + { + RestrictInfo *firstclause = (RestrictInfo *) linitial(mergeclauses); + List *opathkeys; + List *ipathkeys; + PathKey *opathkey; + PathKey *ipathkey; + MergeScanSelCache *cache; + + /* Get the input pathkeys to determine the sort-order details */ + opathkeys = outersortkeys ? outersortkeys : outer_path->pathkeys; + ipathkeys = innersortkeys ? innersortkeys : inner_path->pathkeys; + Assert(opathkeys); + Assert(ipathkeys); + opathkey = (PathKey *) linitial(opathkeys); + ipathkey = (PathKey *) linitial(ipathkeys); + /* debugging check */ + if (opathkey->pk_opfamily != ipathkey->pk_opfamily || + opathkey->pk_eclass->ec_collation != ipathkey->pk_eclass->ec_collation || + opathkey->pk_strategy != ipathkey->pk_strategy || + opathkey->pk_nulls_first != ipathkey->pk_nulls_first) + elog(ERROR, "left and right pathkeys do not match in mergejoin"); + + /* Get the selectivity with caching */ + cache = cached_scansel(root, firstclause, opathkey); + + if (bms_is_subset(firstclause->left_relids, + outer_path->parent->relids)) + { + /* left side of clause is outer */ + outerstartsel = cache->leftstartsel; + outerendsel = cache->leftendsel; + innerstartsel = cache->rightstartsel; + innerendsel = cache->rightendsel; + } + else + { + /* left side of clause is inner */ + outerstartsel = cache->rightstartsel; + outerendsel = cache->rightendsel; + innerstartsel = cache->leftstartsel; + innerendsel = cache->leftendsel; + } + if (jointype == JOIN_LEFT || + jointype == JOIN_ANTI) + { + outerstartsel = 0.0; + outerendsel = 1.0; + } + else if (jointype == JOIN_RIGHT) + { + innerstartsel = 0.0; + innerendsel = 1.0; + } + } + else + { + /* cope with clauseless or full mergejoin */ + outerstartsel = innerstartsel = 0.0; + outerendsel = innerendsel = 1.0; + } + + /* + * Convert selectivities to row counts. We force outer_rows and + * inner_rows to be at least 1, but the skip_rows estimates can be zero. + */ + outer_skip_rows = rint(outer_path_rows * outerstartsel); + inner_skip_rows = rint(inner_path_rows * innerstartsel); + outer_rows = clamp_row_est(outer_path_rows * outerendsel); + inner_rows = clamp_row_est(inner_path_rows * innerendsel); + + Assert(outer_skip_rows <= outer_rows); + Assert(inner_skip_rows <= inner_rows); + + /* + * Readjust scan selectivities to account for above rounding. This is + * normally an insignificant effect, but when there are only a few rows in + * the inputs, failing to do this makes for a large percentage error. + */ + outerstartsel = outer_skip_rows / outer_path_rows; + innerstartsel = inner_skip_rows / inner_path_rows; + outerendsel = outer_rows / outer_path_rows; + innerendsel = inner_rows / inner_path_rows; + + Assert(outerstartsel <= outerendsel); + Assert(innerstartsel <= innerendsel); + + /* cost of source data */ + + if (outersortkeys) /* do we need to sort outer? */ + { + cost_sort(&sort_path, + root, + outersortkeys, + outer_path->total_cost, + outer_path_rows, + outer_path->pathtarget->width, + 0.0, + work_mem, + -1.0); + startup_cost += sort_path.startup_cost; + startup_cost += (sort_path.total_cost - sort_path.startup_cost) + * outerstartsel; + run_cost += (sort_path.total_cost - sort_path.startup_cost) + * (outerendsel - outerstartsel); + } + else + { + startup_cost += outer_path->startup_cost; + startup_cost += (outer_path->total_cost - outer_path->startup_cost) + * outerstartsel; + run_cost += (outer_path->total_cost - outer_path->startup_cost) + * (outerendsel - outerstartsel); + } + + if (innersortkeys) /* do we need to sort inner? */ + { + cost_sort(&sort_path, + root, + innersortkeys, + inner_path->total_cost, + inner_path_rows, + inner_path->pathtarget->width, + 0.0, + work_mem, + -1.0); + startup_cost += sort_path.startup_cost; + startup_cost += (sort_path.total_cost - sort_path.startup_cost) + * innerstartsel; + inner_run_cost = (sort_path.total_cost - sort_path.startup_cost) + * (innerendsel - innerstartsel); + } + else + { + startup_cost += inner_path->startup_cost; + startup_cost += (inner_path->total_cost - inner_path->startup_cost) + * innerstartsel; + inner_run_cost = (inner_path->total_cost - inner_path->startup_cost) + * (innerendsel - innerstartsel); + } + + /* + * We can't yet determine whether rescanning occurs, or whether + * materialization of the inner input should be done. The minimum + * possible inner input cost, regardless of rescan and materialization + * considerations, is inner_run_cost. We include that in + * workspace->total_cost, but not yet in run_cost. + */ + + /* CPU costs left for later */ + + /* Public result fields */ + workspace->startup_cost = startup_cost; + workspace->total_cost = startup_cost + run_cost + inner_run_cost; + /* Save private data for final_cost_mergejoin */ + workspace->run_cost = run_cost; + workspace->inner_run_cost = inner_run_cost; + workspace->outer_rows = outer_rows; + workspace->inner_rows = inner_rows; + workspace->outer_skip_rows = outer_skip_rows; + workspace->inner_skip_rows = inner_skip_rows; +} + +/* + * final_cost_mergejoin + * Final estimate of the cost and result size of a mergejoin path. + * + * Unlike other costsize functions, this routine makes two actual decisions: + * whether the executor will need to do mark/restore, and whether we should + * materialize the inner path. It would be logically cleaner to build + * separate paths testing these alternatives, but that would require repeating + * most of the cost calculations, which are not all that cheap. Since the + * choice will not affect output pathkeys or startup cost, only total cost, + * there is no possibility of wanting to keep more than one path. So it seems + * best to make the decisions here and record them in the path's + * skip_mark_restore and materialize_inner fields. + * + * Mark/restore overhead is usually required, but can be skipped if we know + * that the executor need find only one match per outer tuple, and that the + * mergeclauses are sufficient to identify a match. + * + * We materialize the inner path if we need mark/restore and either the inner + * path can't support mark/restore, or it's cheaper to use an interposed + * Material node to handle mark/restore. + * + * 'path' is already filled in except for the rows and cost fields and + * skip_mark_restore and materialize_inner + * 'workspace' is the result from initial_cost_mergejoin + * 'extra' contains miscellaneous information about the join + */ +void +final_cost_mergejoin(PlannerInfo *root, MergePath *path, + JoinCostWorkspace *workspace, + JoinPathExtraData *extra) +{ + Path *outer_path = path->jpath.outerjoinpath; + Path *inner_path = path->jpath.innerjoinpath; + double inner_path_rows = inner_path->rows; + List *mergeclauses = path->path_mergeclauses; + List *innersortkeys = path->innersortkeys; + Cost startup_cost = workspace->startup_cost; + Cost run_cost = workspace->run_cost; + Cost inner_run_cost = workspace->inner_run_cost; + double outer_rows = workspace->outer_rows; + double inner_rows = workspace->inner_rows; + double outer_skip_rows = workspace->outer_skip_rows; + double inner_skip_rows = workspace->inner_skip_rows; + Cost cpu_per_tuple, + bare_inner_cost, + mat_inner_cost; + QualCost merge_qual_cost; + QualCost qp_qual_cost; + double mergejointuples, + rescannedtuples; + double rescanratio; + + /* Protect some assumptions below that rowcounts aren't zero */ + if (inner_path_rows <= 0) + inner_path_rows = 1; + + /* Mark the path with the correct row estimate */ + if (path->jpath.path.param_info) + path->jpath.path.rows = path->jpath.path.param_info->ppi_rows; + else + path->jpath.path.rows = path->jpath.path.parent->rows; + + /* For partial paths, scale row estimate. */ + if (path->jpath.path.parallel_workers > 0) + { + double parallel_divisor = get_parallel_divisor(&path->jpath.path); + + path->jpath.path.rows = + clamp_row_est(path->jpath.path.rows / parallel_divisor); + } + + /* + * We could include disable_cost in the preliminary estimate, but that + * would amount to optimizing for the case where the join method is + * disabled, which doesn't seem like the way to bet. + */ + if (!enable_mergejoin) + startup_cost += disable_cost; + + /* + * Compute cost of the mergequals and qpquals (other restriction clauses) + * separately. + */ + cost_qual_eval(&merge_qual_cost, mergeclauses, root); + cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo, root); + qp_qual_cost.startup -= merge_qual_cost.startup; + qp_qual_cost.per_tuple -= merge_qual_cost.per_tuple; + + /* + * With a SEMI or ANTI join, or if the innerrel is known unique, the + * executor will stop scanning for matches after the first match. When + * all the joinclauses are merge clauses, this means we don't ever need to + * back up the merge, and so we can skip mark/restore overhead. + */ + if ((path->jpath.jointype == JOIN_SEMI || + path->jpath.jointype == JOIN_ANTI || + extra->inner_unique) && + (list_length(path->jpath.joinrestrictinfo) == + list_length(path->path_mergeclauses))) + path->skip_mark_restore = true; + else + path->skip_mark_restore = false; + + /* + * Get approx # tuples passing the mergequals. We use approx_tuple_count + * here because we need an estimate done with JOIN_INNER semantics. + */ + mergejointuples = approx_tuple_count(root, &path->jpath, mergeclauses); + + /* + * When there are equal merge keys in the outer relation, the mergejoin + * must rescan any matching tuples in the inner relation. This means + * re-fetching inner tuples; we have to estimate how often that happens. + * + * For regular inner and outer joins, the number of re-fetches can be + * estimated approximately as size of merge join output minus size of + * inner relation. Assume that the distinct key values are 1, 2, ..., and + * denote the number of values of each key in the outer relation as m1, + * m2, ...; in the inner relation, n1, n2, ... Then we have + * + * size of join = m1 * n1 + m2 * n2 + ... + * + * number of rescanned tuples = (m1 - 1) * n1 + (m2 - 1) * n2 + ... = m1 * + * n1 + m2 * n2 + ... - (n1 + n2 + ...) = size of join - size of inner + * relation + * + * This equation works correctly for outer tuples having no inner match + * (nk = 0), but not for inner tuples having no outer match (mk = 0); we + * are effectively subtracting those from the number of rescanned tuples, + * when we should not. Can we do better without expensive selectivity + * computations? + * + * The whole issue is moot if we are working from a unique-ified outer + * input, or if we know we don't need to mark/restore at all. + */ + if (IsA(outer_path, UniquePath) || path->skip_mark_restore) + rescannedtuples = 0; + else + { + rescannedtuples = mergejointuples - inner_path_rows; + /* Must clamp because of possible underestimate */ + if (rescannedtuples < 0) + rescannedtuples = 0; + } + + /* + * We'll inflate various costs this much to account for rescanning. Note + * that this is to be multiplied by something involving inner_rows, or + * another number related to the portion of the inner rel we'll scan. + */ + rescanratio = 1.0 + (rescannedtuples / inner_rows); + + /* + * Decide whether we want to materialize the inner input to shield it from + * mark/restore and performing re-fetches. Our cost model for regular + * re-fetches is that a re-fetch costs the same as an original fetch, + * which is probably an overestimate; but on the other hand we ignore the + * bookkeeping costs of mark/restore. Not clear if it's worth developing + * a more refined model. So we just need to inflate the inner run cost by + * rescanratio. + */ + bare_inner_cost = inner_run_cost * rescanratio; + + /* + * When we interpose a Material node the re-fetch cost is assumed to be + * just cpu_operator_cost per tuple, independently of the underlying + * plan's cost; and we charge an extra cpu_operator_cost per original + * fetch as well. Note that we're assuming the materialize node will + * never spill to disk, since it only has to remember tuples back to the + * last mark. (If there are a huge number of duplicates, our other cost + * factors will make the path so