greenplumn selfuncs 源码
greenplumn selfuncs 代码
文件路径：/src/backend/utils/adt/selfuncs.c
/*-------------------------------------------------------------------------
*
* selfuncs.c
* Selectivity functions and index cost estimation functions for
* standard operators and index access methods.
*
* Selectivity routines are registered in the pg_operator catalog
* in the "oprrest" and "oprjoin" attributes.
*
* Index cost functions are located via the index AM's API struct,
* which is obtained from the handler function registered in pg_am.
*
* Portions Copyright (c) 2006-2009, Greenplum inc
* Portions Copyright (c) 2012-Present VMware, Inc. or its affiliates.
* Portions Copyright (c) 1996-2019, PostgreSQL Global Development Group
* Portions Copyright (c) 1994, Regents of the University of California
*
*
* IDENTIFICATION
* src/backend/utils/adt/selfuncs.c
*
*-------------------------------------------------------------------------
*/
/*----------
* Operator selectivity estimation functions are called to estimate the
* selectivity of WHERE clauses whose top-level operator is their operator.
* We divide the problem into two cases:
* Restriction clause estimation: the clause involves vars of just
* one relation.
* Join clause estimation: the clause involves vars of multiple rels.
* Join selectivity estimation is far more difficult and usually less accurate
* than restriction estimation.
*
* When dealing with the inner scan of a nestloop join, we consider the
* join's joinclauses as restriction clauses for the inner relation, and
* treat vars of the outer relation as parameters (a/k/a constants of unknown
* values). So, restriction estimators need to be able to accept an argument
* telling which relation is to be treated as the variable.
*
* The call convention for a restriction estimator (oprrest function) is
*
* Selectivity oprrest (PlannerInfo *root,
* Oid operator,
* List *args,
* int varRelid);
*
* root: general information about the query (rtable and RelOptInfo lists
* are particularly important for the estimator).
* operator: OID of the specific operator in question.
* args: argument list from the operator clause.
* varRelid: if not zero, the relid (rtable index) of the relation to
* be treated as the variable relation. May be zero if the args list
* is known to contain vars of only one relation.
*
* This is represented at the SQL level (in pg_proc) as
*
* float8 oprrest (internal, oid, internal, int4);
*
* The result is a selectivity, that is, a fraction (0 to 1) of the rows
* of the relation that are expected to produce a TRUE result for the
* given operator.
*
* The call convention for a join estimator (oprjoin function) is similar
* except that varRelid is not needed, and instead join information is
* supplied:
*
* Selectivity oprjoin (PlannerInfo *root,
* Oid operator,
* List *args,
* JoinType jointype,
* SpecialJoinInfo *sjinfo);
*
* float8 oprjoin (internal, oid, internal, int2, internal);
*
* (Before Postgres 8.4, join estimators had only the first four of these
* parameters. That signature is still allowed, but deprecated.) The
* relationship between jointype and sjinfo is explained in the comments for
* clause_selectivity() --- the short version is that jointype is usually
* best ignored in favor of examining sjinfo.
*
* Join selectivity for regular inner and outer joins is defined as the
* fraction (0 to 1) of the cross product of the relations that is expected
* to produce a TRUE result for the given operator. For both semi and anti
* joins, however, the selectivity is defined as the fraction of the left-hand
* side relation's rows that are expected to have a match (ie, at least one
* row with a TRUE result) in the right-hand side.
*
* For both oprrest and oprjoin functions, the operator's input collation OID
* (if any) is passed using the standard fmgr mechanism, so that the estimator
* function can fetch it with PG_GET_COLLATION(). Note, however, that all
* statistics in pg_statistic are currently built using the relevant column's
* collation. Thus, in most cases where we are looking at statistics, we
* should ignore the operator collation and use the stats entry's collation.
* We expect that the error induced by doing this is usually not large enough
* to justify complicating matters. In any case, doing otherwise would yield
* entirely garbage results for ordered stats data such as histograms.
*----------
*/
#include "postgres.h"
#include <ctype.h>
#include <math.h>
#include "access/brin.h"
#include "access/gin.h"
#include "access/table.h"
#include "access/tableam.h"
#include "catalog/index.h"
#include "catalog/pg_am.h"
#include "catalog/pg_collation.h"
#include "catalog/pg_constraint.h"
#include "catalog/pg_operator.h"
#include "catalog/pg_statistic.h"
#include "catalog/pg_statistic_ext.h"
#include "executor/executor.h"
#include "executor/nodeAgg.h"
#include "miscadmin.h"
#include "nodes/makefuncs.h"
#include "nodes/nodeFuncs.h"
#include "optimizer/clauses.h"
#include "optimizer/cost.h"
#include "optimizer/optimizer.h"
#include "optimizer/pathnode.h"
#include "optimizer/paths.h"
#include "optimizer/plancat.h"
#include "parser/parse_clause.h"
#include "parser/parsetree.h"
#include "statistics/statistics.h"
#include "utils/builtins.h"
#include "utils/date.h"
#include "utils/datum.h"
#include "utils/faultinjector.h"
#include "utils/fmgroids.h"
#include "utils/index_selfuncs.h"
#include "utils/lsyscache.h"
#include "utils/pg_locale.h"
#include "utils/rel.h"
#include "utils/selfuncs.h"
#include "utils/snapmgr.h"
#include "utils/spccache.h"
#include "utils/syscache.h"
#include "utils/timestamp.h"
#include "utils/typcache.h"
#include "cdb/cdbgroup.h" /* cdbpathlocus_collocates_expressions */
#include "cdb/cdbutil.h"
#include "cdb/cdbvars.h"
#include "optimizer/restrictinfo.h"
/* Hooks for plugins to get control when we ask for stats */
get_relation_stats_hook_type get_relation_stats_hook = NULL;
get_index_stats_hook_type get_index_stats_hook = NULL;
static double eqsel_internal(PG_FUNCTION_ARGS, bool negate);
static double eqjoinsel_inner(Oid opfuncoid,
VariableStatData *vardata1, VariableStatData *vardata2,
double nd1, double nd2,
bool isdefault1, bool isdefault2,
AttStatsSlot *sslot1, AttStatsSlot *sslot2,
Form_pg_statistic stats1, Form_pg_statistic stats2,
bool have_mcvs1, bool have_mcvs2);
static double eqjoinsel_semi(Oid opfuncoid,
VariableStatData *vardata1, VariableStatData *vardata2,
double nd1, double nd2,
bool isdefault1, bool isdefault2,
AttStatsSlot *sslot1, AttStatsSlot *sslot2,
Form_pg_statistic stats1, Form_pg_statistic stats2,
bool have_mcvs1, bool have_mcvs2,
RelOptInfo *inner_rel);
static bool estimate_multivariate_ndistinct(PlannerInfo *root,
RelOptInfo *rel, List **varinfos, double *ndistinct);
static bool convert_to_scalar(Datum value, Oid valuetypid, Oid collid,
double *scaledvalue,
Datum lobound, Datum hibound, Oid boundstypid,
double *scaledlobound, double *scaledhibound);
static double convert_numeric_to_scalar(Datum value, Oid typid, bool *failure);
static void convert_string_to_scalar(char *value,
double *scaledvalue,
char *lobound,
double *scaledlobound,
char *hibound,
double *scaledhibound);
static void convert_bytea_to_scalar(Datum value,
double *scaledvalue,
Datum lobound,
double *scaledlobound,
Datum hibound,
double *scaledhibound);
static double convert_one_string_to_scalar(char *value,
int rangelo, int rangehi);
static double convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
int rangelo, int rangehi);
static char *convert_string_datum(Datum value, Oid typid, Oid collid,
bool *failure);
static void examine_simple_variable(PlannerInfo *root, Var *var,
VariableStatData *vardata);
static bool get_variable_range(PlannerInfo *root, VariableStatData *vardata,
Oid sortop, Datum *min, Datum *max);
static bool get_actual_variable_range(PlannerInfo *root,
VariableStatData *vardata,
Oid sortop,
Datum *min, Datum *max);
static RelOptInfo *find_join_input_rel(PlannerInfo *root, Relids relids);
static void try_fetch_rel_stats(RangeTblEntry *rte, const char *attname,
VariableStatData* vardata);
static void try_fetch_largest_child_stats(PlannerInfo *root, Index parent_rti,
const char *attname, VariableStatData* vardata);
/*
* eqsel - Selectivity of "=" for any data types.
*
* Note: this routine is also used to estimate selectivity for some
* operators that are not "=" but have comparable selectivity behavior,
* such as "~=" (geometric approximate-match). Even for "=", we must
* keep in mind that the left and right datatypes may differ.
*/
Datum
eqsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, false));
}
/*
* Common code for eqsel() and neqsel()
*/
static double
eqsel_internal(PG_FUNCTION_ARGS, bool negate)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
Oid operator = PG_GETARG_OID(1);
List *args = (List *) PG_GETARG_POINTER(2);
int varRelid = PG_GETARG_INT32(3);
VariableStatData vardata;
Node *other;
bool varonleft;
double selec;
/*
* When asked about <>, we do the estimation using the corresponding =
* operator, then convert to <> via "1.0 - eq_selectivity - nullfrac".
*/
if (negate)
{
operator = get_negator(operator);
if (!OidIsValid(operator))
{
/* Use default selectivity (should we raise an error instead?) */
return 1.0 - DEFAULT_EQ_SEL;
}
}
/*
* If expression is not variable = something or something = variable, then
* punt and return a default estimate.
*/
if (!get_restriction_variable(root, args, varRelid,
&vardata, &other, &varonleft))
return negate ? (1.0 - DEFAULT_EQ_SEL) : DEFAULT_EQ_SEL;
/*
* We can do a lot better if the something is a constant. (Note: the
* Const might result from estimation rather than being a simple constant
* in the query.)
*/
if (IsA(other, Const))
selec = var_eq_const(&vardata, operator,
((Const *) other)->constvalue,
((Const *) other)->constisnull,
varonleft, negate);
else
selec = var_eq_non_const(&vardata, operator, other,
varonleft, negate);
ReleaseVariableStats(vardata);
return selec;
}
/*
* var_eq_const --- eqsel for var = const case
*
* This is exported so that some other estimation functions can use it.
*/
double
var_eq_const(VariableStatData *vardata, Oid operator,
Datum constval, bool constisnull,
bool varonleft, bool negate)
{
double selec;
double nullfrac = 0.0;
bool isdefault;
Oid opfuncoid;
/*
* If the constant is NULL, assume operator is strict and return zero, ie,
* operator will never return TRUE. (It's zero even for a negator op.)
*/
if (constisnull)
return 0.0;
/*
* Grab the nullfrac for use below. Note we allow use of nullfrac
* regardless of security check.
*/
if (HeapTupleIsValid(vardata->statsTuple))
{
Form_pg_statistic stats;
stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
nullfrac = stats->stanullfrac;
}
/*
* If we matched the var to a unique index or DISTINCT clause, assume
* there is exactly one match regardless of anything else. (This is
* slightly bogus, since the index or clause's equality operator might be
* different from ours, but it's much more likely to be right than
* ignoring the information.)
*/
if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
{
selec = 1.0 / vardata->rel->tuples;
}
else if (HeapTupleIsValid(vardata->statsTuple) &&
statistic_proc_security_check(vardata,
(opfuncoid = get_opcode(operator))))
{
AttStatsSlot sslot;
bool match = false;
int i;
/*
* Is the constant "=" to any of the column's most common values?
* (Although the given operator may not really be "=", we will assume
* that seeing whether it returns TRUE is an appropriate test. If you
* don't like this, maybe you shouldn't be using eqsel for your
* operator...)
*/
if (get_attstatsslot(&sslot, vardata->statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
{
FmgrInfo eqproc;
fmgr_info(opfuncoid, &eqproc);
for (i = 0; i < sslot.nvalues; i++)
{
/* be careful to apply operator right way 'round */
if (varonleft)
match = DatumGetBool(FunctionCall2Coll(&eqproc,
sslot.stacoll,
sslot.values[i],
constval));
else
match = DatumGetBool(FunctionCall2Coll(&eqproc,
sslot.stacoll,
constval,
sslot.values[i]));
if (match)
break;
}
}
else
{
/* no most-common-value info available */
i = 0; /* keep compiler quiet */
}
if (match)
{
/*
* Constant is "=" to this common value. We know selectivity
* exactly (or as exactly as ANALYZE could calculate it, anyway).
*/
selec = sslot.numbers[i];
}
else
{
/*
* Comparison is against a constant that is neither NULL nor any
* of the common values. Its selectivity cannot be more than
* this:
*/
double sumcommon = 0.0;
double otherdistinct;
for (i = 0; i < sslot.nnumbers; i++)
sumcommon += sslot.numbers[i];
selec = 1.0 - sumcommon - nullfrac;
CLAMP_PROBABILITY(selec);
/*
* and in fact it's probably a good deal less. We approximate that
* all the not-common values share this remaining fraction
* equally, so we divide by the number of other distinct values.
*/
otherdistinct = get_variable_numdistinct(vardata, &isdefault) -
sslot.nnumbers;
if (otherdistinct > 1)
selec /= otherdistinct;
/*
* Another cross-check: selectivity shouldn't be estimated as more
* than the least common "most common value".
*/
if (sslot.nnumbers > 0 && selec > sslot.numbers[sslot.nnumbers - 1])
selec = sslot.numbers[sslot.nnumbers - 1];
}
free_attstatsslot(&sslot);
}
else
{
/*
* No ANALYZE stats available, so make a guess using estimated number
* of distinct values and assuming they are equally common. (The guess
* is unlikely to be very good, but we do know a few special cases.)
*/
selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
}
/* now adjust if we wanted <> rather than = */
if (negate)
selec = 1.0 - selec - nullfrac;
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
return selec;
}
/*
* var_eq_non_const --- eqsel for var = something-other-than-const case
*
* This is exported so that some other estimation functions can use it.
*/
double
var_eq_non_const(VariableStatData *vardata, Oid operator,
Node *other,
bool varonleft, bool negate)
{
double selec;
double nullfrac = 0.0;
bool isdefault;
/*
* Grab the nullfrac for use below.
*/
if (HeapTupleIsValid(vardata->statsTuple))
{
Form_pg_statistic stats;
stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
nullfrac = stats->stanullfrac;
}
/*
* If we matched the var to a unique index or DISTINCT clause, assume
* there is exactly one match regardless of anything else. (This is
* slightly bogus, since the index or clause's equality operator might be
* different from ours, but it's much more likely to be right than
* ignoring the information.)
*/
if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
{
selec = 1.0 / vardata->rel->tuples;
}
else if (HeapTupleIsValid(vardata->statsTuple))
{
double ndistinct;
AttStatsSlot sslot;
/*
* Search is for a value that we do not know a priori, but we will
* assume it is not NULL. Estimate the selectivity as non-null
* fraction divided by number of distinct values, so that we get a
* result averaged over all possible values whether common or
* uncommon. (Essentially, we are assuming that the not-yet-known
* comparison value is equally likely to be any of the possible
* values, regardless of their frequency in the table. Is that a good
* idea?)
*/
selec = 1.0 - nullfrac;
ndistinct = get_variable_numdistinct(vardata, &isdefault);
if (ndistinct > 1)
selec /= ndistinct;
/*
* Cross-check: selectivity should never be estimated as more than the
* most common value's.
*/
if (get_attstatsslot(&sslot, vardata->statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_NUMBERS))
{
if (sslot.nnumbers > 0 && selec > sslot.numbers[0])
selec = sslot.numbers[0];
free_attstatsslot(&sslot);
}
}
else
{
/*
* No ANALYZE stats available, so make a guess using estimated number
* of distinct values and assuming they are equally common. (The guess
* is unlikely to be very good, but we do know a few special cases.)
*/
selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
}
/* now adjust if we wanted <> rather than = */
if (negate)
selec = 1.0 - selec - nullfrac;
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
return selec;
}
/*
* neqsel - Selectivity of "!=" for any data types.
*
* This routine is also used for some operators that are not "!="
* but have comparable selectivity behavior. See above comments
* for eqsel().
*/
Datum
neqsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, true));
}
/*
* scalarineqsel - Selectivity of "<", "<=", ">", ">=" for scalars.
*
* This is the guts of scalarltsel/scalarlesel/scalargtsel/scalargesel.
* The isgt and iseq flags distinguish which of the four cases apply.
*
* The caller has commuted the clause, if necessary, so that we can treat
* the variable as being on the left. The caller must also make sure that
* the other side of the clause is a non-null Const, and dissect that into
* a value and datatype. (This definition simplifies some callers that
* want to estimate against a computed value instead of a Const node.)
*
* This routine works for any datatype (or pair of datatypes) known to
* convert_to_scalar(). If it is applied to some other datatype,
* it will return an approximate estimate based on assuming that the constant
* value falls in the middle of the bin identified by binary search.
*/
static double
scalarineqsel(PlannerInfo *root, Oid operator, bool isgt, bool iseq,
VariableStatData *vardata, Datum constval, Oid consttype)
{
Form_pg_statistic stats;
FmgrInfo opproc;
double mcv_selec,
hist_selec,
sumcommon;
double selec;
if (!HeapTupleIsValid(vardata->statsTuple))
{
/*
* No stats are available. Typically this means we have to fall back
* on the default estimate; but if the variable is CTID then we can
* make an estimate based on comparing the constant to the table size.
*/
if (vardata->var && IsA(vardata->var, Var) &&
((Var *) vardata->var)->varattno == SelfItemPointerAttributeNumber)
{
ItemPointer itemptr;
double block;
double density;
/*
* If the relation's empty, we're going to include all of it.
* (This is mostly to avoid divide-by-zero below.)
*/
if (vardata->rel->pages == 0)
return 1.0;
itemptr = (ItemPointer) DatumGetPointer(constval);
block = ItemPointerGetBlockNumberNoCheck(itemptr);
/*
* Determine the average number of tuples per page (density).
*
* Since the last page will, on average, be only half full, we can
* estimate it to have half as many tuples as earlier pages. So
* give it half the weight of a regular page.
*/
density = vardata->rel->tuples / (vardata->rel->pages - 0.5);
/* If target is the last page, use half the density. */
if (block >= vardata->rel->pages - 1)
density *= 0.5;
/*
* Using the average tuples per page, calculate how far into the
* page the itemptr is likely to be and adjust block accordingly,
* by adding that fraction of a whole block (but never more than a
* whole block, no matter how high the itemptr's offset is). Here
* we are ignoring the possibility of dead-tuple line pointers,
* which is fairly bogus, but we lack the info to do better.
*/
if (density > 0.0)
{
OffsetNumber offset = ItemPointerGetOffsetNumberNoCheck(itemptr);
block += Min(offset / density, 1.0);
}
/*
* Convert relative block number to selectivity. Again, the last
* page has only half weight.
*/
selec = block / (vardata->rel->pages - 0.5);
/*
* The calculation so far gave us a selectivity for the "<=" case.
* We'll have one less tuple for "<" and one additional tuple for
* ">=", the latter of which we'll reverse the selectivity for
* below, so we can simply subtract one tuple for both cases. The
* cases that need this adjustment can be identified by iseq being
* equal to isgt.
*/
if (iseq == isgt && vardata->rel->tuples >= 1.0)
selec -= (1.0 / vardata->rel->tuples);
/* Finally, reverse the selectivity for the ">", ">=" cases. */
if (isgt)
selec = 1.0 - selec;
CLAMP_PROBABILITY(selec);
return selec;
}
/* no stats available, so default result */
return DEFAULT_INEQ_SEL;
}
stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
fmgr_info(get_opcode(operator), &opproc);
/*
* If we have most-common-values info, add up the fractions of the MCV
* entries that satisfy MCV OP CONST. These fractions contribute directly
* to the result selectivity. Also add up the total fraction represented
* by MCV entries.
*/
mcv_selec = mcv_selectivity(vardata, &opproc, constval, true,
&sumcommon);
/*
* If there is a histogram, determine which bin the constant falls in, and
* compute the resulting contribution to selectivity.
*/
hist_selec = ineq_histogram_selectivity(root, vardata,
&opproc, isgt, iseq,
constval, consttype);
/*
* Now merge the results from the MCV and histogram calculations,
* realizing that the histogram covers only the non-null values that are
* not listed in MCV.
*/
selec = 1.0 - stats->stanullfrac - sumcommon;
if (hist_selec >= 0.0)
selec *= hist_selec;
else
{
/*
* If no histogram but there are values not accounted for by MCV,
* arbitrarily assume half of them will match.
*/
selec *= 0.5;
}
selec += mcv_selec;
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
return selec;
}
/*
* mcv_selectivity - Examine the MCV list for selectivity estimates
*
* Determine the fraction of the variable's MCV population that satisfies
* the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft. Also
* compute the fraction of the total column population represented by the MCV
* list. This code will work for any boolean-returning predicate operator.
*
* The function result is the MCV selectivity, and the fraction of the
* total population is returned into *sumcommonp. Zeroes are returned
* if there is no MCV list.
*/
double
mcv_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
Datum constval, bool varonleft,
double *sumcommonp)
{
double mcv_selec,
sumcommon;
AttStatsSlot sslot;
int i;
mcv_selec = 0.0;
sumcommon = 0.0;
if (HeapTupleIsValid(vardata->statsTuple) &&
statistic_proc_security_check(vardata, opproc->fn_oid) &&
get_attstatsslot(&sslot, vardata->statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
{
for (i = 0; i < sslot.nvalues; i++)
{
if (varonleft ?
DatumGetBool(FunctionCall2Coll(opproc,
sslot.stacoll,
sslot.values[i],
constval)) :
DatumGetBool(FunctionCall2Coll(opproc,
sslot.stacoll,
constval,
sslot.values[i])))
mcv_selec += sslot.numbers[i];
sumcommon += sslot.numbers[i];
}
free_attstatsslot(&sslot);
}
*sumcommonp = sumcommon;
return mcv_selec;
}
/*
* histogram_selectivity - Examine the histogram for selectivity estimates
*
* Determine the fraction of the variable's histogram entries that satisfy
* the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft.
*
* This code will work for any boolean-returning predicate operator, whether
* or not it has anything to do with the histogram sort operator. We are
* essentially using the histogram just as a representative sample. However,
* small histograms are unlikely to be all that representative, so the caller
* should be prepared to fall back on some other estimation approach when the
* histogram is missing or very small. It may also be prudent to combine this
* approach with another one when the histogram is small.
*
* If the actual histogram size is not at least min_hist_size, we won't bother
* to do the calculation at all. Also, if the n_skip parameter is > 0, we
* ignore the first and last n_skip histogram elements, on the grounds that
* they are outliers and hence not very representative. Typical values for
* these parameters are 10 and 1.
*
* The function result is the selectivity, or -1 if there is no histogram
* or it's smaller than min_hist_size.
*
* The output parameter *hist_size receives the actual histogram size,
* or zero if no histogram. Callers may use this number to decide how
* much faith to put in the function result.
*
* Note that the result disregards both the most-common-values (if any) and
* null entries. The caller is expected to combine this result with
* statistics for those portions of the column population. It may also be
* prudent to clamp the result range, ie, disbelieve exact 0 or 1 outputs.
*/
double
histogram_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
Datum constval, bool varonleft,
int min_hist_size, int n_skip,
int *hist_size)
{
double result;
AttStatsSlot sslot;
/* check sanity of parameters */
Assert(n_skip >= 0);
Assert(min_hist_size > 2 * n_skip);
if (HeapTupleIsValid(vardata->statsTuple) &&
statistic_proc_security_check(vardata, opproc->fn_oid) &&
get_attstatsslot(&sslot, vardata->statsTuple,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
ATTSTATSSLOT_VALUES))
{
*hist_size = sslot.nvalues;
if (sslot.nvalues >= min_hist_size)
{
int nmatch = 0;
int i;
for (i = n_skip; i < sslot.nvalues - n_skip; i++)
{
if (varonleft ?
DatumGetBool(FunctionCall2Coll(opproc,
sslot.stacoll,
sslot.values[i],
constval)) :
DatumGetBool(FunctionCall2Coll(opproc,
sslot.stacoll,
constval,
sslot.values[i])))
nmatch++;
}
result = ((double) nmatch) / ((double) (sslot.nvalues - 2 * n_skip));
}
else
result = -1;
free_attstatsslot(&sslot);
}
else
{
*hist_size = 0;
result = -1;
}
return result;
}
/*
* ineq_histogram_selectivity - Examine the histogram for scalarineqsel
*
* Determine the fraction of the variable's histogram population that
* satisfies the inequality condition, ie, VAR < (or <=, >, >=) CONST.
* The isgt and iseq flags distinguish which of the four cases apply.
*
* Returns -1 if there is no histogram (valid results will always be >= 0).
*
* Note that the result disregards both the most-common-values (if any) and
* null entries. The caller is expected to combine this result with
* statistics for those portions of the column population.
*
* This is exported so that some other estimation functions can use it.
*/
double
ineq_histogram_selectivity(PlannerInfo *root,
VariableStatData *vardata,
FmgrInfo *opproc, bool isgt, bool iseq,
Datum constval, Oid consttype)
{
double hist_selec;
AttStatsSlot sslot;
hist_selec = -1.0;
/*
* Someday, ANALYZE might store more than one histogram per rel/att,
* corresponding to more than one possible sort ordering defined for the
* column type. However, to make that work we will need to figure out
* which staop to search for --- it's not necessarily the one we have at
* hand! (For example, we might have a '<=' operator rather than the '<'
* operator that will appear in staop.) For now, assume that whatever
* appears in pg_statistic is sorted the same way our operator sorts, or
* the reverse way if isgt is true.
*/
if (HeapTupleIsValid(vardata->statsTuple) &&
statistic_proc_security_check(vardata, opproc->fn_oid) &&
get_attstatsslot(&sslot, vardata->statsTuple,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
ATTSTATSSLOT_VALUES))
{
if (sslot.nvalues > 1)
{
/*
* Use binary search to find the desired location, namely the
* right end of the histogram bin containing the comparison value,
* which is the leftmost entry for which the comparison operator
* succeeds (if isgt) or fails (if !isgt). (If the given operator
* isn't actually sort-compatible with the histogram, you'll get
* garbage results ... but probably not any more garbage-y than
* you would have from the old linear search.)
*
* In this loop, we pay no attention to whether the operator iseq
* or not; that detail will be mopped up below. (We cannot tell,
* anyway, whether the operator thinks the values are equal.)
*
* If the binary search accesses the first or last histogram
* entry, we try to replace that endpoint with the true column min
* or max as found by get_actual_variable_range(). This
* ameliorates misestimates when the min or max is moving as a
* result of changes since the last ANALYZE. Note that this could
* result in effectively including MCVs into the histogram that
* weren't there before, but we don't try to correct for that.
