/*------------------------------------------------------------------------- * * costsize.c * Routines to compute (and set) relation sizes and path costs * * Path costs are measured in arbitrary units established by these basic * parameters: * * seq_page_cost Cost of a sequential page fetch * random_page_cost Cost of a non-sequential page fetch * cpu_tuple_cost Cost of typical CPU time to process a tuple * cpu_index_tuple_cost Cost of typical CPU time to process an index tuple * cpu_operator_cost Cost of CPU time to execute an operator or function * * We expect that the kernel will typically do some amount of read-ahead * optimization; this in conjunction with seek costs means that seq_page_cost * is normally considerably less than random_page_cost. (However, if the * database is fully cached in RAM, it is reasonable to set them equal.) * * We also use a rough estimate "effective_cache_size" of the number of * disk pages in Postgres + OS-level disk cache. (We can't simply use * NBuffers for this purpose because that would ignore the effects of * the kernel's disk cache.) * * Obviously, taking constants for these values is an oversimplification, * but it's tough enough to get any useful estimates even at this level of * detail. Note that all of these parameters are user-settable, in case * the default values are drastically off for a particular platform. * * seq_page_cost and random_page_cost can also be overridden for an individual * tablespace, in case some data is on a fast disk and other data is on a slow * disk. Per-tablespace overrides never apply to temporary work files such as * an external sort or a materialize node that overflows work_mem. * * We compute two separate costs for each path: * total_cost: total estimated cost to fetch all tuples * startup_cost: cost that is expended before first tuple is fetched * In some scenarios, such as when there is a LIMIT or we are implementing * an EXISTS(...) sub-select, it is not necessary to fetch all tuples of the * path's result. A caller can estimate the cost of fetching a partial * result by interpolating between startup_cost and total_cost. In detail: * actual_cost = startup_cost + * (total_cost - startup_cost) * tuples_to_fetch / path->parent->rows; * Note that a base relation's rows count (and, by extension, plan_rows for * plan nodes below the LIMIT node) are set without regard to any LIMIT, so * that this equation works properly. (Also, these routines guarantee not to * set the rows count to zero, so there will be no zero divide.) The LIMIT is * applied as a top-level plan node. * * For largely historical reasons, most of the routines in this module use * the passed result Path only to store their startup_cost and total_cost * results into. All the input data they need is passed as separate * parameters, even though much of it could be extracted from the Path. * An exception is made for the cost_XXXjoin() routines, which expect all * the non-cost 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) 1996-2011, PostgreSQL Global Development Group * Portions Copyright (c) 1994, Regents of the University of California * * IDENTIFICATION * src/backend/optimizer/path/costsize.c * *------------------------------------------------------------------------- */ #include "postgres.h" #include #include "executor/executor.h" #include "executor/nodeHash.h" #include "miscadmin.h" #include "nodes/nodeFuncs.h" #include "optimizer/clauses.h" #include "optimizer/cost.h" #include "optimizer/pathnode.h" #include "optimizer/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) /* * Some Paths return less than the nominal number of rows of their parent * relations; join nodes need to do this to get the correct input count: */ #define PATH_ROWS(path) \ (IsA(path, UniquePath) ? \ ((UniquePath *) (path))->rows : \ (path)->parent->rows) double seq_page_cost = DEFAULT_SEQ_PAGE_COST; double random_page_cost = DEFAULT_RANDOM_PAGE_COST; double cpu_tuple_cost = DEFAULT_CPU_TUPLE_COST; double cpu_index_tuple_cost = DEFAULT_CPU_INDEX_TUPLE_COST; double cpu_operator_cost = DEFAULT_CPU_OPERATOR_COST; int effective_cache_size = DEFAULT_EFFECTIVE_CACHE_SIZE; Cost disable_cost = 1.0e10; bool enable_seqscan = true; bool enable_indexscan = true; bool enable_indexonlyscan = true; bool enable_bitmapscan = true; bool enable_tidscan = true; bool enable_sort = true; bool enable_hashagg = true; bool enable_nestloop = true; bool enable_material = true; bool enable_mergejoin = true; bool enable_hashjoin = true; typedef struct { PlannerInfo *root; QualCost total; } cost_qual_eval_context; static MergeScanSelCache *cached_scansel(PlannerInfo *root, RestrictInfo *rinfo, PathKey *pathkey); static void cost_rescan(PlannerInfo *root, Path *path, Cost *rescan_startup_cost, Cost *rescan_total_cost); static bool cost_qual_eval_walker(Node *node, cost_qual_eval_context *context); static bool adjust_semi_join(PlannerInfo *root, JoinPath *path, SpecialJoinInfo *sjinfo, Selectivity *outer_match_frac, Selectivity *match_count, bool *indexed_join_quals); static double approx_tuple_count(PlannerInfo *root, JoinPath *path, List *quals); static void set_rel_width(PlannerInfo *root, RelOptInfo *rel); static double relation_byte_size(double tuples, int width); static double page_size(double tuples, int width); /* * clamp_row_est * Force a row-count estimate to a sane value. */ double clamp_row_est(double nrows) { /* * Force estimate to be at least one row, to make explain output look * better and to avoid possible divide-by-zero when interpolating costs. * Make it an integer, too. */ if (nrows <= 1.0) nrows = 1.0; else nrows = rint(nrows); return nrows; } /* * cost_seqscan * Determines and returns the cost of scanning a relation sequentially. */ void cost_seqscan(Path *path, PlannerInfo *root, RelOptInfo *baserel) { double spc_seq_page_cost; Cost startup_cost = 0; Cost run_cost = 0; Cost cpu_per_tuple; /* Should only be applied to base relations */ Assert(baserel->relid > 0); Assert(baserel->rtekind == RTE_RELATION); if (!enable_seqscan) startup_cost += disable_cost; /* fetch estimated page cost for tablespace containing table */ get_tablespace_page_costs(baserel->reltablespace, NULL, &spc_seq_page_cost); /* * disk costs */ run_cost += spc_seq_page_cost * baserel->pages; /* CPU costs */ startup_cost += baserel->baserestrictcost.startup; cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple; run_cost += cpu_per_tuple * baserel->tuples; path->startup_cost = startup_cost; path->total_cost = startup_cost + run_cost; } /* * cost_index * Determines and returns the cost of scanning a relation using an index. * * 'path' describes the indexscan under consideration, and is complete * except for the fields to be set by this routine * 'outer_rel' is the outer relation when we are considering using the index * scan as the inside of a nestloop join (hence, some of the indexquals * are join clauses, and we should expect repeated scans of the index); * NULL for a plain index scan * * In addition to startup_cost and total_cost, cost_index() sets the path's * indextotalcost and indexselectivity fields. These values are 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, RelOptInfo *outer_rel) { IndexOptInfo *index = path->indexinfo; RelOptInfo *baserel = index->rel; bool indexonly = (path->path.pathtype == T_IndexOnlyScan); Cost startup_cost = 0; Cost run_cost = 0; Cost indexStartupCost; Cost indexTotalCost; Selectivity indexSelectivity; double indexCorrelation, csquared; double spc_seq_page_cost, spc_random_page_cost; Cost min_IO_cost, max_IO_cost; Cost cpu_per_tuple; double tuples_fetched; double pages_fetched; /* Should only be applied to base relations */ Assert(IsA(baserel, RelOptInfo) && IsA(index, IndexOptInfo)); Assert(baserel->relid > 0); Assert(baserel->rtekind == RTE_RELATION); if (!enable_indexscan) startup_cost += disable_cost; /* we don't need to check enable_indexonlyscan; indxpath.c does that */ /* * Call index-access-method-specific code to estimate the processing cost * for scanning the index, as well as the selectivity of the index (ie, * the fraction of main-table tuples we will have to retrieve) and its * correlation to the main-table tuple order. */ OidFunctionCall7(index->amcostestimate, PointerGetDatum(root), PointerGetDatum(path), PointerGetDatum(outer_rel), PointerGetDatum(&indexStartupCost), PointerGetDatum(&indexTotalCost), PointerGetDatum(&indexSelectivity), PointerGetDatum(&indexCorrelation)); /* * Save amcostestimate's results for possible use in bitmap scan planning. * We don't bother to save indexStartupCost or indexCorrelation, because a * bitmap scan doesn't care about either. */ path->indextotalcost = indexTotalCost; path->indexselectivity = indexSelectivity; /* all costs for touching index itself included here */ startup_cost += indexStartupCost; run_cost += indexTotalCost - indexStartupCost; /* estimate number of main-table tuples fetched */ tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples); /* fetch estimated page costs for tablespace containing table */ get_tablespace_page_costs(baserel->reltablespace, &spc_random_page_cost, &spc_seq_page_cost); /*---------- * Estimate number of main-table pages fetched, and compute I/O cost. * * When the index ordering is uncorrelated with the table ordering, * we use an approximation proposed by Mackert and Lohman (see * index_pages_fetched() for details) to compute the number of pages * fetched, and then charge spc_random_page_cost per page fetched. * * When the index ordering is exactly correlated with the table ordering * (just after a CLUSTER, for example), the number of pages fetched should * be exactly selectivity * table_size. What's more, all but the first * will be sequential fetches, not the random fetches that occur in the * uncorrelated case. So if the number of pages is more than 1, we * ought to charge * spc_random_page_cost + (pages_fetched - 1) * spc_seq_page_cost * For partially-correlated indexes, we ought to charge somewhere between * these two estimates. We currently interpolate linearly between the * estimates based on the correlation squared (XXX is that appropriate?). * * If it's an index-only scan, then we will not need to fetch any heap * pages for which the visibility map shows all tuples are visible. * Hence, reduce the estimated number of heap fetches accordingly. * We use the measured fraction of the entire heap that is all-visible, * which might not be particularly relevant to the subset of the heap * that this query will fetch; but it's not clear how to do better. *---------- */ if (outer_rel != NULL && outer_rel->rows > 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. */ double num_scans = outer_rel->rows; pages_fetched = index_pages_fetched(tuples_fetched * num_scans, baserel->pages, (double) index->pages, root); if (indexonly) pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac)); max_IO_cost = (pages_fetched * spc_random_page_cost) / num_scans; /* * 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 * num_scans, 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) / num_scans; } else { /* * Normal case: apply the Mackert and Lohman formula, and then * interpolate between that and the correlation-derived result. */ pages_fetched = index_pages_fetched(tuples_fetched, baserel->pages, (double) index->pages, root); if (indexonly) pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac)); /* max_IO_cost is for the perfectly uncorrelated case (csquared=0) */ max_IO_cost = pages_fetched * spc_random_page_cost; /* min_IO_cost is for the perfectly correlated case (csquared=1) */ pages_fetched = ceil(indexSelectivity * (double) baserel->pages); if (indexonly) pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac)); if (pages_fetched > 0) { min_IO_cost = spc_random_page_cost; if (pages_fetched > 1) min_IO_cost += (pages_fetched - 1) * spc_seq_page_cost; } else min_IO_cost = 0; } /* * Now interpolate based on estimated index order correlation to get total * disk I/O cost for main table accesses. */ csquared = indexCorrelation * indexCorrelation; run_cost += max_IO_cost + csquared * (min_IO_cost - max_IO_cost); /* * Estimate CPU costs per tuple. * * Normally the indexquals will be removed from the list of restriction * clauses that we have to evaluate as qpquals, so we should subtract * their costs from baserestrictcost. But if we are doing a join then * some of the indexquals are join clauses and