diff options
Diffstat (limited to 'src/backend/optimizer/path/costsize.c')
| -rw-r--r-- | src/backend/optimizer/path/costsize.c | 1458 |
1 files changed, 0 insertions, 1458 deletions
diff --git a/src/backend/optimizer/path/costsize.c b/src/backend/optimizer/path/costsize.c deleted file mode 100644 index 3bf6d2f4dc8..00000000000 --- a/src/backend/optimizer/path/costsize.c +++ /dev/null @@ -1,1458 +0,0 @@ -/*------------------------------------------------------------------------- - * - * costsize.c - * Routines to compute (and set) relation sizes and path costs - * - * Path costs are measured in units of disk accesses: one sequential page - * fetch has cost 1. All else is scaled relative to a page fetch, using - * the scaling parameters - * - * 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 process a typical WHERE operator - * - * 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. - * - * 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. - * - * - * Portions Copyright (c) 1996-2002, PostgreSQL Global Development Group - * Portions Copyright (c) 1994, Regents of the University of California - * - * IDENTIFICATION - * $Header: /cvsroot/pgsql/src/backend/optimizer/path/costsize.c,v 1.85 2002/06/20 20:29:29 momjian Exp $ - * - *------------------------------------------------------------------------- - */ - -#include "postgres.h" - -#include <math.h> - -#include "catalog/pg_statistic.h" -#include "executor/nodeHash.h" -#include "miscadmin.h" -#include "optimizer/clauses.h" -#include "optimizer/cost.h" -#include "optimizer/pathnode.h" -#include "parser/parsetree.h" -#include "utils/selfuncs.h" -#include "utils/lsyscache.h" -#include "utils/syscache.h" - - -#define LOG2(x) (log(x) / 0.693147180559945) -#define LOG6(x) (log(x) / 1.79175946922805) - - -double effective_cache_size = DEFAULT_EFFECTIVE_CACHE_SIZE; -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; - -Cost disable_cost = 100000000.0; - -bool enable_seqscan = true; -bool enable_indexscan = true; -bool enable_tidscan = true; -bool enable_sort = true; -bool enable_nestloop = true; -bool enable_mergejoin = true; -bool enable_hashjoin = true; - - -static Selectivity estimate_hash_bucketsize(Query *root, Var *var); -static bool cost_qual_eval_walker(Node *node, Cost *total); -static Selectivity approx_selectivity(Query *root, List *quals); -static void set_rel_width(Query *root, RelOptInfo *rel); -static double relation_byte_size(double tuples, int width); -static double page_size(double tuples, int width); - - -/* - * cost_seqscan - * Determines and returns the cost of scanning a relation sequentially. - * - * Note: for historical reasons, this routine and the others 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. - */ -void -cost_seqscan(Path *path, Query *root, - RelOptInfo *baserel) -{ - Cost startup_cost = 0; - Cost run_cost = 0; - Cost cpu_per_tuple; - - /* Should only be applied to base relations */ - Assert(length(baserel->relids) == 1); - Assert(baserel->rtekind == RTE_RELATION); - - if (!enable_seqscan) - startup_cost += disable_cost; - - /* - * disk costs - * - * The cost of reading a page sequentially is 1.0, by definition. Note - * that the Unix kernel will typically do some amount of read-ahead - * optimization, so that this cost is less than the true cost of - * reading a page from disk. We ignore that issue here, but must take - * it into account when estimating the cost of non-sequential - * accesses! - */ - run_cost += baserel->pages; /* sequential fetches with cost 1.0 */ - - /* CPU costs */ - cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost; - run_cost += cpu_per_tuple * baserel->tuples; - - path->startup_cost = startup_cost; - path->total_cost = startup_cost + run_cost; -} - -/* - * cost_nonsequential_access - * Estimate the cost of accessing one page at random from a relation - * (or sort temp file) of the given size in pages. - * - * The simplistic model that the cost is random_page_cost is what we want - * to use for large relations; but for small ones that is a serious - * overestimate because of the effects of caching. This routine tries to - * account for that. - * - * Unfortunately we don't have any good way of estimating the effective cache - * size we are working with --- we know that Postgres itself has NBuffers - * internal buffers, but the size of the kernel's disk cache is uncertain, - * and how much of it we get to use is even less certain. We punt the problem - * for now by assuming we are given an effective_cache_size parameter. - * - * Given a guesstimated cache size, we estimate the actual I/O cost per page - * with the entirely ad-hoc equations: - * for rel_size <= effective_cache_size: - * 1 + (random_page_cost/2-1) * (rel_size/effective_cache_size) ** 2 - * for rel_size >= effective_cache_size: - * random_page_cost * (1 - (effective_cache_size/rel_size)/2) - * These give the right asymptotic behavior (=> 1.0 as rel_size becomes - * small, => random_page_cost as it becomes large) and meet in the middle - * with the estimate that the cache is about 50% effective for a relation - * of the same size as effective_cache_size. (XXX this is probably all - * wrong, but I haven't been able to find any theory about how effective - * a disk cache should be presumed to be.) - */ -static Cost -cost_nonsequential_access(double relpages) -{ - double relsize; - - /* don't crash on bad input data */ - if (relpages <= 0.0 || effective_cache_size <= 0.0) - return random_page_cost; - - relsize = relpages / effective_cache_size; - - if (relsize >= 1.0) - return random_page_cost * (1.0 - 0.5 / relsize); - else - return 1.0 + (random_page_cost * 0.5 - 1.0) * relsize * relsize; -} - -/* - * cost_index - * Determines and returns the cost of scanning a relation using an index. - * - * NOTE: an indexscan plan node can actually represent several passes, - * but here we consider the cost of just one pass. - * - * 'root' is the query root - * 'baserel' is the base relation the index is for - * 'index' is the index to be used - * 'indexQuals' is the list of applicable qual clauses (implicit AND semantics) - * 'is_injoin' is T if we are considering using the index scan as the inside - * of a nestloop join (hence, some of the indexQuals are join clauses) - * - * NOTE: '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(Path *path, Query *root, - RelOptInfo *baserel, - IndexOptInfo *index, - List *indexQuals, - bool is_injoin) -{ - Cost startup_cost = 0; - Cost run_cost = 0; - Cost indexStartupCost; - Cost indexTotalCost; - Selectivity indexSelectivity; - double indexCorrelation, - csquared; - Cost min_IO_cost, - max_IO_cost; - Cost cpu_per_tuple; - double tuples_fetched; - double pages_fetched; - double T, - b; - - /* Should only be applied to base relations */ - Assert(IsA(baserel, RelOptInfo) && - IsA(index, IndexOptInfo)); - Assert(length(baserel->relids) == 1); - Assert(baserel->rtekind == RTE_RELATION); - - if (!enable_indexscan && !is_injoin) - startup_cost += disable_cost; - - /* - * 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. - */ - OidFunctionCall8(index->amcostestimate, - PointerGetDatum(root), - PointerGetDatum(baserel), - PointerGetDatum(index), - PointerGetDatum(indexQuals), - PointerGetDatum(&indexStartupCost), - PointerGetDatum(&indexTotalCost), - PointerGetDatum(&indexSelectivity), - PointerGetDatum(&indexCorrelation)); - - /* all costs for touching index itself included here */ - startup_cost += indexStartupCost; - run_cost += indexTotalCost - indexStartupCost; - - /*---------- - * Estimate number of main-table tuples and pages fetched. - * - * When the index ordering is uncorrelated with the table ordering, - * 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) - * - * When the index ordering is exactly correlated with the table ordering - * (just after a CLUSTER, for example), the number of pages fetched should - * be just sT. What's more, these will be sequential fetches, not the - * random fetches that occur in the uncorrelated case. So, depending on - * the extent of correlation, we should estimate the actual I/O cost - * somewhere between s * T * 1.0 and PF * random_cost. We currently - * interpolate linearly between these two endpoints based on the - * correlation squared (XXX is that appropriate?). - * - * In any case the number of tuples fetched is Ns. - *---------- - */ - - tuples_fetched = indexSelectivity * baserel->tuples; - /* Don't believe estimates less than 1... */ - if (tuples_fetched < 1.0) - tuples_fetched = 1.0; - - /* This part is the Mackert and Lohman formula */ - - T = (baserel->pages > 1) ? (double) baserel->pages : 1.0; - b = (effective_cache_size > 1) ? effective_cache_size : 1.0; - - if (T <= b) - { - pages_fetched = - (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched); - if (pages_fetched > T) - pages_fetched = T; - } - 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; - } - } - - /* - * min_IO_cost corresponds to the perfectly correlated case - * (csquared=1), max_IO_cost to the perfectly uncorrelated case - * (csquared=0). Note that we just charge random_page_cost per page - * in the uncorrelated case, rather than using - * cost_nonsequential_access, since we've already accounted for - * caching effects by using the Mackert model. - */ - min_IO_cost = ceil(indexSelectivity * T); - max_IO_cost = pages_fetched * random_page_cost; - - /* - * 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. XXX For a lossy index, not all - * the quals will be removed and so we really shouldn't subtract their - * costs; but detecting that seems more expensive than it's