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openGauss-server/src/common/backend/utils/adt/selfuncs.cpp

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/* -------------------------------------------------------------------------
*
* 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 registered in the pg_am catalog
* in the "amcostestimate" attribute.
*
* Portions Copyright (c) 1996-2012, 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 database's default
* collation. Thus, in most cases where we are looking at statistics, we
* should ignore the actual operator collation and use DEFAULT_COLLATION_OID.
* We expect that the error induced by doing this is usually not large enough
* to justify complicating matters.
* ----------
*/
#include "postgres.h"
#include "knl/knl_variable.h"
#include <ctype.h>
#include <math.h>
#include "access/gin.h"
#include "access/relscan.h"
#include "access/sysattr.h"
#include "catalog/index.h"
#include "catalog/pg_collation.h"
#include "catalog/pg_namespace.h"
#include "catalog/pg_opfamily.h"
#include "catalog/pg_partition_fn.h"
#include "catalog/pg_statistic.h"
#include "catalog/pg_type.h"
#include "catalog/pg_proc.h"
#include "catalog/storage_gtt.h"
#include "executor/executor.h"
#include "foreign/foreign.h"
#include "mb/pg_wchar.h"
#include "miscadmin.h"
#include "nodes/makefuncs.h"
#include "nodes/nodeFuncs.h"
#include "optimizer/clauses.h"
#include "optimizer/cost.h"
#include "optimizer/pathnode.h"
#include "optimizer/paths.h"
#include "optimizer/plancat.h"
#include "optimizer/predtest.h"
#include "optimizer/restrictinfo.h"
#include "optimizer/streamplan.h"
#include "optimizer/var.h"
#include "optimizer/planner.h"
#include "parser/parse_clause.h"
#include "parser/parse_coerce.h"
#include "parser/parsetree.h"
#include "parser/parse_relation.h"
#include "pgstat.h"
#include "utils/acl.h"
#include "utils/builtins.h"
#include "utils/bytea.h"
#include "utils/date.h"
#include "utils/datum.h"
#include "utils/extended_statistics.h"
#include "utils/fmgroids.h"
#include "utils/lsyscache.h"
#include "utils/nabstime.h"
#include "utils/pg_locale.h"
#include "utils/rel.h"
#include "utils/rel_gs.h"
#include "utils/selfuncs.h"
#include "utils/spccache.h"
#include "utils/syscache.h"
#include "utils/timestamp.h"
#include "utils/tqual.h"
#include "utils/typcache.h"
#include "utils/memutils.h"
#ifdef PGXC
#include "pgxc/pgxc.h"
#include "access/transam.h"
#endif
static double var_eq_const(VariableStatData* vardata, Oid opera, Datum constval, bool constisnull, bool varonleft);
static double var_eq_non_const(VariableStatData* vardata, Oid opera, Node* other, bool varonleft);
static double ineq_histogram_selectivity(
PlannerInfo* root, VariableStatData* vardata, FmgrInfo* opproc, bool isgt, Datum constval, Oid consttype);
static double eqjoinsel_inner(
Oid opera, VariableStatData* vardata1, VariableStatData* vardata2, SpecialJoinInfo* sjinfo);
static double eqjoinsel_semi(
Oid opera, VariableStatData* vardata1, VariableStatData* vardata2, RelOptInfo* inner_rel, SpecialJoinInfo* sjinfo);
static double neqjoinsel_semi(
Oid opera, VariableStatData* vardata1, VariableStatData* vardata2, RelOptInfo* inner_rel, SpecialJoinInfo* sjinfo);
static bool convert_to_scalar(Datum value, Oid valuetypid, double* scaledvalue, Datum lobound, Datum hibound,
Oid boundstypid, double* scaledlobound, double* scaledhibound);
static double convert_numeric_to_scalar(Datum value, Oid typid);
static void convert_string_to_scalar(
const 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(const 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);
static double convert_timevalue_to_scalar(Datum value, Oid typid);
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);
RelOptInfo* find_join_input_rel(PlannerInfo* root, Relids relids);
static Selectivity prefix_selectivity(
PlannerInfo* root, VariableStatData* vardata, Oid vartype, Oid opfamily, Const* prefixcon);
static Selectivity like_selectivity(const char* patt, int pattlen, bool case_insensitive);
static Selectivity regex_selectivity(const char* patt, int pattlen, bool case_insensitive, int fixed_prefix_len);
static Datum string_to_datum(const char* str, Oid datatype);
static Const* string_to_const(const char* str, Oid datatype);
static Const* string_to_bytea_const(const char* str, size_t str_len);
static List* specialExpr_group_num(PlannerInfo* root, List* nodeList, double* numdistinct, double rows);
static List* add_unique_group_var(
PlannerInfo* root, List* varinfos, Node* var, VariableStatData* vardata, STATS_EST_TYPE eType = STATS_TYPE_LOCAL);
static double get_join_ratio(VariableStatData* vardata, SpecialJoinInfo* sjinfo);
bool can_use_possion(VariableStatData* vardata, SpecialJoinInfo* sjinfo, double* ratio);
extern Datum pg_stat_get_last_analyze_time(PG_FUNCTION_ARGS);
extern List* find_skew_join_distribute_keys(Plan* plan);
static Oid distinct_unshippable_func[] = {
/* substring func */
TEXTSUBSTRINGFUNCOID,
TEXTSUBSTRINGNOLENFUNCOID,
BITSUBSTRINGFUNOID,
BITSUBSTRINGNOLENFUNCOID,
BYTEASUBSTRINGFUNCOID,
BYTEASUBSTRINGNOLENOID,
TEXTREGEXSUBSTRINGOID,
SUBSTRINGESCAPEFUNCOID,
/* substr func */
SUBSTRFUNCOID,
SUBSTRBWITHLENFUNCOID,
SUBSTRBNOLENFUNCOID,
SUBSTRNOLENFUNCOID,
SUBSTRINNFUNCOID,
SUBSTRINNNOLENFUNCOID,
BYTEASUBSTRFUNCOID,
BYTEASUBSTRNOLENFUNCOID,
/* length */
TEXTLENOID,
LENGTHFUNCOID,
BPLENFUNCOID,
TEXTOCTLENFUNCOID,
BPOCTLENFUNCOID,
CHARLENFUNCOID,
/* datepart */
TIMESTAMPTZPARTFUNCOID,
INTERVALPARTFUNCOID,
TIMESTAMPTZTRUNCFUNCOID,
INTERVALTRUNCFUNCOID,
TIMEZPARTFUNCOID,
DATEPARTFROMABSTIMEFUNCOID,
DATEPARTFROMRELTIMEFUNCOID,
DATEPARTFUNCOID,
TIMEPARTFUNCOID,
TIMESTAMPPARTFUNCOID,
/* position */
POSITIONFUNCOID,
STRPOSFUNCOID,
BITPOSITIONFUNCOID,
BYTEAPOSFUNCOID,
INSTR2FUNCOID,
INSTR3FUNCOID,
INSTR4FUNCOID};
/* Is not different var in the same rel. */
#define VARNEQ(var1, var2) \
(IsA(var1, Var) && IsA(var2, Var)) && (((Var*)(var1))->varno == ((Var*)(var2))->varno) && \
!(_equalSimpleVar(var1, var2))
#define MINIMAL_SEL 1.0e-5 /* selectivity less than this is counted as approxiamtely 0 */
#define SKEW_RATIO 2 /* the skew ratio under this is considered as even distributed */
#define extrapolation_stats_log(funcname, selec, extrapolationselec) \
{ \
ereport(DEBUG2, \
(errmodule(MOD_OPT), \
errmsg("Extrapolation stats[%s]: original selectivity: %lf, " \
"extrapolated selectivity: %lf", \
funcname, \
selec, \
extrapolationselec))); \
}
/*
* 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)
{
PlannerInfo* root = (PlannerInfo*)PG_GETARG_POINTER(0);
Oid opera = PG_GETARG_OID(1);
List* args = (List*)PG_GETARG_POINTER(2);
int var_relid = PG_GETARG_INT32(3);
VariableStatData var_data;
var_data.statsTuple = NULL;
var_data.freefunc = NULL;
var_data.rel = NULL;
var_data.var = NULL;
Node* other = NULL;
bool var_on_left = true;
double selec;
/*
* If expression is not variable = something or something = variable, then
* punt and return a default estimate.
*/
if (!get_restriction_variable(root, args, var_relid, &var_data, &other, &var_on_left)) {
PG_RETURN_FLOAT8(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(&var_data, opera, ((Const*)other)->constvalue, ((Const*)other)->constisnull, var_on_left);
} else {
selec = var_eq_non_const(&var_data, opera, other, var_on_left);
}
ReleaseVariableStats(var_data);
PG_RETURN_FLOAT8((float8)selec);
}
/*
* var_eq_const --- eqsel for var = const case
*
* This is split out so that some other estimation functions can use it.
*/
static double var_eq_const(VariableStatData* var_data, Oid opera, Datum const_val, bool const_is_null, bool var_on_left)
{
double selec;
bool is_default = false;
Oid opfuncoid;
/*
* If the constant is NULL, assume operator is strict and return zero, ie,
* operator will never return TRUE.
*/
if (const_is_null) {
return 0.0;
}
/*
* 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 (var_data->isunique && var_data->rel && var_data->rel->tuples >= 1.0) {
return 1.0 / var_data->rel->tuples;
}
if (HeapTupleIsValid(var_data->statsTuple) &&
StatisticProcSecurityCheck(var_data, (opfuncoid = get_opcode(opera)))) {
Form_pg_statistic stats;
Datum* values = NULL;
int n_values;
float4* numbers = NULL;
int n_numbers;
bool match = false;
int i;
stats = (Form_pg_statistic)GETSTRUCT(var_data->statsTuple);
/*
* 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(var_data->statsTuple,
var_data->atttype,
var_data->atttypmod,
STATISTIC_KIND_MCV,
InvalidOid,
NULL,
&values,
&n_values,
&numbers,
&n_numbers)) {
FmgrInfo eqproc;
fmgr_info(opfuncoid, &eqproc);
for (i = 0; i < n_values; i++) {
/* be careful to apply operator right way 'round */
if (var_on_left) {
match = DatumGetBool(FunctionCall2Coll(&eqproc, DEFAULT_COLLATION_OID, values[i], const_val));
} else {
match = DatumGetBool(FunctionCall2Coll(&eqproc, DEFAULT_COLLATION_OID, const_val, values[i]));
}
if (match) {
break;
}
}
} else {
/* no most-common-value info available */
values = NULL;
numbers = NULL;
i = n_values = n_numbers = 0;
}
if (match) {
/*
* Constant is "=" to this common value. We know selectivity
* exactly (or as exactly as ANALYZE could calculate it, anyway).