shouldn't be subtracted. * Rather than work out exactly how much to subtract, we don't subtract * anything. */ startup_cost += baserel->baserestrictcost.startup; cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple; if (outer_rel == NULL) { QualCost index_qual_cost; cost_qual_eval(&index_qual_cost, path->indexquals, root); /* any startup cost still has to be paid ... */ cpu_per_tuple -= index_qual_cost.per_tuple; } run_cost += cpu_per_tuple * tuples_fetched; path->path.startup_cost = startup_cost; path->path.total_cost = startup_cost + run_cost; } /* * index_pages_fetched * Estimate the number of pages actually fetched after accounting for * cache effects. * * We use an approximation proposed by Mackert and Lohman, "Index Scans * Using a Finite LRU Buffer: A Validated I/O Model", ACM Transactions * on Database Systems, Vol. 14, No. 3, September 1989, Pages 401-424. * The Mackert and Lohman approximation is that the number of pages * fetched is * PF = * min(2TNs/(2T+Ns), T) when T <= b * 2TNs/(2T+Ns) when T > b and Ns <= 2Tb/(2T-b) * b + (Ns - 2Tb/(2T-b))*(T-b)/T when T > b and Ns > 2Tb/(2T-b) * where * T = # pages in table * N = # tuples in table * s = selectivity = fraction of table to be scanned * b = # buffer pages available (we include kernel space here) * * We assume that effective_cache_size is the total number of buffer pages * available for the whole query, and pro-rate that space across all the * tables in the query and the index currently under consideration. (This * ignores space needed for other indexes used by the query, but since we * don't know which indexes will get used, we can't estimate that very well; * and in any case counting all the tables may well be an overestimate, since * depending on the join plan not all the tables may be scanned concurrently.) * * The product Ns is the number of tuples fetched; we pass in that * product rather than calculating it here. "pages" is the number of pages * in the object under consideration (either an index or a table). * "index_pages" is the amount to add to the total table space, which was * computed for us by query_planner. * * Caller is expected to have ensured that tuples_fetched is greater than zero * and rounded to integer (see clamp_row_est). The result will likewise be * greater than zero and integral. */ double index_pages_fetched(double tuples_fetched, BlockNumber pages, double index_pages, PlannerInfo *root) { double pages_fetched; double total_pages; double T, b; /* T is # pages in table, but don't allow it to be zero */ T = (pages > 1) ? (double) pages : 1.0; /* Compute number of pages assumed to be competing for cache space */ total_pages = root->total_table_pages + index_pages; total_pages = Max(total_pages, 1.0); Assert(T <= total_pages); /* b is pro-rated share of effective_cache_size */ b = (double) effective_cache_size *T / total_pages; /* force it positive and integral */ if (b <= 1.0) b = 1.0; else b = ceil(b); /* This part is the Mackert and Lohman formula */ if (T <= b) { pages_fetched = (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched); if (pages_fetched >= T) pages_fetched = T; else pages_fetched = ceil(pages_fetched); } else { double lim; lim = (2.0 * T * b) / (2.0 * T - b); if (tuples_fetched <= lim) { pages_fetched = (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched); } else { pages_fetched = b + (tuples_fetched - lim) * (T - b) / T; } pages_fetched = ceil(pages_fetched); } return pages_fetched; } /* * get_indexpath_pages * Determine the total size of the indexes used in a bitmap index path. * * Note: if the same index is used more than once in a bitmap tree, we will * count it multiple times, which perhaps is the wrong thing ... but it's * not completely clear, and detecting duplicates is difficult, so ignore it * for now. */ static double get_indexpath_pages(Path *bitmapqual) { double result = 0; ListCell *l; if (IsA(bitmapqual, BitmapAndPath)) { BitmapAndPath *apath = (BitmapAndPath *) bitmapqual; foreach(l, apath->bitmapquals) { result += get_indexpath_pages((Path *) lfirst(l)); } } else if (IsA(bitmapqual, BitmapOrPath)) { BitmapOrPath *opath = (BitmapOrPath *) bitmapqual; foreach(l, opath->bitmapquals) { result += get_indexpath_pages((Path *) lfirst(l)); } } else if (IsA(bitmapqual, IndexPath)) { IndexPath *ipath = (IndexPath *) bitmapqual; result = (double) ipath->indexinfo->pages; } else elog(ERROR, "unrecognized node type: %d", nodeTag(bitmapqual)); return result; } /* * cost_bitmap_heap_scan * Determines and returns the cost of scanning a relation using a bitmap * index-then-heap plan. * * 'baserel' is the relation to be scanned * 'bitmapqual' is a tree of IndexPaths, BitmapAndPaths, and BitmapOrPaths * 'outer_rel' is the outer relation when we are considering using the bitmap * scan as the inside of a nestloop join (hence, some of the indexquals * are join clauses, and we should expect repeated scans of the table); * NULL for a plain bitmap scan * * Note: if this is a join inner path, the component IndexPaths in bitmapqual * should have been costed accordingly. */ void cost_bitmap_heap_scan(Path *path, PlannerInfo *root, RelOptInfo *baserel, Path *bitmapqual, RelOptInfo *outer_rel) { Cost startup_cost = 0; Cost run_cost = 0; Cost indexTotalCost; Selectivity indexSelectivity; Cost cpu_per_tuple; Cost cost_per_page; double tuples_fetched; double pages_fetched; double spc_seq_page_cost, spc_random_page_cost; double T; /* Should only be applied to base relations */ Assert(IsA(baserel, RelOptInfo)); Assert(baserel->relid > 0); Assert(baserel->rtekind == RTE_RELATION); if (!enable_bitmapscan) startup_cost += disable_cost; /* * Fetch total cost of obtaining the bitmap, as well as its total * selectivity. */ cost_bitmap_tree_node(bitmapqual, &indexTotalCost, &indexSelectivity); startup_cost += indexTotalCost; /* Fetch estimated page costs for tablespace containing table. */ get_tablespace_page_costs(baserel->reltablespace, &spc_random_page_cost, &spc_seq_page_cost); /* * Estimate number of main-table pages fetched. */ tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples); T = (baserel->pages > 1) ? (double) baserel->pages : 1.0; if (outer_rel != NULL && outer_rel->rows > 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. */ double num_scans = outer_rel->rows; pages_fetched = index_pages_fetched(tuples_fetched * num_scans, baserel->pages, get_indexpath_pages(bitmapqual), root); pages_fetched /= num_scans; } else { /* * For a single scan, the number of heap pages that need to be fetched * is the same as the Mackert and Lohman formula for the case T <= b * (ie, no re-reads needed). */ pages_fetched = (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched); } if (pages_fetched >= T) pages_fetched = T; else pages_fetched = ceil(pages_fetched); /* * For small numbers of pages we should charge spc_random_page_cost * apiece, while if nearly all the table's pages are being read, it's more * appropriate to charge spc_seq_page_cost apiece. The effect is * nonlinear, too. For lack of a better idea, interpolate like this to * determine the cost per page. */ if (pages_fetched >= 2.0) cost_per_page = spc_random_page_cost - (spc_random_page_cost - spc_seq_page_cost) * sqrt(pages_fetched / T); else cost_per_page = spc_random_page_cost; run_cost += pages_fetched * cost_per_page; /* * Estimate CPU costs per tuple. * * Often the indexquals don't need to be rechecked at each tuple ... but * not always, especially not if there are enough tuples involved that the * bitmaps become lossy. For the moment, just assume they will be * rechecked always. */ startup_cost += baserel->baserestrictcost.startup; cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple; run_cost += cpu_per_tuple * tuples_fetched; path->startup_cost = startup_cost; path->total_cost = startup_cost + run_cost; } /* * cost_bitmap_tree_node * Extract cost and selectivity from a bitmap tree node (index/and/or) */ void cost_bitmap_tree_node(Path *path, Cost *cost, Selectivity *selec) { if (IsA(path, IndexPath)) { *cost = ((IndexPath *) path)->indextotalcost; *selec = ((IndexPath *) path)->indexselectivity; /* * Charge a small amount per retrieved tuple to reflect the costs of * manipulating the bitmap. This is mostly to make sure that a bitmap * scan doesn't look to be the same cost as an indexscan to retrieve a * single tuple. */ *cost += 0.1 * cpu_operator_cost * ((IndexPath *) 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. */ 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.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.startup_cost = totalCost; path->path.total_cost = totalCost; } /* * cost_tidscan * Determines and returns the cost of scanning a relation using TIDs. */ void cost_tidscan(Path *path, PlannerInfo *root, RelOptInfo *baserel, List *tidquals) { Cost startup_cost = 0; Cost run_cost = 0; bool isCurrentOf = false; 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); /* Count how many tuples we expect to retrieve */ ntuples = 0; foreach(l, tidquals) { if (IsA(lfirst(l), ScalarArrayOpExpr)) { /* Each element of the array yields 1 tuple */ ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) lfirst(l); Node *arraynode = (Node *) lsecond(saop->args); ntuples += estimate_array_length(arraynode); } else if (IsA(lfirst(l), CurrentOfExpr)) { /* CURRENT OF yields 1 tuple */ isCurrentOf = true; ntuples++; } else { /* It's just CTID = something, count 1 tuple */ ntuples++; } } /* * We must force TID scan for WHERE CURRENT OF, because only nodeTidscan.c * understands how to do it correctly. Therefore, honor enable_tidscan * only when CURRENT OF isn't present. Also note that cost_qual_eval * counts a CurrentOfExpr as having startup cost disable_cost, which we * subtract off here; that's to prevent other plan types such as seqscan * from winning. */ if (isCurrentOf) { Assert(baserel->baserestrictcost.startup >= disable_cost); startup_cost -= disable_cost; } else if (!enable_tidscan) startup_cost += disable_cost; /* * The TID qual expressions will be computed once, any other baserestrict * quals once per retrived tuple. */ cost_qual_eval(&tid_qual_cost, tidquals, root); /* fetch estimated page cost for tablespace containing table */ get_tablespace_page_costs(baserel->reltablespace, &spc_random_page_cost, NULL); /* disk costs --- assume each tuple on a different page */ run_cost += spc_random_page_cost * ntuples; /* CPU costs */ startup_cost += baserel->baserestrictcost.startup + tid_qual_cost.per_tuple; cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple - tid_qual_cost.per_tuple; run_cost += cpu_per_tuple * ntuples; path->startup_cost = startup_cost; path->total_cost = startup_cost + run_cost; } /* * cost_subqueryscan * Determines and returns the cost of scanning a subquery RTE. */ void cost_subqueryscan(Path *path, RelOptInfo *baserel) { Cost startup_cost; Cost run_cost; Cost cpu_per_tuple; /* Should only be applied to base relations that are subqueries */ Assert(baserel->relid > 0); Assert(baserel->rtekind == RTE_SUBQUERY); /* * Cost of path is cost of evaluating the subplan, plus cost of evaluating * any restriction clauses that will be attached to the SubqueryScan node, * plus cpu_tuple_cost to account for selection and projection overhead. */ path->startup_cost = baserel->subplan->startup_cost; path->total_cost = baserel->subplan->total_cost; startup_cost = baserel->baserestrictcost.startup; cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple; run_cost = cpu_per_tuple * baserel->tuples; path->startup_cost += startup_cost; path->total_cost += startup_cost + run_cost; } /* * cost_functionscan * Determines and returns the cost of scanning a function RTE. */ void cost_functionscan(Path *path, PlannerInfo *root, RelOptInfo *baserel) { Cost startup_cost = 0; Cost run_cost = 0; 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); /* * Estimate costs of executing the function expression. * * Currently, nodeFunctionscan.c always executes the function to * completion before returning any rows, and caches the results in a * tuplestore. So the function eval cost is all startup cost, and per-row * costs are minimal. * * XXX in principle we ought to charge tuplestore spill costs if the * number of rows is large. However, given how phony our rowcount * estimates for functions tend to be, there's not a lot of point in that * refinement right now. */ cost_qual_eval_node(&exprcost, rte->funcexpr, root); startup_cost += exprcost.startup + exprcost.per_tuple; /* Add scanning CPU costs */ startup_cost += baserel->baserestrictcost.startup; cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost.per_tuple; run_cost += cpu_per_tuple * baserel->tuples; path->startup_cost = startup_cost; path->total_cost = startup_cost + run_cost; } /* * cost_valuesscan * Determines and returns the cost of scanning a VALUES RTE. */ void cost_valuesscan(Path *path, PlannerInfo *root, RelOptInfo *baserel) { Cost startup_cost = 0; Cost run_cost = 0; Cost cpu_per_tuple; /* Should only be applied to base relations that are values lists */ Assert(baserel->relid > 0); Assert(baserel->rtekind == RTE_VALUES); /* * 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 */ startup_cost += baserel->baserestrictcost.startup; cpu_per_tuple += cpu_tuple_cost + baserel->baserestrictcost.per_tuple; run_cost += cpu_per_tuple * baserel->tuples; path->startup_cost = startup_cost; path->total_cost = startup_cost + run_cost; } /* * cost_ctescan * Determines and returns the cost of scanning a CTE RTE. * * Note: this is used for both self-reference and regular CTEs; the * possible cost differences are below the threshold of what we could * estimate accurately anyway. Note that the costs of evaluating the * referenced CTE query are added into the final plan as initplan costs, * and should NOT be counted here. */ void cost_ctescan(Path *path, PlannerInfo *root, RelOptInfo *baserel) { Cost startup_cost = 0; Cost run_cost = 0; Cost cpu_per_tuple; /* Should only be applied to base relations that are CTEs */ Assert(baserel->relid > 0); Assert(baserel->rtekind == RTE_CTE); /* Charge one CPU tuple cost per row for tuplestore manipulation */ cpu_per_tuple = cpu_tuple_cost; /* Add scanning CPU costs */ startup_cost += baserel->baserestrictcost.startup; cpu_per_tuple += cpu_tuple_cost + baserel->baserestrictcost.per_tuple; run_cost += cpu_per_tuple * baserel->tuples; path->startup_cost = startup_cost; path->total_cost = startup_cost + run_cost; } /* * cost_recursive_union * Determines and returns the cost of performing a recursive union, * and also the estimated output size. * * We are given Plans for the nonrecursive and recursive terms. * * Note that the arguments and output are Plans, not Paths as in most of * the rest of this module. That's because we don't bother setting up a * Path representation for recursive union --- we have only one way to do it. */ void cost_recursive_union(Plan *runion, Plan *nrterm, Plan *rterm) { Cost startup_cost; Cost total_cost; double total_rows; /* We probably have decent estimates for the non-recursive term */ startup_cost = nrterm->startup_cost; total_cost = nrterm->total_cost; total_rows = nrterm->plan_rows; /* * We arbitrarily assume that about 10 recursive iterations will be * needed, and that we've managed to get a good fix on the cost and output * size of each one of them. These are mighty shaky assumptions but it's * hard to see how to do better. */ total_cost += 10 * rterm->total_cost; total_rows += 10 * rterm->plan_rows; /* * Also charge cpu_tuple_cost per row to account for the costs of * manipulating the tuplestores. (We don't worry about possible * spill-to-disk costs.) */ total_cost += cpu_tuple_cost * total_rows; runion->startup_cost = startup_cost; runion->total_cost = total_cost; runion->plan_rows = total_rows; runion->plan_width = Max(nrterm->plan_width, rterm->plan_width); } /* * cost_sort * Determines and returns the cost of sorting a relation, including * the cost of reading the input data. * * If the total volume of data to sort is less than sort_mem, we will do * an in-memory sort, which requires no I/O and about t*log2(t) tuple * comparisons for t tuples. * * If the total volume exceeds sort_mem, we switch to a tape-style merge * algorithm. There will still be about t*log2(t) tuple comparisons in * total, but we will also need to write and read each tuple once per * merge pass. We expect about ceil(logM(r)) merge passes where r is the * number of initial runs formed and M is the merge order used by tuplesort.c. * Since the average initial run should be about twice sort_mem, we have * disk traffic = 2 * relsize * ceil(logM(p / (2*sort_mem))) * cpu = comparison_cost * t * log2(t) * * If the sort is bounded (i.e., only the first k result tuples are needed) * and k tuples can fit into sort_mem, we use a heap method that keeps only * k tuples in the heap; this will require about t*log2(k) tuple comparisons. * * The disk traffic is assumed to be 3/4ths sequential and 1/4th random * accesses (XXX can't we refine that guess?) * * By default, we charge two operator evals per tuple comparison, which should * be in the right ballpark in most cases. The caller can tweak this by * specifying nonzero comparison_cost; typically that's used for any extra * work that has to be done to prepare the inputs to the comparison operators. * * 'pathkeys' is a list of sort keys * 'input_cost' is the total cost for reading the input data * 'tuples' is the number of tuples in the relation * 'width' is the average tuple width in bytes * 'comparison_cost' is the extra cost per comparison, if any * 'sort_mem' is the number of kilobytes of work memory allowed for the sort * 'limit_tuples' is the bound on the number of output tuples; -1 if no bound * * NOTE: some callers currently pass NIL for pathkeys because they * can't conveniently supply the sort keys. Since this routine doesn't * currently do anything with pathkeys anyway, that doesn't matter... * but if it ever does, it should react gracefully to lack of key data. * (Actually, the thing we'd most likely be interested in is just the number * of sort keys, which all callers *could* supply.) */ void cost_sort(Path *path, PlannerInfo *root, List *pathkeys, Cost input_cost, double tuples, int width, Cost comparison_cost, int sort_mem, double limit_tuples) { Cost startup_cost = input_cost; Cost run_cost = 0; double input_bytes = relation_byte_size(tuples, width); double output_bytes; double output_tuples; long sort_mem_bytes = sort_mem * 1024L; if (!enable_sort) startup_cost += disable_cost; /* * We want to be sure the cost of a sort is never estimated as zero, even * if passed-in tuple count is zero. Besides, mustn't do log(0)... */ if (tuples < 2.0) tuples = 2.0; /* Include the default cost-per-comparison */ comparison_cost += 2.0 * cpu_operator_cost; /* Do we have a useful LIMIT? */ if (limit_tuples > 0 && limit_tuples < tuples) { output_tuples = limit_tuples; output_bytes = relation_byte_size(output_tuples, width); } else { output_tuples = tuples; output_bytes = input_bytes; } if (output_bytes > sort_mem_bytes) { /* * We'll have to use a disk-based sort of all the tuples */ double npages = ceil(input_bytes / BLCKSZ); double nruns = (input_bytes / sort_mem_bytes) * 0.5; double mergeorder = tuplesort_merge_order(sort_mem_bytes); double log_runs; double npageaccesses; /* * CPU costs * * Assume about N log2 N comparisons */ startup_cost += comparison_cost * tuples * LOG2(tuples); /* Disk costs */ /* Compute logM(r) as log(r) / log(M) */ if (nruns > mergeorder) log_runs = ceil(log(nruns) / log(mergeorder)); else log_runs = 1.0; npageaccesses = 2.0 * npages * log_runs; /* Assume 3/4ths of accesses are sequential, 1/4th are not */ startup_cost += npageaccesses * (seq_page_cost * 0.75 + random_page_cost * 0.25); } else if (tuples > 2 * output_tuples || input_bytes > sort_mem_bytes) { /* * We'll use a bounded heap-sort keeping just K tuples in memory, for * a total number of tuple comparisons of N log2 K; but the constant * factor is a bit higher than for quicksort. Tweak it so that the * cost curve is continuous at the crossover point. */ startup_cost += comparison_cost * tuples * LOG2(2.0 * output_tuples); } else { /* We'll use plain quicksort on all the input tuples */ startup_cost += comparison_cost * tuples * LOG2(tuples); } /* * Also charge a small amount (arbitrarily set equal to operator cost) per * extracted tuple. We don't charge cpu_tuple_cost because a Sort node * doesn't do qual-checking or projection, so it has less overhead than * most plan nodes. Note it's correct to use tuples not output_tuples * here --- the upper LIMIT will pro-rate the run cost so we'd be double * counting the LIMIT otherwise. */ run_cost += cpu_operator_cost * tuples; path->startup_cost = startup_cost; path->total_cost = startup_cost + run_cost; } /* * cost_merge_append * Determines and returns the cost of a MergeAppend node. * * MergeAppend merges several pre-sorted input streams, using a heap that * at any given instant holds the next tuple from each stream. If there * are N streams, we need about N*log2(N) tuple comparisons to construct * the heap at startup, and then for each output tuple, about log2(N) * comparisons to delete the top heap entry and another log2(N) comparisons * to insert its successor from the same stream. * * (The effective value of N will drop once some of the input streams are * exhausted, but it seems unlikely to be worth trying to account for that.) * * The heap is never spilled to disk, since we assume N is not very large. * So this is much simpler than cost_sort. * * As in cost_sort, we charge two operator evals per tuple comparison. * * 'pathkeys' is a list of sort keys * 'n_streams' is the number of input streams * 'input_startup_cost' is the sum of the input streams' startup costs * 'input_total_cost' is the sum of the input streams' total costs * 'tuples' is the number of tuples in all the streams */ void cost_merge_append(Path *path, PlannerInfo *root, List *pathkeys, int n_streams, Cost input_startup_cost, Cost input_total_cost, double tuples) { Cost startup_cost = 0; Cost run_cost = 0; Cost comparison_cost; double N; double logN; /* * Avoid log(0)... */ N = (n_streams < 2) ? 2.0 : (double) n_streams; logN = LOG2(N); /* Assumed cost per tuple comparison */ comparison_cost = 2.0 * cpu_operator_cost; /* Heap creation cost */ startup_cost += comparison_cost * N * logN; /* Per-tuple heap maintenance cost */ run_cost += tuples * comparison_cost * 2.0 * logN; /* * Also charge a small amount (arbitrarily set equal to operator cost) per * extracted tuple. We don't charge cpu_tuple_cost because a MergeAppend * node doesn't do qual-checking or projection, so it has less overhead * than most plan nodes. */ run_cost += cpu_operator_cost * tuples; path->startup_cost = startup_cost + input_startup_cost; path->total_cost = startup_cost + run_cost + input_total_cost; } /* * cost_material * Determines and returns the cost of materializing a relation, including * the cost of reading the input data. * * If the total volume of data to materialize exceeds work_mem, we will need * to write it to disk, so the cost is much higher in that case. * * Note that here we are estimating the costs for the first scan of the * relation, so the materialization is all overhead --- any savings will * occur only on rescan, which is estimated in cost_rescan. */ void cost_material(Path *path, Cost input_startup_cost, Cost input_total_cost, double tuples, int width) { Cost startup_cost = input_startup_cost; Cost run_cost = input_total_cost - input_startup_cost; double nbytes = relation_byte_size(tuples, width); long work_mem_bytes = work_mem * 1024L; /* * Whether spilling or not, charge 2x cpu_operator_cost per tuple to * reflect bookkeeping overhead. (This rate must be more than what * cost_rescan charges for materialize, ie, cpu_operator_cost per tuple; * if it is exactly the same then there will be a cost tie between * nestloop with A outer, materialized B inner and nestloop with B outer, * materialized A inner. The extra cost ensures we'll prefer * materializing the smaller rel.) Note that this is normally a good deal * less than cpu_tuple_cost; which is OK because a Material plan node * doesn't do qual-checking or projection, so it's got less overhead than * most plan nodes. */ run_cost += 2 * cpu_operator_cost * tuples; /* * If we will spill to disk, charge at the rate of seq_page_cost per page. * This cost is assumed to be evenly spread through the plan run phase, * which isn't exactly accurate but our cost model doesn't allow for * nonuniform costs within the run phase. */ if (nbytes > work_mem_bytes) { double npages = ceil(nbytes / BLCKSZ); run_cost += seq_page_cost * npages; } path->startup_cost = startup_cost; path->total_cost = startup_cost + run_cost; } /* * cost_agg * Determines and returns the cost of