worth. - * Also, 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. - */ - cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost; - - if (!is_injoin) - cpu_per_tuple -= cost_qual_eval(indexQuals); - - run_cost += cpu_per_tuple * tuples_fetched; - - path->startup_cost = startup_cost; - path->total_cost = startup_cost + run_cost; -} - -/* - * cost_tidscan - * Determines and returns the cost of scanning a relation using TIDs. - */ -void -cost_tidscan(Path *path, Query *root, - RelOptInfo *baserel, List *tideval) -{ - Cost startup_cost = 0; - Cost run_cost = 0; - Cost cpu_per_tuple; - int ntuples = length(tideval); - - /* Should only be applied to base relations */ - Assert(length(baserel->relids) == 1); - Assert(baserel->rtekind == RTE_RELATION); - - if (!enable_tidscan) - startup_cost += disable_cost; - - /* disk costs --- assume each tuple on a different page */ - run_cost += random_page_cost * ntuples; - - /* CPU costs */ - cpu_per_tuple = cpu_tuple_cost + baserel->baserestrictcost; - run_cost += cpu_per_tuple * ntuples; - - 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, Query *root, RelOptInfo *baserel) -{ - Cost startup_cost = 0; - Cost run_cost = 0; - Cost cpu_per_tuple; - - /* Should only be applied to base relations that are functions */ - Assert(length(baserel->relids) == 1); - Assert(baserel->rtekind == RTE_FUNCTION); - - /* - * For now, estimate function's cost at one operator eval per function - * call. Someday we should revive the function cost estimate columns in - * pg_proc... - */ - cpu_per_tuple = cpu_operator_cost; - - /* Add scanning CPU costs */ - cpu_per_tuple += cpu_tuple_cost + baserel->baserestrictcost; - run_cost += cpu_per_tuple * baserel->tuples; - - path->startup_cost = startup_cost; - path->total_cost = startup_cost + run_cost; -} - -/* - * cost_sort - * Determines and returns the cost of sorting a relation. - * - * The cost of supplying the input data is NOT included; the caller should - * add that cost to both startup and total costs returned from this routine! - * - * If the total volume of data to sort is less than SortMem, 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 SortMem, 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(log6(r)) merge passes where r is the - * number of initial runs formed (log6 because tuplesort.c uses six-tape - * merging). Since the average initial run should be about twice SortMem, - * we have - * disk traffic = 2 * relsize * ceil(log6(p / (2*SortMem))) - * cpu = comparison_cost * t * log2(t) - * - * The disk traffic is assumed to be half sequential and half random - * accesses (XXX can't we refine that guess?) - * - * We charge two operator evals per tuple comparison, which should be in - * the right ballpark in most cases. - * - * 'pathkeys' is a list of sort keys - * 'tuples' is the number of tuples in the relation - * 'width' is the average tuple width in bytes - * - * 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. - */ -void -cost_sort(Path *path, Query *root, - List *pathkeys, double tuples, int width) -{ - Cost startup_cost = 0; - Cost run_cost = 0; - double nbytes = relation_byte_size(tuples, width); - long sortmembytes = SortMem * 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; - - /* - * CPU costs - * - * Assume about two operator evals per tuple comparison and N log2 N - * comparisons - */ - startup_cost += 2.0 * cpu_operator_cost * tuples * LOG2(tuples); - - /* disk costs */ - if (nbytes > sortmembytes) - { - double npages = ceil(nbytes / BLCKSZ); - double nruns = nbytes / (sortmembytes * 2); - double log_runs = ceil(LOG6(nruns)); - double npageaccesses; - - if (log_runs < 1.0) - log_runs = 1.0; - npageaccesses = 2.0 * npages * log_runs; - /* Assume half are sequential (cost 1), half are not */ - startup_cost += npageaccesses * - (1.0 + cost_nonsequential_access(npages)) * 0.5; - } - - /* - * Also charge a small amount (arbitrarily set equal to operator cost) - * per extracted tuple. - */ - run_cost += cpu_operator_cost * tuples; - - path->startup_cost = startup_cost; - path->total_cost = startup_cost + run_cost; -} - - -/* - * cost_nestloop - * Determines and returns the cost of joining two relations using the - * nested loop algorithm. - * - * 'outer_path' is the path for the outer relation - * 'inner_path' is the path for the inner relation - * 'restrictlist' are the RestrictInfo nodes to be applied at the join - */ -void -cost_nestloop(Path *path, Query *root, - Path *outer_path, - Path *inner_path, - List *restrictlist) -{ - Cost startup_cost = 0; - Cost run_cost = 0; - Cost cpu_per_tuple; - double ntuples; - - if (!enable_nestloop) - startup_cost += disable_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. What's not so clear is whether the inner path's - * startup_cost must be paid again on each rescan