*/
selec = 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 sum_common = 0.0;
double other_distinct;
double extra_polation_selec = 0.0;
for (i = 0; i < n_numbers; i++) {
sum_common += numbers[i];
}
selec = 1.0 - sum_common - stats->stanullfrac;
CLAMP_PROBABILITY(selec);
if (u_sess->attr.attr_sql.enable_extrapolation_stats && selec <= MINIMAL_SEL &&
TypeCategory(var_data->vartype) == TYPCATEGORY_DATETIME) {
/*
* Extrapolation stats for all mcv values without histogram
* only apply if there's no skew
*/
if (n_numbers > 0 && numbers[0] <= numbers[n_numbers - 1] * SKEW_RATIO)
extra_polation_selec = numbers[n_numbers - 1];
}
/*
* 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.
*/
other_distinct =
get_variable_numdistinct(var_data, &is_default, false, 1.0, NULL, STATS_TYPE_GLOBAL) - n_numbers;
if (other_distinct > 1) {
selec /= other_distinct;
}
/*
* Another cross-check: selectivity shouldn't be estimated as more
* than the least common "most common value".
*/
if (n_numbers > 0 && selec > numbers[n_numbers - 1]) {
selec = numbers[n_numbers - 1];
}
if (u_sess->attr.attr_sql.enable_extrapolation_stats) {
extrapolation_stats_log(__FUNCTION__, selec, extra_polation_selec);
}
selec = Max(selec, extra_polation_selec);
}
free_attstatsslot(var_data->atttype, values, n_values, numbers, n_numbers);
} 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.)
* "FuncExpr = Const" selectivity is underestimated due to lack of stats,
* so we hack it to 1.0/3.0
*/
if (IsA(var_data->var, FuncExpr)) {
if (((FuncExpr*)var_data->var)->funcid == SUBSTRFUNCOID ||
((FuncExpr*)var_data->var)->funcid == LENGTHFUNCOID) {
selec = 1.0 / 3.0;
} else {
selec = 1.0 / get_variable_numdistinct(var_data, &is_default, false, 1.0, NULL, STATS_TYPE_GLOBAL);
}
} else {
selec = 1.0 / get_variable_numdistinct(var_data, &is_default, false, 1.0, NULL, STATS_TYPE_GLOBAL);
}
}
/* 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
*/
static double var_eq_non_const(VariableStatData* var_data, Oid opera, Node* other, bool var_on_left)
{
double selec;
bool is_default = false;
/*
* 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 (var_data->isunique && var_data->rel && var_data->rel->tuples >= 1.0) {
return 1.0 / var_data->rel->tuples;
}
if (HeapTupleIsValid(var_data->statsTuple)) {
Form_pg_statistic stats;
double n_distinct;
float4* numbers = NULL;
int n_numbers;
stats = (Form_pg_statistic)GETSTRUCT(var_data->statsTuple);
/*
* 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 - stats->stanullfrac;
n_distinct = get_variable_numdistinct(var_data, &is_default, false, 1.0, NULL, STATS_TYPE_GLOBAL);
if (n_distinct > 1) {
selec /= n_distinct;
}
/*
* Cross-check: selectivity should never be estimated as more than the
* most common value's.
*/
if (get_attstatsslot(var_data->statsTuple,
var_data->atttype,
var_data->atttypmod,
STATISTIC_KIND_MCV,
InvalidOid,
NULL,
NULL,
NULL,
&numbers,
&n_numbers)) {
if (n_numbers > 0 && selec > numbers[0]) {
selec = numbers[0];
}
free_attstatsslot(var_data->atttype, NULL, 0, numbers, n_numbers);
}
} 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(var_data, &is_default, false, 1.0, NULL, STATS_TYPE_GLOBAL);
}
/* 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)
{
PlannerInfo* root = (PlannerInfo*)PG_GETARG_POINTER(0);
Oid opera = PG_GETARG_OID(1);
List* args = (List*)PG_GETARG_POINTER(2);
int var_relid = PG_GETARG_INT32(3);
Oid eq_op;
float8 result;
/*
* We want 1 - eqsel() - nullfrac where the equality operator is the one associated
* with this != operator, that is, its negator.
*/
eq_op = get_negator(opera);
if (eq_op) {
result = DatumGetFloat8(DirectFunctionCall4(
eqsel, PointerGetDatum(root), ObjectIdGetDatum(eq_op), PointerGetDatum(args), Int32GetDatum(var_relid)));
/*
* calculate nullfrac of the variable for var <> const or const <> var.
* First, we should detemine on left or right the variable locates, and
* then do the fraction calculation of null test
*/
Node *left = (Node*)linitial(args);
Node *right = (Node*)lsecond(args);
Node *node = NULL;
Relids varno_left = pull_varnos(left);
Relids varno_right = pull_varnos(right);
if (varno_left != NULL && varno_right == NULL) {
node = left;
} else if (varno_left == NULL && varno_right != NULL) {
node = right;
}
if (node != NULL) {
result += nulltestsel(root, IS_NULL, node, var_relid, JOIN_INNER, NULL);
}
CLAMP_PROBABILITY(result);
bms_free(varno_left);
varno_left = NULL;
bms_free(varno_right);
varno_right = NULL;
} else {
/* Use default selectivity (should we raise an error instead?) */
result = DEFAULT_EQ_SEL;
}
result = 1.0 - result;
PG_RETURN_FLOAT8(result);
}
/*
* scalarineqsel - Selectivity of "<", "<=", ">", ">=" for scalars.
*
* This is the guts of both scalarltsel and scalargtsel. 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 same into a value and
* datatype.
*
* 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 a default estimate.
*/
static double scalarineqsel(
PlannerInfo* root, Oid opera, bool is_gt, VariableStatData* var_data, Datum const_val, Oid const_type)
{
Form_pg_statistic stats;
FmgrInfo op_proc;
double mcv_selec;
double hist_selec;
double sum_common = 0.0;
double last_common = 0.0;
double selec;
Oid equal_operator = InvalidOid;
double equal_selec = 0.0;
bool in_mcv = false;
if (!HeapTupleIsValid(var_data->statsTuple)) {
/* no stats available, so default result */
return DEFAULT_INEQ_SEL;
}
stats = (Form_pg_statistic)GETSTRUCT(var_data->statsTuple);
fmgr_info(get_opcode(opera), &op_proc);
equal_operator = get_equal(opera);
/*
* 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(var_data, &op_proc, const_val, true, &sum_common,
equal_operator, &in_mcv, &last_common);
/*
* 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, var_data, &op_proc, is_gt, const_val, const_type);
/*
* 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 - sum_common;
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;
/*
* The purpose of the following branch is to handle equal range condition
* but the value is not collected in mcv. If cost_param is turned on, the branch
* is going to work for broundary equality selectivity consideration.
* In some cases, the estimated rows would be far away from the actual rows,
* and cause execution performance degragation.
* So the equal selectivity will be used to optimize the range selectivity
* and eliminate the performance degragation.
*/
if (((uint32)u_sess->attr.attr_sql.cost_param & COST_ALTERNATIVE_EQUALRANGE_NOTINMCV) && !in_mcv &&
(get_opname(opera) != NULL && (strcmp(get_opname(opera), ">=") == 0 || strcmp(get_opname(opera), "<=") == 0))) {
equal_selec = var_eq_const(var_data, equal_operator, const_val, false, true);
/*
* For ">=" and "<=" selectivity calculation, it is worth noticing that
* if const_val is not in MCV, the "=" part will be ignored by both
* hist_selec and mcv_selec. To accommodate for such cases, it is needed to
* add the equal selectivity.
*/
selec += equal_selec;
}
/* extrapolation stats */
if (u_sess->attr.attr_sql.enable_extrapolation_stats && is_gt &&
TypeCategory(var_data->vartype) == TYPCATEGORY_DATETIME) {
if (hist_selec < 0.0 && sum_common > 0.0) {
/* no histogram, only mcv, use freq of last mcv as extrapolation selectivity */
extrapolation_stats_log(__FUNCTION__, selec, last_common);
selec = Max(last_common, selec);
} else if (hist_selec >= 0.0 && sum_common == 0.0) {
/* no mcv, only histogram, use 1/distinct as extrapolation selectivity */
bool is_default = false;
double num_distinct = get_variable_numdistinct(var_data, &is_default, false, 1.0, NULL, STATS_TYPE_GLOBAL);
double extra_polation_selec = (num_distinct == 0.0) ? 0.0 : (1.0 - stats->stanullfrac) / num_distinct;
extrapolation_stats_log(__FUNCTION__, selec, extra_polation_selec);
selec = Max(selec, extra_polation_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* var_data, FmgrInfo* op_proc, Datum const_val, bool var_on_left,
double* sum_commonp, Oid equal_operator, bool* in_mcv, double* last_commonp)
{
double mcv_selec = 0.0;
double sum_common = 0.0;
Datum* values = NULL;
int n_values;
float4* numbers = NULL;
int n_numbers;
int i;
FmgrInfo op_equal;
*in_mcv = false;
/*
* In normal cases, however, when calculating selectivity of a > const or
* a < const, if a is a not a mcv the selectivity of a = const is split
* into two parts and is added to the two sides, because of the difference
* between the histogram's continuity and the discrete nature of data.
*
* To resolve this issue, we get rid of half of the equal selectivity to
* each side.