performing an Agg plan node, * including the cost of its input. * * aggcosts can be NULL when there are no actual aggregate functions (i.e., * we are using a hashed Agg node just to do grouping). * * Note: when aggstrategy == AGG_SORTED, caller must ensure that input costs * are for appropriately-sorted input. */ void cost_agg(Path *path, PlannerInfo *root, AggStrategy aggstrategy, const AggClauseCosts *aggcosts, int numGroupCols, double numGroups, Cost input_startup_cost, Cost input_total_cost, double input_tuples) { Cost startup_cost; Cost total_cost; AggClauseCosts dummy_aggcosts; /* Use all-zero per-aggregate costs if NULL is passed */ if (aggcosts == NULL) { Assert(aggstrategy == AGG_HASHED); MemSet(&dummy_aggcosts, 0, sizeof(AggClauseCosts)); aggcosts = &dummy_aggcosts; } /* * The transCost.per_tuple component of aggcosts should be charged once * per input tuple, corresponding to the costs of evaluating the aggregate * transfns and their input expressions (with any startup cost of course * charged but once). The finalCost component is charged once per output * tuple, corresponding to the costs of evaluating the finalfns. * * If we are grouping, we charge an additional cpu_operator_cost per * grouping column per input tuple for grouping comparisons. * * We will produce a single output tuple if not grouping, and a tuple per * group otherwise. We charge cpu_tuple_cost for each output tuple. * * Note: in this cost model, AGG_SORTED and AGG_HASHED have exactly the * same total CPU cost, but AGG_SORTED has lower startup cost. If the * input path is already sorted appropriately, AGG_SORTED should be * preferred (since it has no risk of memory overflow). This will happen * as long as the computed total costs are indeed exactly equal --- but if * there's roundoff error we might do the wrong thing. So be sure that * the computations below form the same intermediate values in the same * order. */ if (aggstrategy == AGG_PLAIN) { startup_cost = input_total_cost; startup_cost += aggcosts->transCost.startup; startup_cost += aggcosts->transCost.per_tuple * input_tuples; startup_cost += aggcosts->finalCost; /* we aren't grouping */ total_cost = startup_cost + cpu_tuple_cost; } else if (aggstrategy == AGG_SORTED) { /* Here we are able to deliver output on-the-fly */ startup_cost = input_startup_cost; total_cost = input_total_cost; /* calcs phrased this way to match HASHED case, see note above */ total_cost += aggcosts->transCost.startup; total_cost += aggcosts->transCost.per_tuple * input_tuples; total_cost += (cpu_operator_cost * numGroupCols) * input_tuples; total_cost += aggcosts->finalCost * numGroups; total_cost += cpu_tuple_cost * numGroups; } else { /* must be AGG_HASHED */ startup_cost = input_total_cost; startup_cost += aggcosts->transCost.startup; startup_cost += aggcosts->transCost.per_tuple * input_tuples; startup_cost += (cpu_operator_cost * numGroupCols) * input_tuples; total_cost = startup_cost; total_cost += aggcosts->finalCost * numGroups; total_cost += cpu_tuple_cost * numGroups; } path->startup_cost = startup_cost; path->total_cost = total_cost; } /* * cost_windowagg * Determines and returns the cost of performing a WindowAgg plan node, * including the cost of its input. * * Input is assumed already properly sorted. */ void cost_windowagg(Path *path, PlannerInfo *root, List *windowFuncs, int numPartCols, int numOrderCols, Cost input_startup_cost, Cost input_total_cost, double input_tuples) { Cost startup_cost; Cost total_cost; ListCell *lc; startup_cost = input_startup_cost; total_cost = input_total_cost; /* * Window functions are assumed to cost their stated execution cost, plus * the cost of evaluating their input expressions, per tuple. Since they * may in fact evaluate their inputs at multiple rows during each cycle, * this could be a drastic underestimate; but without a way to know how * many rows the window function will fetch, it's hard to do better. In * any case, it's a good estimate for all the built-in window functions, * so we'll just do this for now. */ foreach(lc, windowFuncs) { WindowFunc *wfunc = (WindowFunc *) lfirst(lc); Cost wfunccost; QualCost argcosts; Assert(IsA(wfunc, WindowFunc)); wfunccost = get_func_cost(wfunc->winfnoid) * cpu_operator_cost; /* also add the input expressions' cost to per-input-row costs */ cost_qual_eval_node(&argcosts, (Node *) wfunc->args, root); startup_cost += argcosts.startup; wfunccost += argcosts.per_tuple; total_cost += wfunccost * input_tuples; } /* * We also charge cpu_operator_cost per grouping column per tuple for * grouping comparisons, plus cpu_tuple_cost per tuple for general * overhead. * * XXX this neglects costs of spooling the data to disk when it overflows * work_mem. Sooner or later that should get accounted for. */ total_cost += cpu_operator_cost * (numPartCols + numOrderCols) * input_tuples; total_cost += cpu_tuple_cost * input_tuples; path->startup_cost = startup_cost; path->total_cost = total_cost; } /* * cost_group * Determines and returns the cost of performing a Group plan node, * including the cost of its input. * * Note: caller must ensure that input costs are for appropriately-sorted * input. */ void cost_group(Path *path, PlannerInfo *root, int numGroupCols, double numGroups, Cost input_startup_cost, Cost input_total_cost, double input_tuples) { Cost startup_cost; Cost total_cost; startup_cost = input_startup_cost; total_cost = input_total_cost; /* * Charge one cpu_operator_cost per comparison per input tuple. We assume * all columns get compared at most of the tuples. */ total_cost += cpu_operator_cost * input_tuples * numGroupCols; path->startup_cost = startup_cost; path->total_cost = total_cost; } /* * If a nestloop's inner path is an indexscan, be sure to use its estimated * output row count, which may be lower than the restriction-clause-only row * count of its parent. (We don't include this case in the PATH_ROWS macro * because it applies *only* to a nestloop's inner relation.) We have to * be prepared to recurse through Append or MergeAppend nodes in case of an * appendrel. (It's not clear MergeAppend can be seen here, but we may as * well handle it if so.) */ static double nestloop_inner_path_rows(Path *path) { double result; if (IsA(path, IndexPath)) result = ((IndexPath *) path)->rows; else if (IsA(path, BitmapHeapPath)) result = ((BitmapHeapPath *) path)->rows; else if (IsA(path, AppendPath)) { ListCell *l; result = 0; foreach(l, ((AppendPath *) path)->subpaths) { result += nestloop_inner_path_rows((Path *) lfirst(l)); } } else if (IsA(path, MergeAppendPath)) { ListCell *l; result = 0; foreach(l, ((MergeAppendPath *) path)->subpaths) { result += nestloop_inner_path_rows((Path *) lfirst(l)); } } else result = PATH_ROWS(path); return result; } /* * cost_nestloop * Determines and returns the cost of joining two relations using the * nested loop algorithm. * * 'path' is already filled in except for the cost fields * 'sjinfo' is extra info about the join for selectivity estimation */ void cost_nestloop(NestPath *path, PlannerInfo *root, SpecialJoinInfo *sjinfo) { Path *outer_path = path->outerjoinpath; Path *inner_path = path->innerjoinpath; Cost startup_cost = 0; Cost run_cost = 0; Cost inner_rescan_start_cost; Cost inner_rescan_total_cost; Cost inner_run_cost; Cost inner_rescan_run_cost; Cost cpu_per_tuple; QualCost restrict_qual_cost; double outer_path_rows = PATH_ROWS(outer_path); double inner_path_rows = nestloop_inner_path_rows(inner_path); double ntuples; Selectivity outer_match_frac; Selectivity match_count; bool indexed_join_quals; if (!enable_nestloop) startup_cost += disable_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 (adjust_semi_join(root, path, sjinfo, &outer_match_frac, &match_count, &indexed_join_quals)) { double outer_matched_rows; Selectivity inner_scan_frac; /* * SEMI or ANTI join: executor will stop after first match. * * For an outer-rel row that has at least one match, we can expect the * inner scan to stop after a fraction 1/(match_count+1) of the inner * rows, if the matches are evenly distributed. Since they probably * aren't quite evenly distributed, we apply a fuzz factor of 2.0 to * that fraction. (If we used a larger fuzz factor, we'd have to * clamp inner_scan_frac to at most 1.0; but since match_count is at * least 1, no such clamp is needed now.) * * A complicating factor is that rescans may be cheaper than first * scans. If we never scan all the way to the end of the inner rel, * it might be (depending on the plan type) that we'd never pay the * whole inner first-scan run cost. However it is difficult to * estimate whether that will happen, so be conservative and always * charge the whole first-scan cost once. */ run_cost += inner_run_cost; outer_matched_rows = rint(outer_path_rows * outer_match_frac); inner_scan_frac = 2.0 / (match_count + 1.0); /* Add inner run cost for additional outer tuples having matches */ if (outer_matched_rows > 1) run_cost += (outer_matched_rows - 1) * inner_rescan_run_cost * inner_scan_frac; /* Compute number of tuples processed (not number emitted!) */ ntuples = outer_matched_rows * inner_path_rows * inner_scan_frac; /* * For unmatched outer-rel rows, there are two cases. If the inner * path is an indexscan using all the joinquals as indexquals, then an * unmatched row results in an indexscan returning no rows, which is * probably quite cheap. We estimate this case as the same cost to * return the first tuple of a nonempty scan. Otherwise, the executor * will have to scan the whole inner rel; not so cheap. */ if (indexed_join_quals) { run_cost += (outer_path_rows - outer_matched_rows) * inner_rescan_run_cost / inner_path_rows; /* * We won't be evaluating any quals at all for these rows, so * don't add them to ntuples. */ } else { run_cost += (outer_path_rows - outer_matched_rows) * inner_rescan_run_cost; ntuples += (outer_path_rows - outer_matched_rows) * inner_path_rows; } } else { /* Normal case; 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; /* Compute number of tuples processed (not number emitted!) */ ntuples = outer_path_rows * inner_path_rows; } /* CPU costs */ cost_qual_eval(&restrict_qual_cost, path->joinrestrictinfo, root); startup_cost += restrict_qual_cost.startup; cpu_per_tuple = cpu_tuple_cost + restrict_qual_cost.per_tuple; run_cost += cpu_per_tuple * ntuples; path->path.startup_cost = startup_cost; path->path.total_cost = startup_cost + run_cost; } /* * cost_mergejoin * Determines and returns the cost of joining two relations using the * merge join algorithm. * * Unlike other costsize functions, this routine makes one actual decision: * whether we should materialize the inner path. We do that either because * the inner path can't support mark/restore, or because it's cheaper to * use an interposed Material node to handle mark/restore. When the decision * is cost-based it would be logically cleaner to build and cost two separate * paths with and without that flag set; but that would require repeating most * of the calculations here, which are not all that cheap. Since the choice * will not affect output pathkeys or startup cost, only total cost, there is * no possibility of wanting to keep both paths. So it seems best to make * the decision here and record it in the path's materialize_inner field. * * 'path' is already filled in except for the cost fields and materialize_inner * 'sjinfo' is extra info about the join for selectivity estimation * * Notes: path's mergeclauses should be a subset of the joinrestrictinfo list; * outersortkeys and innersortkeys are lists of the keys to be used * to sort the outer and inner relations, or NIL if no explicit * sort is needed because the source path is already ordered. */ void cost_mergejoin(MergePath *path, PlannerInfo *root, SpecialJoinInfo *sjinfo) { Path *outer_path = path->jpath.outerjoinpath; Path *inner_path = path->jpath.innerjoinpath; List *mergeclauses = path->path_mergeclauses; List *outersortkeys = path->outersortkeys; List *innersortkeys = path->innersortkeys; Cost startup_cost = 0; Cost run_cost = 0; Cost cpu_per_tuple, inner_run_cost, bare_inner_cost, mat_inner_cost; QualCost merge_qual_cost; QualCost qp_qual_cost; double outer_path_rows = PATH_ROWS(outer_path); double inner_path_rows = PATH_ROWS(inner_path); double outer_rows, inner_rows, outer_skip_rows, inner_skip_rows; double mergejointuples, rescannedtuples; double rescanratio; Selectivity