expensive that it probably won't get + * chosen anyway.) So we don't use cost_rescan here. + * + * Note: keep this estimate in sync with create_mergejoin_plan's labeling + * of the generated Material node. + */ + mat_inner_cost = inner_run_cost + + cpu_operator_cost * inner_rows * rescanratio; + + /* + * If we don't need mark/restore at all, we don't need materialization. + */ + if (path->skip_mark_restore) + path->materialize_inner = false; + + /* + * Prefer materializing if it looks cheaper, unless the user has asked to + * suppress materialization. + */ + else if (enable_material && mat_inner_cost < bare_inner_cost) + path->materialize_inner = true; + + /* + * Even if materializing doesn't look cheaper, we *must* do it if the + * inner path is to be used directly (without sorting) and it doesn't + * support mark/restore. + * + * Since the inner side must be ordered, and only Sorts and IndexScans can + * create order to begin with, and they both support mark/restore, you + * might think there's no problem --- but you'd be wrong. Nestloop and + * merge joins can *preserve* the order of their inputs, so they can be + * selected as the input of a mergejoin, and they don't support + * mark/restore at present. + * + * We don't test the value of enable_material here, because + * materialization is required for correctness in this case, and turning + * it off does not entitle us to deliver an invalid plan. + */ + else if (innersortkeys == NIL && + !ExecSupportsMarkRestore(inner_path)) + path->materialize_inner = true; + + /* + * Also, force materializing if the inner path is to be sorted and the + * sort is expected to spill to disk. This is because the final merge + * pass can be done on-the-fly if it doesn't have to support mark/restore. + * We don't try to adjust the cost estimates for this consideration, + * though. + * + * Since materialization is a performance optimization in this case, + * rather than necessary for correctness, we skip it if enable_material is + * off. + */ + else if (enable_material && innersortkeys != NIL && + relation_byte_size(inner_path_rows, + inner_path->pathtarget->width) > + (work_mem * 1024L)) + path->materialize_inner = true; + else + path->materialize_inner = false; + + /* Charge the right incremental cost for the chosen case */ + if (path->materialize_inner) + run_cost += mat_inner_cost; + else + run_cost += bare_inner_cost; + + /* CPU costs */ + + /* + * The number of tuple comparisons needed is approximately number of outer + * rows plus number of inner rows plus number of rescanned tuples (can we + * refine this?). At each one, we need to evaluate the mergejoin quals. + */ + startup_cost += merge_qual_cost.startup; + startup_cost += merge_qual_cost.per_tuple * + (outer_skip_rows + inner_skip_rows * rescanratio); + run_cost += merge_qual_cost.per_tuple * + ((outer_rows - outer_skip_rows) + + (inner_rows - inner_skip_rows) * rescanratio); + + /* + * For each tuple that gets through the mergejoin proper, we charge + * cpu_tuple_cost plus the cost of evaluating additional restriction + * clauses that are to be applied at the join. (This is pessimistic since + * not all of the quals may get evaluated at each tuple.) + * + * Note: we could adjust for SEMI/ANTI joins skipping some qual + * evaluations here, but it's probably not worth the trouble. + */ + startup_cost += qp_qual_cost.startup; + cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple; + run_cost += cpu_per_tuple * mergejointuples; + + /* tlist eval costs are paid per output row, not per tuple scanned */ + startup_cost += path->jpath.path.pathtarget->cost.startup; + run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows; + + path->jpath.path.startup_cost = startup_cost; + path->jpath.path.total_cost = startup_cost + run_cost; +} + +/* + * run mergejoinscansel() with caching + */ +static MergeScanSelCache * +cached_scansel(PlannerInfo *root, RestrictInfo *rinfo, PathKey *pathkey) +{ + MergeScanSelCache *cache; + ListCell *lc; + Selectivity leftstartsel, + leftendsel, + rightstartsel, + rightendsel; + MemoryContext oldcontext; + + /* Do we have this result already? */ + foreach(lc, rinfo->scansel_cache) + { + cache = (MergeScanSelCache *) lfirst(lc); + if (cache->opfamily == pathkey->pk_opfamily && + cache->collation == pathkey->pk_eclass->ec_collation && + cache->strategy == pathkey->pk_strategy && + cache->nulls_first == pathkey->pk_nulls_first) + return cache; + } + + /* Nope, do the computation */ + mergejoinscansel(root, + (Node *) rinfo->clause, + pathkey->pk_opfamily, + pathkey->pk_strategy, + pathkey->pk_nulls_first, + &leftstartsel, + &leftendsel, + &rightstartsel, + &rightendsel); + + /* Cache the result in suitably long-lived workspace */ + oldcontext = MemoryContextSwitchTo(root->planner_cxt); + + cache = (MergeScanSelCache *) palloc(sizeof(MergeScanSelCache)); + cache->opfamily = pathkey->pk_opfamily; + cache->collation = pathkey->pk_eclass->ec_collation; + cache->strategy = pathkey->pk_strategy; + cache->nulls_first = pathkey->pk_nulls_first; + cache->leftstartsel = leftstartsel; + cache->leftendsel = leftendsel; + cache->rightstartsel = rightstartsel; + cache->rightendsel = rightendsel; + + rinfo->scansel_cache = lappend(rinfo->scansel_cache, cache); + + MemoryContextSwitchTo(oldcontext); + + return cache; +} + +/* + * initial_cost_hashjoin + * Preliminary estimate of the cost of a hashjoin path. + * + * This must quickly produce lower-bound estimates of the path's startup and + * total costs. If we are unable to eliminate the proposed path from + * consideration using the lower bounds, final_cost_hashjoin will be called + * to obtain the final estimates. + * + * The exact division of labor between this function and final_cost_hashjoin + * is private to them, and represents a tradeoff between speed of the initial + * estimate and getting a tight lower bound. We choose to not examine the + * join quals here (other than by counting the number of hash clauses), + * so we can't do much with CPU costs. We do assume that + * ExecChooseHashTableSize is cheap enough to use here. + * + * 'workspace' is to be filled with startup_cost, total_cost, and perhaps + * other data to be used by final_cost_hashjoin + * 'jointype' is the type of join to be performed + * 'hashclauses' is the list of joinclauses to be used as hash clauses + * 'outer_path' is the outer input to the join + * 'inner_path' is the inner input to the join + * 'extra' contains miscellaneous information about the join + * 'parallel_hash' indicates that inner_path is partial and that a shared + * hash table will be built in parallel + */ +void +initial_cost_hashjoin(PlannerInfo *root, JoinCostWorkspace *workspace, + JoinType jointype, + List *hashclauses, + Path *outer_path, Path *inner_path, + JoinPathExtraData *extra, + bool parallel_hash) +{ + Cost startup_cost = 0; + Cost run_cost = 0; + double outer_path_rows = outer_path->rows; + double inner_path_rows = inner_path->rows; + double inner_path_rows_total = inner_path_rows; + int num_hashclauses = list_length(hashclauses); + int numbuckets; + int numbatches; + int num_skew_mcvs; + size_t space_allowed; /* unused */ + + /* cost of source data */ + startup_cost += outer_path->startup_cost; + run_cost += outer_path->total_cost - outer_path->startup_cost; + startup_cost += inner_path->total_cost; + + /* + * Cost of computing hash function: must do it once per input tuple. We + * charge one cpu_operator_cost for each column's hash function. Also, + * tack on one cpu_tuple_cost per inner row, to model the costs of + * inserting the row into the hashtable. + * + * XXX when a hashclause is more complex than a single operator, we really + * should charge the extra eval costs of the left or right side, as + * appropriate, here. This seems more work than it's worth at the moment. + */ + startup_cost += (cpu_operator_cost * num_hashclauses + cpu_tuple_cost) + * inner_path_rows; + run_cost += cpu_operator_cost * num_hashclauses * outer_path_rows; + + /* + * If this is a parallel hash build, then the value we have for + * inner_rows_total currently refers only to the rows returned by each + * participant. For shared hash table size estimation, we need the total + * number, so we need to undo the division. + */ + if (parallel_hash) + inner_path_rows_total *= get_parallel_divisor(inner_path); + + /* + * Get hash table size that executor would use for inner relation. + * + * XXX for the moment, always assume that skew optimization will be + * performed. As long as SKEW_HASH_MEM_PERCENT is small, it's not worth + * trying to determine that for sure. + * + * XXX at some point it might be interesting to try to account for skew + * optimization in the cost estimate, but for now, we don't. + */ + ExecChooseHashTableSize(inner_path_rows_total, + inner_path->pathtarget->width, + true, /* useskew */ + parallel_hash, /* try_combined_hash_mem */ + outer_path->parallel_workers, + &space_allowed, + &numbuckets, + &numbatches, + &num_skew_mcvs); + + /* + * If inner relation is too big then we will need to "batch" the join, + * which implies writing and reading most of the tuples to disk an extra + * time. Charge seq_page_cost per page, since the I/O should be nice and + * sequential. Writing the inner rel counts as startup cost, all the rest + * as run cost. + */ + if (numbatches > 1) + { + double outerpages = page_size(outer_path_rows, + outer_path->pathtarget->width); + double innerpages = page_size(inner_path_rows, + inner_path->pathtarget->width); + + startup_cost += seq_page_cost * innerpages; + run_cost += seq_page_cost * (innerpages + 2 * outerpages); + } + + /* CPU costs left for later */ + + /* Public result fields */ + workspace->startup_cost = startup_cost; + workspace->total_cost = startup_cost + run_cost; + /* Save private data for final_cost_hashjoin */ + workspace->run_cost = run_cost; + workspace->numbuckets = numbuckets; + workspace->numbatches = numbatches; + workspace->inner_rows_total = inner_path_rows_total; +} + +/* + * final_cost_hashjoin + * Final estimate of the cost and result size of a hashjoin path. + * + * Note: the numbatches estimate is also saved into 'path' for use later + * + * 'path' is already filled in except for the rows and cost fields and + * num_batches + * 'workspace' is the result from initial_cost_hashjoin + * 'extra' contains miscellaneous information about the join + */ +void +final_cost_hashjoin(PlannerInfo *root, HashPath *path, + JoinCostWorkspace *workspace, + JoinPathExtraData *extra) +{ + Path *outer_path = path->jpath.outerjoinpath; + Path *inner_path = path->jpath.innerjoinpath; + double outer_path_rows = outer_path->rows; + double inner_path_rows = inner_path->rows; + double inner_path_rows_total = workspace->inner_rows_total; + List *hashclauses = path->path_hashclauses; + Cost startup_cost = workspace->startup_cost; + Cost run_cost = workspace->run_cost; + int numbuckets = workspace->numbuckets; + int numbatches = workspace->numbatches; + Cost cpu_per_tuple; + QualCost hash_qual_cost; + QualCost qp_qual_cost; + double hashjointuples; + double virtualbuckets; + Selectivity innerbucketsize; + Selectivity innermcvfreq; + ListCell *hcl; + + /* Mark the path with the correct row estimate */ + if (path->jpath.path.param_info) + path->jpath.path.rows = path->jpath.path.param_info->ppi_rows; + else + path->jpath.path.rows = path->jpath.path.parent->rows; + + /* For partial paths, scale row estimate. */ + if (path->jpath.path.parallel_workers > 0) + { + double parallel_divisor = get_parallel_divisor(&path->jpath.path); + + path->jpath.path.rows = + clamp_row_est(path->jpath.path.rows / parallel_divisor); + } + + /* + * We could include disable_cost in the preliminary estimate, but that + * would amount to optimizing for the case where the join method is + * disabled, which doesn't seem like the way to bet. + */ + if (!enable_hashjoin) + startup_cost += disable_cost; + + /* mark the path with estimated # of batches */ + path->num_batches = numbatches; + + /* store the total