* This is only applied when the threshold value is not in MCV.
*/
if (equal_operator != InvalidOid) {
fmgr_info(get_opcode(equal_operator), &op_equal);
}
if (HeapTupleIsValid(var_data->statsTuple) &&
StatisticProcSecurityCheck(var_data, op_proc->fn_oid) &&
get_attstatsslot(var_data->statsTuple,
var_data->atttype,
var_data->atttypmod,
STATISTIC_KIND_MCV,
InvalidOid,
NULL,
&values,
&n_values,
&numbers,
&n_numbers)) {
for (i = 0; i < n_values; i++) {
if (op_equal.fn_oid != InvalidOid && equal_operator != InvalidOid) {
if (var_on_left ?
DatumGetBool(FunctionCall2Coll(&op_equal, DEFAULT_COLLATION_OID, values[i], const_val)) :
DatumGetBool(FunctionCall2Coll(&op_equal, DEFAULT_COLLATION_OID, const_val, values[i]))) {
*in_mcv = true;
}
}
if (var_on_left ?
DatumGetBool(FunctionCall2Coll(op_proc, DEFAULT_COLLATION_OID, values[i], const_val)) :
DatumGetBool(FunctionCall2Coll(op_proc, DEFAULT_COLLATION_OID, const_val, values[i]))) {
mcv_selec += numbers[i];
}
sum_common += numbers[i];
}
if (n_values > 0 && last_commonp != NULL && numbers[0] <= numbers[n_values - 1] * SKEW_RATIO) {
*last_commonp = numbers[n_values - 1];
}
free_attstatsslot(var_data->atttype, values, n_values, numbers, n_numbers);
}
CLAMP_PROBABILITY(sum_common);
*sum_commonp = sum_common;
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* var_data, FmgrInfo* op_proc, Datum const_val, bool var_on_left,
int min_hist_size, int n_skip, int* hist_size)
{
double result;
Datum* values = NULL;
int n_values;
/* check sanity of parameters */
Assert(n_skip >= 0);
Assert(min_hist_size > 2 * n_skip);
if (HeapTupleIsValid(var_data->statsTuple) &&
StatisticProcSecurityCheck(var_data, op_proc->fn_oid) &&
get_attstatsslot(var_data->statsTuple,
var_data->atttype,
var_data->atttypmod,
STATISTIC_KIND_HISTOGRAM,
InvalidOid,
NULL,
&values,
&n_values,
NULL,
NULL)) {
*hist_size = n_values;
if (n_values >= min_hist_size) {
int n_match = 0;
int i;
for (i = n_skip; i < n_values - n_skip; i++) {
if (var_on_left ?
DatumGetBool(FunctionCall2Coll(op_proc, DEFAULT_COLLATION_OID, values[i], const_val)) :
DatumGetBool(FunctionCall2Coll(op_proc, DEFAULT_COLLATION_OID, const_val, values[i]))) {
n_match++;
}
}
result = ((double)n_match) / ((double)(n_values - 2 * n_skip));
} else {
result = -1;
}
free_attstatsslot(var_data->atttype, values, n_values, NULL, 0);
} 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 < CONST or VAR > CONST.
*
* 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.
*/
static double ineq_histogram_selectivity(
PlannerInfo* root, VariableStatData* var_data, FmgrInfo* op_proc, bool is_gt, Datum const_val, Oid const_type)
{
double hist_selec;
Oid hist_op;
Datum* values = NULL;
int n_values;
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 is_gt is TRUE.
*/
if (HeapTupleIsValid(var_data->statsTuple) &&
StatisticProcSecurityCheck(var_data, op_proc->fn_oid) &&
get_attstatsslot(var_data->statsTuple,
var_data->atttype,
var_data->atttypmod,
STATISTIC_KIND_HISTOGRAM,
InvalidOid,
&hist_op,
&values,
&n_values,
NULL,
NULL)) {
if (n_values > 1) {
/*
* Use binary search to find proper location, ie, the first slot
* at which the comparison fails. (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
* from the old linear search.)
*
* 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 hist_frac;
int lo_bound = 0; /* first possible slot to search */
int hi_bound = n_values; /* 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 (n_values == 2) {
have_end = get_actual_variable_range(root, var_data, hist_op, &values[0], &values[1]);
}
while (lo_bound < hi_bound) {
int probe = (lo_bound + hi_bound) / 2;
bool lt_cmp = false;
/*
* 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 && n_values > 2) {
have_end = get_actual_variable_range(root, var_data, hist_op, &values[0], NULL);
} else if (probe == n_values - 1 && n_values > 2) {
have_end = get_actual_variable_range(root, var_data, hist_op, NULL, &values[probe]);
}
lt_cmp = DatumGetBool(FunctionCall2Coll(op_proc, DEFAULT_COLLATION_OID, values[probe], const_val));
if (is_gt) {
lt_cmp = !lt_cmp;
}
if (lt_cmp) {
lo_bound = probe + 1;
} else {
hi_bound = probe;
}
}
if (lo_bound <= 0) {
/* Constant is below lower histogram boundary. */
hist_frac = 0.0;
} else if (lo_bound >= n_values) {
/* Constant is above upper histogram boundary. */
hist_frac = 1.0;
} else {
int i = lo_bound;
double val;
double high;
double low;
double bin_frac;
/*
* We have values[i-1] <= constant <= values[i].
*
* 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(
const_val, const_type, &val, values[i - 1], values[i], var_data->vartype, &low, &high)) {
if (high <= low) {
/* cope if bin boundaries appear identical */
bin_frac = 0.5;
} else if (val <= low) {
bin_frac = 0.0;
} else if (val >= high) {
bin_frac = 1.0;
} else {
bin_frac = (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(bin_frac) || bin_frac < 0.0 || bin_frac > 1.0) {
bin_frac = 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.
*/
bin_frac = 0.5;
}
/*
* Now, compute the overall selectivity across the values
* represented by the histogram. We have i-1 full bins and
* bin_frac partial bin below the constant.
*/
hist_frac = (double)(i - 1) + bin_frac;
hist_frac /= (double)(n_values - 1);
}
/*
* Now hist_frac = fraction of histogram entries below the
* constant.
*
* Account for "<" vs ">"
*/
hist_selec = is_gt ? (1.0 - hist_frac) : hist_frac;
/*
* 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.
*/
if (have_end) {
CLAMP_PROBABILITY(hist_selec);
} else {
if (hist_selec < 0.0001) {
hist_selec = 0.0001;
} else if (hist_selec > 0.9999) {
hist_selec = 0.9999;
}
}
}
free_attstatsslot(var_data->atttype, values, n_values, NULL, 0);
}
return hist_selec;
}
/*
* scalarltsel - Selectivity of "<" (also "<=") for scalars.
*/
Datum scalarltsel(PG_FUNCTION_ARGS)
{
PlannerInfo* root = (PlannerInfo*)PG_GETARG_POINTER(0);
Oid opera = PG_GETARG_OID(1);
List* args = (List*)PG_GETARG_POINTER(2);
int var_relid = PG_GETARG_INT32(3);
VariableStatData var_data;
var_data.statsTuple = NULL;
var_data.freefunc = NULL;
var_data.var = NULL;
var_data.rel = NULL;
Node* other = NULL;
bool var_on_left = true;
Datum const_val;
Oid const_type;
bool is_gt = false;
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, var_relid, &var_data, &other, &var_on_left)) {
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* Can't do anything useful if the something is not a constant, either.
*/
if (!IsA(other, Const)) {
ReleaseVariableStats(var_data);
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(var_data);
PG_RETURN_FLOAT8(0.0);
}
const_val = ((Const*)other)->constvalue;
const_type = ((Const*)other)->consttype;
/*
* Force the var to be on the left to simplify logic in scalarineqsel.
*/
if (var_on_left) {
/* we have var < other */
is_gt = false;
} else {
/* we have other < var, commute to make var > other */
opera = get_commutator(opera);
if (!opera) {
/* Use default selectivity (should we raise an error instead?) */
ReleaseVariableStats(var_data);
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
is_gt = true;
}
selec = scalarineqsel(root, opera, is_gt, &var_data, const_val, const_type);
ReleaseVariableStats(var_data);
PG_RETURN_FLOAT8((float8)selec);
}
/*
* scalargtsel - Selectivity of ">" (also ">=") for integers.
*/
Datum scalargtsel(PG_FUNCTION_ARGS)
{
PlannerInfo* root = (PlannerInfo*)PG_GETARG_POINTER(0);
Oid opera = PG_GETARG_OID(1);
List* args = (List*)PG_GETARG_POINTER(2);
int var_relid = PG_GETARG_INT32(3);
VariableStatData var_data;
var_data.statsTuple = NULL;
var_data.freefunc = NULL;
var_data.rel = NULL;
var_data.var = NULL;
Node* other = NULL;
bool var_on_left = true;
Datum const_val;
Oid const_type;
bool is_gt = false;
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, var_relid, &var_data, &other, &var_on_left)) {
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* Can't do anything useful if the something is not a constant, either.
*/
if (!IsA(other, Const)) {
ReleaseVariableStats(var_data);
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(var_data);
PG_RETURN_FLOAT8(0.0);
}
const_val = ((Const*)other)->constvalue;
const_type = ((Const*)other)->consttype;
/*
* Force the var to be on the left to simplify logic in scalarineqsel.
*/
if (var_on_left) {
/* we have var > other */
is_gt = true;
} else {
/* we have other > var, commute to make var < other */
opera = get_commutator(opera);
if (!opera) {
/* Use default selectivity (should we raise an error instead?) */
ReleaseVariableStats(var_data);
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
is_gt = false;
}
selec = scalarineqsel(root, opera, is_gt, &var_data, const_val, const_type);
ReleaseVariableStats(var_data);
PG_RETURN_FLOAT8((float8)selec);
}
/*
* pattern_sel - Generic code for pattern-match selectivity.