outerstartsel, outerendsel, innerstartsel, innerendsel; Path sort_path; /* dummy for result of cost_sort */ /* Protect some assumptions below that rowcounts aren't zero or NaN */ if (outer_path_rows <= 0 || isnan(outer_path_rows)) outer_path_rows = 1; if (inner_path_rows <= 0 || isnan(inner_path_rows)) inner_path_rows = 1; if (!enable_mergejoin) startup_cost += disable_cost; /* * Compute cost of the mergequals and qpquals (other restriction clauses) * separately. */ cost_qual_eval(&merge_qual_cost, mergeclauses, root); cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo, root); qp_qual_cost.startup -= merge_qual_cost.startup; qp_qual_cost.per_tuple -= merge_qual_cost.per_tuple; /* * Get approx # tuples passing the mergequals. We use approx_tuple_count * here because we need an estimate done with JOIN_INNER semantics. */ mergejointuples = approx_tuple_count(root, &path->jpath, mergeclauses); /* * When there are equal merge keys in the outer relation, the mergejoin * must rescan any matching tuples in the inner relation. This means * re-fetching inner tuples; we have to estimate how often that happens. * * For regular inner and outer joins, the number of re-fetches can be * estimated approximately as size of merge join output minus size of * inner relation. Assume that the distinct key values are 1, 2, ..., and * denote the number of values of each key in the outer relation as m1, * m2, ...; in the inner relation, n1, n2, ... Then we have * * size of join = m1 * n1 + m2 * n2 + ... * * number of rescanned tuples = (m1 - 1) * n1 + (m2 - 1) * n2 + ... = m1 * * n1 + m2 * n2 + ... - (n1 + n2 + ...) = size of join - size of inner * relation * * This equation works correctly for outer tuples having no inner match * (nk = 0), but not for inner tuples having no outer match (mk = 0); we * are effectively subtracting those from the number of rescanned tuples, * when we should not. Can we do better without expensive selectivity * computations? * * The whole issue is moot if we are working from a unique-ified outer * input. */ if (IsA(outer_path, UniquePath)) rescannedtuples = 0; else { rescannedtuples = mergejointuples - inner_path_rows; /* Must clamp because of possible underestimate */ if (rescannedtuples < 0) rescannedtuples = 0; } /* We'll inflate various costs this much to account for rescanning */ rescanratio = 1.0 + (rescannedtuples / inner_path_rows); /* * 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 && path->jpath.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 (path->jpath.jointype == JOIN_LEFT || path->jpath.jointype == JOIN_ANTI) { outerstartsel = 0.0; outerendsel = 1.0; } else if (path->jpath.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; /* cost of source data */ if (outersortkeys) /* do we need to sort outer? */ { cost_sort(&sort_path, root, outersortkeys, outer_path->total_cost, outer_path_rows, outer_path->parent->width, 0.0, work_mem, -1.0); startup_cost += sort_path.startup_cost; startup_cost += (sort_path.total_cost - sort_path.startup_cost) * outerstartsel; run_cost += (sort_path.total_cost - sort_path.startup_cost) * (outerendsel - outerstartsel); } else { startup_cost += outer_path->startup_cost; startup_cost += (outer_path->total_cost - outer_path->startup_cost) * outerstartsel; run_cost += (outer_path->total_cost - outer_path->startup_cost) * (outerendsel - outerstartsel); } if (innersortkeys) /* do we need to sort inner? */ { cost_sort(&sort_path, root, innersortkeys, inner_path->total_cost, inner_path_rows, inner_path->parent->width, 0.0, work_mem, -1.0); startup_cost += sort_path.startup_cost; startup_cost += (sort_path.total_cost - sort_path.startup_cost) * innerstartsel; inner_run_cost = (sort_path.total_cost - sort_path.startup_cost) * (innerendsel - innerstartsel); } else { startup_cost += inner_path->startup_cost; startup_cost += (inner_path->total_cost - inner_path->startup_cost) * innerstartsel; inner_run_cost = (inner_path->total_cost - inner_path->startup_cost) * (innerendsel - innerstartsel); } /* * Decide whether we want to materialize the inner input to shield it from * mark/restore and performing re-fetches. Our cost model for regular * re-fetches is that a re-fetch costs the same as an original fetch, * which is probably an overestimate; but on the other hand we ignore the * bookkeeping costs of mark/restore. Not clear if it's worth developing * a more refined model. So we just need to inflate the inner run cost by * rescanratio. */ bare_inner_cost = inner_run_cost * rescanratio; /* * When we interpose a Material node the re-fetch cost is assumed to be * just cpu_operator_cost per tuple, independently of the underlying * plan's cost; and we charge an extra cpu_operator_cost per original * fetch as well. Note that we're assuming the materialize node will * never spill to disk, since it only has to remember tuples back to the * last mark. (If there are a huge number of duplicates, our other cost * factors will make the path so expensive that it probably won't get * chosen anyway.) So we don't use cost_rescan here. * * Note: keep this estimate in sync with create_mergejoin_plan's labeling * of the generated Material node. */ mat_inner_cost = inner_run_cost + cpu_operator_cost * inner_path_rows * rescanratio; /* * Prefer materializing if it looks cheaper, unless the user has asked to * suppress materialization. */ if (enable_material && mat_inner_cost < bare_inner_cost) path->materialize_inner = true; /* * Even if materializing doesn't look cheaper, we *must* do it if the * inner path is to be used directly (without sorting) and it doesn't * support mark/restore. * * Since the inner side must be ordered, and only Sorts and IndexScans can * create order to begin with, and they both support mark/restore, you * might think there's no problem --- but you'd be wrong. Nestloop and * merge joins can *preserve* the order of their inputs, so they can be * selected as the input of a mergejoin, and they don't support * mark/restore at present. * * We don't test the value of enable_material here, because * materialization is required for correctness in this case, and turning * it off does not entitle us to deliver an invalid plan. */ else if (innersortkeys == NIL && !ExecSupportsMarkRestore(inner_path->pathtype)) path->materialize_inner = true; /* * Also, force materializing if the inner path is to be sorted and the * sort is expected to spill to disk. This is because the final merge * pass can be done on-the-fly if it doesn't have to support mark/restore. * We don't try to adjust the cost estimates for this consideration, * though. * * Since materialization is a performance optimization in this case, * rather than necessary for correctness, we skip it if enable_material is * off. */ else if (enable_material && innersortkeys != NIL && relation_byte_size(inner_path_rows, inner_path->parent->width) > (work_mem * 1024L)) path->materialize_inner = true; else path->materialize_inner = false; /* Charge the right incremental cost for the chosen case */ if (path->materialize_inner) run_cost += mat_inner_cost; else run_cost += bare_inner_cost; /* CPU costs */ /* * The number of tuple comparisons needed is approximately number of outer * rows plus number of inner rows plus number of rescanned tuples (can we * refine this?). At each one, we need to evaluate the mergejoin quals. */ startup_cost += merge_qual_cost.startup; startup_cost += merge_qual_cost.per_tuple * (outer_skip_rows + inner_skip_rows * rescanratio); run_cost += merge_qual_cost.per_tuple * ((outer_rows - outer_skip_rows) + (inner_rows - inner_skip_rows) * rescanratio); /* * For each tuple that gets through the mergejoin proper, we charge * cpu_tuple_cost plus the cost of evaluating additional restriction * clauses that are to be applied at the join. (This is pessimistic since * not all of the quals may get evaluated at each tuple.) * * Note: we could adjust for SEMI/ANTI joins skipping some qual * evaluations here, but it's probably not worth the trouble. */ startup_cost += qp_qual_cost.startup; cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple; run_cost += cpu_per_tuple * mergejointuples; path->jpath.path.startup_cost = startup_cost; path->jpath.path.total_cost = startup_cost + run_cost; } /* * run mergejoinscansel() with caching */ static MergeScanSelCache * cached_scansel(PlannerInfo *root, RestrictInfo *rinfo, PathKey *pathkey) { MergeScanSelCache *cache; ListCell *lc; Selectivity leftstartsel, leftendsel, rightstartsel, rightendsel; MemoryContext oldcontext; /* Do we have this result already? */ foreach(lc, rinfo->scansel_cache) { cache = (MergeScanSelCache *) lfirst(lc); if (cache->opfamily == pathkey->pk_opfamily && cache->collation == pathkey->pk_eclass->ec_collation && cache->strategy == pathkey->pk_strategy && cache->nulls_first == pathkey->pk_nulls_first) return cache; } /* Nope, do the computation */ mergejoinscansel(root, (Node *) rinfo->clause, pathkey->pk_opfamily, pathkey->pk_strategy, pathkey->pk_nulls_first, &leftstartsel, &leftendsel, &rightstartsel, &rightendsel); /* Cache the result in suitably long-lived workspace */ oldcontext = MemoryContextSwitchTo(root->planner_cxt); cache = (MergeScanSelCache *) palloc(sizeof(MergeScanSelCache)); cache->opfamily = pathkey->pk_opfamily; cache->collation = pathkey->pk_eclass->ec_collation; cache->strategy = pathkey->pk_strategy; cache->nulls_first = pathkey->pk_nulls_first; cache->leftstartsel = leftstartsel; cache->leftendsel = leftendsel; cache->rightstartsel = rightstartsel; cache->rightendsel = rightendsel; rinfo->scansel_cache = lappend(rinfo->scansel_cache, cache); MemoryContextSwitchTo(oldcontext); return cache; } /* * cost_hashjoin * Determines and returns the cost of joining two relations using the * hash join algorithm. * * 'path' is already filled in except for the cost fields * 'sjinfo' is extra info about the join for selectivity estimation * * Note: path's hashclauses should be a subset of the joinrestrictinfo list */ void cost_hashjoin(HashPath *path, PlannerInfo *root, SpecialJoinInfo *sjinfo) { Path *outer_path = path->jpath.outerjoinpath; Path *inner_path = path->jpath.innerjoinpath; List *hashclauses = path->path_hashclauses; Cost startup_cost = 0; Cost run_cost = 0; Cost cpu_per_tuple; QualCost hash_qual_cost; QualCost qp_qual_cost; double hashjointuples; double outer_path_rows = PATH_ROWS(outer_path); double inner_path_rows = PATH_ROWS(inner_path); int num_hashclauses = list_length(hashclauses); int numbuckets; int numbatches; int num_skew_mcvs; double virtualbuckets; Selectivity innerbucketsize; Selectivity outer_match_frac; Selectivity match_count; ListCell *hcl; if (!enable_hashjoin) 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; /* cost of source data */ startup_cost += outer_path->startup_cost; run_cost += outer_path->total_cost - outer_path->startup_cost; startup_cost += inner_path->total_cost; /* * Cost of computing hash function: must do it once per input tuple. We * charge one cpu_operator_cost for each column's hash function. Also, * tack on one cpu_tuple_cost per inner row, to model the costs of * inserting the row into the hashtable. * * XXX when a hashclause is more complex than a single operator, we really * should charge the extra eval costs of the left or right side, as * appropriate, here. This seems more work than it's worth at the moment. */ startup_cost += (cpu_operator_cost * num_hashclauses + cpu_tuple_cost) * inner_path_rows; run_cost += cpu_operator_cost * num_hashclauses * outer_path_rows; /* * Get hash table size that executor would use for inner relation. * * XXX for the moment, always assume that skew optimization will be * performed. As long as SKEW_WORK_MEM_PERCENT is small, it's not worth * trying to determine that for sure. * * XXX at some point it might be interesting to try to account for skew * optimization in the cost estimate, but for now, we don't. */ ExecChooseHashTableSize(inner_path_rows, inner_path->parent->width, true, /* useskew */ &numbuckets, &numbatches, &num_skew_mcvs); virtualbuckets = (double) numbuckets *(double) numbatches; /* mark the path with estimated # of batches */ path->num_batches = numbatches; /* * Determine bucketsize fraction for inner relation. We use the smallest * bucketsize estimated for any individual hashclause; this is undoubtedly * conservative. * * BUT: if inner relation has been unique-ified, we can assume it's good * for hashing. This is important both because it's the right