of the inner path. - * This is not true if the inner path is materialized, but probably is - * true otherwise. Since we don't yet have clean handling of the - * decision whether to materialize a path, we can't tell here which - * will happen. As a compromise, charge 50% of the inner startup cost - * for each restart. - */ - startup_cost += outer_path->startup_cost + inner_path->startup_cost; - run_cost += outer_path->total_cost - outer_path->startup_cost; - run_cost += outer_path->parent->rows * - (inner_path->total_cost - inner_path->startup_cost); - if (outer_path->parent->rows > 1) - run_cost += (outer_path->parent->rows - 1) * - inner_path->startup_cost * 0.5; - - /* - * Number of tuples processed (not number emitted!). If 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. - */ - if (IsA(inner_path, IndexPath)) - ntuples = ((IndexPath *) inner_path)->rows; - else - ntuples = inner_path->parent->rows; - ntuples *= outer_path->parent->rows; - - /* CPU costs */ - cpu_per_tuple = cpu_tuple_cost + cost_qual_eval(restrictlist); - run_cost += cpu_per_tuple * ntuples; - - path->startup_cost = startup_cost; - path->total_cost = startup_cost + run_cost; -} - -/* - * cost_mergejoin - * Determines and returns the cost of joining two relations using the - * merge join algorithm. - * - * 'outer_path' is the path for the outer relation - * 'inner_path' is the path for the inner relation - * 'restrictlist' are the RestrictInfo nodes to be applied at the join - * 'mergeclauses' are the RestrictInfo nodes to use as merge clauses - * (this should be a subset of the restrictlist) - * '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(Path *path, Query *root, - Path *outer_path, - Path *inner_path, - List *restrictlist, - List *mergeclauses, - List *outersortkeys, - List *innersortkeys) -{ - Cost startup_cost = 0; - Cost run_cost = 0; - Cost cpu_per_tuple; - RestrictInfo *firstclause; - Var *leftvar; - double outer_rows, - inner_rows; - double ntuples; - Selectivity outerscansel, - innerscansel; - Path sort_path; /* dummy for result of cost_sort */ - - if (!enable_mergejoin) - startup_cost += disable_cost; - - /* - * A merge join will stop as soon as it exhausts either input stream. - * Estimate fraction of the left and right inputs that will actually - * need to be scanned. We use only the first (most significant) - * merge clause for this purpose. - * - * Since this calculation is somewhat expensive, and will be the same - * for all mergejoin paths associated with the merge clause, we cache - * the results in the RestrictInfo node. - */ - firstclause = (RestrictInfo *) lfirst(mergeclauses); - if (firstclause->left_mergescansel < 0) /* not computed yet? */ - mergejoinscansel(root, (Node *) firstclause->clause, - &firstclause->left_mergescansel, - &firstclause->right_mergescansel); - - leftvar = get_leftop(firstclause->clause); - Assert(IsA(leftvar, Var)); - if (VARISRELMEMBER(leftvar->varno, outer_path->parent)) - { - /* left side of clause is outer */ - outerscansel = firstclause->left_mergescansel; - innerscansel = firstclause->right_mergescansel; - } - else - { - /* left side of clause is inner */ - outerscansel = firstclause->right_mergescansel; - innerscansel = firstclause->left_mergescansel; - } - - outer_rows = outer_path->parent->rows * outerscansel; - inner_rows = inner_path->parent->rows * innerscansel; - - /* cost of source data */ - - /* - * Note we are assuming that each source tuple is fetched just once, - * which is not right in the presence of equal keys. If we had a way - * of estimating the proportion of equal keys, we could apply a - * correction factor... - */ - if (outersortkeys) /* do we need to sort outer? */ - { - startup_cost += outer_path->total_cost; - cost_sort(&sort_path, - root, - outersortkeys, - outer_path->parent->rows, - outer_path->parent->width); - startup_cost += sort_path.startup_cost; - run_cost += (sort_path.total_cost - sort_path.startup_cost) - * outerscansel; - } - else - { - startup_cost += outer_path->startup_cost; - run_cost += (outer_path->total_cost - outer_path->startup_cost) - * outerscansel; - } - - if (innersortkeys) /* do we need to sort inner? */ - { - startup_cost += inner_path->total_cost; - cost_sort(&sort_path, - root, - innersortkeys, - inner_path->parent->rows, - inner_path->parent->width); - startup_cost += sort_path.startup_cost; - run_cost += (sort_path.total_cost - sort_path.startup_cost) - * innerscansel; - } - else - { - startup_cost += inner_path->startup_cost; - run_cost += (inner_path->total_cost - inner_path->startup_cost) - * innerscansel; - } - - /* - * The number of tuple comparisons needed depends drastically on the - * number of equal keys in the two source relations, which we have no - * good way of estimating. (XXX could the MCV statistics help?) - * Somewhat