number of tuples (sum of partial row estimates) */ + path->inner_rows_total = inner_path_rows_total; + + /* and compute the number of "virtual" buckets in the whole join */ + virtualbuckets = (double) numbuckets * (double) numbatches; + + /* + * Determine bucketsize fraction and MCV frequency for the inner relation. + * We use the smallest bucketsize or MCV frequency estimated for any + * individual hashclause; this is undoubtedly conservative. + * + * BUT: if inner relation has been unique-ified, we can assume it's good + * for hashing. This is important both because it's the right answer, and + * because we avoid contaminating the cache with a value that's wrong for + * non-unique-ified paths. + */ + if (IsA(inner_path, UniquePath)) + { + innerbucketsize = 1.0 / virtualbuckets; + innermcvfreq = 0.0; + } + else + { + innerbucketsize = 1.0; + innermcvfreq = 1.0; + foreach(hcl, hashclauses) + { + RestrictInfo *restrictinfo = lfirst_node(RestrictInfo, hcl); + Selectivity thisbucketsize; + Selectivity thismcvfreq; + + /* + * First we have to figure out which side of the hashjoin clause + * is the inner side. + * + * Since we tend to visit the same clauses over and over when + * planning a large query, we cache the bucket stats estimates in + * the RestrictInfo node to avoid repeated lookups of statistics. + */ + if (bms_is_subset(restrictinfo->right_relids, + inner_path->parent->relids)) + { + /* righthand side is inner */ + thisbucketsize = restrictinfo->right_bucketsize; + if (thisbucketsize < 0) + { + /* not cached yet */ + estimate_hash_bucket_stats(root, + get_rightop(restrictinfo->clause), + virtualbuckets, + &restrictinfo->right_mcvfreq, + &restrictinfo->right_bucketsize); + thisbucketsize = restrictinfo->right_bucketsize; + } + thismcvfreq = restrictinfo->right_mcvfreq; + } + else + { + Assert(bms_is_subset(restrictinfo->left_relids, + inner_path->parent->relids)); + /* lefthand side is inner */ + thisbucketsize = restrictinfo->left_bucketsize; + if (thisbucketsize < 0) + { + /* not cached yet */ + estimate_hash_bucket_stats(root, + get_leftop(restrictinfo->clause), + virtualbuckets, + &restrictinfo->left_mcvfreq, + &restrictinfo->left_bucketsize); + thisbucketsize = restrictinfo->left_bucketsize; + } + thismcvfreq = restrictinfo->left_mcvfreq; + } + + if (innerbucketsize > thisbucketsize) + innerbucketsize = thisbucketsize; + if (innermcvfreq > thismcvfreq) + innermcvfreq = thismcvfreq; + } + } + + /* + * If the bucket holding the inner MCV would exceed hash_mem, we don't + * want to hash unless there is really no other alternative, so apply + * disable_cost. (The executor normally copes with excessive memory usage + * by splitting batches, but obviously it cannot separate equal values + * that way, so it will be unable to drive the batch size below hash_mem + * when this is true.) + */ + if (relation_byte_size(clamp_row_est(inner_path_rows * innermcvfreq), + inner_path->pathtarget->width) > get_hash_memory_limit()) + startup_cost += disable_cost; + + /* + * Compute cost of the hashquals and qpquals (other restriction clauses) + * separately. + */ + cost_qual_eval(&hash_qual_cost, hashclauses, root); + cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo, root); + qp_qual_cost.startup -= hash_qual_cost.startup; + qp_qual_cost.per_tuple -= hash_qual_cost.per_tuple; + + /* CPU costs */ + + if (path->jpath.jointype == JOIN_SEMI || + path->jpath.jointype == JOIN_ANTI || + extra->inner_unique) + { + double outer_matched_rows; + Selectivity inner_scan_frac; + + /* + * With a SEMI or ANTI join, or if the innerrel is known unique, the + * executor will stop after the first match. + * + * For an outer-rel row that has at least one match, we can expect the + * bucket scan to stop after a fraction 1/(match_count+1) of the + * bucket's rows, if the matches are evenly distributed. Since they + * probably aren't quite evenly distributed, we apply a fuzz factor of + * 2.0 to that fraction. (If we used a larger fuzz factor, we'd have + * to clamp inner_scan_frac to at most 1.0; but since match_count is + * at least 1, no such clamp is needed now.) + */ + outer_matched_rows = rint(outer_path_rows * extra->semifactors.outer_match_frac); + inner_scan_frac = 2.0 / (extra->semifactors.match_count + 1.0); + + startup_cost += hash_qual_cost.startup; + run_cost += hash_qual_cost.per_tuple * outer_matched_rows * + clamp_row_est(inner_path_rows * innerbucketsize * inner_scan_frac) * 0.5; + + /* + * For unmatched outer-rel rows, the picture is quite a lot different. + * In the first place, there is no reason to assume that these rows + * preferentially hit heavily-populated buckets; instead assume they + * are uncorrelated with the inner distribution and so they see an + * average bucket size of inner_path_rows / virtualbuckets. In the + * second place, it seems likely that they will have few if any exact + * hash-code matches and so very few of the tuples in the bucket will + * actually require eval of the hash quals. We don't have any good + * way to estimate how many will, but for the moment assume that the + * effective cost per bucket entry is one-tenth what it is for + * matchable tuples. + */ + run_cost += hash_qual_cost.per_tuple * + (outer_path_rows - outer_matched_rows) * + clamp_row_est(inner_path_rows / virtualbuckets) * 0.05; + + /* Get # of tuples that will pass the basic join */ + if (path->jpath.jointype == JOIN_ANTI) + hashjointuples = outer_path_rows - outer_matched_rows; + else + hashjointuples = outer_matched_rows; + } + else + { + /* + * The number of tuple comparisons needed is the number of outer + * tuples times the typical number of tuples in a hash bucket, which + * is the inner relation size times its bucketsize fraction. At each + * one, we need to evaluate the hashjoin quals. But actually, + * charging the full qual eval cost at each tuple is pessimistic, + * since we don't evaluate the quals unless the hash values match + * exactly. For lack of a better idea, halve the cost estimate to + * allow for that. + */ + startup_cost += hash_qual_cost.startup; + run_cost += hash_qual_cost.per_tuple * outer_path_rows * + clamp_row_est(inner_path_rows * innerbucketsize) * 0.5; + + /* + * Get approx # tuples passing the hashquals. We use + * approx_tuple_count here because we need an estimate done with + * JOIN_INNER semantics. + */ + hashjointuples = approx_tuple_count(root, &path->jpath, hashclauses); + } + + /* + * For each tuple that gets through the hashjoin proper, we charge + * cpu_tuple_cost plus the cost of evaluating additional restriction + * clauses that are to be applied at the join. (This is pessimistic since + * not all of the quals may get evaluated at each tuple.) + */ + startup_cost += qp_qual_cost.startup; + cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple; + run_cost += cpu_per_tuple * hashjointuples; + + /* tlist eval costs are paid per output row, not per tuple scanned */ + startup_cost += path->jpath.path.pathtarget->cost.startup; + run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows; + + path->jpath.path.startup_cost = startup_cost; + path->jpath.path.total_cost = startup_cost + run_cost; +} + + +/* + * cost_subplan + * Figure the costs for a SubPlan (or initplan). + * + * Note: we could dig the subplan's Plan out of the root list, but in practice + * all callers have it handy already, so we make them pass it. + */ +void +cost_subplan(PlannerInfo *root, SubPlan *subplan, Plan *plan) +{ + QualCost sp_cost; + + /* Figure any cost for evaluating the testexpr */ + cost_qual_eval(&sp_cost, + make_ands_implicit((Expr *) subplan->testexpr), + root); + + if (subplan->useHashTable) + { + /* + * If we are using a hash table for the subquery outputs, then the + * cost of evaluating the query is a one-time cost. We charge one + * cpu_operator_cost per tuple for the work of loading the hashtable, + * too. + */ + sp_cost.startup += plan->total_cost + + cpu_operator_cost * plan->plan_rows; + + /* + * The per-tuple costs include the cost of evaluating the lefthand + * expressions, plus the cost of probing the hashtable. We already + * accounted for the lefthand expressions as part of the testexpr, and + * will also have counted one cpu_operator_cost for each comparison + * operator. That is probably too low for the probing cost, but it's + * hard to make a better estimate, so live with it for now. + */ + } + else + { + /* + * Otherwise we will be rescanning the subplan output on each + * evaluation. We need to estimate how much of the output we will + * actually need to scan. NOTE: this logic should agree with the + * tuple_fraction estimates used by make_subplan() in + * plan/subselect.c. + */ + Cost plan_run_cost = plan->total_cost - plan->startup_cost; + + if (subplan->subLinkType == EXISTS_SUBLINK) + { + /* we only need to fetch 1 tuple; clamp to avoid zero divide */ + sp_cost.per_tuple += plan_run_cost / clamp_row_est(plan->plan_rows); + } + else if (subplan->subLinkType == ALL_SUBLINK || + subplan->subLinkType == ANY_SUBLINK) + { + /* assume we need 50% of the tuples */ + sp_cost.per_tuple += 0.50 * plan_run_cost; + /* also charge a cpu_operator_cost per row examined */ + sp_cost.per_tuple += 0.50 * plan->plan_rows * cpu_operator_cost; + } + else + { + /* assume we need all tuples */ + sp_cost.per_tuple += plan_run_cost; + } + + /* + * Also account for subplan's startup cost. If the subplan is + * uncorrelated or undirect correlated, AND its topmost node is one + * that materializes its output, assume that we'll only need to pay + * its startup cost once; otherwise assume we pay the startup cost + * every time. + */ + if (subplan->parParam == NIL && + ExecMaterializesOutput(nodeTag(plan))) + sp_cost.startup += plan->startup_cost; + else + sp_cost.per_tuple += plan->startup_cost; + } + + subplan->startup_cost = sp_cost.startup; + subplan->per_call_cost = sp_cost.per_tuple; +} + + +/* + * cost_rescan + * Given a finished Path, estimate the costs of rescanning it after + * having done so the first time. For some Path types a rescan is + * cheaper than an original scan (if no parameters change), and this + * function embodies knowledge about that. The default is to return + * the same costs stored in the Path. (Note that the cost estimates + * actually stored in Paths are always for first scans.) + * + * This function is not currently intended to model effects such as rescans + * being cheaper due to disk block caching; what we are concerned with is + * plan types wherein the executor caches results explicitly, or doesn't + * redo startup calculations, etc. + */ +static void +cost_rescan(PlannerInfo *root, Path *path, + Cost *rescan_startup_cost, /* output parameters */ + Cost *rescan_total_cost) +{ + switch (path->pathtype) + { + case T_FunctionScan: + + /* + * Currently, nodeFunctionscan.c always executes the function to + * completion before returning any rows, and caches the results in + * a tuplestore. So the function eval cost is all startup cost + * and isn't paid over again on rescans. However, all run costs + * will be paid over again. + */ + *rescan_startup_cost = 0; + *rescan_total_cost = path->total_cost - path->startup_cost; + break; + case T_HashJoin: + + /* + * If it's a single-batch join, we don't need to rebuild the hash + * table during a rescan. + */ + if (((HashPath *) path)->num_batches == 1) + { + /* Startup cost is exactly the cost of hash table building */ + *rescan_startup_cost = 0; + *rescan_total_cost = path->total_cost - path->startup_cost; + } + else + { + /* Otherwise, no special treatment */ + *rescan_startup_cost = path->startup_cost; + *rescan_total_cost = path->total_cost; + } + break; + case T_CteScan: + case T_WorkTableScan: + { + /* + * These plan types materialize their final result in a + * tuplestore or tuplesort object. So the rescan cost is only + * cpu_tuple_cost per tuple, unless the result is large enough + * to spill to disk. + */ + Cost run_cost = cpu_tuple_cost * path->rows; + double nbytes = relation_byte_size(path->rows, + path->pathtarget->width); + long