answer, and * because we avoid contaminating the cache with a value that's wrong for * non-unique-ified paths. */ if (IsA(inner_path, UniquePath)) innerbucketsize = 1.0 / virtualbuckets; else { innerbucketsize = 1.0; foreach(hcl, hashclauses) { RestrictInfo *restrictinfo = (RestrictInfo *) lfirst(hcl); Selectivity thisbucketsize; Assert(IsA(restrictinfo, RestrictInfo)); /* * First we have to figure out which side of the hashjoin clause * is the inner side. * * Since we tend to visit the same clauses over and over when * planning a large query, we cache the bucketsize estimate in the * RestrictInfo node to avoid repeated lookups of statistics. */ if (bms_is_subset(restrictinfo->right_relids, inner_path->parent->relids)) { /* righthand side is inner */ thisbucketsize = restrictinfo->right_bucketsize; if (thisbucketsize < 0) { /* not cached yet */ thisbucketsize = estimate_hash_bucketsize(root, get_rightop(restrictinfo->clause), virtualbuckets); restrictinfo->right_bucketsize = thisbucketsize; } } else { Assert(bms_is_subset(restrictinfo->left_relids, inner_path->parent->relids)); /* lefthand side is inner */ thisbucketsize = restrictinfo->left_bucketsize; if (thisbucketsize < 0) { /* not cached yet */ thisbucketsize = estimate_hash_bucketsize(root, get_leftop(restrictinfo->clause), virtualbuckets); restrictinfo->left_bucketsize = thisbucketsize; } } if (innerbucketsize > thisbucketsize) innerbucketsize = thisbucketsize; } } /* * If inner relation is too big then we will need to "batch" the join, * which implies writing and reading most of the tuples to disk an extra * time. Charge seq_page_cost per page, since the I/O should be nice and * sequential. Writing the inner rel counts as startup cost, all the rest * as run cost. */ if (numbatches > 1) { double outerpages = page_size(outer_path_rows, outer_path->parent->width); double innerpages = page_size(inner_path_rows, inner_path->parent->width); startup_cost += seq_page_cost * innerpages; run_cost += seq_page_cost * (innerpages + 2 * outerpages); } /* CPU costs */ if (adjust_semi_join(root, &path->jpath, sjinfo, &outer_match_frac, &match_count, NULL)) { double outer_matched_rows; Selectivity inner_scan_frac; /* * SEMI or ANTI join: executor will stop after first match. * * For an outer-rel row that has at least one match, we can expect the * bucket scan to stop after a fraction 1/(match_count+1) of the * bucket's rows, if the matches are evenly distributed. Since they * probably aren't quite evenly distributed, we apply a fuzz factor of * 2.0 to that fraction. (If we used a larger fuzz factor, we'd have * to clamp inner_scan_frac to at most 1.0; but since match_count is * at least 1, no such clamp is needed now.) */ outer_matched_rows = rint(outer_path_rows * outer_match_frac); inner_scan_frac = 2.0 / (match_count + 1.0); startup_cost += hash_qual_cost.startup; run_cost += hash_qual_cost.per_tuple * outer_matched_rows * clamp_row_est(inner_path_rows * innerbucketsize * inner_scan_frac) * 0.5; /* * For unmatched outer-rel rows, the picture is quite a lot different. * In the first place, there is no reason to assume that these rows * preferentially hit heavily-populated buckets; instead assume they * are uncorrelated with the inner distribution and so they see an * average bucket size of inner_path_rows / virtualbuckets. In the * second place, it seems likely that they will have few if any exact * hash-code matches and so very few of the tuples in the bucket will * actually require eval of the hash quals. We don't have any good * way to estimate how many will, but for the moment assume that the * effective cost per bucket entry is one-tenth what it is for * matchable tuples. */ run_cost += hash_qual_cost.per_tuple * (outer_path_rows - outer_matched_rows) * clamp_row_est(inner_path_rows / virtualbuckets) * 0.05; /* Get # of tuples that will pass the basic join */ if (path->jpath.jointype == JOIN_SEMI) hashjointuples = outer_matched_rows; else hashjointuples = outer_path_rows - outer_matched_rows; } else { /* * The number of tuple comparisons needed is the number of outer * tuples times the typical number of tuples in a hash bucket, which * is the inner relation size times its bucketsize fraction. At each * one, we need to evaluate the hashjoin quals. But actually, * charging the full qual eval cost at each tuple is pessimistic, * since we don't evaluate the quals unless the hash values match * exactly. For lack of a better idea, halve the cost estimate to * allow for that. */ startup_cost += hash_qual_cost.startup; run_cost += hash_qual_cost.per_tuple * outer_path_rows * clamp_row_est(inner_path_rows * innerbucketsize) * 0.5; /* * Get approx # tuples passing the hashquals. We use * approx_tuple_count here because we need an estimate done with * JOIN_INNER semantics. */ hashjointuples = approx_tuple_count(root, &path->jpath, hashclauses); } /* * For each tuple that gets through the hashjoin proper, we charge * cpu_tuple_cost plus the cost of evaluating additional restriction * clauses that are to be applied at the join. (This is pessimistic since * not all of the quals may get evaluated at each tuple.) */ startup_cost += qp_qual_cost.startup; cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple; run_cost += cpu_per_tuple * hashjointuples; path->jpath.path.startup_cost = startup_cost; path->jpath.path.total_cost = startup_cost + run_cost; } /* * cost_subplan * Figure the costs for a SubPlan (or initplan). * * Note: we could dig the subplan's Plan out of the root list, but in practice * all callers have it handy already, so we make them pass it. */ void cost_subplan(PlannerInfo *root, SubPlan *subplan, Plan *plan) { QualCost sp_cost; /* Figure any cost for evaluating the testexpr */ cost_qual_eval(&sp_cost, make_ands_implicit((Expr *) subplan->testexpr), root); if (subplan->useHashTable) { /* * If we are using a hash table for the subquery outputs, then the * cost of evaluating the query is a one-time cost. We charge one * cpu_operator_cost per tuple for the work of loading the hashtable, * too. */ sp_cost.startup += plan->total_cost + cpu_operator_cost * plan->plan_rows; /* * The per-tuple costs include the cost of evaluating the lefthand * expressions, plus the cost of probing the hashtable. We already * accounted for the lefthand expressions as part of the testexpr, and * will also have counted one cpu_operator_cost for each comparison * operator. That is probably too low for the probing cost, but it's * hard to make a better estimate, so live with it for now. */ } else { /* * Otherwise we will be rescanning the subplan output on each * evaluation. We need to estimate how much of the output we will * actually need to scan. NOTE: this logic should agree with the * tuple_fraction estimates used by make_subplan() in * plan/subselect.c. */ Cost plan_run_cost = plan->total_cost - plan->startup_cost; if (subplan->subLinkType == EXISTS_SUBLINK) { /* we only need to fetch 1 tuple */ sp_cost.per_tuple += plan_run_cost / plan->plan_rows; } else if (subplan->subLinkType == ALL_SUBLINK || subplan->subLinkType == ANY_SUBLINK) { /* assume we need 50% of the tuples */ sp_cost.per_tuple += 0.50 * plan_run_cost; /* also charge a cpu_operator_cost per row examined */ sp_cost.per_tuple += 0.50 * plan->plan_rows * cpu_operator_cost; } else { /* assume we need all tuples */ sp_cost.per_tuple += plan_run_cost; } /* * Also account for subplan's startup cost. If the subplan is * uncorrelated or undirect correlated, AND its topmost node is one * that materializes its output, assume that we'll only need to pay * its startup cost once; otherwise assume we pay the startup cost * every time. */ if (subplan->parParam == NIL && ExecMaterializesOutput(nodeTag(plan))) sp_cost.startup += plan->startup_cost; else sp_cost.per_tuple += plan->startup_cost; } subplan->startup_cost = sp_cost.startup; subplan->per_call_cost = sp_cost.per_tuple; } /* * cost_rescan * Given a finished Path, estimate the costs of rescanning it after * having done so the first time. For some Path types a rescan is * cheaper than an original scan (if no parameters change), and this * function embodies knowledge about that. The default is to return * the same costs stored in the Path. (Note that the cost estimates * actually stored in Paths are always for first scans.) * * This function is not currently intended to model effects such as rescans * being cheaper due to disk block caching; what we are concerned with is * plan types wherein the executor caches results explicitly, or doesn't * redo startup calculations, etc. */ static void cost_rescan(PlannerInfo *root, Path *path, Cost *rescan_startup_cost, /* output parameters */ Cost *rescan_total_cost) { switch (path->pathtype) { case T_FunctionScan: /* * Currently, nodeFunctionscan.c always executes the function to * completion before returning any rows, and caches the results in * a tuplestore. So the function eval cost is all startup cost * and isn't paid over again on rescans. However, all run costs * will be paid over again. */ *rescan_startup_cost = 0; *rescan_total_cost = path->total_cost - path->startup_cost; break; case T_HashJoin: /* * Assume that all of the startup cost represents hash table * building, which we won't have to do over. */ *rescan_startup_cost = 0; *rescan_total_cost = path->total_cost - path->startup_cost; break; case T_CteScan: case T_WorkTableScan: { /* * These plan types materialize their final result in a * tuplestore or tuplesort object. So the rescan cost is only * cpu_tuple_cost per tuple, unless the result is large enough * to spill to disk. */ Cost run_cost = cpu_tuple_cost * path->parent->rows; double nbytes = relation_byte_size(path->parent->rows, path->parent->width); long work_mem_bytes = work_mem * 1024L; if (nbytes > work_mem_bytes) { /* It will spill, so account for re-read cost */ double npages = ceil(nbytes / BLCKSZ); run_cost += seq_page_cost * npages; } *rescan_startup_cost = 0; *rescan_total_cost = run_cost; } break; case T_Material: case T_Sort: { /* * These plan types not only materialize their results, but do * not implement qual filtering or projection. So they are * even cheaper to rescan than the ones above. We charge only * cpu_operator_cost per tuple. (Note: keep that in sync with * the run_cost charge in cost_sort, and also see comments in * cost_material before you change it.) */ Cost run_cost = cpu_operator_cost * path->parent->rows; double nbytes = relation_byte_size(path->parent->rows, path->parent->width); long work_mem_bytes = work_mem * 1024L; if (nbytes > work_mem_bytes) { /* It will spill, so account for re-read cost */ double npages = ceil(nbytes / BLCKSZ); run_cost += seq_page_cost * npages; } *rescan_startup_cost = 0; *rescan_total_cost = run_cost; } break; default: *rescan_startup_cost = path->startup_cost; *rescan_total_cost = path->total_cost; break; } } /* * cost_qual_eval * Estimate the CPU costs of evaluating a WHERE clause. * The input can be either an implicitly-ANDed list of boolean * expressions, or a list of RestrictInfo nodes. (The latter is * preferred since it allows caching of the results.) * The result includes both a one-time (startup) component, * and a per-evaluation component. */ void cost_qual_eval(QualCost *cost, List *quals, PlannerInfo *root) { cost_qual_eval_context context; ListCell *l; context.root = root; context.total.startup = 0; context.total.per_tuple = 0; /* We don't charge any cost for the implicit ANDing at top level ... */ foreach(l, quals) { Node *qual = (Node *) lfirst(l); cost_qual_eval_walker(qual, &context); } *cost = context.total; } /* * cost_qual_eval_node * As above, for a single RestrictInfo or expression. */ void cost_qual_eval_node(QualCost *cost, Node *qual, PlannerInfo *root) { cost_qual_eval_context context; context.root = root; context.total.startup = 0; context.total.per_tuple = 0; cost_qual_eval_walker(qual, &context); *cost = context.total; } static bool cost_qual_eval_walker(Node *node, cost_qual_eval_context *context) { if (node == NULL) return false; /* * RestrictInfo nodes contain an eval_cost field reserved for this * routine's use, so that it's not necessary to evaluate the qual clause's * cost more than once. If the clause's cost hasn't been computed yet, * the field's startup value will contain -1. */ if (IsA(node, RestrictInfo)) { RestrictInfo *rinfo = (RestrictInfo *) node; if (rinfo->eval_cost.startup < 0) { cost_qual_eval_context