*/
double histfrac;
int lobound = 0; /* first possible slot to search */
int hibound = sslot.nvalues; /* last+1 slot to search */
bool have_end = false;
/*
* If there are only two histogram entries, we'll want up-to-date
* values for both. (If there are more than two, we need at most
* one of them to be updated, so we deal with that within the
* loop.)
*/
if (sslot.nvalues == 2)
have_end = get_actual_variable_range(root,
vardata,
sslot.staop,
&sslot.values[0],
&sslot.values[1]);
while (lobound < hibound)
{
int probe = (lobound + hibound) / 2;
bool ltcmp;
/*
* If we find ourselves about to compare to the first or last
* histogram entry, first try to replace it with the actual
* current min or max (unless we already did so above).
*/
if (probe == 0 && sslot.nvalues > 2)
have_end = get_actual_variable_range(root,
vardata,
sslot.staop,
&sslot.values[0],
NULL);
else if (probe == sslot.nvalues - 1 && sslot.nvalues > 2)
have_end = get_actual_variable_range(root,
vardata,
sslot.staop,
NULL,
&sslot.values[probe]);
ltcmp = DatumGetBool(FunctionCall2Coll(opproc,
sslot.stacoll,
sslot.values[probe],
constval));
if (isgt)
ltcmp = !ltcmp;
if (ltcmp)
lobound = probe + 1;
else
hibound = probe;
}
if (lobound <= 0)
{
/*
* Constant is below lower histogram boundary. More
* precisely, we have found that no entry in the histogram
* satisfies the inequality clause (if !isgt) or they all do
* (if isgt). We estimate that that's true of the entire
* table, so set histfrac to 0.0 (which we'll flip to 1.0
* below, if isgt).
*/
histfrac = 0.0;
}
else if (lobound >= sslot.nvalues)
{
/*
* Inverse case: constant is above upper histogram boundary.
*/
histfrac = 1.0;
}
else
{
/* We have values[i-1] <= constant <= values[i]. */
int i = lobound;
double eq_selec = 0;
double val,
high,
low;
double binfrac;
/*
* In the cases where we'll need it below, obtain an estimate
* of the selectivity of "x = constval". We use a calculation
* similar to what var_eq_const() does for a non-MCV constant,
* ie, estimate that all distinct non-MCV values occur equally
* often. But multiplication by "1.0 - sumcommon - nullfrac"
* will be done by our caller, so we shouldn't do that here.
* Therefore we can't try to clamp the estimate by reference
* to the least common MCV; the result would be too small.
*
* Note: since this is effectively assuming that constval
* isn't an MCV, it's logically dubious if constval in fact is
* one. But we have to apply *some* correction for equality,
* and anyway we cannot tell if constval is an MCV, since we
* don't have a suitable equality operator at hand.
*/
if (i == 1 || isgt == iseq)
{
double otherdistinct;
bool isdefault;
AttStatsSlot mcvslot;
/* Get estimated number of distinct values */
otherdistinct = get_variable_numdistinct(vardata,
&isdefault);
/* Subtract off the number of known MCVs */
if (get_attstatsslot(&mcvslot, vardata->statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_NUMBERS))
{
otherdistinct -= mcvslot.nnumbers;
free_attstatsslot(&mcvslot);
}
/* If result doesn't seem sane, leave eq_selec at 0 */
if (otherdistinct > 1)
eq_selec = 1.0 / otherdistinct;
}
/*
* Convert the constant and the two nearest bin boundary
* values to a uniform comparison scale, and do a linear
* interpolation within this bin.
*/
if (convert_to_scalar(constval, consttype, sslot.stacoll,
&val,
sslot.values[i - 1], sslot.values[i],
vardata->vartype,
&low, &high))
{
if (high <= low)
{
/* cope if bin boundaries appear identical */
binfrac = 0.5;
}
else if (val <= low)
binfrac = 0.0;
else if (val >= high)
binfrac = 1.0;
else
{
binfrac = (val - low) / (high - low);
/*
* Watch out for the possibility that we got a NaN or
* Infinity from the division. This can happen
* despite the previous checks, if for example "low"
* is -Infinity.
*/
if (isnan(binfrac) ||
binfrac < 0.0 || binfrac > 1.0)
binfrac = 0.5;
}
}
else
{
/*
* Ideally we'd produce an error here, on the grounds that
* the given operator shouldn't have scalarXXsel
* registered as its selectivity func unless we can deal
* with its operand types. But currently, all manner of
* stuff is invoking scalarXXsel, so give a default
* estimate until that can be fixed.
*/
binfrac = 0.5;
}
/*
* Now, compute the overall selectivity across the values
* represented by the histogram. We have i-1 full bins and
* binfrac partial bin below the constant.
*/
histfrac = (double) (i - 1) + binfrac;
histfrac /= (double) (sslot.nvalues - 1);
/*
* At this point, histfrac is an estimate of the fraction of
* the population represented by the histogram that satisfies
* "x <= constval". Somewhat remarkably, this statement is
* true regardless of which operator we were doing the probes
* with, so long as convert_to_scalar() delivers reasonable
* results. If the probe constant is equal to some histogram
* entry, we would have considered the bin to the left of that
* entry if probing with "<" or ">=", or the bin to the right
* if probing with "<=" or ">"; but binfrac would have come
* out as 1.0 in the first case and 0.0 in the second, leading
* to the same histfrac in either case. For probe constants
* between histogram entries, we find the same bin and get the
* same estimate with any operator.
*
* The fact that the estimate corresponds to "x <= constval"
* and not "x < constval" is because of the way that ANALYZE
* constructs the histogram: each entry is, effectively, the
* rightmost value in its sample bucket. So selectivity
* values that are exact multiples of 1/(histogram_size-1)
* should be understood as estimates including a histogram
* entry plus everything to its left.
*
* However, that breaks down for the first histogram entry,
* which necessarily is the leftmost value in its sample
* bucket. That means the first histogram bin is slightly
* narrower than the rest, by an amount equal to eq_selec.
* Another way to say that is that we want "x <= leftmost" to
* be estimated as eq_selec not zero. So, if we're dealing
* with the first bin (i==1), rescale to make that true while
* adjusting the rest of that bin linearly.
*/
if (i == 1)
histfrac += eq_selec * (1.0 - binfrac);
/*
* "x <= constval" is good if we want an estimate for "<=" or
* ">", but if we are estimating for "<" or ">=", we now need
* to decrease the estimate by eq_selec.
*/
if (isgt == iseq)
histfrac -= eq_selec;
}
/*
* Now the estimate is finished for "<" and "<=" cases. If we are
* estimating for ">" or ">=", flip it.
*/
hist_selec = isgt ? (1.0 - histfrac) : histfrac;
/*
* The histogram boundaries are only approximate to begin with,
* and may well be out of date anyway. Therefore, don't believe
* extremely small or large selectivity estimates --- unless we
* got actual current endpoint values from the table, in which
* case just do the usual sanity clamp. Somewhat arbitrarily, we
* set the cutoff for other cases at a hundredth of the histogram
* resolution.
*/
if (have_end)
CLAMP_PROBABILITY(hist_selec);
else
{
double cutoff = 0.01 / (double) (sslot.nvalues - 1);
if (hist_selec < cutoff)
hist_selec = cutoff;
else if (hist_selec > 1.0 - cutoff)
hist_selec = 1.0 - cutoff;
}
}
free_attstatsslot(&sslot);
}
return hist_selec;
}
/*
* Common wrapper function for the selectivity estimators that simply
* invoke scalarineqsel().
*/
static Datum
scalarineqsel_wrapper(PG_FUNCTION_ARGS, bool isgt, bool iseq)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
Oid operator = PG_GETARG_OID(1);
List *args = (List *) PG_GETARG_POINTER(2);
int varRelid = PG_GETARG_INT32(3);
VariableStatData vardata;
Node *other;
bool varonleft;
Datum constval;
Oid consttype;
double selec;
/*
* If expression is not variable op something or something op variable,
* then punt and return a default estimate.
*/
if (!get_restriction_variable(root, args, varRelid,
&vardata, &other, &varonleft))
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
/*
* Can't do anything useful if the something is not a constant, either.
*/
if (!IsA(other, Const))
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* If the constant is NULL, assume operator is strict and return zero, ie,
* operator will never return TRUE.
*/
if (((Const *) other)->constisnull)
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(0.0);
}
constval = ((Const *) other)->constvalue;
consttype = ((Const *) other)->consttype;
/*
* Force the var to be on the left to simplify logic in scalarineqsel.
*/
if (!varonleft)
{
operator = get_commutator(operator);
if (!operator)
{
/* Use default selectivity (should we raise an error instead?) */
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
isgt = !isgt;
}
/* The rest of the work is done by scalarineqsel(). */
selec = scalarineqsel(root, operator, isgt, iseq,
&vardata, constval, consttype);
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8((float8) selec);
}
/*
* scalarltsel - Selectivity of "<" for scalars.
*/
Datum
scalarltsel(PG_FUNCTION_ARGS)
{
return scalarineqsel_wrapper(fcinfo, false, false);
}
/*
* scalarlesel - Selectivity of "<=" for scalars.
*/
Datum
scalarlesel(PG_FUNCTION_ARGS)
{
return scalarineqsel_wrapper(fcinfo, false, true);
}
/*
* scalargtsel - Selectivity of ">" for scalars.
*/
Datum
scalargtsel(PG_FUNCTION_ARGS)
{
return scalarineqsel_wrapper(fcinfo, true, false);
}
/*
* scalargesel - Selectivity of ">=" for scalars.
*/
Datum
scalargesel(PG_FUNCTION_ARGS)
{
return scalarineqsel_wrapper(fcinfo, true, true);
}
/*
* boolvarsel - Selectivity of Boolean variable.
*
* This can actually be called on any boolean-valued expression. If it
* involves only Vars of the specified relation, and if there are statistics
* about the Var or expression (the latter is possible if it's indexed) then
* we'll produce a real estimate; otherwise it's just a default.
*/
Selectivity
boolvarsel(PlannerInfo *root, Node *arg, int varRelid)
{
VariableStatData vardata;
double selec;
examine_variable(root, arg, varRelid, &vardata);
if (HeapTupleIsValid(vardata.statsTuple))
{
/*
* A boolean variable V is equivalent to the clause V = 't', so we
* compute the selectivity as if that is what we have.
*/
selec = var_eq_const(&vardata, BooleanEqualOperator,
BoolGetDatum(true), false, true, false);
}
else
{
/* Otherwise, the default estimate is 0.5 */
selec = 0.5;
}
ReleaseVariableStats(vardata);
return selec;
}
/*
* booltestsel - Selectivity of BooleanTest Node.
*/
Selectivity
booltestsel(PlannerInfo *root, BoolTestType booltesttype, Node *arg,
int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
{
VariableStatData vardata;
double selec;
examine_variable(root, arg, varRelid, &vardata);
if (HeapTupleIsValid(getStatsTuple(&vardata)))
{
Form_pg_statistic stats;
double freq_null;
AttStatsSlot sslot;
HeapTuple tp = getStatsTuple(&vardata);
stats = (Form_pg_statistic) GETSTRUCT(tp);
freq_null = stats->stanullfrac;
if (get_attstatsslot(&sslot, vardata.statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)
&& sslot.nnumbers > 0)
{
double freq_true;
double freq_false;
/*
* Get first MCV frequency and derive frequency for true.
*/
if (DatumGetBool(sslot.values[0]))
freq_true = sslot.numbers[0];
else
freq_true = 1.0 - sslot.numbers[0] - freq_null;
/*
* Next derive frequency for false. Then use these as appropriate
* to derive frequency for each case.
*/
freq_false = 1.0 - freq_true - freq_null;
switch (booltesttype)
{
case IS_UNKNOWN:
/* select only NULL values */
selec = freq_null;
break;
case IS_NOT_UNKNOWN:
/* select non-NULL values */
selec = 1.0 - freq_null;
break;
case IS_TRUE:
/* select only TRUE values */
selec = freq_true;
break;
case IS_NOT_TRUE:
/* select non-TRUE values */
selec = 1.0 - freq_true;
break;
case IS_FALSE:
/* select only FALSE values */
selec = freq_false;
break;
case IS_NOT_FALSE:
/* select non-FALSE values */
selec = 1.0 - freq_false;
break;
default:
elog(ERROR, "unrecognized booltesttype: %d",
(int) booltesttype);
selec = 0.0; /* Keep compiler quiet */
break;
}
free_attstatsslot(&sslot);
}
else
{
/*
* No most-common-value info available. Still have null fraction
* information, so use it for IS [NOT] UNKNOWN. Otherwise adjust
* for null fraction and assume a 50-50 split of TRUE and FALSE.
*/
switch (booltesttype)
{
case IS_UNKNOWN:
/* select only NULL values */
selec = freq_null;
break;
case IS_NOT_UNKNOWN:
/* select non-NULL values */
selec = 1.0 - freq_null;
break;
case IS_TRUE:
case IS_FALSE:
/* Assume we select half of the non-NULL values */
selec = (1.0 - freq_null) / 2.0;
break;
case IS_NOT_TRUE:
case IS_NOT_FALSE:
/* Assume we select NULLs plus half of the non-NULLs */
/* equiv. to freq_null + (1.0 - freq_null) / 2.0 */
selec = (freq_null + 1.0) / 2.0;
break;
default:
elog(ERROR, "unrecognized booltesttype: %d",
(int) booltesttype);
selec = 0.0; /* Keep compiler quiet */
break;
}
}
}
else
{
/*
* If we can't get variable statistics for the argument, perhaps
* clause_selectivity can do something with it. We ignore the
* possibility of a NULL value when using clause_selectivity, and just
* assume the value is either TRUE or FALSE.
*/
switch (booltesttype)
{
case IS_UNKNOWN:
selec = DEFAULT_UNK_SEL;
break;
case IS_NOT_UNKNOWN:
selec = DEFAULT_NOT_UNK_SEL;
break;
case IS_TRUE:
case IS_NOT_FALSE:
selec = (double) clause_selectivity(root, arg,
varRelid,
jointype, sjinfo,
false /* use_damping */);
break;
case IS_FALSE:
case IS_NOT_TRUE:
selec = 1.0 - (double) clause_selectivity(root, arg,
varRelid,
jointype, sjinfo,
false /* use_damping */);
break;
default:
elog(ERROR, "unrecognized booltesttype: %d",
(int) booltesttype);
selec = 0.0; /* Keep compiler quiet */
break;
}
}
ReleaseVariableStats(vardata);
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
return (Selectivity) selec;
}
/*
* nulltestsel - Selectivity of NullTest Node.
*/
Selectivity
nulltestsel(PlannerInfo *root, NullTestType nulltesttype, Node *arg,
int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
{
VariableStatData vardata;
double selec;
/*
* GPDB_84_MERGE_NOTE: Following hack is removed in the upstream commit e006a24a.
* However, removing this causes cost differences for some ICG queries.
* Hence, keeping the hack in GPDB
* Special hack: an IS NULL test being applied at an outer join should not
* be taken at face value, since it's very likely being used to select the
* outer-side rows that don't have a match, and thus its selectivity has
* nothing whatever to do with the statistics of the original table
* column. We do not have nearly enough context here to determine its
* true selectivity, so for the moment punt and guess at 0.5. Eventually
* the planner should be made to provide enough info about the clause's
* context to let us do better.
*/
if (IS_OUTER_JOIN(jointype) && nulltesttype == IS_NULL)
return (Selectivity) 0.5;
examine_variable(root, arg, varRelid, &vardata);
if (HeapTupleIsValid(getStatsTuple(&vardata)))
{
Form_pg_statistic stats;
HeapTuple tp = getStatsTuple(&vardata);
double freq_null;
stats = (Form_pg_statistic) GETSTRUCT(tp);
freq_null = stats->stanullfrac;
switch (nulltesttype)
{
case IS_NULL:
/*
* Use freq_null directly.
*/
selec = freq_null;
break;
case IS_NOT_NULL:
/*
* Select not unknown (not null) values. Calculate from
* freq_null.
*/
selec = 1.0 - freq_null;
break;
default:
elog(ERROR, "unrecognized nulltesttype: %d",
(int) nulltesttype);
return (Selectivity) 0; /* keep compiler quiet */
}
}
else if (vardata.var && IsA(vardata.var, Var) &&
((Var *) vardata.var)->varattno < 0)
{
/*
* There are no stats for system columns, but we know they are never
* NULL.
*/
selec = (nulltesttype == IS_NULL) ? 0.0 : 1.0;
}
else
{
/*
* No ANALYZE stats available, so make a guess
*/
switch (nulltesttype)
{
case IS_NULL:
selec = DEFAULT_UNK_SEL;
break;
case IS_NOT_NULL:
selec = DEFAULT_NOT_UNK_SEL;
break;
default:
elog(ERROR, "unrecognized nulltesttype: %d",
(int) nulltesttype);
return (Selectivity) 0; /* keep compiler quiet */
}
}
ReleaseVariableStats(vardata);
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
return (Selectivity) selec;
}
/*
* strip_array_coercion - strip binary-compatible relabeling from an array expr
*
* For array values, the parser normally generates ArrayCoerceExpr conversions,
* but it seems possible that RelabelType might show up. Also, the planner
* is not currently tense about collapsing stacked ArrayCoerceExpr nodes,
* so we need to be ready to deal with more than one level.
*/
static Node *
strip_array_coercion(Node *node)
{
for (;;)
{
if (node && IsA(node, ArrayCoerceExpr))
{
ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node;
/*
* If the per-element expression is just a RelabelType on top of
* CaseTestExpr, then we know it's a binary-compatible relabeling.
*/
if (IsA(acoerce->elemexpr, RelabelType) &&
IsA(((RelabelType *) acoerce->elemexpr)->arg, CaseTestExpr))
node = (Node *) acoerce->arg;
else
break;
}
else if (node && IsA(node, RelabelType))
{
/* We don't really expect this case, but may as well cope */
node = (Node *) ((RelabelType *) node)->arg;
}
else
break;
}
return node;
}
/*
* scalararraysel - Selectivity of ScalarArrayOpExpr Node.
*/
Selectivity
scalararraysel(PlannerInfo *root,
ScalarArrayOpExpr *clause,
bool is_join_clause,
int varRelid,
JoinType jointype,
SpecialJoinInfo *sjinfo)
{
Oid operator = clause->opno;
bool useOr = clause->useOr;
bool isEquality = false;
bool isInequality = false;
Node *leftop;
Node *rightop;
Oid nominal_element_type;
Oid nominal_element_collation;
TypeCacheEntry *typentry;
RegProcedure oprsel;
FmgrInfo oprselproc;
Selectivity s1;
Selectivity s1disjoint;
/* First, deconstruct the expression */
Assert(list_length(clause->args) == 2);
leftop = (Node *) linitial(clause->args);
rightop = (Node *) lsecond(clause->args);
/* aggressively reduce both sides to constants */
leftop = estimate_expression_value(root, leftop);
rightop = estimate_expression_value(root, rightop);
/* get nominal (after relabeling) element type of rightop */
nominal_element_type = get_base_element_type(exprType(rightop));
if (!OidIsValid(nominal_element_type))
return (Selectivity) 0.5; /* probably shouldn't happen */
/* get nominal collation, too, for generating constants */
nominal_element_collation = exprCollation(rightop);
/* look through any binary-compatible relabeling of rightop */
rightop = strip_array_coercion(rightop);
/*
* Detect whether the operator is the default equality or inequality
* operator of the array element type.
*/
typentry = lookup_type_cache(nominal_element_type, TYPECACHE_EQ_OPR);
if (OidIsValid(typentry->eq_opr))
{
if (operator == typentry->eq_opr)
isEquality = true;
else if (get_negator(operator) == typentry->eq_opr)
isInequality = true;
}
/*
* If it is equality or inequality, we might be able to estimate this as a
* form of array containment; for instance "const = ANY(column)" can be
* treated as "ARRAY[const] <@ column". scalararraysel_containment tries
* that, and returns the selectivity estimate if successful, or -1 if not.
*/
if ((isEquality || isInequality) && !is_join_clause)
{
s1 = scalararraysel_containment(root, leftop, rightop,
nominal_element_type,
isEquality, useOr, varRelid);
if (s1 >= 0.0)
return s1;
}
/*
* Look up the underlying operator's selectivity estimator. Punt if it
* hasn't got one.
*/
if (is_join_clause)
oprsel = get_oprjoin(operator);
else
oprsel = get_oprrest(operator);
if (!oprsel)
return (Selectivity) 0.5;
fmgr_info(oprsel, &oprselproc);
/*
* In the array-containment check above, we must only believe that an
* operator is equality or inequality if it is the default btree equality
* operator (or its negator) for the element type, since those are the
* operators that array containment will use. But in what follows, we can
* be a little laxer, and also believe that any operators using eqsel() or
* neqsel() as selectivity estimator act like equality or inequality.
*/
if (oprsel == F_EQSEL || oprsel == F_EQJOINSEL)
isEquality = true;
else if (oprsel == F_NEQSEL || oprsel == F_NEQJOINSEL)
isInequality = true;
/*
* We consider three cases:
*
* 1. rightop is an Array constant: deconstruct the array, apply the
* operator's selectivity function for each array element, and merge the
* results in the same way that clausesel.c does for AND/OR combinations.
*
* 2. rightop is an ARRAY[] construct: apply the operator's selectivity
* function for each element of the ARRAY[] construct, and merge.
*
* 3. otherwise, make a guess ...
*/
if (rightop && IsA(rightop, Const))
{
Datum arraydatum = ((Const *) rightop)->constvalue;
bool arrayisnull = ((Const *) rightop)->constisnull;
ArrayType *arrayval;
int16 elmlen;
bool elmbyval;
char elmalign;
int num_elems;
Datum *elem_values;
bool *elem_nulls;
int i;
if (arrayisnull) /* qual can't succeed if null array */
return (Selectivity) 0.0;
arrayval = DatumGetArrayTypeP(arraydatum);
get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
&elmlen, &elmbyval, &elmalign);
deconstruct_array(arrayval,
ARR_ELEMTYPE(arrayval),
elmlen, elmbyval, elmalign,
&elem_values, &elem_nulls, &num_elems);
/*
* For generic operators, we assume the probability of success is
* independent for each array element. But for "= ANY" or "<> ALL",
* if the array elements are distinct (which'd typically be the case)
* then the probabilities are disjoint, and we should just sum them.
*
* If we were being really tense we would try to confirm that the
* elements are all distinct, but that would be expensive and it
* doesn't seem to be worth the cycles; it would amount to penalizing
* well-written queries in favor of poorly-written ones. However, we
* do protect ourselves a little bit by checking whether the
* disjointness assumption leads to an impossible (out of range)
* probability; if so, we fall back to the normal calculation.
*/
s1 = s1disjoint = (useOr ? 0.0 : 1.0);
for (i = 0; i < num_elems; i++)
{
List *args;
Selectivity s2;
args = list_make2(leftop,
makeConst(nominal_element_type,
-1,
nominal_element_collation,
elmlen,
elem_values[i],
elem_nulls[i],
elmbyval));
if (is_join_clause)
s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
clause->inputcollid,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
Int16GetDatum(jointype),
PointerGetDatum(sjinfo)));
else
s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
clause->inputcollid,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
Int32GetDatum(varRelid)));
if (useOr)
{
s1 = s1 + s2 - s1 * s2;
if (isEquality)
s1disjoint += s2;
}
else
{
s1 = s1 * s2;
if (isInequality)
s1disjoint += s2 - 1.0;
}
}
/* accept disjoint-probability estimate if in range */
if ((useOr ? isEquality : isInequality) &&
s1disjoint >= 0.0 && s1disjoint <= 1.0)
s1 = s1disjoint;
}
else if (rightop && IsA(rightop, ArrayExpr) &&
!((ArrayExpr *) rightop)->multidims)
{
ArrayExpr *arrayexpr = (ArrayExpr *) rightop;
int16 elmlen;
bool elmbyval;
ListCell *l;
get_typlenbyval(arrayexpr->element_typeid,
&elmlen, &elmbyval);
/*
* We use the assumption of disjoint probabilities here too, although
* the odds of equal array elements are rather higher if the elements
* are not all constants (which they won't be, else constant folding
* would have reduced the ArrayExpr to a Const). In this path it's
* critical to have the sanity check on the s1disjoint estimate.
*/
s1 = s1disjoint = (useOr ? 0.0 : 1.0);
foreach(l, arrayexpr->elements)
{
Node *elem = (Node *) lfirst(l);
List *args;
Selectivity s2;
/*
* Theoretically, if elem isn't of nominal_element_type we should
* insert a RelabelType, but it seems unlikely that any operator
* estimation function would really care ...
*/
args = list_make2(leftop, elem);
if (is_join_clause)
s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
clause->inputcollid,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
Int16GetDatum(jointype),
PointerGetDatum(sjinfo)));
else
s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
clause->inputcollid,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
Int32GetDatum(varRelid)));
if (useOr)
{
s1 = s1 + s2 - s1 * s2;
if (isEquality)
s1disjoint += s2;
}
else
{
s1 = s1 * s2;
if (isInequality)
s1disjoint += s2 - 1.0;
}
}
/* accept disjoint-probability estimate if in range */
if ((useOr ? isEquality : isInequality) &&
s1disjoint >= 0.0 && s1disjoint <= 1.0)
s1 = s1disjoint;
}
else
{
CaseTestExpr *dummyexpr;
List *args;
Selectivity s2;
int i;
/*
* We need a dummy rightop to pass to the operator selectivity
* routine. It can be pretty much anything that doesn't look like a
* constant; CaseTestExpr is a convenient choice.
*/
dummyexpr = makeNode(CaseTestExpr);
dummyexpr->typeId = nominal_element_type;
dummyexpr->typeMod = -1;
dummyexpr->collation = clause->inputcollid;
args = list_make2(leftop, dummyexpr);
if (is_join_clause)
s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
clause->inputcollid,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
Int16GetDatum(jointype),
PointerGetDatum(sjinfo)));
else
s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
clause->inputcollid,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
Int32GetDatum(varRelid)));
s1 = useOr ? 0.0 : 1.0;
/*
* Arbitrarily assume 10 elements in the eventual array value (see
* also estimate_array_length). We don't risk an assumption of
* disjoint probabilities here.
*/
for (i = 0; i < 10; i++)
{
if (useOr)
s1 = s1 + s2 - s1 * s2;
else
s1 = s1 * s2;
}
}
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(s1);
return s1;
}
/*
* Estimate number of elements in the array yielded by an expression.
*
* It's important that this agree with scalararraysel.