work_mem_bytes = work_mem * 1024L; + + if (nbytes > work_mem_bytes) + { + /* It will spill, so account for re-read cost */ + double npages = ceil(nbytes / BLCKSZ); + + run_cost += seq_page_cost * npages; + } + *rescan_startup_cost = 0; + *rescan_total_cost = run_cost; + } + break; + case T_Material: + case T_Sort: + { + /* + * These plan types not only materialize their results, but do + * not implement qual filtering or projection. So they are + * even cheaper to rescan than the ones above. We charge only + * cpu_operator_cost per tuple. (Note: keep that in sync with + * the run_cost charge in cost_sort, and also see comments in + * cost_material before you change it.) + */ + Cost run_cost = cpu_operator_cost * path->rows; + double nbytes = relation_byte_size(path->rows, + path->pathtarget->width); + long work_mem_bytes = work_mem * 1024L; + + if (nbytes > work_mem_bytes) + { + /* It will spill, so account for re-read cost */ + double npages = ceil(nbytes / BLCKSZ); + + run_cost += seq_page_cost * npages; + } + *rescan_startup_cost = 0; + *rescan_total_cost = run_cost; + } + break; + case T_Memoize: + /* All the hard work is done by cost_memoize_rescan */ + cost_memoize_rescan(root, (MemoizePath *) path, + rescan_startup_cost, rescan_total_cost); + break; + default: + *rescan_startup_cost = path->startup_cost; + *rescan_total_cost = path->total_cost; + break; + } +} + + +/* + * cost_qual_eval + * Estimate the CPU costs of evaluating a WHERE clause. + * The input can be either an implicitly-ANDed list of boolean + * expressions, or a list of RestrictInfo nodes. (The latter is + * preferred since it allows caching of the results.) + * The result includes both a one-time (startup) component, + * and a per-evaluation component. + */ +void +cost_qual_eval(QualCost *cost, List *quals, PlannerInfo *root) +{ + cost_qual_eval_context context; + ListCell *l; + + context.root = root; + context.total.startup = 0; + context.total.per_tuple = 0; + + /* We don't charge any cost for the implicit ANDing at top level ... */ + + foreach(l, quals) + { + Node *qual = (Node *) lfirst(l); + + cost_qual_eval_walker(qual, &context); + } + + *cost = context.total; +} + +/* + * cost_qual_eval_node + * As above, for a single RestrictInfo or expression. + */ +void +cost_qual_eval_node(QualCost *cost, Node *qual, PlannerInfo *root) +{ + cost_qual_eval_context context; + + context.root = root; + context.total.startup = 0; + context.total.per_tuple = 0; + + cost_qual_eval_walker(qual, &context); + + *cost = context.total; +} + +static bool +cost_qual_eval_walker(Node *node, cost_qual_eval_context *context) +{ + if (node == NULL) + return false; + + /* + * RestrictInfo nodes contain an eval_cost field reserved for this + * routine's use, so that it's not necessary to evaluate the qual clause's + * cost more than once. If the clause's cost hasn't been computed yet, + * the field's startup value will contain -1. + */ + if (IsA(node, RestrictInfo)) + { + RestrictInfo *rinfo = (RestrictInfo *) node; + + if (rinfo->eval_cost.startup < 0) + { + cost_qual_eval_context locContext; + + locContext.root = context->root; + locContext.total.startup = 0; + locContext.total.per_tuple = 0; + + /* + * For an OR clause, recurse into the marked-up tree so that we + * set the eval_cost for contained RestrictInfos too. + */ + if (rinfo->orclause) + cost_qual_eval_walker((Node *) rinfo->orclause, &locContext); + else + cost_qual_eval_walker((Node *) rinfo->clause, &locContext); + + /* + * If the RestrictInfo is marked pseudoconstant, it will be tested + * only once, so treat its cost as all startup cost. + */ + if (rinfo->pseudoconstant) + { + /* count one execution during startup */ + locContext.total.startup += locContext.total.per_tuple; + locContext.total.per_tuple = 0; + } + rinfo->eval_cost = locContext.total; + } + context->total.startup += rinfo->eval_cost.startup; + context->total.per_tuple += rinfo->eval_cost.per_tuple; + /* do NOT recurse into children */ + return false; + } + + /* + * For each operator or function node in the given tree, we charge the + * estimated execution cost given by pg_proc.procost (remember to multiply + * this by cpu_operator_cost). + * + * Vars and Consts are charged zero, and so are boolean operators (AND, + * OR, NOT). Simplistic, but a lot better than no model at all. + * + * Should we try to account for the possibility of short-circuit + * evaluation of AND/OR? Probably *not*, because that would make the + * results depend on the clause ordering, and we are not in any position + * to expect that the current ordering of the clauses is the one that's + * going to end up being used. The above per-RestrictInfo caching would + * not mix well with trying to re-order clauses anyway. + * + * Another issue that is entirely ignored here is that if a set-returning + * function is below top level in the tree, the functions/operators above + * it will need to be evaluated multiple times. In practical use, such + * cases arise so seldom as to not be worth the added complexity needed; + * moreover, since our rowcount estimates for functions tend to be pretty + * phony, the results would also be pretty phony. + */ + if (IsA(node, FuncExpr)) + { + add_function_cost(context->root, ((FuncExpr *) node)->funcid, node, + &context->total); + } + else if (IsA(node, OpExpr) || + IsA(node, DistinctExpr) || + IsA(node, NullIfExpr)) + { + /* rely on struct equivalence to treat these all alike */ + set_opfuncid((OpExpr *) node); + add_function_cost(context->root, ((OpExpr *) node)->opfuncid, node, + &context->total); + } + else if (IsA(node, ScalarArrayOpExpr)) + { + ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) node; + Node *arraynode = (Node *) lsecond(saop->args); + QualCost sacosts; + QualCost hcosts; + int estarraylen = estimate_array_length(arraynode); + + set_sa_opfuncid(saop); + sacosts.startup = sacosts.per_tuple = 0; + add_function_cost(context->root, saop->opfuncid, NULL, + &sacosts); + + if (OidIsValid(saop->hashfuncid)) + { + /* Handle costs for hashed ScalarArrayOpExpr */ + hcosts.startup = hcosts.per_tuple = 0; + + add_function_cost(context->root, saop->hashfuncid, NULL, &hcosts); + context->total.startup += sacosts.startup + hcosts.startup; + + /* Estimate the cost of building the hashtable. */ + context->total.startup += estarraylen * hcosts.per_tuple; + + /* + * XXX should we charge a little bit for sacosts.per_tuple when + * building the table, or is it ok to assume there will be zero + * hash collision? + */ + + /* + * Charge for hashtable lookups. Charge a single hash and a + * single comparison. + */ + context->total.per_tuple += hcosts.per_tuple + sacosts.per_tuple; + } + else + { + /* + * Estimate that the operator will be applied to about half of the + * array elements before the answer is determined. + */ + context->total.startup += sacosts.startup; + context->total.per_tuple += sacosts.per_tuple * + estimate_array_length(arraynode) * 0.5; + } + } + else if (IsA(node, Aggref) || + IsA(node, WindowFunc)) + { + /* + * Aggref and WindowFunc nodes are (and should be) treated like Vars, + * ie, zero execution cost in the current model, because they behave + * essentially like Vars at execution. We disregard the costs of + * their input expressions for the same reason. The actual execution + * costs of the aggregate/window functions and their arguments have to + * be factored into plan-node-specific costing of the Agg or WindowAgg + * plan node. + */ + return false; /* don't recurse into children */ + } + else if (IsA(node, GroupingFunc)) + { + /* Treat this as having cost 1 */ + context->total.per_tuple += cpu_operator_cost; + return false; /* don't recurse into children */ + } + else if (IsA(node, CoerceViaIO)) + { + CoerceViaIO *iocoerce = (CoerceViaIO *) node; + Oid iofunc; + Oid typioparam; + bool typisvarlena; + + /* check the result type's input function */ + getTypeInputInfo(iocoerce->resulttype, + &iofunc, &typioparam); + add_function_cost(context->root, iofunc, NULL, + &context->total); + /* check the input type's output function */ + getTypeOutputInfo(exprType((Node *) iocoerce->arg), + &iofunc, &typisvarlena); + add_function_cost(context->root, iofunc, NULL, + &context->total); + } + else if (IsA(node, ArrayCoerceExpr)) + { + ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node; + QualCost perelemcost; + + cost_qual_eval_node(&perelemcost, (Node *) acoerce->elemexpr, + context->root); + context->total.startup += perelemcost.startup; + if (perelemcost.per_tuple > 0) + context->total.per_tuple += perelemcost.per_tuple * + estimate_array_length((Node *) acoerce->arg); + } + else if (IsA(node, RowCompareExpr)) + { + /* Conservatively assume we will check all the columns */ + RowCompareExpr *rcexpr = (RowCompareExpr *) node; + ListCell *lc; + + foreach(lc, rcexpr->opnos) + { + Oid opid = lfirst_oid(lc); + + add_function_cost(context->root, get_opcode(opid), NULL, + &context->total); + } + } + else if (IsA(node, MinMaxExpr) || + IsA(node, SQLValueFunction) || + IsA(node, XmlExpr) || + IsA(node, CoerceToDomain) || + IsA(node, NextValueExpr)) + { + /* Treat all these as having cost 1 */ + context->total.per_tuple += cpu_operator_cost; + } + else if (IsA(node, CurrentOfExpr)) + { + /* Report high cost to prevent selection of anything but TID scan */ + context->total.startup += disable_cost; + } + else if (IsA(node, SubLink)) + { + /* This routine should not be applied to un-planned expressions */ + elog(ERROR, "cannot handle unplanned sub-select"); + } + else if (IsA(node, SubPlan)) + { + /* + * A subplan node in an expression typically indicates that the + * subplan will be executed on each evaluation, so charge accordingly. + * (Sub-selects that can be executed as InitPlans have already been + * removed from the expression.) + */ + SubPlan *subplan = (SubPlan *) node; + + context->total.startup += subplan->startup_cost; + context->total.per_tuple += subplan->per_call_cost; + + /* + * We don't want to recurse into the testexpr, because it was already + * counted in the SubPlan node's costs. So we're done. + */ + return false; + } + else if (IsA(node, AlternativeSubPlan)) + { + /* + * Arbitrarily use the first alternative plan for costing. (We should + * certainly only include one alternative, and we don't yet have + * enough information to know which one the executor is most likely to + * use.) + */ + AlternativeSubPlan *asplan = (AlternativeSubPlan *) node; + + return cost_qual_eval_walker((Node *) linitial(asplan->subplans), + context); + } + else if (IsA(node, PlaceHolderVar)) + { + /* + * A PlaceHolderVar should be given cost zero when considering general + * expression evaluation costs. The expense of doing the contained + * expression is charged as part of the tlist eval costs of the scan + * or join where the PHV is first computed (see set_rel_width and + * add_placeholders_to_joinrel). If we charged it again here, we'd be + * double-counting the cost for each level of plan that the PHV + * bubbles up through. Hence, return without recursing into the + * phexpr. + */ + return false; + } + + /* recurse into children */ + return expression_tree_walker(node, cost_qual_eval_walker, + (void *) context); +} + +/* + * get_restriction_qual_cost + * Compute evaluation costs of a baserel's restriction quals, plus any + * movable join quals that have been pushed down to the scan. + * Results are returned into *qpqual_cost. + * + * This is a convenience subroutine that works for seqscans and other cases + * where all the given quals will be evaluated the hard way. It's not useful + * for cost_index(), for example, where the index machinery takes care of + * some of the quals. We assume baserestrictcost was previously set by + * set_baserel_size_estimates(). + */ +static void +get_restriction_qual_cost(PlannerInfo *root, RelOptInfo *baserel, + ParamPathInfo *param_info, + QualCost *qpqual_cost) +{ + if (param_info) + { + /* Include costs of pushed-down clauses */ + cost_qual_eval(qpqual_cost, param_info->ppi_clauses, root); + + qpqual_cost->startup += baserel->baserestrictcost.startup; + qpqual_cost->per_tuple += baserel->baserestrictcost.per_tuple; + } + else + *qpqual_cost = baserel->baserestrictcost; +} + + +/* + * compute_semi_anti_join_factors + * Estimate how much of the inner input a SEMI, ANTI, or inner_unique join + * can be expected to scan. + * + * In a hash or nestloop SEMI/ANTI join, the executor will stop scanning + * inner rows as soon as it finds a match to the current outer row. + * The same happens if we have detected the inner rel is unique. + * We should therefore adjust some of the cost components for this effect. + * This function computes some estimates needed for these adjustments. + * These estimates will be the same regardless of the particular paths used + * for the outer and inner relation, so we compute these once and then pass + * them to all the join cost estimation functions. + * + * Input parameters: + * joinrel: join relation under consideration + * outerrel: outer relation under consideration + * innerrel: inner relation under consideration + * jointype: if not JOIN_SEMI or JOIN_ANTI, we assume it's inner_unique + * sjinfo: SpecialJoinInfo relevant to this join + * restrictlist: join quals + * Output parameters: + * *semifactors is filled in (see pathnodes.h for field definitions) + */ +void +compute_semi_anti_join_factors(PlannerInfo *root, + RelOptInfo *joinrel, + RelOptInfo *outerrel, + RelOptInfo *innerrel, + JoinType jointype, + SpecialJoinInfo *sjinfo, + List *restrictlist, + SemiAntiJoinFactors *semifactors) +{ + Selectivity jselec; + Selectivity nselec; + Selectivity avgmatch; + SpecialJoinInfo norm_sjinfo; + List *joinquals; + ListCell *l; + + /* + * In an ANTI join, we must ignore clauses that are "pushed down", since + * those won't affect the match logic. In a SEMI join, we do not + * distinguish joinquals from "pushed down" quals, so just use the whole + * restrictinfo list. For other outer join types, we should consider only + * non-pushed-down quals, so that this devolves to an IS_OUTER_JOIN check. + */ + if (IS_OUTER_JOIN(jointype)) + { + joinquals = NIL; + foreach(l, restrictlist) + { + RestrictInfo *rinfo = lfirst_node(RestrictInfo, l); + + if (!RINFO_IS_PUSHED_DOWN(rinfo, joinrel->relids)) + joinquals = lappend(joinquals, rinfo); + } + } + else + joinquals = restrictlist; + + /* + * Get the JOIN_SEMI or JOIN_ANTI selectivity of the join clauses. + */ + jselec = clauselist_selectivity(root, + joinquals, + 0, + (jointype == JOIN_ANTI) ? JOIN_ANTI : JOIN_SEMI, + sjinfo); + + /* + * Also get the normal inner-join selectivity of the join clauses. + */ + norm_sjinfo.type = T_SpecialJoinInfo; + norm_sjinfo.min_lefthand = outerrel->relids; + norm_sjinfo.min_righthand = innerrel->relids; + norm_sjinfo.syn_lefthand = outerrel->relids; + norm_sjinfo.syn_righthand = innerrel->relids; + norm_sjinfo.jointype = JOIN_INNER; + /* we don't bother trying to make the remaining fields valid */ + norm_sjinfo.lhs_strict = false; + norm_sjinfo.delay_upper_joins = false; + norm_sjinfo.semi_can_btree = false; + norm_sjinfo.semi_can_hash = false; + norm_sjinfo.semi_operators = NIL; + norm_sjinfo.semi_rhs_exprs = NIL; + + nselec = clauselist_selectivity(root, + joinquals, + 0, + JOIN_INNER, + &norm_sjinfo); + + /* Avoid leaking a lot of ListCells */ + if (IS_OUTER_JOIN(jointype)) + list_free(joinquals); + + /* + * jselec can be interpreted as the fraction of outer-rel rows that have + * any matches (this is true for both SEMI and ANTI cases). And nselec is + * the fraction of the Cartesian product that matches. So, the average + * number of matches for each outer-rel row that has at least one match is + * nselec * inner_rows / jselec. + * + * Note: it is correct to use the inner rel's "rows" count here, even + * though we might later be considering a parameterized inner path with + * fewer rows. This is because we have included all the join clauses in + * the selectivity estimate. + */ + if (jselec > 0) /* protect against zero divide */ + { + avgmatch = nselec * innerrel->rows / jselec; + /* Clamp to sane range */ + avgmatch = Max(1.0, avgmatch); + } + else + avgmatch = 1.0; + + semifactors->outer_match_frac = jselec; + semifactors->match_count = avgmatch; +} + +/* + * has_indexed_join_quals + * Check whether all the joinquals of a nestloop join are used as + * inner index quals. + * + * If the inner path of a SEMI/ANTI join is an indexscan (including bitmap + * indexscan) that uses all the joinquals as indexquals, we can assume that an + * unmatched outer tuple is cheap to process, whereas otherwise it's probably + * expensive. + */ +static bool +has_indexed_join_quals(NestPath *joinpath) +{ + Relids joinrelids = joinpath->path.parent->relids; + Path *innerpath = joinpath->innerjoinpath; + List *indexclauses; + bool found_one; + ListCell *lc; + + /* If join still has quals to evaluate, it's not fast */ + if (joinpath->joinrestrictinfo != NIL) + return false; + /* Nor if the inner path isn't parameterized at all */ + if (innerpath->param_info == NULL) + return false; + + /* Find the indexclauses list for the inner scan */ + switch (innerpath->pathtype) + { + case T_IndexScan: + case T_IndexOnlyScan: + indexclauses = ((IndexPath *) innerpath)->indexclauses; + break; + case T_BitmapHeapScan: + { + /* Accept only a simple bitmap scan, not AND/OR cases */ + Path *bmqual = ((BitmapHeapPath *) innerpath)->bitmapqual; + + if (IsA(bmqual, IndexPath)) + indexclauses = ((IndexPath *) bmqual)->indexclauses; + else + return false; + break; + } + default: + + /* + * If it's not a simple indexscan, it probably doesn't run quickly + * for zero rows out, even if it's a parameterized path using all + * the joinquals. + */ + return false; + } + + /* + * Examine the inner path's param clauses. Any that are from the outer + * path must be found in the indexclauses list, either exactly or in an + * equivalent form generated by equivclass.c. Also, we must find at least + * one such clause, else it's a clauseless join which isn't fast. + */ + found_one = false; + foreach(lc, innerpath->param_info->ppi_clauses) + { + RestrictInfo *rinfo = (RestrictInfo *) lfirst(lc); + + if (join_clause_is_movable_into(rinfo, + innerpath->parent->relids, + joinrelids)) + { + if (!is_redundant_with_indexclauses(rinfo, indexclauses)) + return false; + found_one = true; + } + } + return found_one; +} + + +/* + * approx_tuple_count + * Quick-and-dirty estimation of the number of join rows passing + * a set of qual conditions. + * + * The quals can be either an implicitly-ANDed list of boolean expressions, + * or a list of RestrictInfo nodes (typically the latter). + * + * We intentionally compute the selectivity under JOIN_INNER rules, even + * if it's some type of outer join. This is appropriate because we are + * trying to figure out how many tuples pass the initial merge or hash + * join step. + * + * This is quick-and-dirty because we bypass clauselist_selectivity, and + * simply multiply the independent clause selectivities together. Now + * clauselist_selectivity often can't do any better than that anyhow, but + * for some situations (such as range constraints) it is smarter. However, + * we can't effectively cache the results of clauselist_selectivity, whereas + * the individual clause selectivities can be and are cached. + * + * Since we are only using the results to estimate how many potential + * output tuples are generated and passed through qpqual checking, it + * seems OK to live with the approximation. + */ +static double +approx_tuple_count(PlannerInfo *root, JoinPath *path, List *quals) +{ + double tuples; + double outer_tuples = path->outerjoinpath->rows; + double inner_tuples = path->innerjoinpath->rows; + SpecialJoinInfo sjinfo; + Selectivity selec = 1.0; + ListCell *l; + + /* + * Make up a SpecialJoinInfo for JOIN_INNER semantics. + */ + sjinfo.type = T_SpecialJoinInfo; + sjinfo.min_lefthand = path->outerjoinpath->parent->relids; + sjinfo.min_righthand = path->innerjoinpath->parent->relids; + sjinfo.syn_lefthand = path->outerjoinpath->parent->relids; + sjinfo.syn_righthand = path->innerjoinpath->parent->relids; + sjinfo.jointype = JOIN_INNER; + /* we don't bother trying to make the remaining fields valid */ + sjinfo.lhs_strict = false; + sjinfo.delay_upper_joins = false; + sjinfo.semi_can_btree = false; + sjinfo.semi_can_hash = false; + sjinfo.semi_operators = NIL; + sjinfo.semi_rhs_exprs = NIL; + + /* Get the approximate selectivity */ + foreach(l, quals) + { + Node *qual = (Node *) lfirst(l); + + /* Note that clause_selectivity will be able to cache its result */ + selec *= clause_selectivity(root, qual, 0, JOIN_INNER, &sjinfo); + } + + /* Apply it to the input relation sizes */ + tuples = selec * outer_tuples * inner_tuples; + + return clamp_row_est(tuples); +} + + +/* + * set_baserel_size_estimates + * Set the size estimates for the given base relation. + * + * The rel's targetlist and restrictinfo list must have been constructed + * already, and rel->tuples must be set. + * + * We set the following fields of the rel node: + * rows: the estimated number of output tuples (after applying + * restriction clauses). + * width: the estimated average output tuple width in bytes. + * baserestrictcost: estimated cost of evaluating baserestrictinfo clauses. + */ +void +set_baserel_size_estimates(PlannerInfo *root, RelOptInfo *rel) +{ + double nrows; + + /* Should only be applied to base relations */ + Assert(rel->relid > 0); + + nrows = rel->tuples * + clauselist_selectivity(root, + rel->baserestrictinfo, + 0, + JOIN_INNER, + NULL); + + rel->rows = clamp_row_est(nrows); + + cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo, root); + + set_rel_width(root, rel); +} + +/* + * get_parameterized_baserel_size + * Make a size estimate for a parameterized scan of a base relation. + * + * 'param_clauses' lists the additional join clauses to be used. + * + * set_baserel_size_estimates must have been applied already. + */ +double +get_parameterized_baserel_size(PlannerInfo *root, RelOptInfo *rel, + List *param_clauses) +{ + List *allclauses; + double nrows; + + /* + * Estimate the number of rows returned by the parameterized scan, knowing + * that it will apply all the extra join clauses as well as the rel's own + * restriction clauses. Note that we force the clauses to be treated as + * non-join clauses during selectivity estimation. + */ + allclauses = list_concat_copy(param_clauses, rel->baserestrictinfo); + nrows = rel->tuples * + clauselist_selectivity(root, + allclauses, + rel->relid, /* do not use 0! */ + JOIN_INNER, + NULL); + nrows = clamp_row_est(nrows); + /* For safety, make sure result is not more than the base estimate */ + if (nrows > rel->rows) + nrows = rel->rows; + return nrows; +} + +/* + * set_joinrel_size_estimates + * Set the size estimates for the given join relation. + * + * The rel's targetlist must have been constructed already, and a + * restriction clause list that matches the given component rels must + * be provided. + * + * Since there is more than one way to make a joinrel for more than two + * base relations, the results we get here could depend on which component + * rel pair is provided. In theory we should get the same answers no matter + * which pair is provided; in practice, since the selectivity estimation + * routines don't handle all cases equally well, we might not. But there's + * not much to be done about it. (Would it make sense to repeat the + * calculations for each pair of input rels that's encountered, and somehow + * average the results? Probably way more trouble than it's worth, and + * anyway we must keep the rowcount estimate the same for all paths for the + * joinrel.) + * + * We set only the rows field here. The reltarget field was already set by + * build_joinrel_tlist, and baserestrictcost is not used for join rels. + */ +void +set_joinrel_size_estimates(PlannerInfo *root, RelOptInfo *rel, + RelOptInfo *outer_rel, + RelOptInfo *inner_rel, + SpecialJoinInfo *sjinfo, + List *restrictlist) +{ + rel->rows = calc_joinrel_size_estimate(root, + rel, + outer_rel, + inner_rel, + outer_rel->rows, + inner_rel->rows, + sjinfo, + restrictlist); +} + +/* + * get_parameterized_joinrel_size + * Make a size estimate for a parameterized scan of a join relation. + * + * 'rel' is the joinrel under consideration. + * 'outer_path', 'inner_path' are (probably also parameterized) Paths that + * produce the relations being joined. + * 'sjinfo' is any SpecialJoinInfo relevant to this join. + * 'restrict_clauses' lists the join clauses that need to be applied at the + * join node (including any movable clauses that were moved down to this join, + * and not including any movable clauses that were pushed down into the + * child paths). + * + * set_joinrel_size_estimates must have been applied already. + */ +double +get_parameterized_joinrel_size(PlannerInfo *root, RelOptInfo *rel, + Path *outer_path, + Path *inner_path, + SpecialJoinInfo *sjinfo, + List *restrict_clauses) +{ + double nrows; + + /* + * Estimate the number of rows returned by the parameterized join as the + * sizes of the input paths times the selectivity of the clauses that have + * ended up at this join node. + * + * As with set_joinrel_size_estimates, the rowcount estimate could depend + * on the pair of input paths provided, though ideally we'd get the same + * estimate for any pair with the same parameterization. + */ + nrows = calc_joinrel_size_estimate(root, + rel, + outer_path->parent, + inner_path->parent, + outer_path->rows, + inner_path->rows, + sjinfo, + restrict_clauses); + /* For safety, make sure result is not more than the base estimate */ + if (nrows > rel->rows) + nrows = rel->rows; + return nrows; +} + +/* + * calc_joinrel_size_estimate + * Workhorse for set_joinrel_size_estimates and + * get_parameterized_joinrel_size. + * + * outer_rel/inner_rel are the relations being joined, but they should be + * assumed to have sizes outer_rows/inner_rows; those numbers might be less + * than what rel->rows says, when we are considering parameterized paths. + */ +static double +calc_joinrel_size_estimate(PlannerInfo *root, + RelOptInfo *joinrel, + RelOptInfo *outer_rel, + RelOptInfo *inner_rel, + double outer_rows, + double inner_rows, + SpecialJoinInfo *sjinfo, + List *restrictlist_in) +{ + /* This apparently-useless variable dodges a compiler bug in VS2013: */ + List *restrictlist = restrictlist_in; + JoinType jointype = sjinfo->jointype; + Selectivity fkselec; + Selectivity jselec; + Selectivity pselec; + double nrows; + + /* + * Compute joinclause selectivity. Note that we are only considering + * clauses that become restriction clauses at this join level; we are not + * double-counting them because they were not considered in estimating the + * sizes of the component rels. + * + * First, see whether any of the joinclauses can be matched to known FK + * constraints. If so, drop those clauses from the restrictlist, and + * instead estimate their selectivity using FK semantics. (We do this + * without regard to whether said clauses are local or "pushed down". + * Probably, an FK-matching clause could never be seen as pushed down at + * an outer join, since it would be strict and hence would be grounds for + * join strength reduction.) fkselec gets the net selectivity for + * FK-matching clauses, or 1.0 if there are none. + */ + fkselec = get_foreign_key_join_selectivity(root, + outer_rel->relids, + inner_rel->relids, + sjinfo, + &restrictlist); + + /* + * For an outer join, we have to distinguish the selectivity of the join's + * own clauses (JOIN/ON conditions) from any clauses that were "pushed + * down". For inner joins we just count them all as joinclauses. + */ + if (IS_OUTER_JOIN(jointype)) + { + List *joinquals = NIL; + List *pushedquals = NIL; + ListCell *l; + + /* Grovel through the clauses to separate into two lists */ + foreach(l, restrictlist) + { + RestrictInfo *rinfo = lfirst_node(RestrictInfo, l); + + if (RINFO_IS_PUSHED_DOWN(rinfo, joinrel->relids)) + pushedquals = lappend(pushedquals, rinfo); + else + joinquals = lappend(joinquals, rinfo); + } + + /* Get the separate selectivities */ + jselec = clauselist_selectivity(root, + joinquals, + 0, + jointype, + sjinfo); + pselec = clauselist_selectivity(root, + pushedquals, + 0, + jointype, + sjinfo); + + /* Avoid leaking a lot of ListCells */ + list_free(joinquals); + list_free(pushedquals); + } + else + { + jselec = clauselist_selectivity(root, + restrictlist, + 0, + jointype, + sjinfo); + pselec = 0.0; /* not used, keep compiler quiet */ + } + + /* + * Basically, we multiply size of Cartesian product by selectivity. + * + * If we are doing an outer join, take that into account: the joinqual + * selectivity has to be clamped using the knowledge that the output must + * be at least as large as the non-nullable input. However, any + * pushed-down quals are applied after the outer join, so their + * selectivity applies fully. + * + * For JOIN_SEMI and JOIN_ANTI, the selectivity is defined as the fraction + * of LHS rows that have matches, and we apply that straightforwardly. + */ + switch (jointype) + { + case JOIN_INNER: + nrows = outer_rows * inner_rows * fkselec * jselec; + /* pselec not used */ + break; + case JOIN_LEFT: + nrows = outer_rows * inner_rows * fkselec * jselec; + if (nrows < outer_rows) + nrows = outer_rows; + nrows *= pselec; + break; + case JOIN_FULL: + nrows = outer_rows * inner_rows * fkselec * jselec; + if (nrows < outer_rows) + nrows = outer_rows; + if (nrows < inner_rows) + nrows = inner_rows; + nrows *= pselec; + break; + case JOIN_SEMI: + nrows = outer_rows * fkselec * jselec; + /* pselec not used */ + break; + case JOIN_ANTI: + nrows = outer_rows * (1.0 - fkselec * jselec); + nrows *= pselec; + break; + default: + /* other values not expected here */ + elog(ERROR, "unrecognized join type: %d", (int) jointype); + nrows = 0; /* keep compiler quiet */ + break; + } + + return clamp_row_est(nrows); +} + +/* + * get_foreign_key_join_selectivity + * Estimate join selectivity for foreign-key-related clauses. + * + * Remove any clauses that can be matched to FK constraints from *restrictlist, + * and return a substitute estimate of their selectivity. 1.0 is returned + * when there are no such clauses. + * + * The reason for treating such clauses specially is that we can get better + * estimates this way than by relying on clauselist_selectivity(), especially + * for multi-column FKs where that function's assumption that the clauses are + * independent falls down badly. But even with single-column FKs, we may be + * able to get a better answer when the pg_statistic stats are missing or out + * of date. + */ +static Selectivity +get_foreign_key_join_selectivity(PlannerInfo *root, + Relids outer_relids, + Relids inner_relids, + SpecialJoinInfo *sjinfo, + List **restrictlist) +{ + Selectivity fkselec = 1.0; + JoinType jointype = sjinfo->jointype; + List *worklist = *restrictlist; + ListCell *lc; + + /* Consider each FK constraint that is known to match the query */ + foreach(lc, root->fkey_list) + { + ForeignKeyOptInfo *fkinfo = (ForeignKeyOptInfo *) lfirst(lc); + bool ref_is_outer; + List *removedlist; + ListCell *cell; + + /* + * This FK is not relevant unless it connects a baserel on one side of + * this join to a baserel on the other side. + */ + if (bms_is_member(fkinfo->con_relid, outer_relids) && + bms_is_member(fkinfo->ref_relid, inner_relids)) + ref_is_outer = false; + else if (bms_is_member(fkinfo->ref_relid, outer_relids) && + bms_is_member(fkinfo->con_relid, inner_relids)) + ref_is_outer = true; + else + continue; + + /* + * If we're dealing with a semi/anti join, and the FK's referenced + * relation is on the outside, then knowledge of the FK doesn't help + * us figure out what we need to know (which is the fraction of outer + * rows that have matches). On the other hand, if the referenced rel + * is on the inside, then all outer rows must have matches in the + * referenced table (ignoring nulls). But any restriction or join + * clauses that filter that table will reduce the fraction of matches. + * We can account for restriction clauses, but it's too hard to guess + * how many table rows would get through a join that's inside the RHS. + * Hence, if either case applies, punt and ignore the FK. + */ + if ((jointype == JOIN_SEMI || jointype == JOIN_ANTI) && + (ref_is_outer || bms_membership(inner_relids) != BMS_SINGLETON)) + continue; + + /* + * Modify the restrictlist by removing clauses that match the FK (and + * putting them into removedlist instead). It seems unsafe to modify + * the originally-passed List structure, so we make a shallow copy the + * first time through. + */ + if (worklist == *restrictlist) + worklist = list_copy(worklist); + + removedlist = NIL; + foreach(cell, worklist) + { + RestrictInfo *rinfo = (RestrictInfo *) lfirst(cell); + bool remove_it = false; + int i; + + /* Drop this clause if it matches any column of the FK */ + for (i = 0; i < fkinfo->nkeys; i++) + { + if (rinfo->parent_ec) + { + /* + * EC-derived clauses can only match by EC. It is okay to + * consider any clause derived from the same EC as + * matching the FK: even if equivclass.c chose to generate + * a clause equating some other pair of Vars, it could + * have generated one equating the FK's Vars. So for + * purposes of estimation, we can act as though it did so. + * + * Note: checking parent_ec is a bit of a cheat because + * there are EC-derived clauses that don't have parent_ec + * set; but such clauses must compare expressions that + * aren't just Vars, so they cannot match the FK anyway. + */ + if (fkinfo->eclass[i] == rinfo->parent_ec) + { + remove_it = true; + break; + } + } + else + { + /* + * Otherwise, see if rinfo was previously matched to FK as + * a "loose" clause. + */ + if (list_member_ptr(fkinfo->rinfos[i], rinfo)) + { + remove_it = true; + break; + } + } + } + if (remove_it) + { + worklist = foreach_delete_current(worklist, cell); + removedlist = lappend(removedlist, rinfo); + } + } + + /* + * If we failed to remove all the matching clauses we expected to + * find, chicken out and ignore this FK; applying its selectivity + * might result in double-counting. Put any clauses we did manage to + * remove back into the worklist. + * + * Since the matching clauses are known not outerjoin-delayed, they + * would normally have appeared in the initial joinclause list. If we + * didn't find them, there are two possibilities: + * + * 1. If the FK match is based on an EC that is ec_has_const, it won't + * have generated any join clauses at all. We discount such ECs while + * checking to see if we have "all" the clauses. (Below, we'll adjust + * the selectivity estimate for this case.) + * + * 2. The clauses were matched to some other FK in a previous + * iteration of this loop, and thus removed from worklist. (A likely + * case is that two FKs are matched to the same EC; there will be only + * one EC-derived clause in the initial list, so the first FK will + * consume it.) Applying both FKs' selectivity independently risks + * underestimating the join size; in particular, this would undo one + * of the main things that ECs were invented for, namely to avoid + * double-counting the selectivity of redundant equality conditions. + * Later we might think of a reasonable way to combine the estimates, + * but for now, just punt, since this is a fairly uncommon situation. + */ + if (removedlist == NIL || + list_length(removedlist) != + (fkinfo->nmatched_ec - fkinfo->nconst_ec + fkinfo->nmatched_ri)) + { + worklist = list_concat(worklist, removedlist); + continue; + } + + /* + * Finally we get to the payoff: estimate selectivity using the + * knowledge that each referencing row will match exactly one row in + * the referenced table. + * + * XXX that's not true in the presence of nulls in the referencing + * column(s), so in principle we should derate the estimate for those. + * However (1) if there are any strict restriction clauses for the + * referencing column(s) elsewhere in the query, derating here would + * be double-counting the null fraction, and (2) it's not very clear + * how to combine null fractions for multiple referencing columns. So + * we do nothing for now about correcting for nulls. + * + * XXX another point here is that if either side of an FK constraint + * is an inheritance parent, we estimate as though the constraint + * covers all its children as well. This is not an unreasonable + * assumption for a referencing table, ie the user probably applied + * identical constraints to all child tables (though perhaps we ought + * to check that). But it's not possible to have done that for a + * referenced table. Fortunately, precisely because that doesn't + * work, it is uncommon in practice to have an FK referencing a parent + * table. So, at least for now, disregard inheritance here. + */ + if (jointype == JOIN_SEMI || jointype == JOIN_ANTI) + { + /* + * For JOIN_SEMI and JOIN_ANTI, we only get here when the FK's + * referenced table is exactly the inside of the join. The join + * selectivity is defined as the fraction of LHS rows that have + * matches. The FK implies that every LHS row has a match *in the + * referenced table*; but any restriction clauses on it will + * reduce the number of matches. Hence we take the join + * selectivity as equal to the selectivity of the table's + * restriction clauses, which is rows / tuples; but we must guard + * against tuples == 0. + */ + RelOptInfo *ref_rel = find_base_rel(root, fkinfo->ref_relid); + double ref_tuples = Max(ref_rel->tuples, 1.0); + + fkselec *= ref_rel->rows / ref_tuples; + } + else + { + /* + * Otherwise, selectivity is exactly 1/referenced-table-size; but + * guard against tuples == 0. Note we should use the raw table + * tuple count, not any estimate of its filtered or joined size. + */ + RelOptInfo *ref_rel = find_base_rel(root, fkinfo->ref_relid); + double ref_tuples = Max(ref_rel->tuples, 1.0); + + fkselec *= 1.0 / ref_tuples; + } + + /* + * If any of the FK columns participated in ec_has_const ECs, then + * equivclass.c will have generated "var = const" restrictions for + * each side of the join, thus reducing the sizes of both input + * relations. Taking the fkselec at face value would amount to + * double-counting the selectivity of the constant restriction for the + * referencing Var. Hence, look for the restriction clause(s) that + * were applied to the referencing Var(s), and divide out their + * selectivity to correct for this. + */ + if (fkinfo->nconst_ec > 0) + { + for (int i = 0; i < fkinfo->nkeys; i++) + { + EquivalenceClass *ec = fkinfo->eclass[i]; + + if (ec && ec->ec_has_const) + { + EquivalenceMember *em = fkinfo->fk_eclass_member[i]; + RestrictInfo *rinfo = find_derived_clause_for_ec_member(ec, + em); + + if (rinfo) + { + Selectivity s0; + + s0 = clause_selectivity(root, + (Node *) rinfo, + 0, + jointype, + sjinfo); + if (s0 > 0) + fkselec /= s0; + } + } + } + } + } + + *restrictlist = worklist; + CLAMP_PROBABILITY(fkselec); + return fkselec; +} + +/* + * set_subquery_size_estimates + * Set the size estimates for a base relation that is a subquery. + * + * The rel's targetlist and restrictinfo list must have been constructed + * already, and the Paths for the subquery must have been completed. + * We look at the subquery's PlannerInfo to extract data. + * + * We set the same fields as set_baserel_size_estimates. + */ +void +set_subquery_size_estimates(PlannerInfo *root, RelOptInfo *rel) +{ + PlannerInfo *subroot = rel->subroot; + RelOptInfo *sub_final_rel; + ListCell *lc; + + /* Should only be applied to base relations that are subqueries */ + Assert(rel->relid > 0); + Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_SUBQUERY); + + /* + * Copy raw number of output rows from subquery. All of its paths should + * have the same output rowcount, so just look at cheapest-total. + */ + sub_final_rel = fetch_upper_rel(subroot, UPPERREL_FINAL, NULL); + rel->tuples = sub_final_rel->cheapest_total_path->rows; + + /* + * Compute per-output-column width estimates by examining the subquery's + * targetlist. For any output that is a plain Var, get the width estimate + * that was made while planning the subquery. Otherwise, we leave it to + * set_rel_width to fill in a datatype-based default estimate. + */ + foreach(lc, subroot->parse->targetList) + { + TargetEntry *te = lfirst_node(TargetEntry, lc); + Node *texpr = (Node *) te->expr; + int32 item_width = 0; + + /* junk columns aren't visible to upper query */ + if (te->resjunk) + continue; + + /* + * The subquery could be an expansion of a view that's had columns + * added to it since the current query was parsed, so that there are + * non-junk tlist columns in it that don't correspond to any column + * visible at our query level. Ignore such columns. + */ + if (te->resno < rel->min_attr || te->resno > rel->max_attr) + continue; + + /* + * XXX This currently doesn't work for subqueries containing set + * operations, because the Vars in their tlists are bogus references + * to the first leaf subquery, which wouldn't give the right answer + * even if we could still get to its PlannerInfo. + * + * Also, the subquery could be an appendrel for which all branches are + * known empty due to constraint exclusion, in which case + * set_append_rel_pathlist will have left the attr_widths set to zero. + * + * In either case, we just leave the width estimate zero until + * set_rel_width fixes it. + */ + if (IsA(texpr, Var) && + subroot->parse->setOperations == NULL) + { + Var *var = (Var *) texpr; + RelOptInfo *subrel = find_base_rel(subroot, var->varno); + + item_width = subrel->attr_widths[var->varattno - subrel->min_attr]; + } + rel->attr_widths[te->resno - rel->min_attr] = item_width; + } + + /* Now estimate number of output rows, etc */ + set_baserel_size_estimates(root, rel); +} + +/* + * set_function_size_estimates + * Set the size estimates for a base relation that is a function call. + * + * The rel's targetlist and restrictinfo list must have been constructed + * already. + * + * We set the same fields as set_baserel_size_estimates. + */ +void +set_function_size_estimates(PlannerInfo *root, RelOptInfo *rel) +{ + RangeTblEntry *rte; + ListCell *lc; + + /* Should only be applied to base relations that are functions */ + Assert(rel->relid > 0); + rte = planner_rt_fetch(rel->relid, root); + Assert(rte->rtekind == RTE_FUNCTION); + + /* + * Estimate number of rows the functions will return. The rowcount of the + * node is that of the largest function result. + */ + rel->tuples = 0; + foreach(lc, rte->functions) + { + RangeTblFunction *rtfunc = (RangeTblFunction *) lfirst(lc); + double ntup = expression_returns_set_rows(root, rtfunc->funcexpr); + + if (ntup > rel->tuples) + rel->tuples = ntup; + } + + /* Now estimate number of output rows, etc */ + set_baserel_size_estimates(root, rel); +} + +/* + * set_function_size_estimates + * Set the size estimates for a base relation that is a function call. + * + * The rel's targetlist and restrictinfo list must have been constructed + * already. + * + * We set the same fields as set_tablefunc_size_estimates. + */ +void +set_tablefunc_size_estimates(PlannerInfo *root, RelOptInfo *rel) +{ + /* Should only be applied to base relations that are functions */ + Assert(rel->relid > 0); + Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_TABLEFUNC); + + rel->tuples = 100; + + /* Now estimate number of output rows, etc */ + set_baserel_size_estimates(root, rel); +} + +/* + * set_values_size_estimates + * Set the size estimates for a base relation that is a values list. + * + * The rel's targetlist and restrictinfo list must have been constructed + * already. + * + * We set the same fields as set_baserel_size_estimates. + */ +void +set_values_size_estimates(PlannerInfo *root, RelOptInfo *rel) +{ + RangeTblEntry *rte; + + /* Should only be applied to base relations that are values lists */ + Assert(rel->relid > 0); + rte = planner_rt_fetch(rel->relid, root); + Assert(rte->rtekind == RTE_VALUES); + + /* + * Estimate number of rows the values list will return. We know this + * precisely based on the list length (well, barring set-returning + * functions in list items, but that's a refinement not catered for + * anywhere else either). + */ + rel->tuples = list_length(rte->values_lists); + + /* Now estimate number of output rows, etc */ + set_baserel_size_estimates(root, rel); +} + +/* + * set_cte_size_estimates + * Set the size estimates for a base relation that is a CTE reference. + * + * The rel's targetlist and restrictinfo list must have been constructed + * already, and we need an estimate of the number of rows returned by the CTE + * (if a regular CTE) or the non-recursive term (if a self-reference). + * + * We set the same fields as set_baserel_size_estimates. + */ +void +set_cte_size_estimates(PlannerInfo *root, RelOptInfo *rel, double cte_rows) +{ + RangeTblEntry *rte; + + /* Should only be applied to base relations that are CTE references */ + Assert(rel->relid > 0); + rte = planner_rt_fetch(rel->relid, root); + Assert(rte->rtekind == RTE_CTE); + + if (rte->self_reference) + { + /* + * In a self-reference, arbitrarily assume the average worktable size + * is about 10 times the nonrecursive term's size. + */ + rel->tuples = 10 * cte_rows; + } + else + { + /* Otherwise just believe the CTE's rowcount estimate */ + rel->tuples = cte_rows; + } + + /* Now estimate number of output rows, etc */ + set_baserel_size_estimates(root, rel); +} + +/* + * set_namedtuplestore_size_estimates + * Set the size estimates for a base relation that is a tuplestore reference. + * + * The rel's targetlist and restrictinfo list must have been constructed + * already. + * + * We set the same fields as set_baserel_size_estimates. + */ +void +set_namedtuplestore_size_estimates(PlannerInfo *root, RelOptInfo *rel) +{ + RangeTblEntry *rte; + + /* Should only be applied to base relations that are tuplestore references */ + Assert(rel->relid > 0); + rte = planner_rt_fetch(rel->relid, root); + Assert(rte->rtekind == RTE_NAMEDTUPLESTORE); + + /* + * Use the estimate provided by the code which is generating the named + * tuplestore. In some cases, the actual number might be available; in + * others the same plan will be re-used, so a "typical" value might be + * estimated and used. + */ + rel->tuples = rte->enrtuples; + if (rel->tuples < 0) + rel->tuples = 1000; + + /* Now estimate number of output rows, etc */ + set_baserel_size_estimates(root, rel); +} + +/* + * set_result_size_estimates + * Set the size estimates for an RTE_RESULT base relation + * + * The rel's targetlist and restrictinfo list must have been constructed + * already. + * + * We set the same fields as set_baserel_size_estimates. + */ +void +set_result_size_estimates(PlannerInfo *root, RelOptInfo *rel) +{ + /* Should only be applied to RTE_RESULT base relations */ + Assert(rel->relid > 0); + Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_RESULT); + + /* RTE_RESULT always generates a single row, natively */ + rel->tuples = 1; + + /* Now estimate number of output rows, etc */ + set_baserel_size_estimates(root, rel); +} + +/* + * set_foreign_size_estimates + * Set the size estimates for a base relation that is a foreign table. + * + * There is not a whole lot that we can do here; the foreign-data wrapper + * is responsible for producing useful estimates. We can do a decent job + * of estimating baserestrictcost, so we set that, and we also set up width + * using what will be purely datatype-driven estimates from the targetlist. + * There is no way to do anything sane with the rows value, so we just put + * a default estimate and hope that the wrapper can improve on it. The + * wrapper's GetForeignRelSize function will be called momentarily. + * + * The rel's targetlist and restrictinfo list must have been constructed + * already. + */ +void +set_foreign_size_estimates(PlannerInfo *root, RelOptInfo *rel) +{ + /* Should only be applied to base relations */ + Assert(rel->relid > 0); + + rel->rows = 1000; /* entirely bogus default estimate */ + + cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo, root); + + set_rel_width(root, rel); +} + + +/* + * set_rel_width + * Set the estimated output width of a base relation. + * + * The estimated output width is the sum of the per-attribute width estimates + * for the actually-referenced columns, plus any PHVs or other expressions + * that have to be calculated at this relation. This is the amount of data + * we'd need to pass upwards in case of a sort, hash, etc. + * + * This function also sets reltarget->cost, so it's a bit misnamed now. + * + * NB: this works best on plain relations because it prefers to look at + * real Vars. For subqueries, set_subquery_size_estimates will already have + * copied up whatever per-column estimates were made within the subquery, + * and for other types of rels there isn't much we can do anyway. We fall + * back on (fairly stupid) datatype-based width estimates if we can't get + * any better number. + * + * The per-attribute width estimates are cached for possible re-use while + * building join relations or post-scan/join pathtargets. + */ +static void +set_rel_width(PlannerInfo *root, RelOptInfo *rel) +{ + Oid reloid = planner_rt_fetch(rel->relid, root)->relid; + int32 tuple_width = 0; + bool have_wholerow_var = false; + ListCell *lc; + + /* Vars are assumed to have cost zero, but other exprs do not */ + rel->reltarget->cost.startup = 0; + rel->reltarget->cost.per_tuple = 0; + + foreach(lc, rel->reltarget->exprs) + { + Node *node = (Node *) lfirst(lc); + + /* + * Ordinarily, a Var in a rel's targetlist must belong to that rel; + * but there are corner cases involving LATERAL references where that + * isn't so. If the Var has the wrong varno, fall through to the + * generic case (it doesn't seem worth the trouble to be any smarter). + */ + if (IsA(node, Var) && + ((Var *) node)->varno == rel->relid) + { + Var *var = (Var *) node; + int ndx; + int32 item_width; + + Assert(var->varattno >= rel->min_attr); + Assert(var->varattno <= rel->max_attr); + + ndx = var->varattno - rel->min_attr; + + /* + * If it's a whole-row Var, we'll deal with it below after we have + * already cached as many attr widths as possible. + */ + if (var->varattno == 0) + { + have_wholerow_var = true; + continue; + } + + /* + * The width may have been cached already (especially if it's a + * subquery), so don't duplicate effort. + */ + if (rel->attr_widths[ndx] > 0) + { + tuple_width += rel->attr_widths[ndx]; + continue; + } + + /* Try to get column width from statistics */ + if (reloid != InvalidOid && var->varattno > 0) + { + item_width = get_attavgwidth(reloid, var->varattno); + if (item_width > 0) + { + rel->attr_widths[ndx] = item_width; + tuple_width += item_width; + continue; + } + } + + /* + * Not a plain relation, or can't find statistics for it. Estimate + * using just the type info. + */ + item_width = get_typavgwidth(var->vartype, var->vartypmod); + Assert(item_width > 0); + rel->attr_widths[ndx] = item_width; + tuple_width += item_width; + } + else if (IsA(node, PlaceHolderVar)) + { + /* + * We will need to evaluate the PHV's contained expression while + * scanning this rel, so be sure to include it in reltarget->cost. + */ + PlaceHolderVar *phv = (PlaceHolderVar *) node; + PlaceHolderInfo *phinfo = find_placeholder_info(root, phv, false); + QualCost cost; + + tuple_width += phinfo->ph_width; + cost_qual_eval_node(&cost, (Node *) phv->phexpr, root); + rel->reltarget->cost.startup += cost.startup; + rel->reltarget->cost.per_tuple += cost.per_tuple; + } + else + { + /* + * We could be looking at an expression pulled up from a subquery, + * or a ROW() representing a whole-row child Var, etc. Do what we + * can using the expression type information. + */ + int32 item_width; + QualCost cost; + + item_width = get_typavgwidth(exprType(node), exprTypmod(node)); + Assert(item_width > 0); + tuple_width += item_width; + /* Not entirely clear if we need to account for cost, but do so */ + cost_qual_eval_node(&cost, node, root); + rel->reltarget->cost.startup += cost.startup; + rel->reltarget->cost.per_tuple += cost.per_tuple; + } + } + + /* + * If we have a whole-row reference, estimate its width as the sum of + * per-column widths plus heap tuple header overhead. + */ + if (have_wholerow_var) + { + int32 wholerow_width = MAXALIGN(SizeofHeapTupleHeader); + + if (reloid != InvalidOid) + { + /* Real relation, so estimate true tuple width */ + wholerow_width += get_relation_data_width(reloid, + rel->attr_widths - rel->min_attr); + } + else + { + /* Do what we can with info for a phony rel */ + AttrNumber i; + + for (i = 1; i <= rel->max_attr; i++) + wholerow_width += rel->attr_widths[i - rel->min_attr]; + } + + rel->attr_widths[0 - rel->min_attr] = wholerow_width; + + /* + * Include the whole-row Var as part of the output tuple. Yes, that + * really is what happens at runtime. + */ + tuple_width += wholerow_width; + } + + Assert(tuple_width >= 0); + rel->reltarget->width = tuple_width; +} + +/* + * set_pathtarget_cost_width + * Set the estimated eval cost and output width of a PathTarget tlist. + * + * As a notational convenience, returns the same PathTarget pointer passed in. + * + * Most, though not quite all, uses of this function occur after we've run + * set_rel_width() for base relations; so we can usually obtain cached width + * estimates for Vars. If we can't, fall back on datatype-based width + * estimates. Present early-planning uses of PathTargets don't need accurate + * widths badly enough to justify going to the catalogs for better data. + */ +PathTarget * +set_pathtarget_cost_width(PlannerInfo *root, PathTarget *target) +{ + int32 tuple_width = 0; + ListCell *lc; + + /* Vars are assumed to have cost zero, but other exprs do not */ + target->cost.startup = 0; + target->cost.per_tuple = 0; + + foreach(lc, target->exprs) + { + Node *node = (Node *) lfirst(lc); + + if (IsA(node, Var)) + { + Var *var = (Var *) node; + int32 item_width; + + /* We should not see any upper-level Vars here */ + Assert(var->varlevelsup == 0); + + /* Try to get data from RelOptInfo cache */ + if (var->varno < root->simple_rel_array_size) + { + RelOptInfo *rel = root->simple_rel_array[var->varno]; + + if (rel != NULL && + var->varattno >= rel->min_attr && + var->varattno <= rel->max_attr) + { + int ndx = var->varattno - rel->min_attr; + + if (rel->attr_widths[ndx] > 0) + { + tuple_width += rel->attr_widths[ndx]; + continue; + } + } + } + + /* + * No cached data available, so estimate using just the type info. + */ + item_width = get_typavgwidth(var->vartype, var->vartypmod); + Assert(item_width > 0); + tuple_width += item_width; + } + else + { + /* + * Handle general expressions using type info. + */ + int32 item_width; + QualCost cost; + + item_width = get_typavgwidth(exprType(node), exprTypmod(node)); + Assert(item_width > 0); + tuple_width += item_width; + + /* Account for cost, too */ + cost_qual_eval_node(&cost, node, root); + target->cost.startup += cost.startup; + target->cost.per_tuple += cost.per_tuple; + } + } + + Assert(tuple_width >= 0); + target->width = tuple_width; + + return target; +} + +/* + * relation_byte_size + * Estimate the storage space in bytes for a given number of tuples + * of a given width (size in bytes). + */ +static double +relation_byte_size(double tuples, int width) +{ + return tuples * (MAXALIGN(width) + MAXALIGN(SizeofHeapTupleHeader)); +} + +/* + * page_size + * Returns an estimate of the number of pages covered by a given + * number of tuples of a given width (size in bytes). + */ +static double +page_size(double tuples, int width) +{ + return ceil(relation_byte_size(tuples, width) / BLCKSZ); +} + +/* + * Estimate the fraction of the work that each worker will do given the + * number of workers budgeted for the path. + */ +static double +get_parallel_divisor(Path *path) +{ + double parallel_divisor = path->parallel_workers; + + /* + * Early experience with parallel query suggests that when there is only + * one worker, the leader often makes a very substantial contribution to + * executing the parallel portion of the plan, but as more workers are + * added, it does less and less, because it's busy reading tuples from the + * workers and doing whatever non-parallel post-processing is needed. By + * the time we reach 4 workers, the leader no longer makes a meaningful + * contribution. Thus, for now, estimate that the leader spends 30% of + * its time servicing each worker, and the remainder executing the + * parallel plan. + */ + if (parallel_leader_participation) + { + double leader_contribution; + + leader_contribution = 1.0 - (0.3 * path->parallel_workers); + if (leader_contribution > 0) + parallel_divisor += leader_contribution; + } + + return parallel_divisor; +} + +/* + * compute_bitmap_pages + * + * compute number of pages fetched from heap in bitmap heap scan. + */ +double +compute_bitmap_pages(PlannerInfo *root, RelOptInfo *baserel, Path *bitmapqual, + int loop_count, Cost *cost, double *tuple) +{ + Cost indexTotalCost; + Selectivity indexSelectivity; + double T; + double pages_fetched; + double tuples_fetched; + double heap_pages; + long maxentries; + + /* + * Fetch total cost of obtaining the bitmap, as well as its total + * selectivity. + */ + cost_bitmap_tree_node(bitmapqual, &indexTotalCost, &indexSelectivity); + + /* + * Estimate number of main-table pages fetched. + */ + tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples); + + T = (baserel->pages > 1) ? (double) baserel->pages : 1.0; + + /* + * For a single scan, the number of heap pages that need to be fetched is + * the same as the Mackert and Lohman formula for the case T <= b (ie, no + * re-reads needed). + */ + pages_fetched = (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched); + + /* + * Calculate the number of pages fetched from the heap. Then based on + * current work_mem estimate get the estimated maxentries in the bitmap. + * (Note that we always do this calculation based on the number of pages + * that would be fetched in a single iteration, even if loop_count > 1. + * That's correct, because only that number of entries will be stored in + * the bitmap at one time.) + */ + heap_pages = Min(pages_fetched, baserel->pages); + maxentries = tbm_calculate_entries(work_mem * 1024L); + + if (loop_count > 1) + { + /* + * For repeated bitmap scans, scale up the number of tuples fetched in + * the Mackert and Lohman formula by the number of scans, so that we + * estimate the number of pages fetched by all the scans. Then + * pro-rate for one scan. + */ + pages_fetched = index_pages_fetched(tuples_fetched * loop_count, + baserel->pages, + get_indexpath_pages(bitmapqual), + root); + pages_fetched /= loop_count; + } + + if (pages_fetched >= T) + pages_fetched = T; + else + pages_fetched = ceil(pages_fetched); + + if (maxentries < heap_pages) + { + double exact_pages; + double lossy_pages; + + /* + * Crude approximation of the number of lossy pages. Because of the + * way tbm_lossify() is coded, the number of lossy pages increases + * very sharply as soon as we run short of memory; this formula has + * that property and seems to perform adequately in testing, but it's + * possible we could do better somehow. + */ + lossy_pages = Max(0, heap_pages - maxentries / 2); + exact_pages = heap_pages - lossy_pages; + + /* + * If there are lossy pages then recompute the number of tuples + * processed by the bitmap heap node. We assume here that the chance + * of a given tuple coming from an exact page is the same as the + * chance that a given page is exact. This might not be true, but + * it's not clear how we can do any better. + */ + if (lossy_pages > 0) + tuples_fetched = + clamp_row_est(indexSelectivity * + (exact_pages / heap_pages) * baserel->tuples + + (lossy_pages / heap_pages) * baserel->tuples); + } + + if (cost) + *cost = indexTotalCost; + if (tuple) + *tuple = tuples_fetched; + + return pages_fetched; +} |