locContext; locContext.root = context->root; locContext.total.startup = 0; locContext.total.per_tuple = 0; /* * For an OR clause, recurse into the marked-up tree so that we * set the eval_cost for contained RestrictInfos too. */ if (rinfo->orclause) cost_qual_eval_walker((Node *) rinfo->orclause, &locContext); else cost_qual_eval_walker((Node *) rinfo->clause, &locContext); /* * If the RestrictInfo is marked pseudoconstant, it will be tested * only once, so treat its cost as all startup cost. */ if (rinfo->pseudoconstant) { /* count one execution during startup */ locContext.total.startup += locContext.total.per_tuple; locContext.total.per_tuple = 0; } rinfo->eval_cost = locContext.total; } context->total.startup += rinfo->eval_cost.startup; context->total.per_tuple += rinfo->eval_cost.per_tuple; /* do NOT recurse into children */ return false; } /* * For each operator or function node in the given tree, we charge the * estimated execution cost given by pg_proc.procost (remember to multiply * this by cpu_operator_cost). * * Vars and Consts are charged zero, and so are boolean operators (AND, * OR, NOT). Simplistic, but a lot better than no model at all. * * Should we try to account for the possibility of short-circuit * evaluation of AND/OR? Probably *not*, because that would make the * results depend on the clause ordering, and we are not in any position * to expect that the current ordering of the clauses is the one that's * going to end up being used. The above per-RestrictInfo caching would * not mix well with trying to re-order clauses anyway. */ if (IsA(node, FuncExpr)) { context->total.per_tuple += get_func_cost(((FuncExpr *) node)->funcid) * cpu_operator_cost; } else if (IsA(node, OpExpr) || IsA(node, DistinctExpr) || IsA(node, NullIfExpr)) { /* rely on struct equivalence to treat these all alike */ set_opfuncid((OpExpr *) node); context->total.per_tuple += get_func_cost(((OpExpr *) node)->opfuncid) * cpu_operator_cost; } else if (IsA(node, ScalarArrayOpExpr)) { /* * Estimate that the operator will be applied to about half of the * array elements before the answer is determined. */ ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) node; Node *arraynode = (Node *) lsecond(saop->args); set_sa_opfuncid(saop); context->total.per_tuple += get_func_cost(saop->opfuncid) * cpu_operator_cost * estimate_array_length(arraynode) * 0.5; } else if (IsA(node, Aggref) || IsA(node, WindowFunc)) { /* * Aggref and WindowFunc nodes are (and should be) treated like Vars, * ie, zero execution cost in the current model, because they behave * essentially like Vars in execQual.c. We disregard the costs of * their input expressions for the same reason. The actual execution * costs of the aggregate/window functions and their arguments have to * be factored into plan-node-specific costing of the Agg or WindowAgg * plan node. */ return false; /* don't recurse into children */ } else if (IsA(node, CoerceViaIO)) { CoerceViaIO *iocoerce = (CoerceViaIO *) node; Oid iofunc; Oid typioparam; bool typisvarlena; /* check the result type's input function */ getTypeInputInfo(iocoerce->resulttype, &iofunc, &typioparam); context->total.per_tuple += get_func_cost(iofunc) * cpu_operator_cost; /* check the input type's output function */ getTypeOutputInfo(exprType((Node *) iocoerce->arg), &iofunc, &typisvarlena); context->total.per_tuple += get_func_cost(iofunc) * cpu_operator_cost; } else if (IsA(node, ArrayCoerceExpr)) { ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node; Node *arraynode = (Node *) acoerce->arg; if (OidIsValid(acoerce->elemfuncid)) context->total.per_tuple += get_func_cost(acoerce->elemfuncid) * cpu_operator_cost * estimate_array_length(arraynode); } else if (IsA(node, RowCompareExpr)) { /* Conservatively assume we will check all the columns */ RowCompareExpr *rcexpr = (RowCompareExpr *) node; ListCell *lc; foreach(lc, rcexpr->opnos) { Oid opid = lfirst_oid(lc); context->total.per_tuple += get_func_cost(get_opcode(opid)) * cpu_operator_cost; } } else if (IsA(node, CurrentOfExpr)) { /* Report high cost to prevent selection of anything but TID scan */ context->total.startup += disable_cost; } else if (IsA(node, SubLink)) { /* This routine should not be applied to un-planned expressions */ elog(ERROR, "cannot handle unplanned sub-select"); } else if (IsA(node, SubPlan)) { /* * A subplan node in an expression typically indicates that the * subplan will be executed on each evaluation, so charge accordingly. * (Sub-selects that can be executed as InitPlans have already been * removed from the expression.) */ SubPlan *subplan = (SubPlan *) node; context->total.startup += subplan->startup_cost; context->total.per_tuple += subplan->per_call_cost; /* * We don't want to recurse into the testexpr, because it was already * counted in the SubPlan node's costs. So we're done. */ return false; } else if (IsA(node, AlternativeSubPlan)) { /* * Arbitrarily use the first alternative plan for costing. (We should * certainly only include one alternative, and we don't yet have * enough information to know which one the executor is most likely to * use.) */ AlternativeSubPlan *asplan = (AlternativeSubPlan *) node; return cost_qual_eval_walker((Node *) linitial(asplan->subplans), context); } /* recurse into children */ return expression_tree_walker(node, cost_qual_eval_walker, (void *) context); } /* * adjust_semi_join * Estimate how much of the inner input a SEMI or ANTI join * can be expected to scan. * * In a hash or nestloop SEMI/ANTI join, the executor will stop scanning * inner rows as soon as it finds a match to the current outer row. * We should therefore adjust some of the cost components for this effect. * This function computes some estimates needed for these adjustments. * * 'path' is already filled in except for the cost fields * 'sjinfo' is extra info about the join for selectivity estimation * * Returns TRUE if this is a SEMI or ANTI join, FALSE if not. * * Output parameters (set only in TRUE-result case): * *outer_match_frac is set to the fraction of the outer tuples that are * expected to have at least one match. * *match_count is set to the average number of matches expected for * outer tuples that have at least one match. * *indexed_join_quals is set to TRUE if all the joinquals are used as * inner index quals, FALSE if not. * * indexed_join_quals can be passed as NULL if that information is not * relevant (it is only useful for the nestloop case). */ static bool adjust_semi_join(PlannerInfo *root, JoinPath *path, SpecialJoinInfo *sjinfo, Selectivity *outer_match_frac, Selectivity *match_count, bool *indexed_join_quals) { JoinType jointype = path->jointype; Selectivity jselec; Selectivity nselec; Selectivity avgmatch; SpecialJoinInfo norm_sjinfo; List *joinquals; ListCell *l; /* Fall out if it's not JOIN_SEMI or JOIN_ANTI */ if (jointype != JOIN_SEMI && jointype != JOIN_ANTI) return false; /* * Note: it's annoying to repeat this selectivity estimation on each call, * when the joinclause list will be the same for all path pairs * implementing a given join. clausesel.c will save us from the worst * effects of this by caching at the RestrictInfo level; but perhaps it'd * be worth finding a way to cache the results at a higher level. */ /* * In an ANTI join, we must ignore clauses that are "pushed down", since * those won't affect the match logic. In a SEMI join, we do not * distinguish joinquals from "pushed down" quals, so just use the whole * restrictinfo list. */ if (jointype == JOIN_ANTI) { joinquals = NIL; foreach(l, path->joinrestrictinfo) { RestrictInfo *rinfo = (RestrictInfo *) lfirst(l); Assert(IsA(rinfo, RestrictInfo)); if (!rinfo->is_pushed_down) joinquals = lappend(joinquals, rinfo); } } else joinquals = path->joinrestrictinfo; /* * Get the JOIN_SEMI or JOIN_ANTI selectivity of the join clauses. */ jselec = clauselist_selectivity(root, joinquals, 0, jointype, sjinfo); /* * Also get the normal inner-join selectivity of the join clauses. */ norm_sjinfo.type = T_SpecialJoinInfo; norm_sjinfo.min_lefthand = path->outerjoinpath->parent->relids; norm_sjinfo.min_righthand = path->innerjoinpath->parent->relids; norm_sjinfo.syn_lefthand = path->outerjoinpath->parent->relids; norm_sjinfo.syn_righthand = path->innerjoinpath->parent->relids; norm_sjinfo.jointype = JOIN_INNER; /* we don't bother trying to make the remaining fields valid */ norm_sjinfo.lhs_strict = false; norm_sjinfo.delay_upper_joins = false; norm_sjinfo.join_quals = NIL; nselec = clauselist_selectivity(root, joinquals, 0, JOIN_INNER, &norm_sjinfo); /* Avoid leaking a lot of ListCells */ if (jointype == JOIN_ANTI) list_free(joinquals); /* * jselec can be interpreted as the fraction of outer-rel rows that have * any matches (this is true for both SEMI and ANTI cases). And nselec is * the fraction of the Cartesian product that matches. So, the average * number of matches for each outer-rel row that has at least one match is * nselec * inner_rows / jselec. * * Note: it is correct to use the inner rel's "rows" count here, not * PATH_ROWS(), even if the inner path under consideration is an inner * indexscan. This is because we have included all the join clauses in * the selectivity estimate, even ones used in an inner indexscan. */ if (jselec > 0) /* protect against zero divide */ { avgmatch = nselec * path->innerjoinpath->parent->rows / jselec; /* Clamp to sane range */ avgmatch = Max(1.0, avgmatch); } else avgmatch = 1.0; *outer_match_frac = jselec; *match_count = avgmatch; /* * If requested, check whether the inner path uses all the joinquals as * indexquals. (If that's true, we can assume that an unmatched outer * tuple is cheap to process, whereas otherwise it's probably expensive.) */ if (indexed_join_quals) { if (path->joinrestrictinfo != NIL) { List *nrclauses; nrclauses = select_nonredundant_join_clauses(root, path->joinrestrictinfo, path->innerjoinpath); *indexed_join_quals = (nrclauses == NIL); } else { /* a clauseless join does NOT qualify */ *indexed_join_quals = false; } } return true; } /* * 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->parent->rows; double inner_tuples = path->innerjoinpath->parent->rows; SpecialJoinInfo sjinfo; Selectivity selec = 1.0; ListCell *l; /* * Make up a SpecialJoinInfo for JOIN_INNER semantics. */ sjinfo.type = T_SpecialJoinInfo; sjinfo.min_lefthand = path->outerjoinpath->parent->relids; sjinfo.min_righthand = path->innerjoinpath->parent->relids; sjinfo.syn_lefthand = path->outerjoinpath->parent->relids; sjinfo.syn_righthand = path->innerjoinpath->parent->relids; sjinfo.jointype = JOIN_INNER; /* we don't bother trying to make the remaining fields valid */ sjinfo.lhs_strict = false; sjinfo.delay_upper_joins = false; sjinfo.join_quals = NIL; /* Get the approximate selectivity */ foreach(l, quals) { Node *qual = (Node *) lfirst(l); /* Note that clause_selectivity will be able to cache its result */ selec *= clause_selectivity(root, qual, 0, JOIN_INNER, &sjinfo); } /* Apply it to the input relation sizes */ tuples = selec * outer_tuples * inner_tuples; return clamp_row_est(tuples); } /* * set_baserel_size_estimates * Set the size estimates for the given base relation. * * The rel's targetlist and restrictinfo list must have been constructed * already, and rel->tuples must be set. * * We set the following fields of the rel node: * rows: the estimated number of output tuples (after applying * restriction clauses). * width: the estimated average output tuple width in bytes. * baserestrictcost: estimated cost of evaluating baserestrictinfo clauses. */ void set_baserel_size_estimates(PlannerInfo *root, RelOptInfo *rel) { double nrows; /* Should only be applied to base relations */ Assert(rel->relid > 0); nrows = rel->tuples * clauselist_selectivity(root, rel->baserestrictinfo, 0, JOIN_INNER, NULL); rel->rows = clamp_row_est(nrows); cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo, root); set_rel_width(root, rel); } /* * set_joinrel_size_estimates * Set the size estimates for the given join relation. * * The rel's targetlist must have been constructed already, and a * restriction clause list that matches the given component rels must * be provided. * * Since there is more than one way to make a joinrel for more than two * base relations, the results we get here could depend on which component * rel pair is provided. In theory we should get the same answers no matter * which pair is provided; in practice, since the