*/
static double pattern_sel(PG_FUNCTION_ARGS, Pattern_Type ptype, bool negate)
{
PlannerInfo* root = (PlannerInfo*)PG_GETARG_POINTER(0);
Oid opera = PG_GETARG_OID(1);
List* args = (List*)PG_GETARG_POINTER(2);
int var_relid = PG_GETARG_INT32(3);
Oid collation = PG_GET_COLLATION();
VariableStatData var_data;
var_data.statsTuple = NULL;
var_data.freefunc = NULL;
var_data.var = NULL;
var_data.rel = NULL;
Node* other = NULL;
bool var_on_left = true;
Datum const_val;
Oid const_type;
Oid var_type;
Oid op_family;
Pattern_Prefix_Status pstatus;
Const* patt = NULL;
Const* prefix = NULL;
Selectivity rest_selec = 0;
double result;
/*
* If this is for a NOT LIKE or similar operator, get the corresponding
* positive-match operator and work with that. Set result to the correct
* default estimate, too.
*/
if (negate) {
opera = get_negator(opera);
if (!OidIsValid(opera)) {
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_OPERATE_NOT_SUPPORTED),
errmsg("pattern_sel called for operator without a negator"))));
}
result = 1.0 - DEFAULT_MATCH_SEL;
} else {
result = DEFAULT_MATCH_SEL;
}
/*
* If expression is not variable op constant, then punt and return a
* default estimate.
*/
if (!get_restriction_variable(root, args, var_relid, &var_data, &other, &var_on_left)) {
return result;
}
if (!var_on_left || !IsA(other, Const)) {
ReleaseVariableStats(var_data);
return result;
}
/*
* 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 (((Const*)other)->constisnull) {
ReleaseVariableStats(var_data);
return 0.0;
}
const_val = ((Const*)other)->constvalue;
const_type = ((Const*)other)->consttype;
/*
* The right-hand const is type text or bytea for all supported operators.
* We do not expect to see binary-compatible types here, since
* const-folding should have relabeled the const to exactly match the
* operator's declared type.
*/
if (const_type != TEXTOID && const_type != BYTEAOID) {
ReleaseVariableStats(var_data);
return result;
}
/*
* Similarly, the exposed type of the left-hand side should be one of
* those we know. (Do not look at var_data.atttype, which might be
* something binary-compatible but different.) We can use it to choose
* the index op_family from which we must draw the comparison operators.
*
* NOTE: It would be more correct to use the PATTERN opfamilies than the
* simple ones, but at the moment ANALYZE will not generate statistics for
* the PATTERN operators. But our results are so approximate anyway that
* it probably hardly matters.
*/
var_type = var_data.vartype;
switch (var_type) {
case TEXTOID:
op_family = TEXT_BTREE_FAM_OID;
break;
case BPCHAROID:
op_family = BPCHAR_BTREE_FAM_OID;
break;
case NAMEOID:
op_family = NAME_BTREE_FAM_OID;
break;
case BYTEAOID:
op_family = BYTEA_BTREE_FAM_OID;
break;
default:
ReleaseVariableStats(var_data);
return result;
}
/*
* Pull out any fixed prefix implied by the pattern, and estimate the
* fractional selectivity of the remainder of the pattern. Unlike many of
* the other functions in this file, we use the pattern operator's actual
* collation for this step. This is not because we expect the collation
* to make a big difference in the selectivity estimate (it seldom would),
* but because we want to be sure we cache compiled regexps under the
* right cache key, so that they can be re-used at runtime.
*/
patt = (Const*)other;
pstatus = pattern_fixed_prefix(patt, ptype, collation, &prefix, &rest_selec);
/*
* If necessary, coerce the prefix constant to the right type.
*/
if ((prefix != NULL) && prefix->consttype != var_type) {
char* prefixstr = NULL;
switch (prefix->consttype) {
case TEXTOID:
prefixstr = TextDatumGetCString(prefix->constvalue);
break;
case BYTEAOID:
prefixstr = DatumGetCString(DirectFunctionCall1(byteaout, prefix->constvalue));
break;
default:
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_UNRECOGNIZED_NODE_TYPE),
errmsg("unrecognized const_type: %u", prefix->consttype))));
ReleaseVariableStats(var_data);
return result;
}
prefix = string_to_const(prefixstr, var_type);
pfree_ext(prefixstr);
}
if (pstatus == Pattern_Prefix_Exact) {
/*
* Pattern specifies an exact match, so pretend operator is '='
*/
Oid eq_opr = get_opfamily_member(op_family, var_type, var_type, BTEqualStrategyNumber);
if (eq_opr == InvalidOid) {
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_OPERATE_NOT_SUPPORTED), errmsg("no = operator for op_family %u", op_family))));
}
result = var_eq_const(&var_data, eq_opr, prefix->constvalue, false, true);
} else {
/*
* Not exact-match pattern. If we have a sufficiently large
* histogram, estimate selectivity for the histogram part of the
* population by counting matches in the histogram. If not, estimate
* selectivity of the fixed prefix and remainder of pattern
* separately, then combine the two to get an estimate of the
* selectivity for the part of the column population represented by
* the histogram. (For small histograms, we combine these
* approaches.)
*
* We then add up data for any most-common-values values; these are
* not in the histogram population, and we can get exact answers for
* them by applying the pattern operator, so there's no reason to
* approximate. (If the MCVs cover a significant part of the total
* population, this gives us a big leg up in accuracy.)
*/
Selectivity selec;
int hist_size;
FmgrInfo op_proc;
double null_frac;
double mcv_selec;
double sum_common;
double last_common;
bool in_mcv = false;
/* Try to use the histogram entries to get selectivity */
fmgr_info(get_opcode(opera), &op_proc);
selec = histogram_selectivity(&var_data, &op_proc, const_val, true, 10, 1, &hist_size);
/* If not at least 100 entries, use the heuristic method */
if (hist_size < 100) {
Selectivity heur_sel;
Selectivity prefix_sel;
if (pstatus == Pattern_Prefix_Partial) {
prefix_sel = prefix_selectivity(root, &var_data, var_type, op_family, prefix);
} else {
prefix_sel = 1.0;
}
heur_sel = prefix_sel * rest_selec;
if (selec < 0) { /* fewer than 10 histogram entries? */
selec = heur_sel;
} else {
/*
* For histogram sizes from 10 to 100, we combine the
* histogram and heuristic selectivities, putting increasingly
* more trust in the histogram for larger sizes.
*/
double hist_weight = hist_size / 100.0;
selec = selec * hist_weight + heur_sel * (1.0 - hist_weight);
}
}
/* In any case, don't believe extremely small or large estimates. */
if (selec < 0.0001) {
selec = 0.0001;
} else if (selec > 0.9999) {
selec = 0.9999;
}
/*
* If we have most-common-values info, add up the fractions of the MCV
* entries that satisfy MCV OP PATTERN. These fractions contribute
* directly to the result selectivity. Also add up the total fraction
* represented by MCV entries.
*/
mcv_selec = mcv_selectivity(&var_data, &op_proc, const_val, true, &sum_common, InvalidOid,
&in_mcv, &last_common);
if (HeapTupleIsValid(var_data.statsTuple)) {
null_frac = ((Form_pg_statistic)GETSTRUCT(var_data.statsTuple))->stanullfrac;
} else {
null_frac = 0.0;
}
/*
* 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 - null_frac - sum_common;
selec += mcv_selec;
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
result = selec;
}
if (prefix != NULL) {
pfree(DatumGetPointer(prefix->constvalue));
pfree_ext(prefix);
}
ReleaseVariableStats(var_data);
return negate ? (1.0 - result) : result;
}
/*
* regexeqsel - Selectivity of regular-expression pattern match.
*/
Datum regexeqsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(pattern_sel(fcinfo, Pattern_Type_Regex, false));
}
/*
* icregexeqsel - Selectivity of case-insensitive regex match.
*/
Datum icregexeqsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(pattern_sel(fcinfo, Pattern_Type_Regex_IC, false));
}
/*
* likesel - Selectivity of LIKE pattern match.
*/
Datum likesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(pattern_sel(fcinfo, Pattern_Type_Like, false));
}
/*
* iclikesel - Selectivity of ILIKE pattern match.
*/
Datum iclikesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(pattern_sel(fcinfo, Pattern_Type_Like_IC, false));
}
/*
* regexnesel - Selectivity of regular-expression pattern non-match.
*/
Datum regexnesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(pattern_sel(fcinfo, Pattern_Type_Regex, true));
}
/*
* icregexnesel - Selectivity of case-insensitive regex non-match.
*/
Datum icregexnesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(pattern_sel(fcinfo, Pattern_Type_Regex_IC, true));
}
/*
* nlikesel - Selectivity of LIKE pattern non-match.
*/
Datum nlikesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(pattern_sel(fcinfo, Pattern_Type_Like, true));
}
/*
* icnlikesel - Selectivity of ILIKE pattern non-match.
*/
Datum icnlikesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(pattern_sel(fcinfo, Pattern_Type_Like_IC, true));
}
/*
* booltestsel - Selectivity of BooleanTest Node.
*/
Selectivity booltestsel(PlannerInfo* root, BoolTestType bool_test_type, Node* arg, int var_relid, JoinType join_type,
SpecialJoinInfo* sj_info)
{
VariableStatData var_data;
var_data.statsTuple = NULL;
var_data.freefunc = NULL;
var_data.rel = NULL;
var_data.var = NULL;
double selec;
examine_variable(root, arg, var_relid, &var_data);
if (HeapTupleIsValid(var_data.statsTuple)) {
Form_pg_statistic stats;
double freq_null;
Datum* values = NULL;
int n_values;
float4* numbers = NULL;
int n_numbers;
stats = (Form_pg_statistic)GETSTRUCT(var_data.statsTuple);
freq_null = stats->stanullfrac;
if (get_attstatsslot(var_data.statsTuple,
var_data.atttype,
var_data.atttypmod,
STATISTIC_KIND_MCV,
InvalidOid,
NULL,
&values,
&n_values,
&numbers,
&n_numbers) &&
n_numbers > 0) {
double freq_true;
double freq_false;
/*
* Get first MCV frequency and derive frequency for true.