arbitrarily, we charge one tuple - * comparison (one cpu_operator_cost) for each tuple in the two source - * relations. This is probably a lower bound. - */ - run_cost += cpu_operator_cost * (outer_rows + inner_rows); - - /* - * 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. It's OK to use an - * approximate selectivity here, since in most cases this is a minor - * component of the cost. NOTE: it's correct to use the unscaled rows - * counts here, not the scaled-down counts we obtained above. - */ - ntuples = approx_selectivity(root, mergeclauses) * - outer_path->parent->rows * inner_path->parent->rows; - - /* CPU costs */ - cpu_per_tuple = cpu_tuple_cost + cost_qual_eval(restrictlist); - run_cost += cpu_per_tuple * ntuples; - - path->startup_cost = startup_cost; - path->total_cost = startup_cost + run_cost; -} - -/* - * cost_hashjoin - * Determines and returns the cost of joining two relations using the - * hash join algorithm. - * - * 'outer_path' is the path for the outer relation - * 'inner_path' is the path for the inner relation - * 'restrictlist' are the RestrictInfo nodes to be applied at the join - * 'hashclauses' is a list of the hash join clause (always a 1-element list) - * (this should be a subset of the restrictlist) - */ -void -cost_hashjoin(Path *path, Query *root, - Path *outer_path, - Path *inner_path, - List *restrictlist, - List *hashclauses) -{ - Cost startup_cost = 0; - Cost run_cost = 0; - Cost cpu_per_tuple; - double ntuples; - double outerbytes = relation_byte_size(outer_path->parent->rows, - outer_path->parent->width); - double innerbytes = relation_byte_size(inner_path->parent->rows, - inner_path->parent->width); - long hashtablebytes = SortMem * 1024L; - RestrictInfo *restrictinfo; - Var *left, - *right; - Selectivity innerbucketsize; - - if (!enable_hashjoin) - startup_cost += disable_cost; - - /* 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 */ - startup_cost += cpu_operator_cost * inner_path->parent->rows; - run_cost += cpu_operator_cost * outer_path->parent->rows; - - /* - * Determine bucketsize fraction for inner relation. First we have to - * figure out which side of the hashjoin clause is the inner side. - */ - Assert(length(hashclauses) == 1); - Assert(IsA(lfirst(hashclauses), RestrictInfo)); - restrictinfo = (RestrictInfo *) lfirst(hashclauses); - /* these must be OK, since check_hashjoinable accepted the clause */ - left = get_leftop(restrictinfo->clause); - right = get_rightop(restrictinfo->clause); - - /* - * 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 (VARISRELMEMBER(right->varno, inner_path->parent)) - { - /* righthand side is inner */ - innerbucketsize = restrictinfo->right_bucketsize; - if (innerbucketsize < 0) - { - /* not cached yet */ - innerbucketsize = estimate_hash_bucketsize(root, right); - restrictinfo->right_bucketsize = innerbucketsize; - } - } - else - { - Assert(VARISRELMEMBER(left->varno, inner_path->parent)); - /* lefthand side is inner */ - innerbucketsize = restrictinfo->left_bucketsize; - if (innerbucketsize < 0) - { - /* not cached yet */ - innerbucketsize = estimate_hash_bucketsize(root, left); - restrictinfo->left_bucketsize = innerbucketsize; - } - } - - /* - * 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. We charge - * one cpu_operator_cost per tuple comparison. - */ - run_cost += cpu_operator_cost * outer_path->parent->rows * - ceil(inner_path->parent->rows * innerbucketsize); - - /* - * 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. It's OK to use an - * approximate selectivity here, since in most cases this is a minor - * component of the cost. - */ - ntuples = approx_selectivity(root, hashclauses) * - outer_path->parent->rows * inner_path->parent->rows; - - /* CPU costs */ - cpu_per_tuple = cpu_tuple_cost + cost_qual_eval(restrictlist); - run_cost += cpu_per_tuple * ntuples; - - /* - * 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 one cost unit per page of I/O (correct since it - * should be nice and sequential...). Writing the inner rel counts as - * startup cost, all the rest as run cost. - */ - if (innerbytes > hashtablebytes) - { - double outerpages = page_size(outer_path->parent->rows, - outer_path->parent->width); - double innerpages = page_size(inner_path->parent->rows, - inner_path->parent->width); - - startup_cost += innerpages; - run_cost += innerpages + 2 * outerpages; - } - - /* - * Bias against putting larger relation on inside. We don't want an - * absolute prohibition, though, since larger relation might have - * better bucketsize --- and we can't trust the size estimates - * unreservedly, anyway. Instead, inflate the startup cost by the - * square root of the size ratio. (Why square root? No real good - * reason, but it seems reasonable...) - */ - if (innerbytes > outerbytes && outerbytes > 0) - startup_cost *= sqrt(innerbytes / outerbytes); - - path->startup_cost = startup_cost; - path->total_cost = startup_cost + run_cost; -} - -/* - * Estimate hash bucketsize fraction (ie, number of entries in a bucket - * divided by total tuples in relation) if the specified Var is used - * as a hash key. - * - * XXX This is really pretty bogus since we're effectively assuming that the - * distribution of hash keys will be the same after applying restriction - * clauses as it was in the underlying relation. However, we are not nearly - * smart enough to figure out how the restrict clauses might change the - * distribution, so this will have to do for now. - * - * We can get the number of buckets the executor will use for the given - * input relation. If the data were perfectly distributed, with the same - * number of tuples going into each available bucket, then the bucketsize - * fraction would be 1/nbuckets. But this happy state of affairs will occur - * only if (a) there are at least nbuckets distinct data values, and (b) - * we have a not-too-skewed data distribution. Otherwise the buckets will - * be nonuniformly occupied. If the other relation in the join has a key - * distribution similar to this one's, then the most-loaded buckets are - * exactly those that will be probed most often. Therefore, the "average" - * bucket size for costing purposes should really be taken as something close - * to the "worst case" bucket size. We try to estimate this by adjusting the - * fraction if there are too few distinct data values, and then scaling up - * by the ratio of the most common value's frequency to the average frequency. - * - * If no statistics are available, use a default estimate of 0.1. This will - * discourage use of a hash rather strongly if the inner relation is large, - * which is what we want. We do not want to hash unless we know that the - * inner rel is well-dispersed (or the alternatives seem much worse). - */ -static Selectivity -estimate_hash_bucketsize(Query *root, Var *var) -{ - Oid relid; - RelOptInfo *rel; - int virtualbuckets; - int physicalbuckets; - int numbatches; - HeapTuple tuple; - Form_pg_statistic stats; - double estfract, - ndistinct, - mcvfreq, - avgfreq; - float4 *numbers; - int nnumbers; - - /* - * Lookup info about var's relation and attribute; if none available, - * return default estimate. - */ - if (!IsA(var, Var)) - return 0.1; - - relid = getrelid(var->varno, root->rtable); - if (relid == InvalidOid) - return 0.1; - - rel = find_base_rel(root, var->varno); - - if (rel->tuples <= 0.0 || rel->rows <= 0.0) - return 0.1; /* ensure we can divide below */ - - /* Get hash table size that executor would use for this relation */ - ExecChooseHashTableSize(rel->rows, rel->width, - &virtualbuckets, - &physicalbuckets, - &numbatches); - - tuple = SearchSysCache(STATRELATT, - ObjectIdGetDatum(relid), - Int16GetDatum(var->varattno), - 0, 0); - if (!HeapTupleIsValid(tuple)) - { - /* - * Perhaps the Var is a system attribute; if so, it will have no - * entry in pg_statistic, but we may be able to guess something - * about its distribution anyway. - */ - switch (var->varattno) - { - case ObjectIdAttributeNumber: - case SelfItemPointerAttributeNumber: - /* these are unique, so buckets should be well-distributed */ - return 1.0 / (double) virtualbuckets; - case TableOidAttributeNumber: - /* hashing this is a terrible idea... */ - return 1.0; - } - return 0.1; - } - stats = (Form_pg_statistic) GETSTRUCT(tuple); - - /* - * Obtain number of distinct data values in raw relation. - */ - ndistinct = stats->stadistinct; - if (ndistinct < 0.0) - ndistinct = -ndistinct * rel->tuples; - - if (ndistinct <= 0.0) /* ensure we can divide */ - { - ReleaseSysCache(tuple); - return 0.1; - } - - /* Also compute avg freq of all distinct data values in raw relation */ - avgfreq = (1.0 - stats->stanullfrac) / ndistinct; - - /* - * Adjust ndistinct to account for restriction clauses. Observe we - * are assuming that the data distribution is affected uniformly by - * the restriction clauses! - * - * XXX Possibly better way, but much more expensive: multiply by - * selectivity of rel's restriction clauses that mention the target - * Var. - */ - ndistinct *= rel->rows / rel->tuples; - - /* - * Initial estimate of bucketsize fraction is 1/nbuckets as long as - * the number of buckets is less than the expected number of distinct - * values; otherwise it is 1/ndistinct. - */ - if (ndistinct > (double) virtualbuckets) - estfract = 1.0 / (double) virtualbuckets; - else - estfract = 1.0 / ndistinct; - - /* - * Look up the frequency of the most common value, if available. - */ - mcvfreq = 0.0; - - if (get_attstatsslot(tuple, var->vartype, var->vartypmod, - STATISTIC_KIND_MCV, InvalidOid, - NULL, NULL, &numbers, &nnumbers)) - { - /* - * The