selectivity estimation * routines don't handle all cases equally well, we might not. But there's * not much to be done about it. (Would it make sense to repeat the * calculations for each pair of input rels that's encountered, and somehow * average the results? Probably way more trouble than it's worth.) * * We set only the rows field here. The width field was already set by * build_joinrel_tlist, and baserestrictcost is not used for join rels. */ void set_joinrel_size_estimates(PlannerInfo *root, RelOptInfo *rel, RelOptInfo *outer_rel, RelOptInfo *inner_rel, SpecialJoinInfo *sjinfo, List *restrictlist) { JoinType jointype = sjinfo->jointype; Selectivity jselec; Selectivity pselec; double nrows; /* * Compute joinclause selectivity. Note that we are only considering * clauses that become restriction clauses at this join level; we are not * double-counting them because they were not considered in estimating the * sizes of the component rels. * * For an outer join, we have to distinguish the selectivity of the join's * own clauses (JOIN/ON conditions) from any clauses that were "pushed * down". For inner joins we just count them all as joinclauses. */ if (IS_OUTER_JOIN(jointype)) { List *joinquals = NIL; List *pushedquals = NIL; ListCell *l; /* Grovel through the clauses to separate into two lists */ foreach(l, restrictlist) { RestrictInfo *rinfo = (RestrictInfo *) lfirst(l); Assert(IsA(rinfo, RestrictInfo)); if (rinfo->is_pushed_down) pushedquals = lappend(pushedquals, rinfo); else joinquals = lappend(joinquals, rinfo); } /* Get the separate selectivities */ jselec = clauselist_selectivity(root, joinquals, 0, jointype, sjinfo); pselec = clauselist_selectivity(root, pushedquals, 0, jointype, sjinfo); /* Avoid leaking a lot of ListCells */ list_free(joinquals); list_free(pushedquals); } else { jselec = clauselist_selectivity(root, restrictlist, 0, jointype, sjinfo); pselec = 0.0; /* not used, keep compiler quiet */ } /* * Basically, we multiply size of Cartesian product by selectivity. * * If we are doing an outer join, take that into account: the joinqual * selectivity has to be clamped using the knowledge that the output must * be at least as large as the non-nullable input. However, any * pushed-down quals are applied after the outer join, so their * selectivity applies fully. * * For JOIN_SEMI and JOIN_ANTI, the selectivity is defined as the fraction * of LHS rows that have matches, and we apply that straightforwardly. */ switch (jointype) { case JOIN_INNER: nrows = outer_rel->rows * inner_rel->rows * jselec; break; case JOIN_LEFT: nrows = outer_rel->rows * inner_rel->rows * jselec; if (nrows < outer_rel->rows) nrows = outer_rel->rows; nrows *= pselec; break; case JOIN_FULL: nrows = outer_rel->rows * inner_rel->rows * jselec; if (nrows < outer_rel->rows) nrows = outer_rel->rows; if (nrows < inner_rel->rows) nrows = inner_rel->rows; nrows *= pselec; break; case JOIN_SEMI: nrows = outer_rel->rows * jselec; /* pselec not used */ break; case JOIN_ANTI: nrows = outer_rel->rows * (1.0 - jselec); nrows *= pselec; break; default: /* other values not expected here */ elog(ERROR, "unrecognized join type: %d", (int) jointype); nrows = 0; /* keep compiler quiet */ break; } rel->rows = clamp_row_est(nrows); } /* * set_subquery_size_estimates * Set the size estimates for a base relation that is a subquery. * * The rel's targetlist and restrictinfo list must have been constructed * already, and the plan for the subquery must have been completed. * We look at the subquery's plan and PlannerInfo to extract data. * * We set the same fields as set_baserel_size_estimates. */ void set_subquery_size_estimates(PlannerInfo *root, RelOptInfo *rel) { PlannerInfo *subroot = rel->subroot; RangeTblEntry *rte; ListCell *lc; /* Should only be applied to base relations that are subqueries */ Assert(rel->relid > 0); rte = planner_rt_fetch(rel->relid, root); Assert(rte->rtekind == RTE_SUBQUERY); /* Copy raw number of output rows from subplan */ rel->tuples = rel->subplan->plan_rows; /* * Compute per-output-column width estimates by examining the subquery's * targetlist. For any output that is a plain Var, get the width estimate * that was made while planning the subquery. Otherwise, we leave it to * set_rel_width to fill in a datatype-based default estimate. */ foreach(lc, subroot->parse->targetList) { TargetEntry *te = (TargetEntry *) lfirst(lc); Node *texpr = (Node *) te->expr; int32 item_width = 0; Assert(IsA(te, TargetEntry)); /* junk columns aren't visible to upper query */ if (te->resjunk) continue; /* * 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]; } Assert(te->resno >= rel->min_attr && te->resno <= rel->max_attr); rel->attr_widths[te->resno - rel->min_attr] = item_width; } /* Now estimate number of output rows, etc */ set_baserel_size_estimates(root, rel); } /* * set_function_size_estimates * Set the size estimates for a base relation that is a function call. * * The rel's targetlist and restrictinfo list must have been constructed * already. * * We set the same fields as set_baserel_size_estimates. */ void set_function_size_estimates(PlannerInfo *root, RelOptInfo *rel) { RangeTblEntry *rte; /* Should only be applied to base relations that are functions */ Assert(rel->relid > 0); rte = planner_rt_fetch(rel->relid, root); Assert(rte->rtekind == RTE_FUNCTION); /* Estimate number of rows the function itself will return */ rel->tuples = clamp_row_est(expression_returns_set_rows(rte->funcexpr)); /* Now estimate number of output rows, etc */ set_baserel_size_estimates(root, rel); } /* * set_values_size_estimates * Set the size estimates for a base relation that is a values list. * * The rel's targetlist and restrictinfo list must have been constructed * already. * * We set the same fields as set_baserel_size_estimates. */ void set_values_size_estimates(PlannerInfo *root, RelOptInfo *rel) { RangeTblEntry *rte; /* Should only be applied to base relations that are values lists */ Assert(rel->relid > 0); rte = planner_rt_fetch(rel->relid, root); Assert(rte->rtekind == RTE_VALUES); /* * Estimate number of rows the values list will return. We know this * precisely based on the list length (well, barring set-returning * functions in list items, but that's a refinement not catered for * anywhere else either). */ rel->tuples = list_length(rte->values_lists); /* Now estimate number of output rows, etc */ set_baserel_size_estimates(root, rel); } /* * set_cte_size_estimates * Set the size estimates for a base relation that is a CTE reference. * * The rel's targetlist and restrictinfo list must have been constructed * already, and we need the completed plan for the CTE (if a regular CTE) * or the non-recursive term (if a self-reference). * * We set the same fields as set_baserel_size_estimates. */ void set_cte_size_estimates(PlannerInfo *root, RelOptInfo *rel, Plan *cteplan) { RangeTblEntry *rte; /* Should only be applied to base relations that are CTE references */ Assert(rel->relid > 0); rte = planner_rt_fetch(rel->relid, root); Assert(rte->rtekind == RTE_CTE); if (rte->self_reference) { /* * In a self-reference, arbitrarily assume the average worktable size * is about 10 times the nonrecursive term's size. */ rel->tuples = 10 * cteplan->plan_rows; } else { /* Otherwise just believe the CTE plan's output estimate */ rel->tuples = cteplan->plan_rows; } /* Now estimate number of output rows, etc */ set_baserel_size_estimates(root, rel); } /* * set_foreign_size_estimates * Set the size estimates for a base relation that is a foreign table. * * There is not a whole lot that we can do here; the foreign-data wrapper * is responsible for producing useful estimates. We can do a decent job * of estimating baserestrictcost, so we set that, and we also set up width * using what will be purely datatype-driven estimates from the targetlist. * There is no way to do anything sane with the rows value, so we just put * a default estimate and hope that the wrapper can improve on it. The * wrapper's PlanForeignScan function will be called momentarily. * * The rel's targetlist and restrictinfo list must have been constructed * already. */ void set_foreign_size_estimates(PlannerInfo *root, RelOptInfo *rel) { /* Should only be applied to base relations */ Assert(rel->relid > 0); rel->rows = 1000; /* entirely bogus default estimate */ cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo, root); set_rel_width(root, rel); } /* * set_rel_width * Set the estimated output width of a base relation. * * The estimated output width is the sum of the per-attribute width estimates * for the actually-referenced columns, plus any PHVs or other expressions * that have to be calculated at this relation. This is the amount of data * we'd need to pass upwards in case of a sort, hash, etc. * * NB: this works best on plain relations because it prefers to look at * real Vars. For subqueries, set_subquery_size_estimates will already have * copied up whatever per-column estimates were made within the subquery, * and for other types of rels there isn't much we can do anyway. We fall * back on (fairly stupid) datatype-based width estimates if we can't get * any better number. * * The per-attribute width estimates are cached for possible re-use while * building join relations. */ static void set_rel_width(PlannerInfo *root, RelOptInfo *rel) { Oid reloid = planner_rt_fetch(rel->relid, root)->relid; int32 tuple_width = 0; bool have_wholerow_var = false; ListCell *lc; foreach(lc, rel->reltargetlist) { Node *node = (Node *) lfirst(lc); if (IsA(node, Var)) { Var *var = (Var *) node; int ndx; int32 item_width; Assert(var->varno == rel->relid); Assert(var->varattno >= rel->min_attr); Assert(var->varattno <= rel->max_attr); ndx = var->varattno - rel->min_attr; /* * If it's a whole-row Var, we'll deal with it below after we have * already cached as many attr widths as possible. */ if (var->varattno == 0) { have_wholerow_var = true; continue; } /* * The width may have been cached already (especially if it's a * subquery), so don't duplicate effort. */ if (rel->attr_widths[ndx] > 0) { tuple_width += rel->attr_widths[ndx]; continue; } /* Try to get column width from statistics */ if (reloid != InvalidOid && var->varattno > 0) { item_width = get_attavgwidth(reloid, var->varattno); if (item_width > 0) { rel->attr_widths[ndx] = item_width; tuple_width += item_width; continue; } } /* * Not a plain relation, or can't find statistics for it. Estimate * using just the type info. */ item_width = get_typavgwidth(var->vartype, var->vartypmod); Assert(item_width > 0); rel->attr_widths[ndx] = item_width; tuple_width += item_width; } else if (IsA(node, PlaceHolderVar)) { PlaceHolderVar *phv = (PlaceHolderVar *) node; PlaceHolderInfo *phinfo = find_placeholder_info(root, phv, false); tuple_width += phinfo->ph_width; } else { /* * We could be looking at an expression pulled up from a subquery, * or a ROW() representing a whole-row child Var, etc. Do what we * can using the expression type information. */ int32 item_width; item_width = get_typavgwidth(exprType(node), exprTypmod(node)); Assert(item_width > 0); tuple_width += item_width; } } /* * If we have a whole-row reference, estimate its width as the sum of * per-column widths plus sizeof(HeapTupleHeaderData). */ if (have_wholerow_var) { int32 wholerow_width = sizeof(HeapTupleHeaderData); if (reloid != InvalidOid) { /* Real relation, so estimate true tuple width */ wholerow_width += get_relation_data_width(reloid, rel->attr_widths - rel->min_attr); } else { /* Do what we can with info for a phony rel */ AttrNumber i; for (i = 1; i <= rel->max_attr; i++) wholerow_width += rel->attr_widths[i - rel->min_attr]; } rel->attr_widths[0 - rel->min_attr] = wholerow_width; /* * Include the whole-row Var as part of the output tuple. Yes, that * really is what happens at runtime. */ tuple_width += wholerow_width; } Assert(tuple_width >= 0); rel->width = tuple_width; } /* * relation_byte_size * Estimate the storage space in bytes for a given number of tuples * of a given width (size in bytes). */ static double relation_byte_size(double tuples, int width) { return tuples * (MAXALIGN(width) + MAXALIGN(sizeof(HeapTupleHeaderData))); } /* * page_size * Returns an estimate of the number of pages covered by a given * number of tuples of a given width (size in bytes). */ static double page_size(double tuples, int width) { return ceil(relation_byte_size(tuples, width) / BLCKSZ); }