*/
if (DatumGetBool(values[0])) {
freq_true = numbers[0];
} else {
freq_true = 1.0 - 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 (bool_test_type) {
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:
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_UNRECOGNIZED_NODE_TYPE),
errmsg("unrecognized bool_test_type: %d", (int)bool_test_type))));
selec = 0.0; /* Keep compiler quiet */
break;
}
free_attstatsslot(var_data.atttype, values, n_values, numbers, n_numbers);
} 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 an even split for boolean tests.
*/
switch (bool_test_type) {
case IS_UNKNOWN:
/*
* Use freq_null directly.
*/
selec = freq_null;
break;
case IS_NOT_UNKNOWN:
/*
* Select not unknown (not null) values. Calculate from
* freq_null.
*/
selec = 1.0 - freq_null;
break;
case IS_TRUE:
case IS_NOT_TRUE:
case IS_FALSE:
case IS_NOT_FALSE:
selec = (1.0 - freq_null) / 2.0;
break;
default:
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_UNRECOGNIZED_NODE_TYPE),
errmsg("unrecognized bool_test_type: %d", (int)bool_test_type))));
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 (bool_test_type) {
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, var_relid, join_type, sj_info, false);
break;
case IS_FALSE:
case IS_NOT_TRUE:
selec = 1.0 - (double)clause_selectivity(root, arg, var_relid, join_type, sj_info, false);
break;
default:
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_UNRECOGNIZED_NODE_TYPE),
errmsg("unrecognized bool_test_type: %d", (int)bool_test_type))));
selec = 0.0; /* Keep compiler quiet */
break;
}
}
ReleaseVariableStats(var_data);
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
return (Selectivity)selec;
}
/*
* nulltestsel - Selectivity of NullTest Node.
*/
Selectivity nulltestsel(PlannerInfo* root, NullTestType null_test_type, Node* arg, int var_relid, JoinType join_type,
SpecialJoinInfo* sj_info)
{
VariableStatData var_data;
var_data.statsTuple = NULL;
var_data.freefunc = NULL;
var_data.rel = NULL;
var_data.var = NULL;
double selec;
examine_variable(root, arg, var_relid, &var_data);
if (HeapTupleIsValid(var_data.statsTuple)) {
Form_pg_statistic stats;
double freq_null;
stats = (Form_pg_statistic)GETSTRUCT(var_data.statsTuple);
freq_null = stats->stanullfrac;
switch (null_test_type) {
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:
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_UNRECOGNIZED_NODE_TYPE),
errmsg("unrecognized null_test_type: %d", (int)null_test_type))));
return (Selectivity)0; /* keep compiler quiet */
}
} else {
/*
* No ANALYZE stats available, so make a guess
*/
switch (null_test_type) {
case IS_NULL:
selec = DEFAULT_UNK_SEL;
break;
case IS_NOT_NULL:
selec = DEFAULT_NOT_UNK_SEL;
break;
default:
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_UNRECOGNIZED_NODE_TYPE),
errmsg("unrecognized null_test_type: %d", (int)null_test_type))));
return (Selectivity)0; /* keep compiler quiet */
}
}
ReleaseVariableStats(var_data);
/* 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*)node)->elemfuncid == InvalidOid) {
node = (Node*)((ArrayCoerceExpr*)node)->arg;
} 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 var_relid,
JoinType join_type, SpecialJoinInfo* sj_info)
{
Oid opera = clause->opno;
bool use_or = clause->useOr;
bool is_equality = false;
bool is_in_equality = false;
Node* left_op = NULL;
Node* right_op = NULL;
Oid nominal_element_type;
Oid nominal_element_collation;
TypeCacheEntry* type_entry = NULL;
RegProcedure opr_sel;
FmgrInfo opr_sel_proc;
Selectivity s1;
Selectivity s1_disjoint;
/* First, deconstruct the expression */
Assert(list_length(clause->args) == 2);
left_op = (Node*)linitial(clause->args);
right_op = (Node*)lsecond(clause->args);
/* get nominal (after relabeling) element type of right_op */
nominal_element_type = get_base_element_type(exprType(right_op));
if (!OidIsValid(nominal_element_type)) {
return (Selectivity)0.5; /* probably shouldn't happen */
}
/* get nominal collation, too, for generating constants */
nominal_element_collation = exprCollation(right_op);
/* look through any binary-compatible relabeling of right_op */
right_op = strip_array_coercion(right_op);
/*
* Detect whether the operator is the default equality or inequality
* operator of the array element type.
*/
type_entry = lookup_type_cache(nominal_element_type, TYPECACHE_EQ_OPR);
if (OidIsValid(type_entry->eq_opr)) {
if (opera == type_entry->eq_opr) {
is_equality = true;
}
else if (get_negator(opera) == type_entry->eq_opr) {
is_in_equality = 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 ((is_equality || is_in_equality) && !is_join_clause) {
s1 = scalararraysel_containment(root, left_op, right_op, nominal_element_type, is_equality, use_or, var_relid);
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) {
opr_sel = get_oprjoin(opera);
} else {
opr_sel = get_oprrest(opera);
}
if (!opr_sel) {
return (Selectivity)0.5;
}
fmgr_info(opr_sel, &opr_sel_proc);
/*
* 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 (opr_sel == F_EQSEL || opr_sel == F_EQJOINSEL) {
is_equality = true;
} else if (opr_sel == F_NEQSEL || opr_sel == F_NEQJOINSEL) {
is_in_equality = true;
}
/*
* We consider three cases:
*
* 1. right_op 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. right_op 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 (right_op && IsA(right_op, Const)) {
Datum array_datum = ((Const*)right_op)->constvalue;
bool array_is_null = ((Const*)right_op)->constisnull;
ArrayType* array_val = NULL;
int16 elm_len;
bool elm_by_val = false;
char elm_align;
int num_elems;
Datum* elem_values = NULL;
bool* elem_nulls = NULL;
int i;
if (array_is_null) { /* qual can't succeed if null array */
return (Selectivity)0.0;
}
array_val = DatumGetArrayTypeP(array_datum);
get_typlenbyvalalign(ARR_ELEMTYPE(array_val), &elm_len, &elm_by_val, &elm_align);
deconstruct_array(
array_val, ARR_ELEMTYPE(array_val), elm_len, elm_by_val, elm_align, &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 = s1_disjoint = (use_or ? 0.0 : 1.0);
for (i = 0; i < num_elems; i++) {
List* args = NIL;
Selectivity s2;
args = list_make2(left_op,
makeConst(nominal_element_type,
-1,
nominal_element_collation,
elm_len,
elem_values[i],
elem_nulls[i],
elm_by_val));
if (is_join_clause) {
s2 = DatumGetFloat8(FunctionCall5Coll(&opr_sel_proc,
clause->inputcollid,
PointerGetDatum(root),
ObjectIdGetDatum(opera),
PointerGetDatum(args),
Int16GetDatum(join_type),
PointerGetDatum(sj_info)));
} else {
s2 = DatumGetFloat8(FunctionCall4Coll(&opr_sel_proc,
clause->inputcollid,
PointerGetDatum(root),
ObjectIdGetDatum(opera),
PointerGetDatum(args),
Int32GetDatum(var_relid)));
}
if (use_or) {
s1 = s1 + s2 - s1 * s2;
if (is_equality) {
s1_disjoint += s2;
}
} else {
s1 = s1 * s2;
if (is_in_equality) {
s1_disjoint += s2 - 1.0;
}
}
}
/* accept disjoint-probability estimate if in range */
if ((use_or ? is_equality : is_in_equality) && s1_disjoint >= 0.0 && s1_disjoint <= 1.0) {
s1 = s1_disjoint;
}
/* free the unused buffer */
pfree_ext(elem_nulls);
} else if (right_op && IsA(right_op, ArrayExpr) && !((ArrayExpr*)right_op)->multidims) {
ArrayExpr* array_expr = (ArrayExpr*)right_op;
int16 elm_len;
bool elm_by_val = false;
ListCell* l = NULL;
get_typlenbyval(array_expr->element_typeid, &elm_len, &elm_by_val);
/*
* 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 s1_disjoint estimate.
*/
s1 = s1_disjoint = (use_or ? 0.0 : 1.0);
foreach (l, array_expr->elements) {
Node* elem = (Node*)lfirst(l);
List* args = NIL;
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(left_op, elem);
if (is_join_clause) {
s2 = DatumGetFloat8(FunctionCall5Coll(&opr_sel_proc,
clause->inputcollid,
PointerGetDatum(root),
ObjectIdGetDatum(opera),
PointerGetDatum(args),
Int16GetDatum(join_type),
PointerGetDatum(sj_info)));
} else {
s2 = DatumGetFloat8(FunctionCall4Coll(&opr_sel_proc,
clause->inputcollid,
PointerGetDatum(root),
ObjectIdGetDatum(opera),
PointerGetDatum(args),
Int32GetDatum(var_relid)));
}
if (use_or) {
s1 = s1 + s2 - s1 * s2;
if (is_equality) {
s1_disjoint += s2;
}
} else {
s1 = s1 * s2;
if (is_in_equality) {
s1_disjoint += s2 - 1.0;
}
}
}
/* accept disjoint-probability estimate if in range */
if ((use_or ? is_equality : is_in_equality) && s1_disjoint >= 0.0 && s1_disjoint <= 1.0) {
s1 = s1_disjoint;
}
} else {
CaseTestExpr* dummy_expr = NULL;
List* args = NIL;
Selectivity s2;
int i;
/*
* We need a dummy right_op 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.