*/
int
estimate_array_length(Node *arrayexpr)
{
/* look through any binary-compatible relabeling of arrayexpr */
arrayexpr = strip_array_coercion(arrayexpr);
if (arrayexpr && IsA(arrayexpr, Const))
{
Datum arraydatum = ((Const *) arrayexpr)->constvalue;
bool arrayisnull = ((Const *) arrayexpr)->constisnull;
ArrayType *arrayval;
if (arrayisnull)
return 0;
arrayval = DatumGetArrayTypeP(arraydatum);
return ArrayGetNItems(ARR_NDIM(arrayval), ARR_DIMS(arrayval));
}
else if (arrayexpr && IsA(arrayexpr, ArrayExpr) &&
!((ArrayExpr *) arrayexpr)->multidims)
{
return list_length(((ArrayExpr *) arrayexpr)->elements);
}
else
{
/* default guess --- see also scalararraysel */
return 10;
}
}
/*
* rowcomparesel - Selectivity of RowCompareExpr Node.
*
* We estimate RowCompare selectivity by considering just the first (high
* order) columns, which makes it equivalent to an ordinary OpExpr. While
* this estimate could be refined by considering additional columns, it
* seems unlikely that we could do a lot better without multi-column
* statistics.
*/
Selectivity
rowcomparesel(PlannerInfo *root,
RowCompareExpr *clause,
int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
{
Selectivity s1;
Oid opno = linitial_oid(clause->opnos);
Oid inputcollid = linitial_oid(clause->inputcollids);
List *opargs;
bool is_join_clause;
/* Build equivalent arg list for single operator */
opargs = list_make2(linitial(clause->largs), linitial(clause->rargs));
/*
* Decide if it's a join clause. This should match clausesel.c's
* treat_as_join_clause(), except that we intentionally consider only the
* leading columns and not the rest of the clause.
*/
if (varRelid != 0)
{
/*
* Caller is forcing restriction mode (eg, because we are examining an
* inner indexscan qual).
*/
is_join_clause = false;
}
else if (sjinfo == NULL)
{
/*
* It must be a restriction clause, since it's being evaluated at a
* scan node.
*/
is_join_clause = false;
}
else
{
/*
* Otherwise, it's a join if there's more than one relation used.
*/
is_join_clause = (NumRelids((Node *) opargs) > 1);
}
if (is_join_clause)
{
/* Estimate selectivity for a join clause. */
s1 = join_selectivity(root, opno,
opargs,
inputcollid,
jointype,
sjinfo);
}
else
{
/* Estimate selectivity for a restriction clause. */
s1 = restriction_selectivity(root, opno,
opargs,
inputcollid,
varRelid);
}
return s1;
}
/*
* eqjoinsel - Join selectivity of "="
*/
Datum
eqjoinsel(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
Oid operator = PG_GETARG_OID(1);
List *args = (List *) PG_GETARG_POINTER(2);
#ifdef NOT_USED
JoinType jointype = (JoinType) PG_GETARG_INT16(3);
#endif
SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
double selec;
double selec_inner;
VariableStatData vardata1;
VariableStatData vardata2;
double nd1;
double nd2;
bool isdefault1;
bool isdefault2;
Oid opfuncoid;
AttStatsSlot sslot1;
AttStatsSlot sslot2;
Form_pg_statistic stats1 = NULL;
Form_pg_statistic stats2 = NULL;
bool have_mcvs1 = false;
bool have_mcvs2 = false;
bool join_is_reversed;
RelOptInfo *inner_rel;
get_join_variables(root, args, sjinfo,
&vardata1, &vardata2, &join_is_reversed);
nd1 = get_variable_numdistinct(&vardata1, &isdefault1);
nd2 = get_variable_numdistinct(&vardata2, &isdefault2);
opfuncoid = get_opcode(operator);
memset(&sslot1, 0, sizeof(sslot1));
memset(&sslot2, 0, sizeof(sslot2));
if (HeapTupleIsValid(vardata1.statsTuple))
{
/* note we allow use of nullfrac regardless of security check */
stats1 = (Form_pg_statistic) GETSTRUCT(vardata1.statsTuple);
if (statistic_proc_security_check(&vardata1, opfuncoid))
have_mcvs1 = get_attstatsslot(&sslot1, vardata1.statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
}
if (HeapTupleIsValid(vardata2.statsTuple))
{
/* note we allow use of nullfrac regardless of security check */
stats2 = (Form_pg_statistic) GETSTRUCT(vardata2.statsTuple);
if (statistic_proc_security_check(&vardata2, opfuncoid))
have_mcvs2 = get_attstatsslot(&sslot2, vardata2.statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
}
/* We need to compute the inner-join selectivity in all cases */
selec_inner = eqjoinsel_inner(opfuncoid,
&vardata1, &vardata2,
nd1, nd2,
isdefault1, isdefault2,
&sslot1, &sslot2,
stats1, stats2,
have_mcvs1, have_mcvs2);
switch (sjinfo->jointype)
{
case JOIN_INNER:
case JOIN_LEFT:
case JOIN_FULL:
selec = selec_inner;
break;
case JOIN_SEMI:
case JOIN_ANTI:
case JOIN_LASJ_NOTIN:
/*
* Look up the join's inner relation. min_righthand is sufficient
* information because neither SEMI nor ANTI joins permit any
* reassociation into or out of their RHS, so the righthand will
* always be exactly that set of rels.
*/
inner_rel = find_join_input_rel(root, sjinfo->min_righthand);
if (!join_is_reversed)
selec = eqjoinsel_semi(opfuncoid,
&vardata1, &vardata2,
nd1, nd2,
isdefault1, isdefault2,
&sslot1, &sslot2,
stats1, stats2,
have_mcvs1, have_mcvs2,
inner_rel);
else
{
Oid commop = get_commutator(operator);
Oid commopfuncoid = OidIsValid(commop) ? get_opcode(commop) : InvalidOid;
selec = eqjoinsel_semi(commopfuncoid,
&vardata2, &vardata1,
nd2, nd1,
isdefault2, isdefault1,
&sslot2, &sslot1,
stats2, stats1,
have_mcvs2, have_mcvs1,
inner_rel);
}
/*
* We should never estimate the output of a semijoin to be more
* rows than we estimate for an inner join with the same input
* rels and join condition; it's obviously impossible for that to
* happen. The former estimate is N1 * Ssemi while the latter is
* N1 * N2 * Sinner, so we may clamp Ssemi <= N2 * Sinner. Doing
* this is worthwhile because of the shakier estimation rules we
* use in eqjoinsel_semi, particularly in cases where it has to
* punt entirely.
*/
selec = Min(selec, inner_rel->rows * selec_inner);
break;
default:
/* other values not expected here */
elog(ERROR, "unrecognized join type: %d",
(int) sjinfo->jointype);
selec = 0; /* keep compiler quiet */
break;
}
free_attstatsslot(&sslot1);
free_attstatsslot(&sslot2);
ReleaseVariableStats(vardata1);
ReleaseVariableStats(vardata2);
CLAMP_PROBABILITY(selec);
PG_RETURN_FLOAT8((float8) selec);
}
/*
* eqjoinsel_inner --- eqjoinsel for normal inner join
*
* We also use this for LEFT/FULL outer joins; it's not presently clear
* that it's worth trying to distinguish them here.
*/
static double
eqjoinsel_inner(Oid opfuncoid,
VariableStatData *vardata1, VariableStatData *vardata2,
double nd1, double nd2,
bool isdefault1, bool isdefault2,
AttStatsSlot *sslot1, AttStatsSlot *sslot2,
Form_pg_statistic stats1, Form_pg_statistic stats2,
bool have_mcvs1, bool have_mcvs2)
{
double selec;
if (have_mcvs1 && have_mcvs2)
{
/*
* We have most-common-value lists for both relations. Run through
* the lists to see which MCVs actually join to each other with the
* given operator. This allows us to determine the exact join
* selectivity for the portion of the relations represented by the MCV
* lists. We still have to estimate for the remaining population, but
* in a skewed distribution this gives us a big leg up in accuracy.
* For motivation see the analysis in Y. Ioannidis and S.
* Christodoulakis, "On the propagation of errors in the size of join
* results", Technical Report 1018, Computer Science Dept., University
* of Wisconsin, Madison, March 1991 (available from ftp.cs.wisc.edu).
*/
FmgrInfo eqproc;
bool *hasmatch1;
bool *hasmatch2;
double nullfrac1 = stats1->stanullfrac;
double nullfrac2 = stats2->stanullfrac;
double matchprodfreq,
matchfreq1,
matchfreq2,
unmatchfreq1,
unmatchfreq2,
otherfreq1,
otherfreq2,
totalsel1,
totalsel2;
int i,
nmatches;
fmgr_info(opfuncoid, &eqproc);
hasmatch1 = (bool *) palloc0(sslot1->nvalues * sizeof(bool));
hasmatch2 = (bool *) palloc0(sslot2->nvalues * sizeof(bool));
/*
* Note we assume that each MCV will match at most one member of the
* other MCV list. If the operator isn't really equality, there could
* be multiple matches --- but we don't look for them, both for speed
* and because the math wouldn't add up...
*/
matchprodfreq = 0.0;
nmatches = 0;
for (i = 0; i < sslot1->nvalues; i++)
{
int j;
for (j = 0; j < sslot2->nvalues; j++)
{
if (hasmatch2[j])
continue;
if (DatumGetBool(FunctionCall2Coll(&eqproc,
sslot1->stacoll,
sslot1->values[i],
sslot2->values[j])))
{
hasmatch1[i] = hasmatch2[j] = true;
matchprodfreq += sslot1->numbers[i] * sslot2->numbers[j];
nmatches++;
break;
}
}
}
CLAMP_PROBABILITY(matchprodfreq);
/* Sum up frequencies of matched and unmatched MCVs */
matchfreq1 = unmatchfreq1 = 0.0;
for (i = 0; i < sslot1->nvalues; i++)
{
if (hasmatch1[i])
matchfreq1 += sslot1->numbers[i];
else
unmatchfreq1 += sslot1->numbers[i];
}
CLAMP_PROBABILITY(matchfreq1);
CLAMP_PROBABILITY(unmatchfreq1);
matchfreq2 = unmatchfreq2 = 0.0;
for (i = 0; i < sslot2->nvalues; i++)
{
if (hasmatch2[i])
matchfreq2 += sslot2->numbers[i];
else
unmatchfreq2 += sslot2->numbers[i];
}
CLAMP_PROBABILITY(matchfreq2);
CLAMP_PROBABILITY(unmatchfreq2);
pfree(hasmatch1);
pfree(hasmatch2);
/*
* Compute total frequency of non-null values that are not in the MCV
* lists.
*/
otherfreq1 = 1.0 - nullfrac1 - matchfreq1 - unmatchfreq1;
otherfreq2 = 1.0 - nullfrac2 - matchfreq2 - unmatchfreq2;
CLAMP_PROBABILITY(otherfreq1);
CLAMP_PROBABILITY(otherfreq2);
/*
* We can estimate the total selectivity from the point of view of
* relation 1 as: the known selectivity for matched MCVs, plus
* unmatched MCVs that are assumed to match against random members of
* relation 2's non-MCV population, plus non-MCV values that are
* assumed to match against random members of relation 2's unmatched
* MCVs plus non-MCV values.
*/
totalsel1 = matchprodfreq;
if (nd2 > sslot2->nvalues)
totalsel1 += unmatchfreq1 * otherfreq2 / (nd2 - sslot2->nvalues);
if (nd2 > nmatches)
totalsel1 += otherfreq1 * (otherfreq2 + unmatchfreq2) /
(nd2 - nmatches);
/* Same estimate from the point of view of relation 2. */
totalsel2 = matchprodfreq;
if (nd1 > sslot1->nvalues)
totalsel2 += unmatchfreq2 * otherfreq1 / (nd1 - sslot1->nvalues);
if (nd1 > nmatches)
totalsel2 += otherfreq2 * (otherfreq1 + unmatchfreq1) /
(nd1 - nmatches);
/*
* Use the smaller of the two estimates. This can be justified in
* essentially the same terms as given below for the no-stats case: to
* a first approximation, we are estimating from the point of view of
* the relation with smaller nd.
*/
selec = (totalsel1 < totalsel2) ? totalsel1 : totalsel2;
}
else
{
/*
* We do not have MCV lists for both sides. Estimate the join
* selectivity as MIN(1/nd1,1/nd2)*(1-nullfrac1)*(1-nullfrac2). This
* is plausible if we assume that the join operator is strict and the
* non-null values are about equally distributed: a given non-null
* tuple of rel1 will join to either zero or N2*(1-nullfrac2)/nd2 rows
* of rel2, so total join rows are at most
* N1*(1-nullfrac1)*N2*(1-nullfrac2)/nd2 giving a join selectivity of
* not more than (1-nullfrac1)*(1-nullfrac2)/nd2. By the same logic it
* is not more than (1-nullfrac1)*(1-nullfrac2)/nd1, so the expression
* with MIN() is an upper bound. Using the MIN() means we estimate
* from the point of view of the relation with smaller nd (since the
* larger nd is determining the MIN). It is reasonable to assume that
* most tuples in this rel will have join partners, so the bound is
* probably reasonably tight and should be taken as-is.
*
* XXX Can we be smarter if we have an MCV list for just one side? It
* seems that if we assume equal distribution for the other side, we
* end up with the same answer anyway.
*/
double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
double nullfrac2 = stats2 ? stats2->stanullfrac : 0.0;
selec = (1.0 - nullfrac1) * (1.0 - nullfrac2);
if (nd1 > nd2)
selec /= nd1;
else
selec /= nd2;
}
return selec;
}
/*
* eqjoinsel_semi --- eqjoinsel for semi join
*
* (Also used for anti join, which we are supposed to estimate the same way.)
* Caller has ensured that vardata1 is the LHS variable.
* Unlike eqjoinsel_inner, we have to cope with opfuncoid being InvalidOid.
*/
static double
eqjoinsel_semi(Oid opfuncoid,
VariableStatData *vardata1, VariableStatData *vardata2,
double nd1, double nd2,
bool isdefault1, bool isdefault2,
AttStatsSlot *sslot1, AttStatsSlot *sslot2,
Form_pg_statistic stats1, Form_pg_statistic stats2,
bool have_mcvs1, bool have_mcvs2,
RelOptInfo *inner_rel)
{
double selec;
/*
* We clamp nd2 to be not more than what we estimate the inner relation's
* size to be. This is intuitively somewhat reasonable since obviously
* there can't be more than that many distinct values coming from the
* inner rel. The reason for the asymmetry (ie, that we don't clamp nd1
* likewise) is that this is the only pathway by which restriction clauses
* applied to the inner rel will affect the join result size estimate,
* since set_joinrel_size_estimates will multiply SEMI/ANTI selectivity by
* only the outer rel's size. If we clamped nd1 we'd be double-counting
* the selectivity of outer-rel restrictions.
*
* We can apply this clamping both with respect to the base relation from
* which the join variable comes (if there is just one), and to the
* immediate inner input relation of the current join.
*
* If we clamp, we can treat nd2 as being a non-default estimate; it's not
* great, maybe, but it didn't come out of nowhere either. This is most
* helpful when the inner relation is empty and consequently has no stats.
*/
if (vardata2->rel)
{
if (nd2 >= vardata2->rel->rows)
{
nd2 = vardata2->rel->rows;
isdefault2 = false;
}
}
if (nd2 >= inner_rel->rows)
{
nd2 = inner_rel->rows;
isdefault2 = false;
}
if (have_mcvs1 && have_mcvs2 && OidIsValid(opfuncoid))
{
/*
* We have most-common-value lists for both relations. Run through
* the lists to see which MCVs actually join to each other with the
* given operator. This allows us to determine the exact join
* selectivity for the portion of the relations represented by the MCV
* lists. We still have to estimate for the remaining population, but
* in a skewed distribution this gives us a big leg up in accuracy.
*/
FmgrInfo eqproc;
bool *hasmatch1;
bool *hasmatch2;
double nullfrac1 = stats1->stanullfrac;
double matchfreq1,
uncertainfrac,
uncertain;
int i,
nmatches,
clamped_nvalues2;
/*
* The clamping above could have resulted in nd2 being less than
* sslot2->nvalues; in which case, we assume that precisely the nd2
* most common values in the relation will appear in the join input,
* and so compare to only the first nd2 members of the MCV list. Of
* course this is frequently wrong, but it's the best bet we can make.
*/
clamped_nvalues2 = Min(sslot2->nvalues, nd2);
fmgr_info(opfuncoid, &eqproc);
hasmatch1 = (bool *) palloc0(sslot1->nvalues * sizeof(bool));
hasmatch2 = (bool *) palloc0(clamped_nvalues2 * sizeof(bool));
/*
* Note we assume that each MCV will match at most one member of the
* other MCV list. If the operator isn't really equality, there could
* be multiple matches --- but we don't look for them, both for speed
* and because the math wouldn't add up...
*/
nmatches = 0;
for (i = 0; i < sslot1->nvalues; i++)
{
int j;
for (j = 0; j < clamped_nvalues2; j++)
{
if (hasmatch2[j])
continue;
if (DatumGetBool(FunctionCall2Coll(&eqproc,
sslot1->stacoll,
sslot1->values[i],
sslot2->values[j])))
{
hasmatch1[i] = hasmatch2[j] = true;
nmatches++;
break;
}
}
}
/* Sum up frequencies of matched MCVs */
matchfreq1 = 0.0;
for (i = 0; i < sslot1->nvalues; i++)
{
if (hasmatch1[i])
matchfreq1 += sslot1->numbers[i];
}
CLAMP_PROBABILITY(matchfreq1);
pfree(hasmatch1);
pfree(hasmatch2);
/*
* Now we need to estimate the fraction of relation 1 that has at
* least one join partner. We know for certain that the matched MCVs
* do, so that gives us a lower bound, but we're really in the dark
* about everything else. Our crude approach is: if nd1 <= nd2 then
* assume all non-null rel1 rows have join partners, else assume for
* the uncertain rows that a fraction nd2/nd1 have join partners. We
* can discount the known-matched MCVs from the distinct-values counts
* before doing the division.
*
* Crude as the above is, it's completely useless if we don't have
* reliable ndistinct values for both sides. Hence, if either nd1 or
* nd2 is default, punt and assume half of the uncertain rows have
* join partners.
*/
if (!isdefault1 && !isdefault2)
{
nd1 -= nmatches;
nd2 -= nmatches;
if (nd1 <= nd2 || nd2 < 0)
uncertainfrac = 1.0;
else
uncertainfrac = nd2 / nd1;
}
else
uncertainfrac = 0.5;
uncertain = 1.0 - matchfreq1 - nullfrac1;
CLAMP_PROBABILITY(uncertain);
selec = matchfreq1 + uncertainfrac * uncertain;
}
else
{
/*
* Without MCV lists for both sides, we can only use the heuristic
* about nd1 vs nd2.
*/
double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
if (!isdefault1 && !isdefault2)
{
if (nd1 <= nd2 || nd2 < 0)
selec = 1.0 - nullfrac1;
else
selec = (nd2 / nd1) * (1.0 - nullfrac1);
}
else
selec = 0.5 * (1.0 - nullfrac1);
}
return selec;
}
/*
* neqjoinsel - Join selectivity of "!="
*/
Datum
neqjoinsel(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
Oid operator = PG_GETARG_OID(1);
List *args = (List *) PG_GETARG_POINTER(2);
JoinType jointype = (JoinType) PG_GETARG_INT16(3);
SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
float8 result;
if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
{
/*
* For semi-joins, if there is more than one distinct value in the RHS
* relation then every non-null LHS row must find a row to join since
* it can only be equal to one of them. We'll assume that there is
* always more than one distinct RHS value for the sake of stability,
* though in theory we could have special cases for empty RHS
* (selectivity = 0) and single-distinct-value RHS (selectivity =
* fraction of LHS that has the same value as the single RHS value).
*
* For anti-joins, if we use the same assumption that there is more
* than one distinct key in the RHS relation, then every non-null LHS
* row must be suppressed by the anti-join.
*
* So either way, the selectivity estimate should be 1 - nullfrac.
*/
VariableStatData leftvar;
VariableStatData rightvar;
bool reversed;
HeapTuple statsTuple;
double nullfrac;
get_join_variables(root, args, sjinfo, &leftvar, &rightvar, &reversed);
statsTuple = reversed ? rightvar.statsTuple : leftvar.statsTuple;
if (HeapTupleIsValid(statsTuple))
nullfrac = ((Form_pg_statistic) GETSTRUCT(statsTuple))->stanullfrac;
else
nullfrac = 0.0;
ReleaseVariableStats(leftvar);
ReleaseVariableStats(rightvar);
result = 1.0 - nullfrac;
}
else
{
/*
* We want 1 - eqjoinsel() where the equality operator is the one
* associated with this != operator, that is, its negator.
*/
Oid eqop = get_negator(operator);
if (eqop)
{
result = DatumGetFloat8(DirectFunctionCall5(eqjoinsel,
PointerGetDatum(root),
ObjectIdGetDatum(eqop),
PointerGetDatum(args),
Int16GetDatum(jointype),
PointerGetDatum(sjinfo)));
}
else
{
/* Use default selectivity (should we raise an error instead?) */
result = DEFAULT_EQ_SEL;
}
result = 1.0 - result;
}
PG_RETURN_FLOAT8(result);
}
/*
* scalarltjoinsel - Join selectivity of "<" for scalars
*/
Datum
scalarltjoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* scalarlejoinsel - Join selectivity of "<=" for scalars
*/
Datum
scalarlejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* scalargtjoinsel - Join selectivity of ">" for scalars
*/
Datum
scalargtjoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* scalargejoinsel - Join selectivity of ">=" for scalars
*/
Datum
scalargejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* mergejoinscansel - Scan selectivity of merge join.
*
* A merge join will stop as soon as it exhausts either input stream.
* Therefore, if we can estimate the ranges of both input variables,
* we can estimate how much of the input will actually be read. This
* can have a considerable impact on the cost when using indexscans.
*
* Also, we can estimate how much of each input has to be read before the
* first join pair is found, which will affect the join's startup time.
*
* clause should be a clause already known to be mergejoinable. opfamily,
* strategy, and nulls_first specify the sort ordering being used.
*
* The outputs are:
* *leftstart is set to the fraction of the left-hand variable expected
* to be scanned before the first join pair is found (0 to 1).
* *leftend is set to the fraction of the left-hand variable expected
* to be scanned before the join terminates (0 to 1).
* *rightstart, *rightend similarly for the right-hand variable.
*/
void
mergejoinscansel(PlannerInfo *root, Node *clause,
Oid opfamily, int strategy, bool nulls_first,
Selectivity *leftstart, Selectivity *leftend,
Selectivity *rightstart, Selectivity *rightend)
{
Node *left,
*right;
VariableStatData leftvar,
rightvar;
int op_strategy;
Oid op_lefttype;
Oid op_righttype;
Oid opno,
lsortop,
rsortop,
lstatop,
rstatop,
ltop,
leop,
revltop,
revleop;
bool isgt;
Datum leftmin,
leftmax,
rightmin,
rightmax;
double selec;
/* Set default results if we can't figure anything out. */
/* XXX should default "start" fraction be a bit more than 0? */
*leftstart = *rightstart = 0.0;
*leftend = *rightend = 1.0;
/* Deconstruct the merge clause */
if (!is_opclause(clause))
return; /* shouldn't happen */
opno = ((OpExpr *) clause)->opno;
left = get_leftop((Expr *) clause);
right = get_rightop((Expr *) clause);
if (!right)
return; /* shouldn't happen */
/* Look for stats for the inputs */
examine_variable(root, left, 0, &leftvar);
examine_variable(root, right, 0, &rightvar);
/* Extract the operator's declared left/right datatypes */
get_op_opfamily_properties(opno, opfamily, false,
&op_strategy,
&op_lefttype,
&op_righttype);
Assert(op_strategy == BTEqualStrategyNumber);
/*
* Look up the various operators we need. If we don't find them all, it
* probably means the opfamily is broken, but we just fail silently.
*
* Note: we expect that pg_statistic histograms will be sorted by the '<'
* operator, regardless of which sort direction we are considering.
*/
switch (strategy)
{
case BTLessStrategyNumber:
isgt = false;
if (op_lefttype == op_righttype)
{
/* easy case */
ltop = get_opfamily_member(opfamily,
op_lefttype, op_righttype,
BTLessStrategyNumber);
leop = get_opfamily_member(opfamily,
op_lefttype, op_righttype,
BTLessEqualStrategyNumber);
lsortop = ltop;
rsortop = ltop;
lstatop = lsortop;
rstatop = rsortop;
revltop = ltop;
revleop = leop;
}
else
{
ltop = get_opfamily_member(opfamily,
op_lefttype, op_righttype,
BTLessStrategyNumber);
leop = get_opfamily_member(opfamily,
op_lefttype, op_righttype,
BTLessEqualStrategyNumber);
lsortop = get_opfamily_member(opfamily,
op_lefttype, op_lefttype,
BTLessStrategyNumber);
rsortop = get_opfamily_member(opfamily,
op_righttype, op_righttype,
BTLessStrategyNumber);
lstatop = lsortop;
rstatop = rsortop;
revltop = get_opfamily_member(opfamily,
op_righttype, op_lefttype,
BTLessStrategyNumber);
revleop = get_opfamily_member(opfamily,
op_righttype, op_lefttype,
BTLessEqualStrategyNumber);
}
break;
case BTGreaterStrategyNumber:
/* descending-order case */
isgt = true;
if (op_lefttype == op_righttype)
{
/* easy case */
ltop = get_opfamily_member(opfamily,
op_lefttype, op_righttype,
BTGreaterStrategyNumber);
leop = get_opfamily_member(opfamily,
op_lefttype, op_righttype,
BTGreaterEqualStrategyNumber);
lsortop = ltop;
rsortop = ltop;
lstatop = get_opfamily_member(opfamily,
op_lefttype, op_lefttype,
BTLessStrategyNumber);
rstatop = lstatop;
revltop = ltop;
revleop = leop;
}
else
{
ltop = get_opfamily_member(opfamily,
op_lefttype, op_righttype,
BTGreaterStrategyNumber);
leop = get_opfamily_member(opfamily,
op_lefttype, op_righttype,
BTGreaterEqualStrategyNumber);
lsortop = get_opfamily_member(opfamily,
op_lefttype, op_lefttype,
BTGreaterStrategyNumber);
rsortop = get_opfamily_member(opfamily,
op_righttype, op_righttype,
BTGreaterStrategyNumber);
lstatop = get_opfamily_member(opfamily,
op_lefttype, op_lefttype,
BTLessStrategyNumber);
rstatop = get_opfamily_member(opfamily,
op_righttype, op_righttype,
BTLessStrategyNumber);
revltop = get_opfamily_member(opfamily,
op_righttype, op_lefttype,
BTGreaterStrategyNumber);
revleop = get_opfamily_member(opfamily,
op_righttype, op_lefttype,
BTGreaterEqualStrategyNumber);
}
break;
default:
goto fail; /* shouldn't get here */
}
if (!OidIsValid(lsortop) ||
!OidIsValid(rsortop) ||
!OidIsValid(lstatop) ||
!OidIsValid(rstatop) ||
!OidIsValid(ltop) ||
!OidIsValid(leop) ||
!OidIsValid(revltop) ||
!OidIsValid(revleop))
goto fail; /* insufficient info in catalogs */
/* Try to get ranges of both inputs */
if (!isgt)
{
if (!get_variable_range(root, &leftvar, lstatop,
&leftmin, &leftmax))
goto fail; /* no range available from stats */
if (!get_variable_range(root, &rightvar, rstatop,
&rightmin, &rightmax))
goto fail; /* no range available from stats */
}
else
{
/* need to swap the max and min */
if (!get_variable_range(root, &leftvar, lstatop,
&leftmax, &leftmin))
goto fail; /* no range available from stats */
if (!get_variable_range(root, &rightvar, rstatop,
&rightmax, &rightmin))
goto fail; /* no range available from stats */
}
/*
* Now, the fraction of the left variable that will be scanned is the
* fraction that's <= the right-side maximum value. But only believe
* non-default estimates, else stick with our 1.0.