first MCV stat is for the most common value. - */ - if (nnumbers > 0) - mcvfreq = numbers[0]; - free_attstatsslot(var->vartype, NULL, 0, - numbers, nnumbers); - } - - /* - * Adjust estimated bucketsize upward to account for skewed - * distribution. - */ - if (avgfreq > 0.0 && mcvfreq > avgfreq) - estfract *= mcvfreq / avgfreq; - - ReleaseSysCache(tuple); - - return (Selectivity) estfract; -} - - -/* - * cost_qual_eval - * Estimate the CPU cost of evaluating a WHERE clause (once). - * The input can be either an implicitly-ANDed list of boolean - * expressions, or a list of RestrictInfo nodes. - */ -Cost -cost_qual_eval(List *quals) -{ - Cost total = 0; - List *l; - - /* We don't charge any cost for the implicit ANDing at top level ... */ - - foreach(l, quals) - { - Node *qual = (Node *) lfirst(l); - - /* - * 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 will contain -1. - */ - if (qual && IsA(qual, RestrictInfo)) - { - RestrictInfo *restrictinfo = (RestrictInfo *) qual; - - if (restrictinfo->eval_cost < 0) - { - restrictinfo->eval_cost = 0; - cost_qual_eval_walker((Node *) restrictinfo->clause, - &restrictinfo->eval_cost); - } - total += restrictinfo->eval_cost; - } - else - { - /* If it's a bare expression, must always do it the hard way */ - cost_qual_eval_walker(qual, &total); - } - } - return total; -} - -static bool -cost_qual_eval_walker(Node *node, Cost *total) -{ - if (node == NULL) - return false; - - /* - * Our basic strategy is to charge one cpu_operator_cost for each - * operator or function node in the given tree. 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? - */ - if (IsA(node, Expr)) - { - Expr *expr = (Expr *) node; - - switch (expr->opType) - { - case OP_EXPR: - case FUNC_EXPR: - *total += cpu_operator_cost; - break; - case OR_EXPR: - case AND_EXPR: - case NOT_EXPR: - break; - case SUBPLAN_EXPR: - - /* - * A subplan node in an expression indicates that the - * subplan will be executed on each evaluation, so charge - * accordingly. (We assume that sub-selects that can be - * executed as InitPlans have already been removed from - * the expression.) - * - * NOTE: this logic should agree with the estimates used by - * make_subplan() in plan/subselect.c. - */ - { - SubPlan *subplan = (SubPlan *) expr->oper; - Plan *plan = subplan->plan; - Cost subcost; - - if (subplan->sublink->subLinkType == EXISTS_SUBLINK) - { - /* we only need to fetch 1 tuple */ - subcost = plan->startup_cost + - (plan->total_cost - plan->startup_cost) / plan->plan_rows; - } - else if (subplan->sublink->subLinkType == ALL_SUBLINK || - subplan->sublink->subLinkType == ANY_SUBLINK) - { - /* assume we need 50% of the tuples */ - subcost = plan->startup_cost + - 0.50 * (plan->total_cost - plan->startup_cost); - /* XXX what if subplan has been materialized? */ - } - else - { - /* assume we need all tuples */ - subcost = plan->total_cost; - } - *total += subcost; - } - break; - } - /* fall through to examine args of Expr node */ - } - return expression_tree_walker(node, cost_qual_eval_walker, - (void *) total); -} - - -/* - * approx_selectivity - * Quick-and-dirty estimation of clause selectivities. - * The input can be either an implicitly-ANDed list of boolean - * expressions, or a list of RestrictInfo nodes (typically the latter). - * - * The "quick" part comes from caching the selectivity estimates so we can - * avoid recomputing them later. (Since the same clauses are typically - * examined over and over in different possible join trees, this makes a - * big difference.) - * - * The "dirty" part comes from the fact that the selectivities of multiple - * clauses are estimated independently and multiplied together. Now - * clauselist_selectivity often can't do any better than that anyhow, but - * for some situations (such as range constraints) it is smarter. - * - * 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 Selectivity -approx_selectivity(Query *root, List *quals) -{ - Selectivity total = 1.0; - List *l; - - foreach(l, quals) - { - Node *qual = (Node *) lfirst(l); - Selectivity selec; - - /* - * RestrictInfo nodes contain a this_selec field reserved for this - * routine's use, so that it's not necessary to evaluate the qual - * clause's selectivity more than once. If the clause's - * selectivity hasn't been computed yet, the field will contain - * -1. - */ - if (qual && IsA(qual, RestrictInfo)) - { - RestrictInfo *restrictinfo = (RestrictInfo *) qual; - - if (restrictinfo->this_selec < 0) - restrictinfo->this_selec = - clause_selectivity(root, - (Node *) restrictinfo->clause, - 0); - selec = restrictinfo->this_selec; - } - else - { - /* If it's a bare expression, must always do it the hard way */ - selec = clause_selectivity(root, qual, 0); - } - total *= selec; - } - return total; -} - - -/* - * 