*/
dummy_expr = makeNode(CaseTestExpr);
dummy_expr->typeId = nominal_element_type;
dummy_expr->typeMod = -1;
dummy_expr->collation = clause->inputcollid;
args = list_make2(left_op, dummy_expr);
if (is_join_clause) {
s2 = DatumGetFloat8(FunctionCall5Coll(&opr_sel_proc,
clause->inputcollid,
PointerGetDatum(root),
ObjectIdGetDatum(opera),
PointerGetDatum(args),
Int16GetDatum(join_type),
PointerGetDatum(sj_info)));
} else {
s2 = DatumGetFloat8(FunctionCall4Coll(&opr_sel_proc,
clause->inputcollid,
PointerGetDatum(root),
ObjectIdGetDatum(opera),
PointerGetDatum(args),
Int32GetDatum(var_relid)));
}
s1 = use_or ? 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 (use_or) {
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* array_expr)
{
/* look through any binary-compatible relabeling of array_expr */
array_expr = strip_array_coercion(array_expr);
if (array_expr && IsA(array_expr, Const)) {
Datum array_datum = ((Const*)array_expr)->constvalue;
bool array_is_null = ((Const*)array_expr)->constisnull;
ArrayType* array_val = NULL;
if (array_is_null) {
return 0;
}
array_val = DatumGetArrayTypeP(array_datum);
return ArrayGetNItems(ARR_NDIM(array_val), ARR_DIMS(array_val));
} else if (array_expr && IsA(array_expr, ArrayExpr) && !((ArrayExpr*)array_expr)->multidims) {
return list_length(((ArrayExpr*)array_expr)->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 var_relid, JoinType join_type, SpecialJoinInfo* sj_info)
{
Selectivity s1;
Oid op_no = linitial_oid(clause->opnos);
Oid input_collid = linitial_oid(clause->inputcollids);
List* op_args = NIL;
bool is_join_clause = false;
/* Build equivalent arg list for single operator */
op_args = 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 (var_relid != 0) {
/*
* Caller is forcing restriction mode (eg, because we are examining an
* inner indexscan qual).
*/
is_join_clause = false;
} else if (sj_info == 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*)op_args) > 1);
}
if (is_join_clause) {
/* Estimate selectivity for a join clause. */
s1 = join_selectivity(root, op_no, op_args, input_collid, join_type, sj_info);
} else {
/* Estimate selectivity for a restriction clause. */
s1 = restriction_selectivity(root, op_no, op_args, input_collid, var_relid);
}
return s1;
}
/*
* eqjoinsel - Join selectivity of "="
*/
Datum eqjoinsel(PG_FUNCTION_ARGS)
{
PlannerInfo* root = (PlannerInfo*)PG_GETARG_POINTER(0);
Oid opera = PG_GETARG_OID(1);
List* args = (List*)PG_GETARG_POINTER(2);
#ifdef NOT_USED
JoinType join_type = (JoinType)PG_GETARG_INT16(3);
#endif
SpecialJoinInfo* sj_info = (SpecialJoinInfo*)PG_GETARG_POINTER(4);
double selec;
VariableStatData var_data1;
VariableStatData var_data2;
bool join_is_reversed = false;
RelOptInfo* inner_rel = NULL;
get_join_variables(root, args, sj_info, &var_data1, &var_data2, &join_is_reversed);
switch (sj_info->jointype) {
case JOIN_INNER:
case JOIN_LEFT:
case JOIN_FULL:
selec = eqjoinsel_inner(opera, &var_data1, &var_data2, sj_info);
break;
case JOIN_SEMI:
case JOIN_ANTI:
case JOIN_LEFT_ANTI_FULL:
/*
* 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, sj_info->min_righthand);
if (!join_is_reversed) {
selec = eqjoinsel_semi(opera, &var_data1, &var_data2, inner_rel, sj_info);
} else {
selec = eqjoinsel_semi(get_commutator(opera), &var_data2, &var_data1, inner_rel, sj_info);
}
break;
default:
/* other values not expected here */
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_UNRECOGNIZED_NODE_TYPE),
errmsg("unrecognized join type: %d", (int)sj_info->jointype))));
selec = 0; /* keep compiler quiet */
break;
}
ReleaseVariableStats(var_data1);
ReleaseVariableStats(var_data2);
CLAMP_PROBABILITY(selec);
PG_RETURN_FLOAT8((float8)selec);
}
/*
* @Description: Set equal var ratio which will be append to RelOptInfo's vareqratio.
* @in vardata: var data info.
* @in ratio: join ratio according to estimation.
* @in sjinfo: the join info for current relation join with others.
*/
void set_equal_varratio(VariableStatData* var_data, Relids other_relids, double ratio, SpecialJoinInfo* sj_info)
{
Node* var = var_data->var;
MemoryContext cxt = NULL;
Relids join_relids = NULL;
VarEqRatio* var_eq_ratio = NULL;
Assert(IsA(var, Var));
if (sj_info == NULL || ratio == 0.0) {
return;
}
cxt = MemoryContextSwitchTo(MemoryContextOriginal((char*)var_data->rel));
join_relids = bms_union(var_data->rel->relids, other_relids);
ListCell* lc = NULL;
foreach (lc, var_data->rel->varEqRatio) {
var_eq_ratio = (VarEqRatio*)lfirst(lc);
/* we need not save if it have the same joinrel and joinratio, otherwise, we only save the minimum joinratio. */
if (bms_equal(var_eq_ratio->joinrelids, join_relids) && _equalSimpleVar(var, var_eq_ratio->var)) {
/* save the minimue ratio. */
if (ratio < var_eq_ratio->ratio) {
var_eq_ratio->ratio = ratio;
}
bms_free(join_relids);
join_relids = NULL;
return;
}
}
var_eq_ratio = (VarEqRatio*)palloc0(sizeof(VarEqRatio));
var_eq_ratio->var = (Var*)var;
var_eq_ratio->ratio = ratio;
var_eq_ratio->joinrelids = join_relids;
var_data->rel->varEqRatio = lappend(var_data->rel->varEqRatio, var_eq_ratio);
(void)MemoryContextSwitchTo(cxt);
}
/*
* 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 opera, VariableStatData* var_data1, VariableStatData* var_data2, SpecialJoinInfo* sj_info)
{
double selec;
double nd1;
double nd2;
bool is_default1 = false;
bool is_default2 = false;
Oid opfuncoid;
Form_pg_statistic stats1 = NULL;
Form_pg_statistic stats2 = NULL;
bool have_mcvs1 = false;
Datum* values1 = NULL;
/*
* 'nvalues1' once set, can not be altered later
* because 'nvalues1' will be used to free values[] array
* we add a new varaiable 'nvalues1_frac' so you can do the calculation
* this is the same for nvalues2 and nvalues2_frac
*/
int n_values1 = 0;
double n_values1_frac = 0.0;
float4* numbers1 = NULL;
int n_numbers1 = 0;
bool have_mcvs2 = false;
Datum* values2 = NULL;
int n_values2 = 0;
double nvalues2_frac = 0.0;
float4* numbers2 = NULL;
int n_numbers2 = 0;
double rel_frac1 = 1.0;
double rel_frac2 = 1.0;
double rel_frac;
nd1 = get_variable_numdistinct(
var_data1, &is_default1, true, get_join_ratio(var_data1, sj_info), sj_info, STATS_TYPE_GLOBAL);
nd2 = get_variable_numdistinct(
var_data2, &is_default2, true, get_join_ratio(var_data2, sj_info), sj_info, STATS_TYPE_GLOBAL);
opfuncoid = get_opcode(opera);
if (HeapTupleIsValid(var_data1->statsTuple)) {
/* note we allow use of nullfrac regardless of security check */
stats1 = (Form_pg_statistic)GETSTRUCT(var_data1->statsTuple);
if (StatisticProcSecurityCheck(var_data1, opfuncoid)) {
have_mcvs1 = get_attstatsslot(var_data1->statsTuple,
var_data1->atttype,
var_data1->atttypmod,
STATISTIC_KIND_MCV,
InvalidOid,
NULL,
&values1,
&n_values1,
&numbers1,
&n_numbers1);
}
if (var_data1->rel != NULL) {
rel_frac1 = var_data1->rel->rows / var_data1->rel->tuples;
/* force rel_frac to be within range of [0, 1] */
CLAMP_PROBABILITY(rel_frac1);
}
}
if (HeapTupleIsValid(var_data2->statsTuple)) {
/* note we allow use of nullfrac regardless of security check */
stats2 = (Form_pg_statistic)GETSTRUCT(var_data2->statsTuple);
if (StatisticProcSecurityCheck(var_data2, opfuncoid)) {
have_mcvs2 = get_attstatsslot(var_data2->statsTuple,
var_data2->atttype,
var_data2->atttypmod,
STATISTIC_KIND_MCV,
InvalidOid,
NULL,
&values2,
&n_values2,
&numbers2,
&n_numbers2);
}
if (var_data2->rel != NULL) {
rel_frac2 = var_data2->rel->rows / var_data2->rel->tuples;
/* force rel_frac to be within range of [0, 1] */
CLAMP_PROBABILITY(rel_frac2);
}
}
rel_frac = rel_frac1 * rel_frac2;
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 eq_proc;
bool* has_match1 = NULL;
bool* has_match2 = NULL;
double null_frac1 = stats1->stanullfrac;
double null_frac2 = stats2->stanullfrac;
double match_prod_freq, match_freq1, match_freq2, unmatch_freq1, unmatch_freq2, other_freq1, other_freq2,
total_sel1, total_sel2;
int i, n_matches;
double tmp_nmatches;
fmgr_info(opfuncoid, &eq_proc);
has_match1 = (bool*)palloc0(n_values1 * sizeof(bool));
has_match2 = (bool*)palloc0(n_values2 * 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...