*/
selec = scalarineqsel(root, leop, isgt, true, &leftvar,
rightmax, op_righttype);
if (selec != DEFAULT_INEQ_SEL)
*leftend = selec;
/* And similarly for the right variable. */
selec = scalarineqsel(root, revleop, isgt, true, &rightvar,
leftmax, op_lefttype);
if (selec != DEFAULT_INEQ_SEL)
*rightend = selec;
/*
* Only one of the two "end" fractions can really be less than 1.0;
* believe the smaller estimate and reset the other one to exactly 1.0. If
* we get exactly equal estimates (as can easily happen with self-joins),
* believe neither.
*/
if (*leftend > *rightend)
*leftend = 1.0;
else if (*leftend < *rightend)
*rightend = 1.0;
else
*leftend = *rightend = 1.0;
/*
* Also, the fraction of the left variable that will be scanned before the
* first join pair is found is the fraction that's < the right-side
* minimum value. But only believe non-default estimates, else stick with
* our own default.
*/
selec = scalarineqsel(root, ltop, isgt, false, &leftvar,
rightmin, op_righttype);
if (selec != DEFAULT_INEQ_SEL)
*leftstart = selec;
/* And similarly for the right variable. */
selec = scalarineqsel(root, revltop, isgt, false, &rightvar,
leftmin, op_lefttype);
if (selec != DEFAULT_INEQ_SEL)
*rightstart = selec;
/*
* Only one of the two "start" fractions can really be more than zero;
* believe the larger estimate and reset the other one to exactly 0.0. If
* we get exactly equal estimates (as can easily happen with self-joins),
* believe neither.
*/
if (*leftstart < *rightstart)
*leftstart = 0.0;
else if (*leftstart > *rightstart)
*rightstart = 0.0;
else
*leftstart = *rightstart = 0.0;
/*
* If the sort order is nulls-first, we're going to have to skip over any
* nulls too. These would not have been counted by scalarineqsel, and we
* can safely add in this fraction regardless of whether we believe
* scalarineqsel's results or not. But be sure to clamp the sum to 1.0!
*/
if (nulls_first)
{
Form_pg_statistic stats;
HeapTuple leftStatsTuple;
HeapTuple rightStatsTuple;
leftStatsTuple = getStatsTuple(&leftvar);
if (HeapTupleIsValid(leftStatsTuple))
{
stats = (Form_pg_statistic) GETSTRUCT(leftStatsTuple);
*leftstart += stats->stanullfrac;
CLAMP_PROBABILITY(*leftstart);
*leftend += stats->stanullfrac;
CLAMP_PROBABILITY(*leftend);
}
rightStatsTuple = getStatsTuple(&rightvar);
if (HeapTupleIsValid(rightStatsTuple))
{
stats = (Form_pg_statistic) GETSTRUCT(rightStatsTuple);
*rightstart += stats->stanullfrac;
CLAMP_PROBABILITY(*rightstart);
*rightend += stats->stanullfrac;
CLAMP_PROBABILITY(*rightend);
}
}
/* Disbelieve start >= end, just in case that can happen */
if (*leftstart >= *leftend)
{
*leftstart = 0.0;
*leftend = 1.0;
}
if (*rightstart >= *rightend)
{
*rightstart = 0.0;
*rightend = 1.0;
}
fail:
ReleaseVariableStats(leftvar);
ReleaseVariableStats(rightvar);
}
/*
* Helper routine for estimate_num_groups: add an item to a list of
* GroupVarInfos, but only if it's not known equal to any of the existing
* entries.
*/
typedef struct
{
Node *var; /* might be an expression, not just a Var */
RelOptInfo *rel; /* relation it belongs to */
double ndistinct; /* # distinct values */
} GroupVarInfo;
static List *
add_unique_group_var(PlannerInfo *root, List *varinfos,
Node *var, VariableStatData *vardata)
{
GroupVarInfo *varinfo;
double ndistinct;
bool isdefault;
ListCell *lc;
ndistinct = get_variable_numdistinct(vardata, &isdefault);
/* cannot use foreach here because of possible list_delete */
lc = list_head(varinfos);
while (lc)
{
varinfo = (GroupVarInfo *) lfirst(lc);
/* must advance lc before list_delete possibly pfree's it */
lc = lnext(lc);
/* Drop exact duplicates */
if (equal(var, varinfo->var))
return varinfos;
/*
* Drop known-equal vars, but only if they belong to different
* relations (see comments for estimate_num_groups)
*/
if (vardata->rel != varinfo->rel &&
exprs_known_equal(root, var, varinfo->var))
{
if (varinfo->ndistinct <= ndistinct)
{
/* Keep older item, forget new one */
return varinfos;
}
else
{
/* Delete the older item */
varinfos = list_delete_ptr(varinfos, varinfo);
}
}
}
varinfo = (GroupVarInfo *) palloc(sizeof(GroupVarInfo));
varinfo->var = var;
varinfo->rel = vardata->rel;
varinfo->ndistinct = ndistinct;
varinfos = lappend(varinfos, varinfo);
return varinfos;
}
/*
* estimate_num_groups - Estimate number of groups in a grouped query
*
* Given a query having a GROUP BY clause, estimate how many groups there
* will be --- ie, the number of distinct combinations of the GROUP BY
* expressions.
*
* This routine is also used to estimate the number of rows emitted by
* a DISTINCT filtering step; that is an isomorphic problem. (Note:
* actually, we only use it for DISTINCT when there's no grouping or
* aggregation ahead of the DISTINCT.)
*
* Inputs:
* root - the query
* groupExprs - list of expressions being grouped by
* input_rows - number of rows estimated to arrive at the group/unique
* filter step
* pgset - NULL, or a List** pointing to a grouping set to filter the
* groupExprs against
*
* Given the lack of any cross-correlation statistics in the system, it's
* impossible to do anything really trustworthy with GROUP BY conditions
* involving multiple Vars. We should however avoid assuming the worst
* case (all possible cross-product terms actually appear as groups) since
* very often the grouped-by Vars are highly correlated. Our current approach
* is as follows:
* 1. Expressions yielding boolean are assumed to contribute two groups,
* independently of their content, and are ignored in the subsequent
* steps. This is mainly because tests like "col IS NULL" break the
* heuristic used in step 2 especially badly.
* 2. Reduce the given expressions to a list of unique Vars used. For
* example, GROUP BY a, a + b is treated the same as GROUP BY a, b.
* It is clearly correct not to count the same Var more than once.
* It is also reasonable to treat f(x) the same as x: f() cannot
* increase the number of distinct values (unless it is volatile,
* which we consider unlikely for grouping), but it probably won't
* reduce the number of distinct values much either.
* As a special case, if a GROUP BY expression can be matched to an
* expressional index for which we have statistics, then we treat the
* whole expression as though it were just a Var.
* 3. If the list contains Vars of different relations that are known equal
* due to equivalence classes, then drop all but one of the Vars from each
* known-equal set, keeping the one with smallest estimated # of values
* (since the extra values of the others can't appear in joined rows).
* Note the reason we only consider Vars of different relations is that
* if we considered ones of the same rel, we'd be double-counting the
* restriction selectivity of the equality in the next step.
* 4. For Vars within a single source rel, we multiply together the numbers
* of values, clamp to the number of rows in the rel (divided by 10 if
* more than one Var), and then multiply by a factor based on the
* selectivity of the restriction clauses for that rel. When there's
* more than one Var, the initial product is probably too high (it's the
* worst case) but clamping to a fraction of the rel's rows seems to be a
* helpful heuristic for not letting the estimate get out of hand. (The
* factor of 10 is derived from pre-Postgres-7.4 practice.) The factor
* we multiply by to adjust for the restriction selectivity assumes that
* the restriction clauses are independent of the grouping, which may not
* be a valid assumption, but it's hard to do better.
* 5. If there are Vars from multiple rels, we repeat step 4 for each such
* rel, and multiply the results together.
* Note that rels not containing grouped Vars are ignored completely, as are
* join clauses. Such rels cannot increase the number of groups, and we
* assume such clauses do not reduce the number either (somewhat bogus,
* but we don't have the info to do better).
*/
double
estimate_num_groups(PlannerInfo *root, List *groupExprs, double input_rows,
List **pgset)
{
List *varinfos = NIL;
double srf_multiplier = 1.0;
double numdistinct;
ListCell *l;
int i;
/*
* We don't ever want to return an estimate of zero groups, as that tends
* to lead to division-by-zero and other unpleasantness. The input_rows
* estimate is usually already at least 1, but clamp it just in case it
* isn't.
*/
input_rows = clamp_row_est(input_rows);
/*
* If no grouping columns, there's exactly one group. (This can't happen
* for normal cases with GROUP BY or DISTINCT, but it is possible for
* corner cases with set operations.)
*/
if (groupExprs == NIL || (pgset && list_length(*pgset) < 1))
return 1.0;
/*
* Count groups derived from boolean grouping expressions. For other
* expressions, find the unique Vars used, treating an expression as a Var
* if we can find stats for it. For each one, record the statistical
* estimate of number of distinct values (total in its table, without
* regard for filtering).
*/
numdistinct = 1.0;
i = 0;
foreach(l, groupExprs)
{
Node *groupexpr = (Node *) lfirst(l);
double this_srf_multiplier;
VariableStatData vardata;
List *varshere;
ListCell *l2;
/* is expression in this grouping set? */
if (pgset && !list_member_int(*pgset, i++))
continue;
/*
* Set-returning functions in grouping columns are a bit problematic.
* The code below will effectively ignore their SRF nature and come up
* with a numdistinct estimate as though they were scalar functions.
* We compensate by scaling up the end result by the largest SRF
* rowcount estimate. (This will be an overestimate if the SRF
* produces multiple copies of any output value, but it seems best to
* assume the SRF's outputs are distinct. In any case, it's probably
* pointless to worry too much about this without much better
* estimates for SRF output rowcounts than we have today.)
*/
this_srf_multiplier = expression_returns_set_rows(root, groupexpr);
if (srf_multiplier < this_srf_multiplier)
srf_multiplier = this_srf_multiplier;
/* Short-circuit for expressions returning boolean */
if (exprType(groupexpr) == BOOLOID)
{
numdistinct *= 2.0;
continue;
}
/*
* If examine_variable is able to deduce anything about the GROUP BY
* expression, treat it as a single variable even if it's really more
* complicated.
*/
examine_variable(root, groupexpr, 0, &vardata);
if (HeapTupleIsValid(getStatsTuple(&vardata)) || vardata.isunique)
{
varinfos = add_unique_group_var(root, varinfos,
groupexpr, &vardata);
ReleaseVariableStats(vardata);
continue;
}
ReleaseVariableStats(vardata);
/*
* Else pull out the component Vars. Handle PlaceHolderVars by
* recursing into their arguments (effectively assuming that the
* PlaceHolderVar doesn't change the number of groups, which boils
* down to ignoring the possible addition of nulls to the result set).
*/
varshere = pull_var_clause(groupexpr,
PVC_RECURSE_AGGREGATES |
PVC_RECURSE_WINDOWFUNCS |
PVC_RECURSE_PLACEHOLDERS);
/*
* If we find any variable-free GROUP BY item, then either it is a
* constant (and we can ignore it) or it contains a volatile function;
* in the latter case we punt and assume that each input row will
* yield a distinct group.
*/
if (varshere == NIL)
{
if (contain_volatile_functions(groupexpr))
return input_rows;
continue;
}
/*
* Else add variables to varinfos list
*/
foreach(l2, varshere)
{
Node *var = (Node *) lfirst(l2);
examine_variable(root, var, 0, &vardata);
varinfos = add_unique_group_var(root, varinfos, var, &vardata);
ReleaseVariableStats(vardata);
}
}
/*
* If now no Vars, we must have an all-constant or all-boolean GROUP BY
* list.
*/
if (varinfos == NIL)
{
/* Apply SRF multiplier as we would do in the long path */
numdistinct *= srf_multiplier;
/* Round off */
numdistinct = ceil(numdistinct);
/* Guard against out-of-range answers */
if (numdistinct > input_rows)
numdistinct = input_rows;
if (numdistinct < 1.0)
numdistinct = 1.0;
return numdistinct;
}
/*
* Group Vars by relation and estimate total numdistinct.
*
* For each iteration of the outer loop, we process the frontmost Var in
* varinfos, plus all other Vars in the same relation. We remove these
* Vars from the newvarinfos list for the next iteration. This is the
* easiest way to group Vars of same rel together.
*/
do
{
GroupVarInfo *varinfo1 = (GroupVarInfo *) linitial(varinfos);
RelOptInfo *rel = varinfo1->rel;
double reldistinct = 1;
double relmaxndistinct = reldistinct;
int relvarcount = 0;
List *newvarinfos = NIL;
List *relvarinfos = NIL;
/*
* Split the list of varinfos in two - one for the current rel, one
* for remaining Vars on other rels.
*/
relvarinfos = lcons(varinfo1, relvarinfos);
for_each_cell(l, lnext(list_head(varinfos)))
{
GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
if (varinfo2->rel == varinfo1->rel)
{
/* varinfos on current rel */
relvarinfos = lcons(varinfo2, relvarinfos);
}
else
{
/* not time to process varinfo2 yet */
newvarinfos = lcons(varinfo2, newvarinfos);
}
}
/*
* Get the numdistinct estimate for the Vars of this rel. We
* iteratively search for multivariate n-distinct with maximum number
* of vars; assuming that each var group is independent of the others,
* we multiply them together. Any remaining relvarinfos after no more
* multivariate matches are found are assumed independent too, so
* their individual ndistinct estimates are multiplied also.
*
* While iterating, count how many separate numdistinct values we
* apply. We apply a fudge factor below, but only if we multiplied
* more than one such values.
*/
while (relvarinfos)
{
double mvndistinct;
if (estimate_multivariate_ndistinct(root, rel, &relvarinfos,
&mvndistinct))
{
reldistinct *= mvndistinct;
if (relmaxndistinct < mvndistinct)
relmaxndistinct = mvndistinct;
relvarcount++;
}
else
{
foreach(l, relvarinfos)
{
GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
reldistinct *= varinfo2->ndistinct;
if (relmaxndistinct < varinfo2->ndistinct)
relmaxndistinct = varinfo2->ndistinct;
relvarcount++;
}
/* we're done with this relation */
relvarinfos = NIL;
}
}
/*
* Sanity check --- don't divide by zero if empty relation.
*/
Assert(IS_SIMPLE_REL(rel));
if (rel->tuples > 0)
{
/*
* Clamp to size of rel, or size of rel / 10 if multiple Vars. The
* fudge factor is because the Vars are probably correlated but we
* don't know by how much. We should never clamp to less than the
* largest ndistinct value for any of the Vars, though, since
* there will surely be at least that many groups.
*/
double clamp = rel->tuples;
if (relvarcount > 1)
{
clamp *= 0.1;
if (clamp < relmaxndistinct)
{
clamp = relmaxndistinct;
/* for sanity in case some ndistinct is too large: */
if (clamp > rel->tuples)
clamp = rel->tuples;
}
}
if (reldistinct > clamp)
reldistinct = clamp;
/*
* Update the estimate based on the restriction selectivity,
* guarding against division by zero when reldistinct is zero.
* Also skip this if we know that we are returning all rows.
*/
if (reldistinct > 0 && rel->rows < rel->tuples)
{
/*
* Given a table containing N rows with n distinct values in a
* uniform distribution, if we select p rows at random then
* the expected number of distinct values selected is
*
* n * (1 - product((N-N/n-i)/(N-i), i=0..p-1))
*
* = n * (1 - (N-N/n)! / (N-N/n-p)! * (N-p)! / N!)
*
* See "Approximating block accesses in database
* organizations", S. B. Yao, Communications of the ACM,
* Volume 20 Issue 4, April 1977 Pages 260-261.
*
* Alternatively, re-arranging the terms from the factorials,
* this may be written as
*
* n * (1 - product((N-p-i)/(N-i), i=0..N/n-1))
*
* This form of the formula is more efficient to compute in
* the common case where p is larger than N/n. Additionally,
* as pointed out by Dell'Era, if i << N for all terms in the
* product, it can be approximated by
*
* n * (1 - ((N-p)/N)^(N/n))
*
* See "Expected distinct values when selecting from a bag
* without replacement", Alberto Dell'Era,
* http://www.adellera.it/investigations/distinct_balls/.
*
* The condition i << N is equivalent to n >> 1, so this is a
* good approximation when the number of distinct values in
* the table is large. It turns out that this formula also
* works well even when n is small.
*/
reldistinct *=
(1 - pow((rel->tuples - rel->rows) / rel->tuples,
rel->tuples / reldistinct));
}
reldistinct = clamp_row_est(reldistinct);
/*
* Update estimate of total distinct groups.
*/
numdistinct *= reldistinct;
}
varinfos = newvarinfos;
} while (varinfos != NIL);
/* Now we can account for the effects of any SRFs */
numdistinct *= srf_multiplier;
/* Round off */
numdistinct = ceil(numdistinct);
/* Guard against out-of-range answers */
if (numdistinct > input_rows)
numdistinct = input_rows;
if (numdistinct < 1.0)
numdistinct = 1.0;
return numdistinct;
}
/*
* Estimate hash bucket statistics when the specified expression is used
* as a hash key for the given number of buckets.
*
* This attempts to determine two values:
*
* 1. The frequency of the most common value of the expression (returns
* zero into *mcv_freq if we can't get that).
*
* 2. The "bucketsize fraction", ie, average number of entries in a bucket
* divided by total tuples in relation.
*
* 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).
*
* The caller should also check that the mcv_freq is not so large that the
* most common value would by itself require an impractically large bucket.
* In a hash join, the executor can split buckets if they get too big, but
* obviously that doesn't help for a bucket that contains many duplicates of
* the same value.
*/
void
estimate_hash_bucket_stats(PlannerInfo *root, Node *hashkey, double nbuckets,
Selectivity *mcv_freq,
Selectivity *bucketsize_frac)
{
VariableStatData vardata;
double estfract,
ndistinct,
stanullfrac,
avgfreq;
bool isdefault;
AttStatsSlot sslot;
examine_variable(root, hashkey, 0, &vardata);
/* Look up the frequency of the most common value, if available */
*mcv_freq = 0.0;
if (HeapTupleIsValid(vardata.statsTuple))
{
if (get_attstatsslot(&sslot, vardata.statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_NUMBERS))
{
/*
* The first MCV stat is for the most common value.
*/
if (sslot.nnumbers > 0)
*mcv_freq = sslot.numbers[0];
free_attstatsslot(&sslot);
}
}
/* Get number of distinct values */
ndistinct = get_variable_numdistinct(&vardata, &isdefault);
/*
* If ndistinct isn't real, punt. We normally return 0.1, but if the
* mcv_freq is known to be even higher than that, use it instead.
*/
if (isdefault)
{
*bucketsize_frac = (Selectivity) Max(0.1, *mcv_freq);
ReleaseVariableStats(vardata);
return;
}
if (HeapTupleIsValid(getStatsTuple(&vardata)))
{
HeapTuple tp = getStatsTuple(&vardata);
Form_pg_statistic stats;
stats = (Form_pg_statistic) GETSTRUCT(tp);
stanullfrac = stats->stanullfrac;
}
else
stanullfrac = 0.0;
/* Compute avg freq of all distinct data values in raw relation */
avgfreq = (1.0 - 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.
*/
if (vardata.rel && vardata.rel->tuples > 0)
{
ndistinct *= vardata.rel->rows / vardata.rel->tuples;
ndistinct = clamp_row_est(ndistinct);
}
/*
* 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 > nbuckets)
estfract = 1.0 / nbuckets;
else
estfract = 1.0 / ndistinct;
/*
* Adjust estimated bucketsize upward to account for skewed distribution.
*/
if (avgfreq > 0.0 && *mcv_freq > avgfreq)
estfract *= *mcv_freq / 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;
*bucketsize_frac = (Selectivity) estfract;
ReleaseVariableStats(vardata);
}
/*
* estimate_hashagg_tablesize
* estimate the number of bytes that a hash aggregate hashtable will
* require based on the agg_costs, path width and number of groups.
*
* We return the result as "double" to forestall any possible overflow
* problem in the multiplication by dNumGroups.
*
* XXX this may be over-estimating the size now that hashagg knows to omit
* unneeded columns from the hashtable. Also for mixed-mode grouping sets,
* grouping columns not in the hashed set are counted here even though hashagg
* won't store them. Is this a problem?
*/
double
estimate_hashagg_tablesize(Path *path, const AggClauseCosts *agg_costs,
double dNumGroups)
{
Size hashentrysize = hash_agg_entry_size(
agg_costs->numAggs, path->pathtarget->width, agg_costs->transitionSpace);
/*
* Note that this disregards the effect of fill-factor and growth policy
* of the hash table. That's probably ok, given that the default
* fill-factor is relatively high. It'd be hard to meaningfully factor in
* "double-in-size" growth policies here.
*/
return hashentrysize * dNumGroups;
}
/*-------------------------------------------------------------------------
*
* Support routines
*
*-------------------------------------------------------------------------
*/
/*
* Find applicable ndistinct statistics for the given list of VarInfos (which
* must all belong to the given rel), and update *ndistinct to the estimate of
* the MVNDistinctItem that best matches. If a match it found, *varinfos is
* updated to remove the list of matched varinfos.
*
* Varinfos that aren't for simple Vars are ignored.
*
* Return true if we're able to find a match, false otherwise.
*/
static bool
estimate_multivariate_ndistinct(PlannerInfo *root, RelOptInfo *rel,
List **varinfos, double *ndistinct)
{
ListCell *lc;
Bitmapset *attnums = NULL;
int nmatches;
Oid statOid = InvalidOid;
MVNDistinct *stats;
Bitmapset *matched = NULL;
/* bail out immediately if the table has no extended statistics */
if (!rel->statlist)
return false;
/* Determine the attnums we're looking for */
foreach(lc, *varinfos)
{
GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc);
Assert(varinfo->rel == rel);
if (IsA(varinfo->var, Var))
{
attnums = bms_add_member(attnums,
((Var *) varinfo->var)->varattno);
}
}
/* look for the ndistinct statistics matching the most vars */
nmatches = 1; /* we require at least two matches */
foreach(lc, rel->statlist)
{
StatisticExtInfo *info = (StatisticExtInfo *) lfirst(lc);
Bitmapset *shared;
int nshared;
/* skip statistics of other kinds */
if (info->kind != STATS_EXT_NDISTINCT)
continue;
/* compute attnums shared by the vars and the statistics object */
shared = bms_intersect(info->keys, attnums);
nshared = bms_num_members(shared);
/*
* Does this statistics object match more columns than the currently
* best object? If so, use this one instead.
*
* XXX This should break ties using name of the object, or something
* like that, to make the outcome stable.
*/
if (nshared > nmatches)
{
statOid = info->statOid;
nmatches = nshared;
matched = shared;
}
}
/* No match? */
if (statOid == InvalidOid)
return false;
Assert(nmatches > 1 && matched != NULL);
stats = statext_ndistinct_load(statOid);
/*
* If we have a match, search it for the specific item that matches (there
* must be one), and construct the output values.
*/
if (stats)
{
int i;
List *newlist = NIL;
MVNDistinctItem *item = NULL;
/* Find the specific item that exactly matches the combination */
for (i = 0; i < stats->nitems; i++)
{
MVNDistinctItem *tmpitem = &stats->items[i];
if (bms_subset_compare(tmpitem->attrs, matched) == BMS_EQUAL)
{
item = tmpitem;
break;
}
}
/* make sure we found an item */
if (!item)
elog(ERROR, "corrupt MVNDistinct entry");
/* Form the output varinfo list, keeping only unmatched ones */
foreach(lc, *varinfos)
{
GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc);
AttrNumber attnum;
if (!IsA(varinfo->var, Var))
{
newlist = lappend(newlist, varinfo);
continue;
}
attnum = ((Var *) varinfo->var)->varattno;
if (!bms_is_member(attnum, matched))
newlist = lappend(newlist, varinfo);
}
*varinfos = newlist;
*ndistinct = item->ndistinct;
return true;
}
return false;
}
/*
* convert_to_scalar
* Convert non-NULL values of the indicated types to the comparison
* scale needed by scalarineqsel().
* Returns "true" if successful.
*
* XXX this routine is a hack: ideally we should look up the conversion
* subroutines in pg_type.
*
* All numeric datatypes are simply converted to their equivalent
* "double" values. (NUMERIC values that are outside the range of "double"
* are clamped to +/- HUGE_VAL.)
*
* String datatypes are converted to have hi and lo bound be constants, with
* the scaledvalue equally either hi or lo, depending on the value of isgt
* (done so that the caller will include the entire bucket in the final
* computed selectivity, even after inverting for the isgt case)
*
* The bytea datatype is just enough different from strings that it has
* to be treated separately.
*
* The several datatypes representing absolute times are all converted
* to Timestamp, which is actually a double, and then we just use that
* double value. Note this will give correct results even for the "special"
* values of Timestamp, since those are chosen to compare correctly;
* see timestamp_cmp.
*
* The several datatypes representing relative times (intervals) are all
* converted to measurements expressed in seconds.