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. - * - * 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(Query *root, RelOptInfo *rel) -{ - /* Should only be applied to base relations */ - Assert(length(rel->relids) == 1); - - rel->rows = rel->tuples * - restrictlist_selectivity(root, - rel->baserestrictinfo, - lfirsti(rel->relids)); - - /* - * Force estimate to be at least one row, to make explain output look - * better and to avoid possible divide-by-zero when interpolating - * cost. - */ - if (rel->rows < 1.0) - rel->rows = 1.0; - - rel->baserestrictcost = cost_qual_eval(rel->baserestrictinfo); - - 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 the same relnode fields as set_baserel_size_estimates() does. - */ -void -set_joinrel_size_estimates(Query *root, RelOptInfo *rel, - RelOptInfo *outer_rel, - RelOptInfo *inner_rel, - JoinType jointype, - List *restrictlist) -{ - double temp; - - /* Start with the Cartesian product */ - temp = outer_rel->rows * inner_rel->rows; - - /* - * Apply join restrictivity. 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. - */ - temp *= restrictlist_selectivity(root, - restrictlist, - 0); - - /* - * If we are doing an outer join, take that into account: the output - * must be at least as large as the non-nullable input. (Is there any - * chance of being even smarter?) - */ - switch (jointype) - { - case JOIN_INNER: - break; - case JOIN_LEFT: - if (temp < outer_rel->rows) - temp = outer_rel->rows; - break; - case JOIN_RIGHT: - if (temp < inner_rel->rows) - temp = inner_rel->rows; - break; - case JOIN_FULL: - if (temp < outer_rel->rows) - temp = outer_rel->rows; - if (temp < inner_rel->rows) - temp = inner_rel->rows; - break; - default: - elog(ERROR, "set_joinrel_size_estimates: unsupported join type %d", - (int) jointype); - break; - } - - /* - * Force estimate to be at least one row, to make explain output look - * better and to avoid possible divide-by-zero when interpolating - * cost. - */ - if (temp < 1.0) - temp = 1.0; - - rel->rows = temp; - - /* - * We could apply set_rel_width() to compute the output tuple width - * from scratch, but at present it's always just the sum of the input - * widths, so why work harder than necessary? If relnode.c is ever - * taught to remove unneeded columns from join targetlists, go back to - * using set_rel_width here. - */ - rel->width = outer_rel->width + inner_rel->width; -} - -/* - * 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 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_function_size_estimates(Query *root, RelOptInfo *rel) -{ - /* Should only be applied to base relations that are functions */ - Assert(length(rel->relids) == 1); - Assert(rel->rtekind == RTE_FUNCTION); - - /* - * Estimate number of rows the function itself will return. - * - * XXX no idea how to do this yet; but should at least check whether - * function returns set or not... - */ - rel->tuples = 1000; - - /* Now estimate number of output rows */ - rel->rows = rel->tuples * - restrictlist_selectivity(root, - rel->baserestrictinfo, - lfirsti(rel->relids)); - - /* - * Force estimate to be at least one row, to make explain output look - * better and to avoid possible divide-by-zero when interpolating - * cost. - */ - if (rel->rows < 1.0) - rel->rows = 1.0; - - rel->baserestrictcost = cost_qual_eval(rel->baserestrictinfo); - - set_rel_width(root, rel); -} - - -/* - * set_rel_width - * Set the estimated output width of the relation. - * - * NB: this works best on base relations because it prefers to look at - * real Vars. It will fail to make use of pg_statistic info when applied - * to a subquery relation, even if the subquery outputs are simple vars - * that we could have gotten info for. Is it worth trying to be smarter - * about subqueries? - */ -static void -set_rel_width(Query *root, RelOptInfo *rel) -{ - int32 tuple_width = 0; - List *tllist; - - foreach(tllist, rel->targetlist) - { - TargetEntry *tle = (TargetEntry *) lfirst(tllist); - int32 item_width; - - /* - * If it's a Var, try to get statistical info from pg_statistic. - */ - if (tle->expr && IsA(tle->expr, Var)) - { - Var *var = (Var *) tle->expr; - Oid relid; - - relid = getrelid(var->varno, root->rtable); - if (relid != InvalidOid) - { - item_width = get_attavgwidth(relid, var->varattno); - if (item_width > 0) - { - tuple_width += item_width; - continue; - } - } - } - - /* - * Not a Var, or can't find statistics for it. Estimate using - * just the type info. - */ - item_width = get_typavgwidth(tle->resdom->restype, - tle->resdom->restypmod); - Assert(item_width > 0); - tuple_width += item_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 * ((double) MAXALIGN(width + sizeof(HeapTupleData))); -} - -/* - * 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); -} |