*/
match_prod_freq = 0.0;
n_matches = 0;
for (i = 0; i < n_values1; i++) {
int j;
for (j = 0; j < n_values2; j++) {
if (has_match2[j]) {
continue;
}
if (DatumGetBool(FunctionCall2Coll(&eq_proc, DEFAULT_COLLATION_OID, values1[i], values2[j]))) {
has_match1[i] = has_match2[j] = true;
match_prod_freq += numbers1[i] * numbers2[j];
n_matches++;
break;
}
}
}
/* adjust match freq according to relation's filter fraction */
tmp_nmatches = (double)n_matches * rel_frac;
if (n_matches != 0) {
match_prod_freq *= rel_frac;
}
CLAMP_PROBABILITY(match_prod_freq);
/* Sum up frequencies of matched and unmatched MCVs */
match_freq1 = unmatch_freq1 = 0.0;
for (i = 0; i < n_values1; i++) {
if (has_match1[i]) {
match_freq1 += numbers1[i];
} else {
unmatch_freq1 += numbers1[i];
}
}
CLAMP_PROBABILITY(match_freq1);
CLAMP_PROBABILITY(unmatch_freq1);
match_freq2 = unmatch_freq2 = 0.0;
for (i = 0; i < n_values2; i++) {
if (has_match2[i]) {
match_freq2 += numbers2[i];
} else {
unmatch_freq2 += numbers2[i];
}
}
CLAMP_PROBABILITY(match_freq2);
CLAMP_PROBABILITY(unmatch_freq2);
pfree_ext(has_match1);
pfree_ext(has_match2);
/*
* Compute total frequency of non-null values that are not in the MCV
* lists.
*/
other_freq1 = 1.0 - null_frac1 - match_freq1 - unmatch_freq1;
other_freq2 = 1.0 - null_frac2 - match_freq2 - unmatch_freq2;
CLAMP_PROBABILITY(other_freq1);
CLAMP_PROBABILITY(other_freq2);
/*
* 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.
*/
n_values1_frac = (double)n_values1 * rel_frac1;
nvalues2_frac = (double)n_values2 * rel_frac2;
total_sel1 = match_prod_freq;
if (nd2 > nvalues2_frac) {
total_sel1 += unmatch_freq1 * other_freq2 / (nd2 - nvalues2_frac) * rel_frac;
}
if (nd2 > tmp_nmatches) {
total_sel1 += other_freq1 * (other_freq2 + unmatch_freq2) / (nd2 - tmp_nmatches) * rel_frac;
}
/* Same estimate from the point of view of relation 2. */
total_sel2 = match_prod_freq;
if (nd1 > n_values1_frac) {
total_sel2 += unmatch_freq2 * other_freq1 / (nd1 - n_values1_frac) * rel_frac;
}
if (nd1 > tmp_nmatches) {
total_sel2 += other_freq2 * (other_freq1 + unmatch_freq1) / (nd1 - tmp_nmatches) * rel_frac;
}
/*
* 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.
*/
if (rel_frac == 0) {
selec = 0;
} else {
selec = (total_sel1 < total_sel2) ? (total_sel1 / rel_frac) : (total_sel2 / rel_frac);
}
/*
* calculate join ratio for both two tables, admitting that smaller distinct
* values will be all joined out
*/
double join_ratio1 = 0.0;
double join_ratio2 = 0.0;
if (n_matches != 0 && rel_frac1 != 0 && rel_frac2 != 0) {
join_ratio1 = match_freq1 * tmp_nmatches / (n_matches * rel_frac1);
join_ratio2 = match_freq2 * tmp_nmatches / (n_matches * rel_frac2);
}
if (nd1 > nd2) {
if (nd1 != tmp_nmatches) {
join_ratio1 += other_freq1 * (nd2 - tmp_nmatches) / (nd1 - tmp_nmatches);
}
join_ratio2 += other_freq2;
} else if (nd1 < nd2) {
if (nd2 != tmp_nmatches) {
join_ratio2 += other_freq2 * (nd1 - tmp_nmatches) / (nd2 - tmp_nmatches);
}
join_ratio1 += other_freq1;
}
CLAMP_PROBABILITY(join_ratio1);
CLAMP_PROBABILITY(join_ratio2);
set_varratio_after_calc_selectivity(var_data1, RatioType_Join, join_ratio1, sj_info);
set_varratio_after_calc_selectivity(var_data2, RatioType_Join, join_ratio2, sj_info);
/* Bloom filter can be used only when var = var. */
if (IsA(var_data1->var, Var) && IsA(var_data2->var, Var)) {
/* Set var's ratio which will be used by bloom filter set. */
if ((var_data2->rel != NULL) && (var_data1->rel != NULL)) {
set_equal_varratio(var_data1, var_data2->rel->relids, join_ratio1, sj_info);
set_equal_varratio(var_data2, var_data1->rel->relids, join_ratio2, sj_info);
}
}
} 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 null_frac1 = stats1 ? stats1->stanullfrac : 0.0;
double null_frac2 = stats2 ? stats2->stanullfrac : 0.0;
selec = (1.0 - null_frac1) * (1.0 - null_frac2);
if (nd1 > nd2) {
selec /= nd1;
/* we can cache the var ratio only if both var1 and var2 have distinct value. */
if (!is_default1 && !is_default2) {
double ratio1 = nd2 / nd1 * (1.0 - null_frac1);
set_varratio_after_calc_selectivity(var_data1, RatioType_Join, ratio1, sj_info);
if (IsA(var_data1->var, Var) && IsA(var_data2->var, Var)) {
/* Set var's ratio which will be used by bloom filter set. */
set_equal_varratio(var_data1, var_data2->rel->relids, ratio1, sj_info);
}
}
} else {
selec /= nd2;
/* we can cache the var ratio only if both var1 and var2 have distinct value. */
if (!is_default1 && !is_default2) {
double ratio2 = nd1 / nd2 * (1.0 - null_frac2);
set_varratio_after_calc_selectivity(var_data2, RatioType_Join, ratio2, sj_info);
if (IsA(var_data1->var, Var) && IsA(var_data2->var, Var)) {
set_equal_varratio(var_data2, var_data1->rel->relids, ratio2, sj_info);
}
}
}
}
if (have_mcvs1) {
free_attstatsslot(var_data1->atttype, values1, n_values1, numbers1, n_numbers1);
}
if (have_mcvs2) {
free_attstatsslot(var_data2->atttype, values2, n_values2, numbers2, n_numbers2);
}
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 operator being InvalidOid.
*/
static double eqjoinsel_semi(Oid opera, VariableStatData* var_data1, VariableStatData* var_data2, RelOptInfo* inner_rel,
SpecialJoinInfo* sj_info)
{
double selec;
double nd1;
double nd2;
bool is_default1 = false;
bool is_default2 = false;
Oid opfuncoid;
Form_pg_statistic stats1 = NULL;
bool have_mcvs1 = false;
Datum* values1 = NULL;
int n_values1 = 0;
float4* numbers1 = NULL;
int n_numbers1 = 0;
bool have_mcvs2 = false;
Datum* values2 = NULL;
int n_values2 = 0;
float4* numbers2 = NULL;
int n_numbers2 = 0;
nd1 = get_variable_numdistinct(
var_data1, &is_default1, true, get_join_ratio(var_data1, sj_info), sj_info, STATS_TYPE_GLOBAL);
nd2 = get_variable_numdistinct(
var_data2, &is_default2, true, get_join_ratio(var_data2, sj_info), sj_info, STATS_TYPE_GLOBAL);
opfuncoid = OidIsValid(opera) ? get_opcode(opera) : InvalidOid;
/*
* 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 (var_data2->rel) {
nd2 = Min(nd2, var_data2->rel->rows);
}
nd2 = Min(nd2, inner_rel->rows);
if (HeapTupleIsValid(var_data1->statsTuple)) {
stats1 = (Form_pg_statistic)GETSTRUCT(var_data1->statsTuple);
if (StatisticProcSecurityCheck(var_data1, opfuncoid)) {
have_mcvs1 = get_attstatsslot(var_data1->statsTuple,
var_data1->atttype,
var_data1->atttypmod,
STATISTIC_KIND_MCV,
InvalidOid,
NULL,
&values1,
&n_values1,
&numbers1,
&n_numbers1);
}
}
if (HeapTupleIsValid(var_data2->statsTuple) && StatisticProcSecurityCheck(var_data2, opfuncoid)) {
have_mcvs2 = get_attstatsslot(var_data2->statsTuple,
var_data2->atttype,
var_data2->atttypmod,
STATISTIC_KIND_MCV,
InvalidOid,
NULL,
&values2,
&n_values2,
&numbers2,
&n_numbers2);
}
if (have_mcvs1 && have_mcvs2 && OidIsValid(opera)) {
/*
* 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 eq_proc;
bool* has_match1 = NULL;
bool* has_match2 = NULL;
double null_frac1 = stats1->stanullfrac;
double match_freq1;
double uncertain_frac;
double uncertain;
int i;
int n_matches;
int clamped_nvalues2;
/*
* The clamping above could have resulted in nd2 being less than
* n_values2; 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(n_values2, nd2);
fmgr_info(opfuncoid, &eq_proc);
has_match1 = (bool*)palloc0(n_values1 * sizeof(bool));
has_match2 = (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...
*/
n_matches = 0;
for (i = 0; i < n_values1; i++) {
int j;
for (j = 0; j < clamped_nvalues2; j++) {
if (has_match2[j]) {
continue;
}
if (DatumGetBool(FunctionCall2Coll(&eq_proc, DEFAULT_COLLATION_OID, values1[i], values2[j]))) {
has_match1[i] = has_match2[j] = true;
n_matches++;
break;
}
}
}
/* Sum up frequencies of matched MCVs */
match_freq1 = 0.0;
for (i = 0; i < n_values1; i++) {
if (has_match1[i]) {
match_freq1 += numbers1[i];
}
}
CLAMP_PROBABILITY(match_freq1);
pfree_ext(has_match1);
pfree_ext(has_match2);
/*
* 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 (!is_default1 && !is_default2) {
nd1 -= n_matches;
nd2 -= n_matches;
if (nd1 <= nd2 || nd2 < 0) {
uncertain_frac = 1.0;
} else {
uncertain_frac = nd2 / nd1;
}
} else {
uncertain_frac = 0.5;
}
uncertain = 1.0 - match_freq1 - null_frac1;
CLAMP_PROBABILITY(uncertain);
selec = match_freq1 + uncertain_frac * uncertain;
} else {
/*
* Without MCV lists for both sides, we can only use the heuristic
* about nd1 vs nd2.
