* Portions Copyright (c) 1994, Regents of the University of California
*
* IDENTIFICATION
- * $PostgreSQL: pgsql/src/backend/optimizer/path/costsize.c,v 1.123 2004/01/19 03:52:28 tgl Exp $
+ * $PostgreSQL: pgsql/src/backend/optimizer/path/costsize.c,v 1.129 2004/06/01 03:02:52 tgl Exp $
*
*-------------------------------------------------------------------------
*/
bool enable_hashjoin = true;
-static Selectivity estimate_hash_bucketsize(Query *root, Var *var,
- int nbuckets);
static bool cost_qual_eval_walker(Node *node, QualCost *total);
static Selectivity approx_selectivity(Query *root, List *quals,
JoinType jointype);
Cost startup_cost = 0;
Cost run_cost = 0;
Cost cpu_per_tuple;
- int ntuples = length(tideval);
+ int ntuples = list_length(tideval);
/* Should only be applied to base relations */
Assert(baserel->relid > 0);
* 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 SortMem, we will do
+ * If the total volume of data to sort is less than work_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 SortMem, we switch to a tape-style merge
+ * If the total volume exceeds work_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(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,
+ * merging). Since the average initial run should be about twice work_mem,
* we have
- * disk traffic = 2 * relsize * ceil(log6(p / (2*SortMem)))
+ * disk traffic = 2 * relsize * ceil(log6(p / (2*work_mem)))
* cpu = comparison_cost * t * log2(t)
*
* The disk traffic is assumed to be half sequential and half random
Cost startup_cost = input_cost;
Cost run_cost = 0;
double nbytes = relation_byte_size(tuples, width);
- long sortmembytes = SortMem * 1024L;
+ long work_mem_bytes = work_mem * 1024L;
if (!enable_sort)
startup_cost += disable_cost;
startup_cost += 2.0 * cpu_operator_cost * tuples * LOG2(tuples);
/* disk costs */
- if (nbytes > sortmembytes)
+ if (nbytes > work_mem_bytes)
{
double npages = ceil(nbytes / BLCKSZ);
- double nruns = nbytes / (sortmembytes * 2);
+ double nruns = nbytes / (work_mem_bytes * 2);
double log_runs = ceil(LOG6(nruns));
double npageaccesses;
* 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 SortMem, we will need
+ * 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.
*/
void
Cost startup_cost = input_cost;
Cost run_cost = 0;
double nbytes = relation_byte_size(tuples, width);
- long sortmembytes = SortMem * 1024L;
+ long work_mem_bytes = work_mem * 1024L;
/* disk costs */
- if (nbytes > sortmembytes)
+ if (nbytes > work_mem_bytes)
{
double npages = ceil(nbytes / BLCKSZ);
* 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);
-
- if (bms_is_subset(firstclause->left_relids, outer_path->parent->relids))
+ if (mergeclauses)
{
- /* left side of clause is outer */
- outerscansel = firstclause->left_mergescansel;
- innerscansel = firstclause->right_mergescansel;
+ firstclause = (RestrictInfo *) linitial(mergeclauses);
+ if (firstclause->left_mergescansel < 0) /* not computed yet? */
+ mergejoinscansel(root, (Node *) firstclause->clause,
+ &firstclause->left_mergescansel,
+ &firstclause->right_mergescansel);
+
+ if (bms_is_subset(firstclause->left_relids, outer_path->parent->relids))
+ {
+ /* 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;
+ }
}
else
{
- /* left side of clause is inner */
- outerscansel = firstclause->right_mergescansel;
- innerscansel = firstclause->left_mergescansel;
+ /* cope with clauseless mergejoin */
+ outerscansel = innerscansel = 1.0;
}
/* convert selectivity to row count; must scan at least one row */
outer_path->parent->width);
double innerbytes = relation_byte_size(inner_path_rows,
inner_path->parent->width);
- int num_hashclauses = length(hashclauses);
+ int num_hashclauses = list_length(hashclauses);
int virtualbuckets;
int physicalbuckets;
int numbatches;
Selectivity innerbucketsize;
Selectivity joininfactor;
- List *hcl;
+ ListCell *hcl;
if (!enable_hashjoin)
startup_cost += disable_cost;
/* not cached yet */
thisbucketsize =
estimate_hash_bucketsize(root,
- (Var *) get_rightop(restrictinfo->clause),
+ get_rightop(restrictinfo->clause),
virtualbuckets);
restrictinfo->right_bucketsize = thisbucketsize;
}
/* not cached yet */
thisbucketsize =
estimate_hash_bucketsize(root,
- (Var *) get_leftop(restrictinfo->clause),
+ get_leftop(restrictinfo->clause),
virtualbuckets);
restrictinfo->left_bucketsize = thisbucketsize;
}
path->jpath.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 are passed 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, int nbuckets)
-{
- Oid relid;
- RelOptInfo *rel;
- HeapTuple tuple;
- Form_pg_statistic stats;
- double estfract,
- ndistinct,
- mcvfreq,
- avgfreq;
- float4 *numbers;
- int nnumbers;
-
- /* Ignore any binary-compatible relabeling */
- if (var && IsA(var, RelabelType))
- var = (Var *) ((RelabelType *) var)->arg;
-
- /*
- * Lookup info about var's relation and attribute; if none available,
- * return default estimate.
- */
- if (var == NULL || !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 */
-
- tuple = SearchSysCache(STATRELATT,
- ObjectIdGetDatum(relid),
- Int16GetDatum(var->varattno),
- 0, 0);
- if (!HeapTupleIsValid(tuple))
- {
- /*
- * If the attribute is known unique because of an index,
- * we can treat it as well-distributed.
- */
- if (has_unique_index(rel, var->varattno))
- return 1.0 / (double) nbuckets;
-
- /*
- * 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) nbuckets;
- 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) nbuckets)
- estfract = 1.0 / (double) nbuckets;
- 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;
-
- /*
- * Clamp bucketsize to sane range (the above adjustment could easily
- * produce an out-of-range result). We set the lower bound a little
- * above zero, since zero isn't a very sane result.
- */
- if (estfract < 1.0e-6)
- estfract = 1.0e-6;
- else if (estfract > 1.0)
- estfract = 1.0;
-
- ReleaseSysCache(tuple);
-
- return (Selectivity) estfract;
-}
-
/*
* cost_qual_eval
void
cost_qual_eval(QualCost *cost, List *quals)
{
- List *l;
+ ListCell *l;
cost->startup = 0;
cost->per_tuple = 0;
approx_selectivity(Query *root, List *quals, JoinType jointype)
{
Selectivity total = 1.0;
- List *l;
+ ListCell *l;
foreach(l, quals)
{
set_rel_width(Query *root, RelOptInfo *rel)
{
int32 tuple_width = 0;
- List *tllist;
+ ListCell *tllist;
- foreach(tllist, FastListValue(&rel->reltargetlist))
+ foreach(tllist, rel->reltargetlist)
{
Var *var = (Var *) lfirst(tllist);
int ndx = var->varattno - rel->min_attr;
projects / postgresql / blobdiff