*
*/
static bool
convert_to_scalar(Datum value, Oid valuetypid, Oid collid, double *scaledvalue,
Datum lobound, Datum hibound, Oid boundstypid,
double *scaledlobound, double *scaledhibound)
{
bool failure = false;
/*
* Both the valuetypid and the boundstypid should exactly match the
* declared input type(s) of the operator we are invoked for. However,
* extensions might try to use scalarineqsel as estimator for operators
* with input type(s) we don't handle here; in such cases, we want to
* return false, not fail. In any case, we mustn't assume that valuetypid
* and boundstypid are identical.
*
* XXX The histogram we are interpolating between points of could belong
* to a column that's only binary-compatible with the declared type. In
* essence we are assuming that the semantics of binary-compatible types
* are enough alike that we can use a histogram generated with one type's
* operators to estimate selectivity for the other's. This is outright
* wrong in some cases --- in particular signed versus unsigned
* interpretation could trip us up. But it's useful enough in the
* majority of cases that we do it anyway. Should think about more
* rigorous ways to do it.
*/
switch (valuetypid)
{
/*
* Built-in numeric types
*/
case BOOLOID:
case INT2OID:
case INT4OID:
case INT8OID:
case FLOAT4OID:
case FLOAT8OID:
case NUMERICOID:
case OIDOID:
case REGPROCOID:
case REGPROCEDUREOID:
case REGOPEROID:
case REGOPERATOROID:
case REGCLASSOID:
case REGTYPEOID:
case REGCONFIGOID:
case REGDICTIONARYOID:
case REGROLEOID:
case REGNAMESPACEOID:
*scaledvalue = convert_numeric_to_scalar(value, valuetypid,
&failure);
*scaledlobound = convert_numeric_to_scalar(lobound, boundstypid,
&failure);
*scaledhibound = convert_numeric_to_scalar(hibound, boundstypid,
&failure);
return !failure;
/*
* Built-in string types
*/
case CHAROID:
case BPCHAROID:
case VARCHAROID:
case TEXTOID:
case NAMEOID:
{
char *valstr = convert_string_datum(value, valuetypid,
collid, &failure);
char *lostr = convert_string_datum(lobound, boundstypid,
collid, &failure);
char *histr = convert_string_datum(hibound, boundstypid,
collid, &failure);
/*
* Bail out if any of the values is not of string type. We
* might leak converted strings for the other value(s), but
* that's not worth troubling over.
*/
if (failure)
return false;
convert_string_to_scalar(valstr, scaledvalue,
lostr, scaledlobound,
histr, scaledhibound);
pfree(valstr);
pfree(lostr);
pfree(histr);
return true;
}
/*
* Built-in bytea type
*/
case BYTEAOID:
{
/* We only support bytea vs bytea comparison */
if (boundstypid != BYTEAOID)
return false;
convert_bytea_to_scalar(value, scaledvalue,
lobound, scaledlobound,
hibound, scaledhibound);
return true;
}
/*
* Built-in time types
*/
case TIMESTAMPOID:
case TIMESTAMPTZOID:
case DATEOID:
case INTERVALOID:
case TIMEOID:
case TIMETZOID:
*scaledvalue = convert_timevalue_to_scalar(value, valuetypid,
&failure);
*scaledlobound = convert_timevalue_to_scalar(lobound, boundstypid,
&failure);
*scaledhibound = convert_timevalue_to_scalar(hibound, boundstypid,
&failure);
return !failure;
/*
* Built-in network types
*/
case INETOID:
case CIDROID:
case MACADDROID:
case MACADDR8OID:
*scaledvalue = convert_network_to_scalar(value, valuetypid,
&failure);
*scaledlobound = convert_network_to_scalar(lobound, boundstypid,
&failure);
*scaledhibound = convert_network_to_scalar(hibound, boundstypid,
&failure);
return !failure;
}
/* Don't know how to convert */
*scaledvalue = *scaledlobound = *scaledhibound = 0;
return false;
}
/*
* Do convert_to_scalar()'s work for any numeric data type.
*
* On failure (e.g., unsupported typid), set *failure to true;
* otherwise, that variable is not changed.
*/
static double
convert_numeric_to_scalar(Datum value, Oid typid, bool *failure)
{
switch (typid)
{
case BOOLOID:
return (double) DatumGetBool(value);
case INT2OID:
return (double) DatumGetInt16(value);
case INT4OID:
return (double) DatumGetInt32(value);
case INT8OID:
return (double) DatumGetInt64(value);
case FLOAT4OID:
return (double) DatumGetFloat4(value);
case FLOAT8OID:
return (double) DatumGetFloat8(value);
case NUMERICOID:
/* Note: out-of-range values will be clamped to +-HUGE_VAL */
return (double)
DatumGetFloat8(DirectFunctionCall1(numeric_float8_no_overflow,
value));
case OIDOID:
case REGPROCOID:
case REGPROCEDUREOID:
case REGOPEROID:
case REGOPERATOROID:
case REGCLASSOID:
case REGTYPEOID:
case REGCONFIGOID:
case REGDICTIONARYOID:
case REGROLEOID:
case REGNAMESPACEOID:
/* we can treat OIDs as integers... */
return (double) DatumGetObjectId(value);
}
*failure = true;
return 0;
}
/*
* Do convert_to_scalar()'s work for any character-string data type.
*
* String datatypes are converted to a scale that ranges from 0 to 1,
* where we visualize the bytes of the string as fractional digits.
*
* We do not want the base to be 256, however, since that tends to
* generate inflated selectivity estimates; few databases will have
* occurrences of all 256 possible byte values at each position.
* Instead, use the smallest and largest byte values seen in the bounds
* as the estimated range for each byte, after some fudging to deal with
* the fact that we probably aren't going to see the full range that way.
*
* An additional refinement is that we discard any common prefix of the
* three strings before computing the scaled values. This allows us to
* "zoom in" when we encounter a narrow data range. An example is a phone
* number database where all the values begin with the same area code.
* (Actually, the bounds will be adjacent histogram-bin-boundary values,
* so this is more likely to happen than you might think.)
*/
static void
convert_string_to_scalar(char *value,
double *scaledvalue,
char *lobound,
double *scaledlobound,
char *hibound,
double *scaledhibound)
{
int rangelo,
rangehi;
char *sptr;
rangelo = rangehi = (unsigned char) hibound[0];
for (sptr = lobound; *sptr; sptr++)
{
if (rangelo > (unsigned char) *sptr)
rangelo = (unsigned char) *sptr;
if (rangehi < (unsigned char) *sptr)
rangehi = (unsigned char) *sptr;
}
for (sptr = hibound; *sptr; sptr++)
{
if (rangelo > (unsigned char) *sptr)
rangelo = (unsigned char) *sptr;
if (rangehi < (unsigned char) *sptr)
rangehi = (unsigned char) *sptr;
}
/* If range includes any upper-case ASCII chars, make it include all */
if (rangelo <= 'Z' && rangehi >= 'A')
{
if (rangelo > 'A')
rangelo = 'A';
if (rangehi < 'Z')
rangehi = 'Z';
}
/* Ditto lower-case */
if (rangelo <= 'z' && rangehi >= 'a')
{
if (rangelo > 'a')
rangelo = 'a';
if (rangehi < 'z')
rangehi = 'z';
}
/* Ditto digits */
if (rangelo <= '9' && rangehi >= '0')
{
if (rangelo > '0')
rangelo = '0';
if (rangehi < '9')
rangehi = '9';
}
/*
* If range includes less than 10 chars, assume we have not got enough
* data, and make it include regular ASCII set.
*/
if (rangehi - rangelo < 9)
{
rangelo = ' ';
rangehi = 127;
}
/*
* Now strip any common prefix of the three strings.
*/
while (*lobound)
{
if (*lobound != *hibound || *lobound != *value)
break;
lobound++, hibound++, value++;
}
/*
* Now we can do the conversions.
*/
*scaledvalue = convert_one_string_to_scalar(value, rangelo, rangehi);
*scaledlobound = convert_one_string_to_scalar(lobound, rangelo, rangehi);
*scaledhibound = convert_one_string_to_scalar(hibound, rangelo, rangehi);
}
static double
convert_one_string_to_scalar(char *value, int rangelo, int rangehi)
{
int slen = strlen(value);
double num,
denom,
base;
if (slen <= 0)
return 0.0; /* empty string has scalar value 0 */
/*
* There seems little point in considering more than a dozen bytes from
* the string. Since base is at least 10, that will give us nominal
* resolution of at least 12 decimal digits, which is surely far more
* precision than this estimation technique has got anyway (especially in
* non-C locales). Also, even with the maximum possible base of 256, this
* ensures denom cannot grow larger than 256^13 = 2.03e31, which will not
* overflow on any known machine.
*/
if (slen > 12)
slen = 12;
/* Convert initial characters to fraction */
base = rangehi - rangelo + 1;
num = 0.0;
denom = base;
while (slen-- > 0)
{
int ch = (unsigned char) *value++;
if (ch < rangelo)
ch = rangelo - 1;
else if (ch > rangehi)
ch = rangehi + 1;
num += ((double) (ch - rangelo)) / denom;
denom *= base;
}
return num;
}
/*
* Convert a string-type Datum into a palloc'd, null-terminated string.
*
* On failure (e.g., unsupported typid), set *failure to true;
* otherwise, that variable is not changed. (We'll return NULL on failure.)
*
* When using a non-C locale, we must pass the string through strxfrm()
* before continuing, so as to generate correct locale-specific results.
*/
static char *
convert_string_datum(Datum value, Oid typid, Oid collid, bool *failure)
{
char *val;
switch (typid)
{
case CHAROID:
val = (char *) palloc(2);
val[0] = DatumGetChar(value);
val[1] = '\0';
break;
case BPCHAROID:
case VARCHAROID:
case TEXTOID:
val = TextDatumGetCString(value);
break;
case NAMEOID:
{
NameData *nm = (NameData *) DatumGetPointer(value);
val = pstrdup(NameStr(*nm));
break;
}
default:
*failure = true;
return NULL;
}
if (!lc_collate_is_c(collid))
{
char *xfrmstr;
size_t xfrmlen;
size_t xfrmlen2 PG_USED_FOR_ASSERTS_ONLY;
/*
* XXX: We could guess at a suitable output buffer size and only call
* strxfrm twice if our guess is too small.
*
* XXX: strxfrm doesn't support UTF-8 encoding on Win32, it can return
* bogus data or set an error. This is not really a problem unless it
* crashes since it will only give an estimation error and nothing
* fatal.
*/
#if _MSC_VER == 1400 /* VS.Net 2005 */
/*
*
* http://connect.microsoft.com/VisualStudio/feedback/ViewFeedback.aspx?FeedbackID=99694
*/
{
char x[1];
xfrmlen = strxfrm(x, val, 0);
}
#else
xfrmlen = strxfrm(NULL, val, 0);
#endif
#ifdef WIN32
/*
* On Windows, strxfrm returns INT_MAX when an error occurs. Instead
* of trying to allocate this much memory (and fail), just return the
* original string unmodified as if we were in the C locale.
*/
if (xfrmlen == INT_MAX)
return val;
#endif
xfrmstr = (char *) palloc(xfrmlen + 1);
xfrmlen2 = strxfrm(xfrmstr, val, xfrmlen + 1);
/*
* Some systems (e.g., glibc) can return a smaller value from the
* second call than the first; thus the Assert must be <= not ==.
*/
Assert(xfrmlen2 <= xfrmlen);
pfree(val);
val = xfrmstr;
}
return val;
}
/*
* Do convert_to_scalar()'s work for any bytea data type.
*
* Very similar to the old convert_string_to_scalar except we can't assume
* null-termination and therefore pass explicit lengths around.
*
* Also, assumptions about likely "normal" ranges of characters have been
* removed - a data range of 0..255 is always used, for now. (Perhaps
* someday we will add information about actual byte data range to
* pg_statistic.)
*/
static void
convert_bytea_to_scalar(Datum value,
double *scaledvalue,
Datum lobound,
double *scaledlobound,
Datum hibound,
double *scaledhibound)
{
bytea *valuep = DatumGetByteaPP(value);
bytea *loboundp = DatumGetByteaPP(lobound);
bytea *hiboundp = DatumGetByteaPP(hibound);
int rangelo,
rangehi,
valuelen = VARSIZE_ANY_EXHDR(valuep),
loboundlen = VARSIZE_ANY_EXHDR(loboundp),
hiboundlen = VARSIZE_ANY_EXHDR(hiboundp),
i,
minlen;
unsigned char *valstr = (unsigned char *) VARDATA_ANY(valuep);
unsigned char *lostr = (unsigned char *) VARDATA_ANY(loboundp);
unsigned char *histr = (unsigned char *) VARDATA_ANY(hiboundp);
/*
* Assume bytea data is uniformly distributed across all byte values.
*/
rangelo = 0;
rangehi = 255;
/*
* Now strip any common prefix of the three strings.
*/
minlen = Min(Min(valuelen, loboundlen), hiboundlen);
for (i = 0; i < minlen; i++)
{
if (*lostr != *histr || *lostr != *valstr)
break;
lostr++, histr++, valstr++;
loboundlen--, hiboundlen--, valuelen--;
}
/*
* Now we can do the conversions.
*/
*scaledvalue = convert_one_bytea_to_scalar(valstr, valuelen, rangelo, rangehi);
*scaledlobound = convert_one_bytea_to_scalar(lostr, loboundlen, rangelo, rangehi);
*scaledhibound = convert_one_bytea_to_scalar(histr, hiboundlen, rangelo, rangehi);
}
static double
convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
int rangelo, int rangehi)
{
double num,
denom,
base;
if (valuelen <= 0)
return 0.0; /* empty string has scalar value 0 */
/*
* Since base is 256, need not consider more than about 10 chars (even
* this many seems like overkill)
*/
if (valuelen > 10)
valuelen = 10;
/* Convert initial characters to fraction */
base = rangehi - rangelo + 1;
num = 0.0;
denom = base;
while (valuelen-- > 0)
{
int ch = *value++;
if (ch < rangelo)
ch = rangelo - 1;
else if (ch > rangehi)
ch = rangehi + 1;
num += ((double) (ch - rangelo)) / denom;
denom *= base;
}
return num;
}
/*
* Do convert_to_scalar()'s work for any timevalue data type.
*
* On failure (e.g., unsupported typid), set *failure to true;
* otherwise, that variable is not changed.
*/
double
convert_timevalue_to_scalar(Datum value, Oid typid, bool *failure)
{
switch (typid)
{
case TIMESTAMPOID:
return DatumGetTimestamp(value);
case TIMESTAMPTZOID:
return DatumGetTimestampTz(value);
case DATEOID:
return date2timestamp_no_overflow(DatumGetDateADT(value));
case INTERVALOID:
{
Interval *interval = DatumGetIntervalP(value);
/*
* Convert the month part of Interval to days using assumed
* average month length of 365.25/12.0 days. Not too
* accurate, but plenty good enough for our purposes.
*/
return interval->time + interval->day * (double) USECS_PER_DAY +
interval->month * ((DAYS_PER_YEAR / (double) MONTHS_PER_YEAR) * USECS_PER_DAY);
}
case TIMEOID:
return DatumGetTimeADT(value);
case TIMETZOID:
{
TimeTzADT *timetz = DatumGetTimeTzADTP(value);
/* use GMT-equivalent time */
return (double) (timetz->time + (timetz->zone * 1000000.0));
}
}
*failure = true;
return 0;
}
/*
* get_restriction_variable
* Examine the args of a restriction clause to see if it's of the
* form (variable op pseudoconstant) or (pseudoconstant op variable),
* where "variable" could be either a Var or an expression in vars of a
* single relation. If so, extract information about the variable,
* and also indicate which side it was on and the other argument.
*
* Inputs:
* root: the planner info
* args: clause argument list
* varRelid: see specs for restriction selectivity functions
*
* Outputs: (these are valid only if true is returned)
* *vardata: gets information about variable (see examine_variable)
* *other: gets other clause argument, aggressively reduced to a constant
* *varonleft: set true if variable is on the left, false if on the right
*
* Returns true if a variable is identified, otherwise false.
*
* Note: if there are Vars on both sides of the clause, we must fail, because
* callers are expecting that the other side will act like a pseudoconstant.
*/
bool
get_restriction_variable(PlannerInfo *root, List *args, int varRelid,
VariableStatData *vardata, Node **other,
bool *varonleft)
{
Node *left,
*right;
VariableStatData rdata;
/* Fail if not a binary opclause (probably shouldn't happen) */
if (list_length(args) != 2)
return false;
left = (Node *) linitial(args);
right = (Node *) lsecond(args);
/*
* Examine both sides. Note that when varRelid is nonzero, Vars of other
* relations will be treated as pseudoconstants.
*/
examine_variable(root, left, varRelid, vardata);
examine_variable(root, right, varRelid, &rdata);
/*
* If one side is a variable and the other not, we win.
*/
if (vardata->rel && rdata.rel == NULL)
{
*varonleft = true;
*other = estimate_expression_value(root, rdata.var);
/* Assume we need no ReleaseVariableStats(rdata) here */
return true;
}
if (vardata->rel == NULL && rdata.rel)
{
*varonleft = false;
*other = estimate_expression_value(root, vardata->var);
/* Assume we need no ReleaseVariableStats(*vardata) here */
*vardata = rdata;
return true;
}
/* Oops, clause has wrong structure (probably var op var) */
ReleaseVariableStats(*vardata);
ReleaseVariableStats(rdata);
return false;
}
/*
* get_join_variables
* Apply examine_variable() to each side of a join clause.
* Also, attempt to identify whether the join clause has the same
* or reversed sense compared to the SpecialJoinInfo.
*
* We consider the join clause "normal" if it is "lhs_var OP rhs_var",
* or "reversed" if it is "rhs_var OP lhs_var". In complicated cases
* where we can't tell for sure, we default to assuming it's normal.
*/
void
get_join_variables(PlannerInfo *root, List *args, SpecialJoinInfo *sjinfo,
VariableStatData *vardata1, VariableStatData *vardata2,
bool *join_is_reversed)
{
Node *left,
*right;
if (list_length(args) != 2)
elog(ERROR, "join operator should take two arguments");
left = (Node *) linitial(args);
right = (Node *) lsecond(args);
examine_variable(root, left, 0, vardata1);
examine_variable(root, right, 0, vardata2);
if (vardata1->rel &&
bms_is_subset(vardata1->rel->relids, sjinfo->syn_righthand))
*join_is_reversed = true; /* var1 is on RHS */
else if (vardata2->rel &&
bms_is_subset(vardata2->rel->relids, sjinfo->syn_lefthand))
*join_is_reversed = true; /* var2 is on LHS */
else
*join_is_reversed = false;
}
/*
* This method returns a pointer to the largest child relation for an inherited (incl partitioned)
* relation. If there are multiple levels in the hierarchy, we delve down recursively till we
* find the largest (as determined from the path structure).
* Input: a partitioned table
* Output: largest child partition. If there are no child partition because all of them have been eliminated, then
* returns NULL.
*/
static RelOptInfo *
largest_child_relation(PlannerInfo *root, Path *path, bool recursing)
{
List *subpaths;
ListCell *subpath_lc;
RelOptInfo *largest_child_in_subpath = NULL;
double max_rows = -1.0;
/* Guard against stack overflow due to overly complex inheritance trees */
check_stack_depth();
while (IsA(path, ProjectionPath))
path = ((ProjectionPath *) path)->subpath;
/*
* Add the children of an Append or MergeAppend path to the list
* of paths to process.
*/
if (IsA(path, AppendPath))
{
subpaths = ((AppendPath *) path)->subpaths;
}
else if (IsA(path, MergeAppendPath))
{
subpaths = ((MergeAppendPath *) path)->subpaths;
}
else
{
if (recursing)
return path->parent;
else
return NULL;
}
foreach(subpath_lc, subpaths)
{
Path *subpath = lfirst(subpath_lc);
RelOptInfo *candidate_child;
candidate_child = largest_child_relation(root, subpath, true);
if (candidate_child && candidate_child->rows > max_rows)
{
max_rows = candidate_child->rows;
largest_child_in_subpath = candidate_child;
}
}
return largest_child_in_subpath;
}
/*
* The purpose of this method is to make the statistics (on a specific column) of a child partition
* representative of the parent relation. This entails the following assumptions:
* 1. if ndistinct<=-1.0 in child partition, the column is a unique column in the child partition. We
* expect the column to remain distinct in the master as well.
* 2. if -1.0 < ndistinct < 0.0, the absolute number of ndistinct values in the child partition is a fraction
* of the number of rows in the partition. We expect that the absolute number of ndistinct in the master
* to stay the same. Therefore, we convert this to a positive number.
* The method get_variable_numdistinct will multiply this by the number of tuples in the master relation.
* 3. if ndistinct is positive, it indicates a small absolute number of distinct values. We expect these
* values to be repeated in all partitions. Therefore, we expect no change in the ndistinct in the master.
*
* Input:
* statsTuple, which is a heaptuple representing statistics on a child relation. It expects statstuple to be non-null.
* scalefactor, which is in the range (0.0,1.0]
*
* Output:
* This method modifies the tuple passed to it.
*/
static void inline adjust_partition_table_statistic_for_parent(HeapTuple statsTuple, double childtuples)
{
Form_pg_statistic stats;
Assert(HeapTupleIsValid(statsTuple));
stats = (Form_pg_statistic) GETSTRUCT(statsTuple);
if (stats->stadistinct <= -1.0)
{
/*
* Case 1 as described above.
*/
return;
}
else if (stats->stadistinct < 0.0)
{
/*
* Case 2 as described above.
*/
stats->stadistinct = ((double) -1.0) * stats->stadistinct * childtuples;
}
else
{
/**
* Case 3 as described above.
*/
return;
}
}
/*
* examine_variable
* Try to look up statistical data about an expression.
* Fill in a VariableStatData struct to describe the expression.
*
* Inputs:
* root: the planner info
* node: the expression tree to examine
* varRelid: see specs for restriction selectivity functions
*
* Outputs: *vardata is filled as follows:
* var: the input expression (with any binary relabeling stripped, if
* it is or contains a variable; but otherwise the type is preserved)
* rel: RelOptInfo for relation containing variable; NULL if expression
* contains no Vars (NOTE this could point to a RelOptInfo of a
* subquery, not one in the current query).
* statsTuple: the pg_statistic entry for the variable, if one exists;
* otherwise NULL.
* freefunc: pointer to a function to release statsTuple with.
* vartype: exposed type of the expression; this should always match
* the declared input type of the operator we are estimating for.
* atttype, atttypmod: actual type/typmod of the "var" expression. This is
* commonly the same as the exposed type of the variable argument,
* but can be different in binary-compatible-type cases.
* isunique: true if we were able to match the var to a unique index or a
* single-column DISTINCT clause, implying its values are unique for
* this query. (Caution: this should be trusted for statistical
* purposes only, since we do not check indimmediate nor verify that
* the exact same definition of equality applies.)
* acl_ok: true if current user has permission to read the column(s)
* underlying the pg_statistic entry. This is consulted by
* statistic_proc_security_check().
*
* Caller is responsible for doing ReleaseVariableStats() before exiting.
*/
void
examine_variable(PlannerInfo *root, Node *node, int varRelid,
VariableStatData *vardata)
{
Node *basenode;
Relids varnos;
RelOptInfo *onerel;
/* Make sure we don't return dangling pointers in vardata */
MemSet(vardata, 0, sizeof(VariableStatData));
/* Save the exposed type of the expression */
vardata->vartype = exprType(node);
vardata->numdistinctFromPrimaryKey = -1.0; /* ignore by default*/
/* Look inside any binary-compatible relabeling */
if (IsA(node, RelabelType))
basenode = (Node *) ((RelabelType *) node)->arg;
else
basenode = node;
/* Fast path for a simple Var */
if (IsA(basenode, Var) &&
(varRelid == 0 || varRelid == ((Var *) basenode)->varno))
{
Var *var = (Var *) basenode;
/* Set up result fields other than the stats tuple */
vardata->var = basenode; /* return Var without relabeling */
vardata->rel = find_base_rel(root, var->varno);
vardata->atttype = var->vartype;
vardata->atttypmod = var->vartypmod;
vardata->isunique = has_unique_index(vardata->rel, var->varattno);
/* Try to locate some stats */
examine_simple_variable(root, var, vardata);
return;
}
/*
* Okay, it's a more complicated expression. Determine variable
* membership. Note that when varRelid isn't zero, only vars of that
* relation are considered "real" vars.
*/
varnos = pull_varnos(basenode);
onerel = NULL;
switch (bms_membership(varnos))
{
case BMS_EMPTY_SET:
/* No Vars at all ... must be pseudo-constant clause */
break;
case BMS_SINGLETON:
if (varRelid == 0 || bms_is_member(varRelid, varnos))
{
onerel = find_base_rel(root,
(varRelid ? varRelid : bms_singleton_member(varnos)));
vardata->rel = onerel;
node = basenode; /* strip any relabeling */
}
/* else treat it as a constant */
break;
case BMS_MULTIPLE:
if (varRelid == 0)
{
/* treat it as a variable of a join relation */
vardata->rel = find_join_rel(root, varnos);
node = basenode; /* strip any relabeling */
}
else if (bms_is_member(varRelid, varnos))
{
/* ignore the vars belonging to other relations */
vardata->rel = find_base_rel(root, varRelid);
node = basenode; /* strip any relabeling */
/* note: no point in expressional-index search here */
}
/* else treat it as a constant */
break;
}
bms_free(varnos);
vardata->var = node;
vardata->atttype = exprType(node);
vardata->atttypmod = exprTypmod(node);
if (onerel)
{
/*
* We have an expression in vars of a single relation. Try to match
* it to expressional index columns, in hopes of finding some
* statistics.
*
* Note that we consider all index columns including INCLUDE columns,
* since there could be stats for such columns. But the test for
* uniqueness needs to be warier.
*
* XXX it's conceivable that there are multiple matches with different
* index opfamilies; if so, we need to pick one that matches the
* operator we are estimating for. FIXME later.
*/
ListCell *ilist;
foreach(ilist, onerel->indexlist)
{
IndexOptInfo *index = (IndexOptInfo *) lfirst(ilist);
ListCell *indexpr_item;
int pos;
indexpr_item = list_head(index->indexprs);
if (indexpr_item == NULL)
continue; /* no expressions here... */
for (pos = 0; pos < index->ncolumns; pos++)
{
if (index->indexkeys[pos] == 0)
{
Node *indexkey;
if (indexpr_item == NULL)
elog(ERROR, "too few entries in indexprs list");
indexkey = (Node *) lfirst(indexpr_item);
if (indexkey && IsA(indexkey, RelabelType))
indexkey = (Node *) ((RelabelType *) indexkey)->arg;
if (equal(node, indexkey))
{
/*
* Found a match ... is it a unique index? Tests here
* should match has_unique_index().
*/
if (index->unique &&
index->nkeycolumns == 1 &&
pos == 0 &&
(index->indpred == NIL || index->predOK))
vardata->isunique = true;
/*
* Has it got stats? We only consider stats for
* non-partial indexes, since partial indexes probably
* don't reflect whole-relation statistics; the above
* check for uniqueness is the only info we take from
* a partial index.
*
* An index stats hook, however, must make its own
* decisions about what to do with partial indexes.
*/
if (get_index_stats_hook &&
(*get_index_stats_hook) (root, index->indexoid,
pos + 1, vardata))
{
/*
* The hook took control of acquiring a stats
* tuple. If it did supply a tuple, it'd better
* have supplied a freefunc.
*/
if (HeapTupleIsValid(vardata->statsTuple) &&
!vardata->freefunc)
elog(ERROR, "no function provided to release variable stats with");
}
else if (index->indpred == NIL)
{
vardata->statsTuple =
SearchSysCache3(STATRELATTINH,
ObjectIdGetDatum(index->indexoid),
Int16GetDatum(pos + 1),
BoolGetDatum(false));
vardata->freefunc = ReleaseSysCache;
if (HeapTupleIsValid(vardata->statsTuple))
{
/* Get index's table for permission check */
RangeTblEntry *rte;
Oid userid;
rte = planner_rt_fetch(index->rel->relid, root);
Assert(rte->rtekind == RTE_RELATION);
/*
* Use checkAsUser if it's set, in case we're
* accessing the table via a view.
*/
userid = rte->checkAsUser ? rte->checkAsUser : GetUserId();
/*
* For simplicity, we insist on the whole
* table being selectable, rather than trying
* to identify which column(s) the index
* depends on. Also require all rows to be
* selectable --- there must be no
* securityQuals from security barrier views
* or RLS policies.
*/
vardata->acl_ok =
rte->securityQuals == NIL &&
(pg_class_aclcheck(rte->relid, userid,
ACL_SELECT) == ACLCHECK_OK);
}
else
{
/* suppress leakproofness checks later */
vardata->acl_ok = true;
}
}
if (vardata->statsTuple)
break;
}
indexpr_item = lnext(indexpr_item);
}
}
if (HeapTupleIsValid(getStatsTuple(vardata)))
break;
}
}
}
/*
* examine_simple_variable
* Handle a simple Var for examine_variable
*
* This is split out as a subroutine so that we can recurse to deal with
* Vars referencing subqueries.
*
* We already filled in all the fields of *vardata except for the stats tuple.
*/
static void
examine_simple_variable(PlannerInfo *root, Var *var,
VariableStatData *vardata)
{
RangeTblEntry *rte = root->simple_rte_array[var->varno];
Assert(IsA(rte, RangeTblEntry));
/*
* If this attribute has a foreign key relationship, then first look
* at primary key statistics. If there exist stats on that attribute,
* we utilize those. If not, continue.
*/
if (gp_statistics_use_fkeys)
{
Oid pkrelid = InvalidOid;
AttrNumber pkattno = -1;
if (ConstraintGetPrimaryKeyOf(rte->relid, var->varattno, &pkrelid, &pkattno))
{
HeapTuple pkStatsTuple;
/* SELECT reltuples FROM pg_class */
pkStatsTuple = SearchSysCache1(RELOID, ObjectIdGetDatum(pkrelid));
if (HeapTupleIsValid(pkStatsTuple))
{
Form_pg_class classForm = (Form_pg_class) GETSTRUCT(pkStatsTuple);
if (classForm->reltuples > 0)
{
vardata->numdistinctFromPrimaryKey = classForm->reltuples;
}
}
ReleaseSysCache(pkStatsTuple);
}
}
if (get_relation_stats_hook &&
(*get_relation_stats_hook) (root, rte, var->varattno, vardata))
{
/*
* The hook took control of acquiring a stats tuple. If it did supply
* a tuple, it'd better have supplied a freefunc.
*/
if (HeapTupleIsValid(vardata->statsTuple) &&
!vardata->freefunc)
elog(ERROR, "no function provided to release variable stats with");
}
else if (rte->rtekind == RTE_RELATION)
{
/*
* Plain table or parent of an inheritance appendrel, so look up the
* column in pg_statistic
*/
vardata->statsTuple = NULL;
if (rte->inh && gp_statistics_pullup_from_child_partition)
{
/*
* GPDB: #13467
* If var->varattno is 0 (e.g.,SELECT DISTINCT <Table_name> FROM <Table_name>),
* we will get an ERROR when we invoke get_attname with missing_ok == false,
* so the NULL string is all we need.
*/
bool missing_ok = var->varattno == 0 ? true : false;
const char *attname = get_attname(rte->relid, var->varattno, missing_ok);
/*
* The GUC gp_statistics_pullup_from_child_partition is
* set false defaultly. If it is true, we always try
* to use largest child's stat.
*/
try_fetch_largest_child_stats(root, var->varno, attname, vardata);
}
if (vardata->statsTuple == NULL)
{
vardata->statsTuple = SearchSysCache3(STATRELATTINH,
ObjectIdGetDatum(rte->relid),
Int16GetDatum(var->varattno),
BoolGetDatum(rte->inh));
vardata->freefunc = ReleaseSysCache;
}
if (HeapTupleIsValid(vardata->statsTuple))
{
Oid userid;
/*
* Check if user has permission to read this column. We require
* all rows to be accessible, so there must be no securityQuals
* from security barrier views or RLS policies. Use checkAsUser
* if it's set, in case we're accessing the table via a view.
*/
userid = rte->checkAsUser ? rte->checkAsUser : GetUserId();
vardata->acl_ok =
rte->securityQuals == NIL &&
((pg_class_aclcheck(rte->relid, userid,
ACL_SELECT) == ACLCHECK_OK) ||
(pg_attribute_aclcheck(rte->relid, var->varattno, userid,
ACL_SELECT) == ACLCHECK_OK));
}
else
{
/* suppress any possible leakproofness checks later */
vardata->acl_ok = true;
}
}
else if (rte->rtekind == RTE_SUBQUERY && !rte->inh)
{
/*
* Plain subquery (not one that was converted to an appendrel).
*/
Query *subquery = rte->subquery;
RelOptInfo *rel;
TargetEntry *ste;
/*
* Punt if it's a whole-row var rather than a plain column reference.
*/
if (var->varattno == InvalidAttrNumber)
return;
/*
* Punt if subquery uses set operations or GROUP BY, as these will
* mash underlying columns' stats beyond recognition. (Set ops are
* particularly nasty; if we forged ahead, we would return stats
* relevant to only the leftmost subselect...) DISTINCT is also
* problematic, but we check that later because there is a possibility
* of learning something even with it.
*/
if (subquery->setOperations ||
subquery->groupClause)
return;
/*
* OK, fetch RelOptInfo for subquery. Note that we don't change the
* rel returned in vardata, since caller expects it to be a rel of the
* caller's query level. Because we might already be recursing, we
* can't use that rel pointer either, but have to look up the Var's
* rel afresh.
*/
rel = find_base_rel(root, var->varno);
/* If the subquery hasn't been planned yet, we have to punt */
if (rel->subroot == NULL)
return;
Assert(IsA(rel->subroot, PlannerInfo));
/*
* Switch our attention to the subquery as mangled by the planner. It
* was okay to look at the pre-planning version for the tests above,
* but now we need a Var that will refer to the subroot's live
* RelOptInfos. For instance, if any subquery pullup happened during
* planning, Vars in the targetlist might have gotten replaced, and we
* need to see the replacement expressions.
*/
subquery = rel->subroot->parse;
Assert(IsA(subquery, Query));
/* Get the subquery output expression referenced by the upper Var */
ste = get_tle_by_resno(subquery->targetList, var->varattno);
if (ste == NULL || ste->resjunk)
elog(ERROR, "subquery %s does not have attribute %d",
rte->eref->aliasname, var->varattno);
var = (Var *) ste->expr;
/*
* If subquery uses DISTINCT, we can't make use of any stats for the
* variable ... but, if it's the only DISTINCT column, we are entitled
* to consider it unique. We do the test this way so that it works
* for cases involving DISTINCT ON.
*/
if (subquery->distinctClause)
{
if (list_length(subquery->distinctClause) == 1 &&
targetIsInSortList(ste, InvalidOid, subquery->distinctClause))
vardata->isunique = true;
/* cannot go further */
return;
}
/*
* If the sub-query originated from a view with the security_barrier
* attribute, we must not look at the variable's statistics, though it
* seems all right to notice the existence of a DISTINCT clause. So
* stop here.
*
* This is probably a harsher restriction than necessary; it's
* certainly OK for the selectivity estimator (which is a C function,
* and therefore omnipotent anyway) to look at the statistics. But
* many selectivity estimators will happily *invoke the operator
* function* to try to work out a good estimate - and that's not OK.
* So for now, don't dig down for stats.
*/
if (rte->security_barrier)
return;
/* Can only handle a simple Var of subquery's query level */
if (var && IsA(var, Var) &&
var->varlevelsup == 0)
{
/*
* OK, recurse into the subquery. Note that the original setting
* of vardata->isunique (which will surely be false) is left
* unchanged in this situation. That's what we want, since even
* if the underlying column is unique, the subquery may have
* joined to other tables in a way that creates duplicates.
*/
examine_simple_variable(rel->subroot, var, vardata);
}
}
else
{
/*
* Otherwise, the Var comes from a FUNCTION, VALUES, or CTE RTE. (We
* won't see RTE_JOIN here because join alias Vars have already been
* flattened.) There's not much we can do with function outputs, but
* maybe someday try to be smarter about VALUES and/or CTEs.
*/
}
}
/*
* Check whether it is permitted to call func_oid passing some of the
* pg_statistic data in vardata. We allow this either if the user has SELECT
* privileges on the table or column underlying the pg_statistic data or if
* the function is marked leak-proof.
*/
bool
statistic_proc_security_check(VariableStatData *vardata, Oid func_oid)
{
if (vardata->acl_ok)
return true;
if (!OidIsValid(func_oid))
return false;
if (get_func_leakproof(func_oid))
return true;
ereport(DEBUG2,
(errmsg_internal("not using statistics because function \"%s\" is not leak-proof",
get_func_name(func_oid))));
return false;
}
/*
* get_variable_numdistinct
* Estimate the number of distinct values of a variable.
*
* vardata: results of examine_variable
* *isdefault: set to true if the result is a default rather than based on
* anything meaningful.
*
* NB: be careful to produce a positive integral result, since callers may
* compare the result to exact integer counts, or might divide by it.
*/
double
get_variable_numdistinct(VariableStatData *vardata, bool *isdefault)
{
double stadistinct;
double stanullfrac = 0.0;
double ntuples;
*isdefault = false;
/**
* If we have an estimate from the primary key, then that is the most accurate value.
*/
if (gp_statistics_use_fkeys &&
vardata->numdistinctFromPrimaryKey > 0.0)
{
return vardata->numdistinctFromPrimaryKey;
}
/*
* Determine the stadistinct value to use. There are cases where we can
* get an estimate even without a pg_statistic entry, or can get a better
* value than is in pg_statistic. Grab stanullfrac too if we can find it
* (otherwise, assume no nulls, for lack of any better idea).
*/
if (HeapTupleIsValid(getStatsTuple(vardata)))
{
/* Use the pg_statistic entry */
Form_pg_statistic stats;
HeapTuple tp = getStatsTuple(vardata);
stats = (Form_pg_statistic) GETSTRUCT(tp);
stadistinct = stats->stadistinct;
stanullfrac = stats->stanullfrac;
}
else if (vardata->vartype == BOOLOID)
{
/*
* Special-case boolean columns: presumably, two distinct values.
*
* Are there any other datatypes we should wire in special estimates
* for?
*/
stadistinct = 2.0;
}
else if (vardata->rel && vardata->rel->rtekind == RTE_VALUES)
{
/*
* If the Var represents a column of a VALUES RTE, assume it's unique.
* This could of course be very wrong, but it should tend to be true
* in well-written queries. We could consider examining the VALUES'
* contents to get some real statistics; but that only works if the
* entries are all constants, and it would be pretty expensive anyway.
*/
stadistinct = -1.0; /* unique (and all non null) */
}
else
{
/*
* We don't keep statistics for system columns, but in some cases we
* can infer distinctness anyway.
*/
if (vardata->var && IsA(vardata->var, Var))
{
switch (((Var *) vardata->var)->varattno)
{
case SelfItemPointerAttributeNumber:
stadistinct = -1.0; /* unique (and all non null) */
break;
case TableOidAttributeNumber:
stadistinct = 1.0; /* only 1 value */
break;
case GpSegmentIdAttributeNumber: /*CDB*/
stadistinct = getgpsegmentCount();
break;
default:
stadistinct = 0.0; /* means "unknown" */
break;
}
}
else
stadistinct = 0.0; /* means "unknown" */
/*
* XXX consider using estimate_num_groups on expressions?
*/
}
/*
* If there is a unique index or DISTINCT clause for the variable, assume
* it is unique no matter what pg_statistic says; the statistics could be
* out of date, or we might have found a partial unique index that proves
* the var is unique for this query. However, we'd better still believe
* the null-fraction statistic.
*/
if (vardata->isunique)
stadistinct = -1.0 * (1.0 - stanullfrac);
/*
* If we had an absolute estimate, use that.
*/
if (stadistinct > 0.0)
return clamp_row_est(stadistinct);
/*
* Otherwise we need to get the relation size; punt if not available.
*/
if (vardata->rel == NULL)
{
*isdefault = true;
return DEFAULT_NUM_DISTINCT;
}
ntuples = vardata->rel->tuples;
if (ntuples <= 0.0)
{
*isdefault = true;
return DEFAULT_NUM_DISTINCT;
}
/*
* If we had a relative estimate, use that.
*/
if (stadistinct < 0.0)
return clamp_row_est(-stadistinct * ntuples);
/*
* With no data, estimate ndistinct = ntuples if the table is small, else
* use default. We use DEFAULT_NUM_DISTINCT as the cutoff for "small" so
* that the behavior isn't discontinuous.
*/
if (ntuples < DEFAULT_NUM_DISTINCT)
return clamp_row_est(ntuples);
*isdefault = true;
return DEFAULT_NUM_DISTINCT;
}
/*
* get_variable_range
* Estimate the minimum and maximum value of the specified variable.
* If successful, store values in *min and *max, and return true.
* If no data available, return false.
*
* sortop is the "<" comparison operator to use. This should generally
* be "<" not ">", as only the former is likely to be found in pg_statistic.
*/
static bool
get_variable_range(PlannerInfo *root, VariableStatData *vardata, Oid sortop,
Datum *min, Datum *max)
{
Datum tmin = 0;
Datum tmax = 0;
bool have_data = false;
int16 typLen;
bool typByVal;
Oid opfuncoid;
AttStatsSlot sslot;
int i;
HeapTuple tp = getStatsTuple(vardata);
/*
* XXX It's very tempting to try to use the actual column min and max, if
* we can get them relatively-cheaply with an index probe. However, since
* this function is called many times during join planning, that could
* have unpleasant effects on planning speed. Need more investigation
* before enabling this.
*/
#ifdef NOT_USED
if (get_actual_variable_range(root, vardata, sortop, min, max))
return true;
#endif
if (!HeapTupleIsValid(tp))
{
/* no stats available, so default result */
return false;
}
/*
* If we can't apply the sortop to the stats data, just fail. In
* principle, if there's a histogram and no MCVs, we could return the
* histogram endpoints without ever applying the sortop ... but it's
* probably not worth trying, because whatever the caller wants to do with
* the endpoints would likely fail the security check too.
*/
if (!statistic_proc_security_check(vardata,
(opfuncoid = get_opcode(sortop))))
return false;
get_typlenbyval(vardata->atttype, &typLen, &typByVal);
/*
* If there is a histogram, grab the first and last values.
*
* If there is a histogram that is sorted with some other operator than
* the one we want, fail --- this suggests that there is data we can't
* use.
*/
if (get_attstatsslot(&sslot, vardata->statsTuple,
STATISTIC_KIND_HISTOGRAM, sortop,
ATTSTATSSLOT_VALUES))
{
/*
* GPDB: GPDB allows users to modify pg_statistics.stavalues with
* UPDATEs (PostgreSQL complaints about the table row type not
* matching). So just in case that the type of the values in
* pg_statistics isn't what we'd expect, give an error rather than
* crash. That shouldn't happen, but better safe than sorry.
*
* GPDB_91_MERGE_FIXME: this is the second place we've added this. Does
* it need to be pulled into get_attstatsslot() itself?
*/
if (!IsBinaryCoercible(sslot.valuetype, vardata->atttype))
elog(ERROR, "invalid histogram of type %s, for attribute of type %s",
format_type_be(sslot.valuetype), format_type_be(vardata->atttype));
if (sslot.nvalues > 0)
{
tmin = datumCopy(sslot.values[0], typByVal, typLen);
tmax = datumCopy(sslot.values[sslot.nvalues - 1], typByVal, typLen);
have_data = true;
}
free_attstatsslot(&sslot);
}
else if (get_attstatsslot(&sslot, vardata->statsTuple,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
0))
{
free_attstatsslot(&sslot);
return false;
}
/*
* If we have most-common-values info, look for extreme MCVs. This is
* needed even if we also have a histogram, since the histogram excludes
* the MCVs. However, usually the MCVs will not be the extreme values, so
* avoid unnecessary data copying.
*/
if (get_attstatsslot(&sslot, vardata->statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_VALUES))
{
bool tmin_is_mcv = false;
bool tmax_is_mcv = false;
FmgrInfo opproc;
fmgr_info(opfuncoid, &opproc);
/*
* GPDB: See the identical check, above, for histogram data.
*/
if (!IsBinaryCoercible(sslot.valuetype, vardata->atttype))
elog(ERROR, "invalid MCV array of type %s, for attribute of type %s",
format_type_be(sslot.valuetype), format_type_be(vardata->atttype));
for (i = 0; i < sslot.nvalues; i++)
{
if (!have_data)
{
tmin = tmax = sslot.values[i];
tmin_is_mcv = tmax_is_mcv = have_data = true;
continue;
}
if (DatumGetBool(FunctionCall2Coll(&opproc,
sslot.stacoll,
sslot.values[i], tmin)))
{
tmin = sslot.values[i];
tmin_is_mcv = true;
}
if (DatumGetBool(FunctionCall2Coll(&opproc,
sslot.stacoll,
tmax, sslot.values[i])))
{
tmax = sslot.values[i];
tmax_is_mcv = true;
}
}
if (tmin_is_mcv)
tmin = datumCopy(tmin, typByVal, typLen);
if (tmax_is_mcv)
tmax = datumCopy(tmax, typByVal, typLen);
free_attstatsslot(&sslot);
}
*min = tmin;
*max = tmax;
return have_data;
}
/*
* get_actual_variable_range
* Attempt to identify the current *actual* minimum and/or maximum
* of the specified variable, by looking for a suitable btree index
* and fetching its low and/or high values.
* If successful, store values in *min and *max, and return true.
* (Either pointer can be NULL if that endpoint isn't needed.)
* If no data available, return false.
*
* sortop is the "<" comparison operator to use.
*/
static bool
get_actual_variable_range(PlannerInfo *root, VariableStatData *vardata,
Oid sortop,
Datum *min, Datum *max)
{
bool have_data = false;
RelOptInfo *rel = vardata->rel;
RangeTblEntry *rte;
ListCell *lc;
/* No hope if no relation or it doesn't have indexes */
if (rel == NULL || rel->indexlist == NIL)
return false;
/* If it has indexes it must be a plain relation */
rte = root->simple_rte_array[rel->relid];
Assert(rte->rtekind == RTE_RELATION);
/* Search through the indexes to see if any match our problem */
foreach(lc, rel->indexlist)
{
IndexOptInfo *index = (IndexOptInfo *) lfirst(lc);
ScanDirection indexscandir;
/* Ignore non-btree indexes */
if (index->relam != BTREE_AM_OID)
continue;
/*
* Ignore partial indexes --- we only want stats that cover the entire
* relation.
*/
if (index->indpred != NIL)
continue;
/*
* The index list might include hypothetical indexes inserted by a
* get_relation_info hook --- don't try to access them.
*/
if (index->hypothetical)
continue;
/*
* The first index column must match the desired variable and sort
* operator --- but we can use a descending-order index.
*/
if (!match_index_to_operand(vardata->var, 0, index))
continue;
switch (get_op_opfamily_strategy(sortop, index->sortopfamily[0]))
{
case BTLessStrategyNumber:
if (index->reverse_sort[0])
indexscandir = BackwardScanDirection;
else
indexscandir = ForwardScanDirection;
break;
case BTGreaterStrategyNumber:
if (index->reverse_sort[0])
indexscandir = ForwardScanDirection;
else
indexscandir = BackwardScanDirection;
break;
default:
/* index doesn't match the sortop */
continue;
}
/*
* Found a suitable index to extract data from. We'll need an EState
* and a bunch of other infrastructure.
*/
{
EState *estate;
ExprContext *econtext;
MemoryContext tmpcontext;
MemoryContext oldcontext;
Relation heapRel;
Relation indexRel;
IndexInfo *indexInfo;
TupleTableSlot *slot;
int16 typLen;
bool typByVal;
ScanKeyData scankeys[1];
IndexScanDesc index_scan;
Datum values[INDEX_MAX_KEYS];
bool isnull[INDEX_MAX_KEYS];
SnapshotData SnapshotNonVacuumable;
estate = CreateExecutorState();
econtext = GetPerTupleExprContext(estate);
/* Make sure any cruft is generated in the econtext's memory */
tmpcontext = econtext->ecxt_per_tuple_memory;
oldcontext = MemoryContextSwitchTo(tmpcontext);
/*
* Open the table and index so we can read from them. We should
* already have some type of lock on each.
*/
heapRel = table_open(rte->relid, NoLock);
indexRel = index_open(index->indexoid, NoLock);
/* extract index key information from the index's pg_index info */
indexInfo = BuildIndexInfo(indexRel);
/* some other stuff */
slot = table_slot_create(heapRel, NULL);
econtext->ecxt_scantuple = slot;
get_typlenbyval(vardata->atttype, &typLen, &typByVal);
InitNonVacuumableSnapshot(SnapshotNonVacuumable, RecentGlobalXmin);
/* set up an IS NOT NULL scan key so that we ignore nulls */
ScanKeyEntryInitialize(&scankeys[0],
SK_ISNULL | SK_SEARCHNOTNULL,
1, /* index col to scan */
InvalidStrategy, /* no strategy */
InvalidOid, /* no strategy subtype */
InvalidOid, /* no collation */
InvalidOid, /* no reg proc for this */
(Datum) 0); /* constant */
have_data = true;
/* If min is requested ... */
if (min)
{
/*
* In principle, we should scan the index with our current
* active snapshot, which is the best approximation we've got
* to what the query will see when executed. But that won't
* be exact if a new snap is taken before running the query,
* and it can be very expensive if a lot of recently-dead or
* uncommitted rows exist at the beginning or end of the index
* (because we'll laboriously fetch each one and reject it).
* Instead, we use SnapshotNonVacuumable. That will accept
* recently-dead and uncommitted rows as well as normal
* visible rows. On the other hand, it will reject known-dead
* rows, and thus not give a bogus answer when the extreme
* value has been deleted (unless the deletion was quite
* recent); that case motivates not using SnapshotAny here.
*
* A crucial point here is that SnapshotNonVacuumable, with
* RecentGlobalXmin as horizon, yields the inverse of the
* condition that the indexscan will use to decide that index
* entries are killable (see heap_hot_search_buffer()).
* Therefore, if the snapshot rejects a tuple and we have to
* continue scanning past it, we know that the indexscan will
* mark that index entry killed. That means that the next
* get_actual_variable_range() call will not have to visit
* that heap entry. In this way we avoid repetitive work when
* this function is used a lot during planning.
*/
index_scan = index_beginscan(heapRel, indexRel,
&SnapshotNonVacuumable,
1, 0);
index_rescan(index_scan, scankeys, 1, NULL, 0);
/* Fetch first tuple in sortop's direction */
if (index_getnext_slot(index_scan, indexscandir, slot))
{
/* Extract the index column values from the slot */
FormIndexDatum(indexInfo, slot, estate,
values, isnull);
/* Shouldn't have got a null, but be careful */
if (isnull[0])
elog(ERROR, "found unexpected null value in index \"%s\"",
RelationGetRelationName(indexRel));
/* Copy the index column value out to caller's context */
MemoryContextSwitchTo(oldcontext);
*min = datumCopy(values[0], typByVal, typLen);
MemoryContextSwitchTo(tmpcontext);
}
else
have_data = false;
index_endscan(index_scan);
}
/* If max is requested, and we didn't find the index is empty */
if (max && have_data)
{
index_scan = index_beginscan(heapRel, indexRel,
&SnapshotNonVacuumable,
1, 0);
index_rescan(index_scan, scankeys, 1, NULL, 0);
/* Fetch first tuple in reverse direction */
if (index_getnext_slot(index_scan, -indexscandir, slot))
{
/* Extract the index column values from the slot */
FormIndexDatum(indexInfo, slot, estate,
values, isnull);
/* Shouldn't have got a null, but be careful */
if (isnull[0])
elog(ERROR, "found unexpected null value in index \"%s\"",
RelationGetRelationName(indexRel));
/* Copy the index column value out to caller's context */
MemoryContextSwitchTo(oldcontext);
*max = datumCopy(values[0], typByVal, typLen);
MemoryContextSwitchTo(tmpcontext);
}
else
have_data = false;
index_endscan(index_scan);
}
/* Clean everything up */
ExecDropSingleTupleTableSlot(slot);
index_close(indexRel, NoLock);
table_close(heapRel, NoLock);
MemoryContextSwitchTo(oldcontext);
FreeExecutorState(estate);
/* And we're done */
break;
}
}
return have_data;
}
/*
* find_join_input_rel
* Look up the input relation for a join.
*
* We assume that the input relation's RelOptInfo must have been constructed
* already.
*/
static RelOptInfo *
find_join_input_rel(PlannerInfo *root, Relids relids)
{
RelOptInfo *rel = NULL;
switch (bms_membership(relids))
{
case BMS_EMPTY_SET:
/* should not happen */
break;
case BMS_SINGLETON:
rel = find_base_rel(root, bms_singleton_member(relids));
break;
case BMS_MULTIPLE:
rel = find_join_rel(root, relids);
break;
}
if (rel == NULL)
elog(ERROR, "could not find RelOptInfo for given relids");
return rel;
}
/*-------------------------------------------------------------------------
*
* Index cost estimation functions
*
*-------------------------------------------------------------------------
*/
/*
* Extract the actual indexquals (as RestrictInfos) from an IndexClause list
*/
List *
get_quals_from_indexclauses(List *indexclauses)
{
List *result = NIL;
ListCell *lc;
foreach(lc, indexclauses)
{
IndexClause *iclause = lfirst_node(IndexClause, lc);
ListCell *lc2;
foreach(lc2, iclause->indexquals)
{
RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
result = lappend(result, rinfo);
}
}
return result;
}
/*
* Compute the total evaluation cost of the comparison operands in a list
* of index qual expressions. Since we know these will be evaluated just
* once per scan, there's no need to distinguish startup from per-row cost.
*
* This can be used either on the result of get_quals_from_indexclauses(),
* or directly on an indexorderbys list. In both cases, we expect that the
* index key expression is on the left side of binary clauses.
*/
Cost
index_other_operands_eval_cost(PlannerInfo *root, List *indexquals)
{
Cost qual_arg_cost = 0;
ListCell *lc;
foreach(lc, indexquals)
{
Expr *clause = (Expr *) lfirst(lc);
Node *other_operand;
QualCost index_qual_cost;
/*
* Index quals will have RestrictInfos, indexorderbys won't. Look
* through RestrictInfo if present.
*/
if (IsA(clause, RestrictInfo))
clause = ((RestrictInfo *) clause)->clause;
if (IsA(clause, OpExpr))
{
OpExpr *op = (OpExpr *) clause;
other_operand = (Node *) lsecond(op->args);
}
else if (IsA(clause, RowCompareExpr))
{
RowCompareExpr *rc = (RowCompareExpr *) clause;
other_operand = (Node *) rc->rargs;
}
else if (IsA(clause, ScalarArrayOpExpr))
{
ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
other_operand = (Node *) lsecond(saop->args);
}
else if (IsA(clause, NullTest))
{
other_operand = NULL;
}
else
{
elog(ERROR, "unsupported indexqual type: %d",
(int) nodeTag(clause));
other_operand = NULL; /* keep compiler quiet */
}
cost_qual_eval_node(&index_qual_cost, other_operand, root);
qual_arg_cost += index_qual_cost.startup + index_qual_cost.per_tuple;
}
return qual_arg_cost;
}
void
genericcostestimate(PlannerInfo *root,
IndexPath *path,
double loop_count,
GenericCosts *costs)
{
IndexOptInfo *index = path->indexinfo;
List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
List *indexOrderBys = path->indexorderbys;
Cost indexStartupCost;
Cost indexTotalCost;
Selectivity indexSelectivity;
double indexCorrelation;
double numIndexPages;
double numIndexTuples;
double spc_random_page_cost;
double num_sa_scans;
double num_outer_scans;
double num_scans;
double qual_op_cost;
double qual_arg_cost;
List *selectivityQuals;
ListCell *l;
/*
* If the index is partial, AND the index predicate with the explicitly
* given indexquals to produce a more accurate idea of the index
* selectivity.
*/
selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
/*
* Check for ScalarArrayOpExpr index quals, and estimate the number of
* index scans that will be performed.
*/
num_sa_scans = 1;
foreach(l, indexQuals)
{
RestrictInfo *rinfo = (RestrictInfo *) lfirst(l);
if (IsA(rinfo->clause, ScalarArrayOpExpr))
{
ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) rinfo->clause;
int alength = estimate_array_length(lsecond(saop->args));
if (alength > 1)
num_sa_scans *= alength;
}
}
/* Estimate the fraction of main-table tuples that will be visited */
indexSelectivity = clauselist_selectivity(root, selectivityQuals,
index->rel->relid,
JOIN_INNER,
NULL,
false /* use_damping */);
/*
* If caller didn't give us an estimate, estimate the number of index
* tuples that will be visited. We do it in this rather peculiar-looking
* way in order to get the right answer for partial indexes.
*/
numIndexTuples = costs->numIndexTuples;
if (numIndexTuples <= 0.0)
{
numIndexTuples = indexSelectivity * index->rel->tuples;
/*
* The above calculation counts all the tuples visited across all
* scans induced by ScalarArrayOpExpr nodes. We want to consider the
* average per-indexscan number, so adjust. This is a handy place to
* round to integer, too. (If caller supplied tuple estimate, it's
* responsible for handling these considerations.)
*/
numIndexTuples = rint(numIndexTuples / num_sa_scans);
}
/*
* We can bound the number of tuples by the index size in any case. Also,
* always estimate at least one tuple is touched, even when
* indexSelectivity estimate is tiny.
*/
if (numIndexTuples > index->tuples)
numIndexTuples = index->tuples;
if (numIndexTuples < 1.0)
numIndexTuples = 1.0;
/*
* Estimate the number of index pages that will be retrieved.
*
* We use the simplistic method of taking a pro-rata fraction of the total
* number of index pages. In effect, this counts only leaf pages and not
* any overhead such as index metapage or upper tree levels.
*
* In practice access to upper index levels is often nearly free because
* those tend to stay in cache under load; moreover, the cost involved is
* highly dependent on index type. We therefore ignore such costs here
* and leave it to the caller to add a suitable charge if needed.
*/
if (index->pages > 1 && index->tuples > 1)
numIndexPages = ceil(numIndexTuples * index->pages / index->tuples);
else
numIndexPages = 1.0;
/* fetch estimated page cost for tablespace containing index */
get_tablespace_page_costs(index->reltablespace,
&spc_random_page_cost,
NULL);
/*
* Now compute the disk access costs.
*
* The above calculations are all per-index-scan. However, if we are in a
* nestloop inner scan, we can expect the scan to be repeated (with
* different search keys) for each row of the outer relation. Likewise,
* ScalarArrayOpExpr quals result in multiple index scans. This creates
* the potential for cache effects to reduce the number of disk page
* fetches needed. We want to estimate the average per-scan I/O cost in
* the presence of caching.
*
* We use the Mackert-Lohman formula (see costsize.c for details) to
* estimate the total number of page fetches that occur. While this
* wasn't what it was designed for, it seems a reasonable model anyway.
* Note that we are counting pages not tuples anymore, so we take N = T =
* index size, as if there were one "tuple" per page.
*/
num_outer_scans = loop_count;
num_scans = num_sa_scans * num_outer_scans;
if (num_scans > 1)
{
double pages_fetched;
/* total page fetches ignoring cache effects */
pages_fetched = numIndexPages * num_scans;
/* use Mackert and Lohman formula to adjust for cache effects */
pages_fetched = index_pages_fetched(pages_fetched,
index->pages,
(double) index->pages,
root);
/*
* Now compute the total disk access cost, and then report a pro-rated
* share for each outer scan. (Don't pro-rate for ScalarArrayOpExpr,
* since that's internal to the indexscan.)
*/
indexTotalCost = (pages_fetched * spc_random_page_cost)
/ num_outer_scans;
}
else
{
/*
* For a single index scan, we just charge spc_random_page_cost per
* page touched.
*/
indexTotalCost = numIndexPages * spc_random_page_cost;
}
/*
* CPU cost: any complex expressions in the indexquals will need to be
* evaluated once at the start of the scan to reduce them to runtime keys
* to pass to the index AM (see nodeIndexscan.c). We model the per-tuple
* CPU costs as cpu_index_tuple_cost plus one cpu_operator_cost per
* indexqual operator. Because we have numIndexTuples as a per-scan
* number, we have to multiply by num_sa_scans to get the correct result
* for ScalarArrayOpExpr cases. Similarly add in costs for any index
* ORDER BY expressions.
*
* Note: this neglects the possible costs of rechecking lossy operators.
* Detecting that that might be needed seems more expensive than it's
* worth, though, considering all the other inaccuracies here ...
*/
qual_arg_cost = index_other_operands_eval_cost(root, indexQuals) +
index_other_operands_eval_cost(root, indexOrderBys);
qual_op_cost = cpu_operator_cost *
(list_length(indexQuals) + list_length(indexOrderBys));
indexStartupCost = qual_arg_cost;
indexTotalCost += qual_arg_cost;
indexTotalCost += numIndexTuples * num_sa_scans * (cpu_index_tuple_cost + qual_op_cost);
/*
* Generic assumption about index correlation: there isn't any.
*/
indexCorrelation = 0.0;
/*
* Return everything to caller.
*/
costs->indexStartupCost = indexStartupCost;
costs->indexTotalCost = indexTotalCost;
costs->indexSelectivity = indexSelectivity;
costs->indexCorrelation = indexCorrelation;
costs->numIndexPages = numIndexPages;
costs->numIndexTuples = numIndexTuples;
costs->spc_random_page_cost = spc_random_page_cost;
costs->num_sa_scans = num_sa_scans;
}
/*
* If the index is partial, add its predicate to the given qual list.
*
* ANDing the index predicate with the explicitly given indexquals produces
* a more accurate idea of the index's selectivity. However, we need to be
* careful not to insert redundant clauses, because clauselist_selectivity()
* is easily fooled into computing a too-low selectivity estimate. Our
* approach is to add only the predicate clause(s) that cannot be proven to
* be implied by the given indexquals. This successfully handles cases such
* as a qual "x = 42" used with a partial index "WHERE x >= 40 AND x < 50".
* There are many other cases where we won't detect redundancy, leading to a
* too-low selectivity estimate, which will bias the system in favor of using
* partial indexes where possible. That is not necessarily bad though.
*
* Note that indexQuals contains RestrictInfo nodes while the indpred
* does not, so the output list will be mixed. This is OK for both
* predicate_implied_by() and clauselist_selectivity(), but might be
* problematic if the result were passed to other things.
*/
List *
add_predicate_to_index_quals(IndexOptInfo *index, List *indexQuals)
{
List *predExtraQuals = NIL;
ListCell *lc;
if (index->indpred == NIL)
return indexQuals;
foreach(lc, index->indpred)
{
Node *predQual = (Node *) lfirst(lc);
List *oneQual = list_make1(predQual);
if (!predicate_implied_by(oneQual, indexQuals, false))
predExtraQuals = list_concat(predExtraQuals, oneQual);
}
/* list_concat avoids modifying the passed-in indexQuals list */
return list_concat(predExtraQuals, indexQuals);
}
void
btcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
Cost *indexStartupCost, Cost *indexTotalCost,
Selectivity *indexSelectivity, double *indexCorrelation,
double *indexPages)
{
IndexOptInfo *index = path->indexinfo;
GenericCosts costs;
Oid relid;
AttrNumber colnum;
VariableStatData vardata;
double numIndexTuples;
Cost descentCost;
List *indexBoundQuals;
int indexcol;
bool eqQualHere;
bool found_saop;
bool found_is_null_op;
double num_sa_scans;
ListCell *lc;
/*
* For a btree scan, only leading '=' quals plus inequality quals for the
* immediately next attribute contribute to index selectivity (these are
* the "boundary quals" that determine the starting and stopping points of
* the index scan). Additional quals can suppress visits to the heap, so
* it's OK to count them in indexSelectivity, but they should not count
* for estimating numIndexTuples. So we must examine the given indexquals
* to find out which ones count as boundary quals. We rely on the
* knowledge that they are given in index column order.
*
* For a RowCompareExpr, we consider only the first column, just as
* rowcomparesel() does.
*
* If there's a ScalarArrayOpExpr in the quals, we'll actually perform N
* index scans not one, but the ScalarArrayOpExpr's operator can be
* considered to act the same as it normally does.
*/
indexBoundQuals = NIL;
indexcol = 0;
eqQualHere = false;
found_saop = false;
found_is_null_op = false;
num_sa_scans = 1;
foreach(lc, path->indexclauses)
{
IndexClause *iclause = lfirst_node(IndexClause, lc);
ListCell *lc2;
if (indexcol != iclause->indexcol)
{
/* Beginning of a new column's quals */
if (!eqQualHere)
break; /* done if no '=' qual for indexcol */
eqQualHere = false;
indexcol++;
if (indexcol != iclause->indexcol)
break; /* no quals at all for indexcol */
}
/* Examine each indexqual associated with this index clause */
foreach(lc2, iclause->indexquals)
{
RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
Expr *clause = rinfo->clause;
Oid clause_op = InvalidOid;
int op_strategy;
if (IsA(clause, OpExpr))
{
OpExpr *op = (OpExpr *) clause;
clause_op = op->opno;
}
else if (IsA(clause, RowCompareExpr))
{
RowCompareExpr *rc = (RowCompareExpr *) clause;
clause_op = linitial_oid(rc->opnos);
}
else if (IsA(clause, ScalarArrayOpExpr))
{
ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
Node *other_operand = (Node *) lsecond(saop->args);
int alength = estimate_array_length(other_operand);
clause_op = saop->opno;
found_saop = true;
/* count number of SA scans induced by indexBoundQuals only */
if (alength > 1)
num_sa_scans *= alength;
}
else if (IsA(clause, NullTest))
{
NullTest *nt = (NullTest *) clause;
if (nt->nulltesttype == IS_NULL)
{
found_is_null_op = true;
/* IS NULL is like = for selectivity purposes */
eqQualHere = true;
}
}
else
elog(ERROR, "unsupported indexqual type: %d",
(int) nodeTag(clause));
/* check for equality operator */
if (OidIsValid(clause_op))
{
op_strategy = get_op_opfamily_strategy(clause_op,
index->opfamily[indexcol]);
Assert(op_strategy != 0); /* not a member of opfamily?? */
if (op_strategy == BTEqualStrategyNumber)
eqQualHere = true;
}
indexBoundQuals = lappend(indexBoundQuals, rinfo);
}
}
/*
* If index is unique and we found an '=' clause for each column, we can
* just assume numIndexTuples = 1 and skip the expensive
* clauselist_selectivity calculations. However, a ScalarArrayOp or
* NullTest invalidates that theory, even though it sets eqQualHere.
*/
if (index->unique &&
indexcol == index->nkeycolumns - 1 &&
eqQualHere &&
!found_saop &&
!found_is_null_op)
numIndexTuples = 1.0;
else
{
List *selectivityQuals;
Selectivity btreeSelectivity;
/*
* If the index is partial, AND the index predicate with the
* index-bound quals to produce a more accurate idea of the number of
* rows covered by the bound conditions.
*/
selectivityQuals = add_predicate_to_index_quals(index, indexBoundQuals);
btreeSelectivity = clauselist_selectivity(root, selectivityQuals,
index->rel->relid,
JOIN_INNER,
NULL,
false /* use_damping */);
numIndexTuples = btreeSelectivity * index->rel->tuples;
/*
* As in genericcostestimate(), we have to adjust for any
* ScalarArrayOpExpr quals included in indexBoundQuals, and then round
* to integer.
*/
numIndexTuples = rint(numIndexTuples / num_sa_scans);
}
/*
* Now do generic index cost estimation.
*/
MemSet(&costs, 0, sizeof(costs));
costs.numIndexTuples = numIndexTuples;
genericcostestimate(root, path, loop_count, &costs);
/*
* Add a CPU-cost component to represent the costs of initial btree
* descent. We don't charge any I/O cost for touching upper btree levels,
* since they tend to stay in cache, but we still have to do about log2(N)
* comparisons to descend a btree of N leaf tuples. We charge one
* cpu_operator_cost per comparison.
*
* If there are ScalarArrayOpExprs, charge this once per SA scan. The
* ones after the first one are not startup cost so far as the overall
* plan is concerned, so add them only to "total" cost.
*/
if (index->tuples > 1) /* avoid computing log(0) */
{
descentCost = ceil(log(index->tuples) / log(2.0)) * cpu_operator_cost;
costs.indexStartupCost += descentCost;
costs.indexTotalCost += costs.num_sa_scans * descentCost;
}
/*
* Even though we're not charging I/O cost for touching upper btree pages,
* it's still reasonable to charge some CPU cost per page descended
* through. Moreover, if we had no such charge at all, bloated indexes
* would appear to have the same search cost as unbloated ones, at least
* in cases where only a single leaf page is expected to be visited. This
* cost is somewhat arbitrarily set at 50x cpu_operator_cost per page
* touched. The number of such pages is btree tree height plus one (ie,
* we charge for the leaf page too). As above, charge once per SA scan.
*/
descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost;
costs.indexStartupCost += descentCost;
costs.indexTotalCost += costs.num_sa_scans * descentCost;
/*
* If we can get an estimate of the first column's ordering correlation C
* from pg_statistic, estimate the index correlation as C for a
* single-column index, or C * 0.75 for multiple columns. (The idea here
* is that multiple columns dilute the importance of the first column's
* ordering, but don't negate it entirely. Before 8.0 we divided the
* correlation by the number of columns, but that seems too strong.)
*/
MemSet(&vardata, 0, sizeof(vardata));
if (index->indexkeys[0] != 0)
{
/* Simple variable --- look to stats for the underlying table */
RangeTblEntry *rte = planner_rt_fetch(index->rel->relid, root);
Assert(rte->rtekind == RTE_RELATION);
relid = rte->relid;
Assert(relid != InvalidOid);
colnum = index->indexkeys[0];
if (get_relation_stats_hook &&
(*get_relation_stats_hook) (root, rte, colnum, &vardata))
{
/*
* The hook took control of acquiring a stats tuple. If it did
* supply a tuple, it'd better have supplied a freefunc.
*/
if (HeapTupleIsValid(vardata.statsTuple) &&
!vardata.freefunc)
elog(ERROR, "no function provided to release variable stats with");
}
else
{
vardata.statsTuple = SearchSysCache3(STATRELATTINH,
ObjectIdGetDatum(relid),
Int16GetDatum(colnum),
BoolGetDatum(rte->inh));
vardata.freefunc = ReleaseSysCache;
}
}
else
{
/* Expression --- maybe there are stats for the index itself */
relid = index->indexoid;
colnum = 1;
if (get_index_stats_hook &&
(*get_index_stats_hook) (root, relid, colnum, &vardata))
{
/*
* The hook took control of acquiring a stats tuple. If it did
* supply a tuple, it'd better have supplied a freefunc.
*/
if (HeapTupleIsValid(vardata.statsTuple) &&
!vardata.freefunc)
elog(ERROR, "no function provided to release variable stats with");
}
else
{
vardata.statsTuple = SearchSysCache3(STATRELATTINH,
ObjectIdGetDatum(relid),
Int16GetDatum(colnum),
BoolGetDatum(false));
vardata.freefunc = ReleaseSysCache;
}
}
if (HeapTupleIsValid(vardata.statsTuple))
{
Oid sortop;
AttStatsSlot sslot;
sortop = get_opfamily_member(index->opfamily[0],
index->opcintype[0],
index->opcintype[0],
BTLessStrategyNumber);
if (OidIsValid(sortop) &&
get_attstatsslot(&sslot, vardata.statsTuple,
STATISTIC_KIND_CORRELATION, sortop,
ATTSTATSSLOT_NUMBERS))
{
double varCorrelation;
Assert(sslot.nnumbers == 1);
varCorrelation = sslot.numbers[0];
if (index->reverse_sort[0])
varCorrelation = -varCorrelation;
if (index->nkeycolumns > 1)
costs.indexCorrelation = varCorrelation * 0.75;
else
costs.indexCorrelation = varCorrelation;
free_attstatsslot(&sslot);
}
}
ReleaseVariableStats(vardata);
*indexStartupCost = costs.indexStartupCost;
*indexTotalCost = costs.indexTotalCost;
*indexSelectivity = costs.indexSelectivity;
*indexCorrelation = costs.indexCorrelation;
*indexPages = costs.numIndexPages;
}
void
hashcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
Cost *indexStartupCost, Cost *indexTotalCost,
Selectivity *indexSelectivity, double *indexCorrelation,
double *indexPages)
{
GenericCosts costs;
MemSet(&costs, 0, sizeof(costs));
genericcostestimate(root, path, loop_count, &costs);
/*
* A hash index has no descent costs as such, since the index AM can go
* directly to the target bucket after computing the hash value. There
* are a couple of other hash-specific costs that we could conceivably add
* here, though:
*
* Ideally we'd charge spc_random_page_cost for each page in the target
* bucket, not just the numIndexPages pages that genericcostestimate
* thought we'd visit. However in most cases we don't know which bucket
* that will be. There's no point in considering the average bucket size
* because the hash AM makes sure that's always one page.
*
* Likewise, we could consider charging some CPU for each index tuple in
* the bucket, if we knew how many there were. But the per-tuple cost is
* just a hash value comparison, not a general datatype-dependent
* comparison, so any such charge ought to be quite a bit less than
* cpu_operator_cost; which makes it probably not worth worrying about.
*
* A bigger issue is that chance hash-value collisions will result in
* wasted probes into the heap. We don't currently attempt to model this
* cost on the grounds that it's rare, but maybe it's not rare enough.
* (Any fix for this ought to consider the generic lossy-operator problem,
* though; it's not entirely hash-specific.)
*/
*indexStartupCost = costs.indexStartupCost;
*indexTotalCost = costs.indexTotalCost;
*indexSelectivity = costs.indexSelectivity;
*indexCorrelation = costs.indexCorrelation;
*indexPages = costs.numIndexPages;
}
void
gistcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
Cost *indexStartupCost, Cost *indexTotalCost,
Selectivity *indexSelectivity, double *indexCorrelation,
double *indexPages)
{
IndexOptInfo *index = path->indexinfo;
GenericCosts costs;
Cost descentCost;
MemSet(&costs, 0, sizeof(costs));
genericcostestimate(root, path, loop_count, &costs);
/*
* We model index descent costs similarly to those for btree, but to do
* that we first need an idea of the tree height. We somewhat arbitrarily
* assume that the fanout is 100, meaning the tree height is at most
* log100(index->pages).
*
* Although this computation isn't really expensive enough to require
* caching, we might as well use index->tree_height to cache it.
*/
if (index->tree_height < 0) /* unknown? */
{
if (index->pages > 1) /* avoid computing log(0) */
index->tree_height = (int) (log(index->pages) / log(100.0));
else
index->tree_height = 0;
}
/*
* Add a CPU-cost component to represent the costs of initial descent. We
* just use log(N) here not log2(N) since the branching factor isn't
* necessarily two anyway. As for btree, charge once per SA scan.
*/
if (index->tuples > 1) /* avoid computing log(0) */
{
descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
costs.indexStartupCost += descentCost;
costs.indexTotalCost += costs.num_sa_scans * descentCost;
}
/*
* Likewise add a per-page charge, calculated the same as for btrees.
*/
descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost;
costs.indexStartupCost += descentCost;
costs.indexTotalCost += costs.num_sa_scans * descentCost;
*indexStartupCost = costs.indexStartupCost;
*indexTotalCost = costs.indexTotalCost;
*indexSelectivity = costs.indexSelectivity;
*indexCorrelation = costs.indexCorrelation;
*indexPages = costs.numIndexPages;
}
void
spgcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
Cost *indexStartupCost, Cost *indexTotalCost,
Selectivity *indexSelectivity, double *indexCorrelation,
double *indexPages)
{
IndexOptInfo *index = path->indexinfo;
GenericCosts costs;
Cost descentCost;
MemSet(&costs, 0, sizeof(costs));
genericcostestimate(root, path, loop_count, &costs);
/*
* We model index descent costs similarly to those for btree, but to do
* that we first need an idea of the tree height. We somewhat arbitrarily
* assume that the fanout is 100, meaning the tree height is at most
* log100(index->pages).
*
* Although this computation isn't really expensive enough to require
* caching, we might as well use index->tree_height to cache it.
*/
if (index->tree_height < 0) /* unknown? */
{
if (index->pages > 1) /* avoid computing log(0) */
index->tree_height = (int) (log(index->pages) / log(100.0));
else
index->tree_height = 0;
}
/*
* Add a CPU-cost component to represent the costs of initial descent. We
* just use log(N) here not log2(N) since the branching factor isn't
* necessarily two anyway. As for btree, charge once per SA scan.
*/
if (index->tuples > 1) /* avoid computing log(0) */
{
descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
costs.indexStartupCost += descentCost;
costs.indexTotalCost += costs.num_sa_scans * descentCost;
}
/*
* Likewise add a per-page charge, calculated the same as for btrees.
*/
descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost;
costs.indexStartupCost += descentCost;
costs.indexTotalCost += costs.num_sa_scans * descentCost;
*indexStartupCost = costs.indexStartupCost;
*indexTotalCost = costs.indexTotalCost;
*indexSelectivity = costs.indexSelectivity;
*indexCorrelation = costs.indexCorrelation;
*indexPages = costs.numIndexPages;
}
/*
* Support routines for gincostestimate
*/
typedef struct
{
bool haveFullScan;
double partialEntries;
double exactEntries;
double searchEntries;
double arrayScans;
} GinQualCounts;
/*
* Estimate the number of index terms that need to be searched for while
* testing the given GIN query, and increment the counts in *counts
* appropriately. If the query is unsatisfiable, return false.
*/
static bool
gincost_pattern(IndexOptInfo *index, int indexcol,
Oid clause_op, Datum query,
GinQualCounts *counts)
{
Oid extractProcOid;
Oid collation;
int strategy_op;
Oid lefttype,
righttype;
int32 nentries = 0;
bool *partial_matches = NULL;
Pointer *extra_data = NULL;
bool *nullFlags = NULL;
int32 searchMode = GIN_SEARCH_MODE_DEFAULT;
int32 i;
Assert(indexcol < index->nkeycolumns);
/*
* Get the operator's strategy number and declared input data types within
* the index opfamily. (We don't need the latter, but we use
* get_op_opfamily_properties because it will throw error if it fails to
* find a matching pg_amop entry.)
*/
get_op_opfamily_properties(clause_op, index->opfamily[indexcol], false,
&strategy_op, &lefttype, &righttype);
/*
* GIN always uses the "default" support functions, which are those with
* lefttype == righttype == the opclass' opcintype (see
* IndexSupportInitialize in relcache.c).
*/
extractProcOid = get_opfamily_proc(index->opfamily[indexcol],
index->opcintype[indexcol],
index->opcintype[indexcol],
GIN_EXTRACTQUERY_PROC);
if (!OidIsValid(extractProcOid))
{
/* should not happen; throw same error as index_getprocinfo */
elog(ERROR, "missing support function %d for attribute %d of index \"%s\"",
GIN_EXTRACTQUERY_PROC, indexcol + 1,
get_rel_name(index->indexoid));
}
/*
* Choose collation to pass to extractProc (should match initGinState).
*/
if (OidIsValid(index->indexcollations[indexcol]))
collation = index->indexcollations[indexcol];
else
collation = DEFAULT_COLLATION_OID;
OidFunctionCall7Coll(extractProcOid,
collation,
query,
PointerGetDatum(&nentries),
UInt16GetDatum(strategy_op),
PointerGetDatum(&partial_matches),
PointerGetDatum(&extra_data),
PointerGetDatum(&nullFlags),
PointerGetDatum(&searchMode));
if (nentries <= 0 && searchMode == GIN_SEARCH_MODE_DEFAULT)
{
/* No match is possible */
return false;
}
for (i = 0; i < nentries; i++)
{
/*
* For partial match we haven't any information to estimate number of
* matched entries in index, so, we just estimate it as 100
*/
if (partial_matches && partial_matches[i])
counts->partialEntries += 100;
else
counts->exactEntries++;
counts->searchEntries++;
}
if (searchMode == GIN_SEARCH_MODE_INCLUDE_EMPTY)
{
/* Treat "include empty" like an exact-match item */
counts->exactEntries++;
counts->searchEntries++;
}
else if (searchMode != GIN_SEARCH_MODE_DEFAULT)
{
/* It's GIN_SEARCH_MODE_ALL */
counts->haveFullScan = true;
}
return true;
}
/*
* Estimate the number of index terms that need to be searched for while
* testing the given GIN index clause, and increment the counts in *counts
* appropriately. If the query is unsatisfiable, return false.
*/
static bool
gincost_opexpr(PlannerInfo *root,
IndexOptInfo *index,
int indexcol,
OpExpr *clause,
GinQualCounts *counts)
{
Oid clause_op = clause->opno;
Node *operand = (Node *) lsecond(clause->args);
/* aggressively reduce to a constant, and look through relabeling */
operand = estimate_expression_value(root, operand);
if (IsA(operand, RelabelType))
operand = (Node *) ((RelabelType *) operand)->arg;
/*
* It's impossible to call extractQuery method for unknown operand. So
* unless operand is a Const we can't do much; just assume there will be
* one ordinary search entry from the operand at runtime.
*/
if (!IsA(operand, Const))
{
counts->exactEntries++;
counts->searchEntries++;
return true;
}
/* If Const is null, there can be no matches */
if (((Const *) operand)->constisnull)
return false;
/* Otherwise, apply extractQuery and get the actual term counts */
return gincost_pattern(index, indexcol, clause_op,
((Const *) operand)->constvalue,
counts);
}
/*
* Estimate the number of index terms that need to be searched for while
* testing the given GIN index clause, and increment the counts in *counts
* appropriately. If the query is unsatisfiable, return false.
*
* A ScalarArrayOpExpr will give rise to N separate indexscans at runtime,
* each of which involves one value from the RHS array, plus all the
* non-array quals (if any). To model this, we average the counts across
* the RHS elements, and add the averages to the counts in *counts (which
* correspond to per-indexscan costs). We also multiply counts->arrayScans
* by N, causing gincostestimate to scale up its estimates accordingly.
*/
static bool
gincost_scalararrayopexpr(PlannerInfo *root,
IndexOptInfo *index,
int indexcol,
ScalarArrayOpExpr *clause,
double numIndexEntries,
GinQualCounts *counts)
{
Oid clause_op = clause->opno;
Node *rightop = (Node *) lsecond(clause->args);
ArrayType *arrayval;
int16 elmlen;
bool elmbyval;
char elmalign;
int numElems;
Datum *elemValues;
bool *elemNulls;
GinQualCounts arraycounts;
int numPossible = 0;
int i;
Assert(clause->useOr);
/* aggressively reduce to a constant, and look through relabeling */
rightop = estimate_expression_value(root, rightop);
if (IsA(rightop, RelabelType))
rightop = (Node *) ((RelabelType *) rightop)->arg;
/*
* It's impossible to call extractQuery method for unknown operand. So
* unless operand is a Const we can't do much; just assume there will be
* one ordinary search entry from each array entry at runtime, and fall
* back on a probably-bad estimate of the number of array entries.
*/
if (!IsA(rightop, Const))
{
counts->exactEntries++;
counts->searchEntries++;
counts->arrayScans *= estimate_array_length(rightop);
return true;
}
/* If Const is null, there can be no matches */
if (((Const *) rightop)->constisnull)
return false;
/* Otherwise, extract the array elements and iterate over them */
arrayval = DatumGetArrayTypeP(((Const *) rightop)->constvalue);
get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
&elmlen, &elmbyval, &elmalign);
deconstruct_array(arrayval,
ARR_ELEMTYPE(arrayval),
elmlen, elmbyval, elmalign,
&elemValues, &elemNulls, &numElems);
memset(&arraycounts, 0, sizeof(arraycounts));
for (i = 0; i < numElems; i++)
{
GinQualCounts elemcounts;
/* NULL can't match anything, so ignore, as the executor will */
if (elemNulls[i])
continue;
/* Otherwise, apply extractQuery and get the actual term counts */
memset(&elemcounts, 0, sizeof(elemcounts));
if (gincost_pattern(index, indexcol, clause_op, elemValues[i],
&elemcounts))
{
/* We ignore array elements that are unsatisfiable patterns */
numPossible++;
if (elemcounts.haveFullScan)
{
/*
* Full index scan will be required. We treat this as if
* every key in the index had been listed in the query; is
* that reasonable?
*/
elemcounts.partialEntries = 0;
elemcounts.exactEntries = numIndexEntries;
elemcounts.searchEntries = numIndexEntries;
}
arraycounts.partialEntries += elemcounts.partialEntries;
arraycounts.exactEntries += elemcounts.exactEntries;
arraycounts.searchEntries += elemcounts.searchEntries;
}
}
if (numPossible == 0)
{
/* No satisfiable patterns in the array */
return false;
}
/*
* Now add the averages to the global counts. This will give us an
* estimate of the average number of terms searched for in each indexscan,
* including contributions from both array and non-array quals.
*/
counts->partialEntries += arraycounts.partialEntries / numPossible;
counts->exactEntries += arraycounts.exactEntries / numPossible;
counts->searchEntries += arraycounts.searchEntries / numPossible;
counts->arrayScans *= numPossible;
return true;
}
/*
* GIN has search behavior completely different from other index types
*/
void
gincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
Cost *indexStartupCost, Cost *indexTotalCost,
Selectivity *indexSelectivity, double *indexCorrelation,
double *indexPages)
{
IndexOptInfo *index = path->indexinfo;
List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
List *selectivityQuals;
double numPages = index->pages,
numTuples = index->tuples;
double numEntryPages,
numDataPages,
numPendingPages,
numEntries;
GinQualCounts counts;
bool matchPossible;
double partialScale;
double entryPagesFetched,
dataPagesFetched,
dataPagesFetchedBySel;
double qual_op_cost,
qual_arg_cost,
spc_random_page_cost,
outer_scans;
Relation indexRel;
GinStatsData ginStats;
ListCell *lc;
/*
* Obtain statistical information from the meta page, if possible. Else
* set ginStats to zeroes, and we'll cope below.
*/
if (!index->hypothetical)
{
/* Lock should have already been obtained in plancat.c */
indexRel = index_open(index->indexoid, NoLock);
ginGetStats(indexRel, &ginStats);
index_close(indexRel, NoLock);
}
else
{
memset(&ginStats, 0, sizeof(ginStats));
}
/*
* Assuming we got valid (nonzero) stats at all, nPendingPages can be
* trusted, but the other fields are data as of the last VACUUM. We can
* scale them up to account for growth since then, but that method only
* goes so far; in the worst case, the stats might be for a completely
* empty index, and scaling them will produce pretty bogus numbers.
* Somewhat arbitrarily, set the cutoff for doing scaling at 4X growth; if
* it's grown more than that, fall back to estimating things only from the
* assumed-accurate index size. But we'll trust nPendingPages in any case
* so long as it's not clearly insane, ie, more than the index size.
*/
if (ginStats.nPendingPages < numPages)
numPendingPages = ginStats.nPendingPages;
else
numPendingPages = 0;
if (numPages > 0 && ginStats.nTotalPages <= numPages &&
ginStats.nTotalPages > numPages / 4 &&
ginStats.nEntryPages > 0 && ginStats.nEntries > 0)
{
/*
* OK, the stats seem close enough to sane to be trusted. But we
* still need to scale them by the ratio numPages / nTotalPages to
* account for growth since the last VACUUM.
*/
double scale = numPages / ginStats.nTotalPages;
numEntryPages = ceil(ginStats.nEntryPages * scale);
numDataPages = ceil(ginStats.nDataPages * scale);
numEntries = ceil(ginStats.nEntries * scale);
/* ensure we didn't round up too much */
numEntryPages = Min(numEntryPages, numPages - numPendingPages);
numDataPages = Min(numDataPages,
numPages - numPendingPages - numEntryPages);
}
else
{
/*
* We might get here because it's a hypothetical index, or an index
* created pre-9.1 and never vacuumed since upgrading (in which case
* its stats would read as zeroes), or just because it's grown too
* much since the last VACUUM for us to put our faith in scaling.
*
* Invent some plausible internal statistics based on the index page
* count (and clamp that to at least 10 pages, just in case). We
* estimate that 90% of the index is entry pages, and the rest is data
* pages. Estimate 100 entries per entry page; this is rather bogus
* since it'll depend on the size of the keys, but it's more robust
* than trying to predict the number of entries per heap tuple.
*/
numPages = Max(numPages, 10);
numEntryPages = floor((numPages - numPendingPages) * 0.90);
numDataPages = numPages - numPendingPages - numEntryPages;
numEntries = floor(numEntryPages * 100);
}
/* In an empty index, numEntries could be zero. Avoid divide-by-zero */
if (numEntries < 1)
numEntries = 1;
/*
* If the index is partial, AND the index predicate with the index-bound
* quals to produce a more accurate idea of the number of rows covered by
* the bound conditions.
*/
selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
/* Estimate the fraction of main-table tuples that will be visited */
*indexSelectivity = clauselist_selectivity(root, selectivityQuals,
index->rel->relid,
JOIN_INNER,
NULL,
false);
/* fetch estimated page cost for tablespace containing index */
get_tablespace_page_costs(index->reltablespace,
&spc_random_page_cost,
NULL);
/*
* Generic assumption about index correlation: there isn't any.
*/
*indexCorrelation = 0.0;
/*
* Examine quals to estimate number of search entries & partial matches
*/
memset(&counts, 0, sizeof(counts));
counts.arrayScans = 1;
matchPossible = true;
foreach(lc, path->indexclauses)
{
IndexClause *iclause = lfirst_node(IndexClause, lc);
ListCell *lc2;
foreach(lc2, iclause->indexquals)
{
RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
Expr *clause = rinfo->clause;
if (IsA(clause, OpExpr))
{
matchPossible = gincost_opexpr(root,
index,
iclause->indexcol,
(OpExpr *) clause,
&counts);
if (!matchPossible)
break;
}
else if (IsA(clause, ScalarArrayOpExpr))
{
matchPossible = gincost_scalararrayopexpr(root,
index,
iclause->indexcol,
(ScalarArrayOpExpr *) clause,
numEntries,
&counts);
if (!matchPossible)
break;
}
else
{
/* shouldn't be anything else for a GIN index */
elog(ERROR, "unsupported GIN indexqual type: %d",
(int) nodeTag(clause));
}
}
}
/* Fall out if there were any provably-unsatisfiable quals */
if (!matchPossible)
{
*indexStartupCost = 0;
*indexTotalCost = 0;
*indexSelectivity = 0;
return;
}
if (counts.haveFullScan || indexQuals == NIL)
{
/*
* Full index scan will be required. We treat this as if every key in
* the index had been listed in the query; is that reasonable?
*/
counts.partialEntries = 0;
counts.exactEntries = numEntries;
counts.searchEntries = numEntries;
}
/* Will we have more than one iteration of a nestloop scan? */
outer_scans = loop_count;
/*
* Compute cost to begin scan, first of all, pay attention to pending
* list.
*/
entryPagesFetched = numPendingPages;
/*
* Estimate number of entry pages read. We need to do
* counts.searchEntries searches. Use a power function as it should be,
* but tuples on leaf pages usually is much greater. Here we include all
* searches in entry tree, including search of first entry in partial
* match algorithm
*/
entryPagesFetched += ceil(counts.searchEntries * rint(pow(numEntryPages, 0.15)));
/*
* Add an estimate of entry pages read by partial match algorithm. It's a
* scan over leaf pages in entry tree. We haven't any useful stats here,
* so estimate it as proportion. Because counts.partialEntries is really
* pretty bogus (see code above), it's possible that it is more than
* numEntries; clamp the proportion to ensure sanity.
*/
partialScale = counts.partialEntries / numEntries;
partialScale = Min(partialScale, 1.0);
entryPagesFetched += ceil(numEntryPages * partialScale);
/*
* Partial match algorithm reads all data pages before doing actual scan,
* so it's a startup cost. Again, we haven't any useful stats here, so
* estimate it as proportion.
*/
dataPagesFetched = ceil(numDataPages * partialScale);
/*
* Calculate cache effects if more than one scan due to nestloops or array
* quals. The result is pro-rated per nestloop scan, but the array qual
* factor shouldn't be pro-rated (compare genericcostestimate).
*/
if (outer_scans > 1 || counts.arrayScans > 1)
{
entryPagesFetched *= outer_scans * counts.arrayScans;
entryPagesFetched = index_pages_fetched(entryPagesFetched,
(BlockNumber) numEntryPages,
numEntryPages, root);
entryPagesFetched /= outer_scans;
dataPagesFetched *= outer_scans * counts.arrayScans;
dataPagesFetched = index_pages_fetched(dataPagesFetched,
(BlockNumber) numDataPages,
numDataPages, root);
dataPagesFetched /= outer_scans;
}
/*
* Here we use random page cost because logically-close pages could be far
* apart on disk.
*/
*indexStartupCost = (entryPagesFetched + dataPagesFetched) * spc_random_page_cost;
/*
* Now compute the number of data pages fetched during the scan.
*
* We assume every entry to have the same number of items, and that there
* is no overlap between them. (XXX: tsvector and array opclasses collect
* statistics on the frequency of individual keys; it would be nice to use
* those here.)
*/
dataPagesFetched = ceil(numDataPages * counts.exactEntries / numEntries);
/*
* If there is a lot of overlap among the entries, in particular if one of
* the entries is very frequent, the above calculation can grossly
* under-estimate. As a simple cross-check, calculate a lower bound based
* on the overall selectivity of the quals. At a minimum, we must read
* one item pointer for each matching entry.
*
* The width of each item pointer varies, based on the level of
* compression. We don't have statistics on that, but an average of
* around 3 bytes per item is fairly typical.
*/
dataPagesFetchedBySel = ceil(*indexSelectivity *
(numTuples / (BLCKSZ / 3)));
if (dataPagesFetchedBySel > dataPagesFetched)
dataPagesFetched = dataPagesFetchedBySel;
/* Account for cache effects, the same as above */
if (outer_scans > 1 || counts.arrayScans > 1)
{
dataPagesFetched *= outer_scans * counts.arrayScans;
dataPagesFetched = index_pages_fetched(dataPagesFetched,
(BlockNumber) numDataPages,
numDataPages, root);
dataPagesFetched /= outer_scans;
}
/* And apply random_page_cost as the cost per page */
*indexTotalCost = *indexStartupCost +
dataPagesFetched * spc_random_page_cost;
/*
* Add on index qual eval costs, much as in genericcostestimate. But we
* can disregard indexorderbys, since GIN doesn't support those.
*/
qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
qual_op_cost = cpu_operator_cost * list_length(indexQuals);
*indexStartupCost += qual_arg_cost;
*indexTotalCost += qual_arg_cost;
*indexTotalCost += (numTuples * *indexSelectivity) * (cpu_index_tuple_cost + qual_op_cost);
*indexPages = dataPagesFetched;
}
void
bmcostestimate(struct PlannerInfo *root,
struct IndexPath *path,
double loop_count,
Cost *indexStartupCost,
Cost *indexTotalCost,
Selectivity *indexSelectivity,
double *indexCorrelation,
double *indexPages)
{
IndexOptInfo *index = path->indexinfo;
RelOptInfo *baserel = index->rel;
RangeTblEntry *rte PG_USED_FOR_ASSERTS_ONLY = planner_rt_fetch(baserel->relid, root);
GenericCosts costs;
Assert(rte->rtekind == RTE_RELATION);
Assert(rte->relid != InvalidOid);
/*
* Now do generic index cost estimation.
*/
MemSet(&costs, 0, sizeof(costs));
/*
* We create a LOV for each distinct key in bitmap index. And the LOV point
* to the bitmap vector pages. Since each bitmap vector has the same length,
* although we do compress for the bits, but we can assume each distinct
* key has approximately same number of bitmap vector pages(although there
* must be some counterexamples). So the indexPages should be:
* selectedDistinctValues / numDistinctValues * index->pages.
*
* But the issue is we can't estimate both of the distinct values from stats
* through estimate_num_groups since it produces larger estimates. Especially
* for selectedDistinctValues.
*
* Image below cases:
* 1. indexSelectivity also correspond to how may distinct values get selected.
* Then the result of genericcostestimate's indexPages will be accurate.
* 2. indexSelectivity is high but only match a small number of distinct values.
* This means the bitmap vector is sparse. So the total index pages number should
* be small.
* 3. indexSelectivity is low but match lots of distinct values. This also means
* the bitmap vector is sparse, and the total index pages number should be small.
*
* The estimate in genericcostestimate should works fine for above cases although
* it's not accurate.
*/
genericcostestimate(root, path, loop_count, &costs);
*indexStartupCost = costs.indexStartupCost;
*indexTotalCost = costs.indexTotalCost;
#ifdef FAULT_INJECTOR
/* Simulate an bitmapAnd plan by changing bitmap cost. */
if (FaultInjector_InjectFaultIfSet("simulate_bitmap_and",
DDLNotSpecified,
"",
"") == FaultInjectorTypeSkip)
{
*indexTotalCost = 0;
}
#endif
*indexSelectivity = costs.indexSelectivity;
*indexCorrelation = costs.indexCorrelation;
*indexPages = costs.numIndexPages;
}
/*
* BRIN has search behavior completely different from other index types
*/
void
brincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
Cost *indexStartupCost, Cost *indexTotalCost,
Selectivity *indexSelectivity, double *indexCorrelation,
double *indexPages)
{
IndexOptInfo *index = path->indexinfo;
List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
double numPages = index->pages;
RelOptInfo *baserel = index->rel;
RangeTblEntry *rte = planner_rt_fetch(baserel->relid, root);
Cost spc_seq_page_cost;
Cost spc_random_page_cost;
double qual_arg_cost;
double qualSelectivity;
BrinStatsData statsData;
double indexRanges;
double minimalRanges;
double estimatedRanges;
double selec;
Relation indexRel;
ListCell *l;
VariableStatData vardata;
Assert(rte->rtekind == RTE_RELATION);
/* fetch estimated page cost for the tablespace containing the index */
get_tablespace_page_costs(index->reltablespace,
&spc_random_page_cost,
&spc_seq_page_cost);
/*
* Obtain some data from the index itself. A lock should have already
* been obtained on the index in plancat.c.
*/
indexRel = index_open(index->indexoid, NoLock);
brinGetStats(indexRel, &statsData);
index_close(indexRel, NoLock);
/*
* Compute index correlation
*
* Because we can use all index quals equally when scanning, we can use
* the largest correlation (in absolute value) among columns used by the
* query. Start at zero, the worst possible case. If we cannot find any
* correlation statistics, we will keep it as 0.
*/
*indexCorrelation = 0;
foreach(l, path->indexclauses)
{
IndexClause *iclause = lfirst_node(IndexClause, l);
AttrNumber attnum = index->indexkeys[iclause->indexcol];
/* attempt to lookup stats in relation for this index column */
if (attnum != 0)
{
/* Simple variable -- look to stats for the underlying table */
if (get_relation_stats_hook &&
(*get_relation_stats_hook) (root, rte, attnum, &vardata))
{
/*
* The hook took control of acquiring a stats tuple. If it
* did supply a tuple, it'd better have supplied a freefunc.
*/
if (HeapTupleIsValid(vardata.statsTuple) && !vardata.freefunc)
elog(ERROR,
"no function provided to release variable stats with");
}
else
{
vardata.statsTuple =
SearchSysCache3(STATRELATTINH,
ObjectIdGetDatum(rte->relid),
Int16GetDatum(attnum),
BoolGetDatum(false));
vardata.freefunc = ReleaseSysCache;
}
}
else
{
/*
* Looks like we've found an expression column in the index. Let's
* see if there's any stats for it.
*/
/* get the attnum from the 0-based index. */
attnum = iclause->indexcol + 1;
if (get_index_stats_hook &&
(*get_index_stats_hook) (root, index->indexoid, attnum, &vardata))
{
/*
* The hook took control of acquiring a stats tuple. If it
* did supply a tuple, it'd better have supplied a freefunc.
*/
if (HeapTupleIsValid(vardata.statsTuple) &&
!vardata.freefunc)
elog(ERROR, "no function provided to release variable stats with");
}
else
{
vardata.statsTuple = SearchSysCache3(STATRELATTINH,
ObjectIdGetDatum(index->indexoid),
Int16GetDatum(attnum),
BoolGetDatum(false));
vardata.freefunc = ReleaseSysCache;
}
}
if (HeapTupleIsValid(vardata.statsTuple))
{
AttStatsSlot sslot;
if (get_attstatsslot(&sslot, vardata.statsTuple,
STATISTIC_KIND_CORRELATION, InvalidOid,
ATTSTATSSLOT_NUMBERS))
{
double varCorrelation = 0.0;
if (sslot.nnumbers > 0)
varCorrelation = Abs(sslot.numbers[0]);
if (varCorrelation > *indexCorrelation)
*indexCorrelation = varCorrelation;
free_attstatsslot(&sslot);
}
}
ReleaseVariableStats(vardata);
}
qualSelectivity = clauselist_selectivity(root, indexQuals,
baserel->relid,
JOIN_INNER, NULL,
false /* use_damping */);
/* work out the actual number of ranges in the index */
indexRanges = Max(ceil((double) baserel->pages / statsData.pagesPerRange),
1.0);
/*
* Now calculate the minimum possible ranges we could match with if all of
* the rows were in the perfect order in the table's heap.
*/
minimalRanges = ceil(indexRanges * qualSelectivity);
/*
* Now estimate the number of ranges that we'll touch by using the
* indexCorrelation from the stats. Careful not to divide by zero (note
* we're using the absolute value of the correlation).
*/
if (*indexCorrelation < 1.0e-10)
estimatedRanges = indexRanges;
else
estimatedRanges = Min(minimalRanges / *indexCorrelation, indexRanges);
/* we expect to visit this portion of the table */
selec = estimatedRanges / indexRanges;
CLAMP_PROBABILITY(selec);
*indexSelectivity = selec;
/*
* Compute the index qual costs, much as in genericcostestimate, to add to
* the index costs. We can disregard indexorderbys, since BRIN doesn't
* support those.
*/
qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
/*
* Compute the startup cost as the cost to read the whole revmap
* sequentially, including the cost to execute the index quals.
*/
*indexStartupCost =
spc_seq_page_cost * statsData.revmapNumPages * loop_count;
*indexStartupCost += qual_arg_cost;
/*
* To read a BRIN index there might be a bit of back and forth over
* regular pages, as revmap might point to them out of sequential order;
* calculate the total cost as reading the whole index in random order.
*/
*indexTotalCost = *indexStartupCost +
spc_random_page_cost * (numPages - statsData.revmapNumPages) * loop_count;
/*
* Charge a small amount per range tuple which we expect to match to. This
* is meant to reflect the costs of manipulating the bitmap. The BRIN scan
* will set a bit for each page in the range when we find a matching
* range, so we must multiply the charge by the number of pages in the
* range.
*/
*indexTotalCost += 0.1 * cpu_operator_cost * estimatedRanges *
statsData.pagesPerRange;
*indexPages = index->pages;
}
/*
* estimate_num_groups_on_segment
*
* - groupNum : the number of groups globally
* - rows : the number of tuples globally
* - locus : how are the groups distributed?
*
* Estimate how many groups are on each segment, when the group keys do not contain
* distribution keys. Understand such condition, we can consider data is roughly
* random-distributed among all segments. The accurate formula to compute the
* expectation is (1-((numsegments-1)/numsegments)^(rows/groupNum))*groupNum.
*
* The above formula can be deduced using indicate-variable method. Let's focus on
* one specific segment, say seg0, and let Xi be a random variable:
* - Xi = 1, seg0 contains tuple from group i
* - Xi = 0, seg0 does not contain tuple from group i
* E(X1+X2+...+X_groupNum) is just what we want to compute. Thus the formula
* is easy to prove.
*/
double
estimate_num_groups_on_segment(double dNumGroupsTotal, double rows, CdbPathLocus locus)
{
if (CdbPathLocus_IsPartitioned(locus))
{
double numsegments = CdbPathLocus_NumSegments(locus);
double totalrows = rows * numsegments;
double numPerGroup = totalrows / dNumGroupsTotal;
double group_num;
group_num = (1-pow((numsegments-1)/numsegments, numPerGroup))*dNumGroupsTotal;
group_num = clamp_row_est(group_num);
return group_num;
}
else
return dNumGroupsTotal;
}
static void
try_fetch_rel_stats(RangeTblEntry *rte, const char *attname, VariableStatData* vardata)
{
AttrNumber attno;
Assert(rte != NULL);
/* attname may be NULL when 'SELECT DISTINCT <table_name> from <table_name>', and attno is set zero directly */
if (attname == NULL)
attno = InvalidAttrNumber;
else
attno = get_attnum(rte->relid, attname);
vardata->statsTuple = SearchSysCache3(STATRELATTINH,
ObjectIdGetDatum(rte->relid),
Int16GetDatum(attno),
BoolGetDatum(rte->inh));
vardata->freefunc = ReleaseSysCache;
}
static void
try_fetch_largest_child_stats(PlannerInfo *root, Index parent_rti,
const char *attname, VariableStatData* vardata)
{
RelOptInfo *parent_rel;
RelOptInfo *child_rel;
parent_rel = find_base_rel(root, parent_rti);
if (parent_rel->cheapest_total_path == NULL)
return;
child_rel = largest_child_relation(root, parent_rel->cheapest_total_path, false);
if (child_rel)
{
RangeTblEntry *child_rte = NULL;
child_rte = root->simple_rte_array[child_rel->relid];
try_fetch_rel_stats(child_rte, attname, vardata);
if (vardata->statsTuple != NULL)
{
adjust_partition_table_statistic_for_parent(vardata->statsTuple,
child_rel->tuples);
}
}
}
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