*/
double null_frac1 = stats1 ? stats1->stanullfrac : 0.0;
if (!is_default1 && !is_default2) {
if (nd1 <= nd2 || nd2 < 0) {
selec = 1.0 - null_frac1;
} else {
selec = (nd2 / nd1) * (1.0 - null_frac1);
}
} else if (!is_default1 && is_default2 && var_data2->rel) {
selec = (var_data2->rel->rows / nd1) * (1.0 - null_frac1);
if (selec > 0.5 * (1.0 - null_frac1)) {
selec = 0.5 * (1.0 - null_frac1);
}
} else {
selec = 0.5 * (1.0 - null_frac1);
}
}
if (IsA(var_data1->var, Var) && IsA(var_data2->var, Var) && var_data2->rel) {
/* Set var's ratio which will be used by bloom filter set. */
set_equal_varratio(var_data1, var_data2->rel->relids, selec, sj_info);
}
if (have_mcvs1) {
free_attstatsslot(var_data1->atttype, values1, n_values1, numbers1, n_numbers1);
}
if (have_mcvs2) {
free_attstatsslot(var_data2->atttype, values2, n_values2, numbers2, n_numbers2);
}
return selec;
}
/*
* neqjoinsel_semi --- neqjoinsel for semi join
* 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 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).
* (Also used for anti join, which we are supposed to estimate the same way.)
* Caller has ensured that vardata1 is the LHS variable.
*/
static double neqjoinsel_semi(Oid opera, VariableStatData* var_data1, VariableStatData* var_data2,
RelOptInfo* inner_rel, SpecialJoinInfo* sj_info)
{
double nd1;
double nd2;
double selec;
bool is_default1 = false;
bool is_default2 = false;
Form_pg_statistic stats1 = NULL;
double null_frac1;
if (HeapTupleIsValid(var_data1->statsTuple)) {
stats1 = (Form_pg_statistic)GETSTRUCT(var_data1->statsTuple);
}
null_frac1 = stats1 ? stats1->stanullfrac : 0.0;
nd1 = get_variable_numdistinct(
var_data1, &is_default1, true, get_join_ratio(var_data1, sj_info), sj_info, STATS_TYPE_GLOBAL);
nd2 = get_variable_numdistinct(
var_data2, &is_default2, true, get_join_ratio(var_data2, sj_info), sj_info, STATS_TYPE_GLOBAL);
/*
* When one of the sides contains zero distinct values, the selectivity is zero;
* when there is more than 1 distinct value in the rhs, it is assumed that all
* distinct values are selected by neq semi join, a distinct value on lfs
* can only match at most one value on the rhs, so the exist not equal condition
* can always be fulfilled;
* when there is only one distinct value on the rhs and more than one on the lfs,
* it is assumed that the one distinct value on rhs can filter out 1/nd1 on the
* lfs;
* when there is only 1 distinct value on both sides, we use 0.5 as an
* approximation, it is resonable to assume that there is 50% of chance that the
* two distinct values one both ends can match.
* nd less than 1 is considered as 1, which might need further analysis.
*/
selec = 1 - null_frac1;
if (nd1 == 0 || nd2 == 0) {
PG_RETURN_FLOAT8((float8)0);
} else if (nd2 > 1) {
PG_RETURN_FLOAT8((float8)selec);
} else if (nd1 > 1) {
selec *= (nd1 - 1) / nd1;
} else {
selec *= 0.5;
}
PG_RETURN_FLOAT8((float8)selec);
}
/*
* neqjoinsel - Join selectivity of "!="
*/
Datum neqjoinsel(PG_FUNCTION_ARGS)
{
/* Instead of calculate the neq join selectiveity as 1 - eq_join_sel, we can calculate the neq probability
* for each distinct value and average them out. This yields a more accurate estimation of selectivity,
* especially in the case of self-join. We want to compare this model with the original model for other situations
* before we settle on this model
* Please note that cost_param=1 option used to provide an alternative way for neqjoin selectivity computation,
* which is deprecated on 2019-02-18. Please go back in time to check if interested.
*/
PlannerInfo* root = (PlannerInfo*)PG_GETARG_POINTER(0);
Oid opera = PG_GETARG_OID(1);
List* args = (List*)PG_GETARG_POINTER(2);
#ifdef NOT_USED
JoinType join_type = (JoinType)PG_GETARG_INT16(3);
#endif
SpecialJoinInfo* sj_info = (SpecialJoinInfo*)PG_GETARG_POINTER(4);
double selec = 0.0;
VariableStatData var_data1;
VariableStatData var_data2;
bool join_is_reversed = false;
RelOptInfo* inner_rel = NULL;
Oid eq_op;
get_join_variables(root, args, sj_info, &var_data1, &var_data2, &join_is_reversed);
eq_op = get_negator(opera);
if (eq_op) {
switch (sj_info->jointype) {
case JOIN_INNER:
case JOIN_LEFT:
case JOIN_FULL:
/*
* We want 1 - eqjoinsel() where the equality operator is the one
* associated with this != operator, that is, its negator.
*/
eq_op = get_negator(opera);
if (eq_op) {
selec = DatumGetFloat8(DirectFunctionCall5(eqjoinsel,
PointerGetDatum(root),
ObjectIdGetDatum(eq_op),
PointerGetDatum(args),
Int16GetDatum(sj_info->jointype),
PointerGetDatum(sj_info)));
selec = 1 - selec;
} else {
/* Use default selectivity (should we raise an error instead?) */
selec = 1 - DEFAULT_EQ_SEL;
}
break;
case JOIN_SEMI:
case JOIN_ANTI:
case JOIN_LEFT_ANTI_FULL:
/*
* 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, sj_info->min_righthand);
if (!join_is_reversed) {
selec = neqjoinsel_semi(eq_op, &var_data1, &var_data2, inner_rel, sj_info);
} else {
selec = neqjoinsel_semi(get_commutator(eq_op), &var_data2, &var_data1, inner_rel, sj_info);
}
/*
* We clamp the selectivity of neqsemijoin to be not more than 0.995
* to avoid destructive underestimation for the corresponded antijoin.
*/
if (selec > MAX_NEQ_SEMI_SEL) {
selec = MAX_NEQ_SEMI_SEL;
}
break;
default:
/* other values not expected here */
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_UNRECOGNIZED_NODE_TYPE),
errmsg("unrecognized join type: %d", (int)sj_info->jointype))));
break;
}
} else {
/* Use default selectivity (should we raise an error instead?) */
selec = 1 - DEFAULT_EQ_SEL;
}
ReleaseVariableStats(var_data1);
ReleaseVariableStats(var_data2);
CLAMP_PROBABILITY(selec);
PG_RETURN_FLOAT8((float8)selec);
}
/*
* scalarltjoinsel - Join selectivity of "<" and "<=" for scalars
*/
Datum scalarltjoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* scalargtjoinsel - Join selectivity of ">" and ">=" for scalars
*/
Datum scalargtjoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* patternjoinsel - Generic code for pattern-match join selectivity.
*/
static double patternjoinsel(PG_FUNCTION_ARGS, Pattern_Type ptype, bool negate)
{
/* For the moment we just punt. */
return negate ? (1.0 - DEFAULT_MATCH_SEL) : DEFAULT_MATCH_SEL;
}
/*
* regexeqjoinsel - Join selectivity of regular-expression pattern match.
*/
Datum regexeqjoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Regex, false));
}
/*
* icregexeqjoinsel - Join selectivity of case-insensitive regex match.
*/
Datum icregexeqjoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Regex_IC, false));
}
/*
* likejoinsel - Join selectivity of LIKE pattern match.
*/
Datum likejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Like, false));
}
/*
* iclikejoinsel - Join selectivity of ILIKE pattern match.
*/
Datum iclikejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Like_IC, false));
}
/*
* regexnejoinsel - Join selectivity of regex non-match.
*/
Datum regexnejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Regex, true));
}
/*
* icregexnejoinsel - Join selectivity of case-insensitive regex non-match.
*/
Datum icregexnejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Regex_IC, true));
}
/*
* nlikejoinsel - Join selectivity of LIKE pattern non-match.
*/
Datum nlikejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Like, true));
}
/*
* icnlikejoinsel - Join selectivity of ILIKE pattern non-match.
*/
Datum icnlikejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Like_IC, true));
}
/*
* 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 = NULL;
Node* right = NULL;
VariableStatData left_var, right_var;
int op_strategy;
Oid op_lefttype;
Oid op_righttype;
Oid op_no, l_sort_op, r_sort_op, l_stat_op, r_stat_op, l_top, l_eop, r_evl_top, r_evl_eop;
bool isgt = false;
Datum left_min;
Datum left_max;
Datum right_min;
Datum right_max;
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 */
}
op_no = ((OpExpr*)clause)->opno;
left = get_leftop((Expr*)clause);
right = get_rightop((Expr*)clause);
if (right == NULL) {
return; /* shouldn't happen */
}
/* Look for stats for the inputs */
examine_variable(root, left, 0, &left_var);
examine_variable(root, right, 0, &right_var);
/* Extract the operator's declared left/right datatypes */
get_op_opfamily_properties(op_no, 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 */
l_top = get_opfamily_member(opfamily, op_lefttype, op_righttype, BTLessStrategyNumber);
l_eop = get_opfamily_member(opfamily, op_lefttype, op_righttype, BTLessEqualStrategyNumber);
l_sort_op = l_top;
r_sort_op = l_top;
l_stat_op = l_sort_op;
r_stat_op = r_sort_op;
r_evl_top = l_top;
r_evl_eop = l_eop;
} else {
l_top = get_opfamily_member(opfamily, op_lefttype, op_righttype, BTLessStrategyNumber);
l_eop = get_opfamily_member(opfamily, op_lefttype, op_righttype, BTLessEqualStrategyNumber);
l_sort_op = get_opfamily_member(opfamily, op_lefttype, op_lefttype, BTLessStrategyNumber);
r_sort_op = get_opfamily_member(opfamily, op_righttype, op_righttype, BTLessStrategyNumber);
l_stat_op = l_sort_op;
r_stat_op = r_sort_op;
r_evl_top = get_opfamily_member(opfamily, op_righttype, op_lefttype, BTLessStrategyNumber);
r_evl_eop = get_opfamily_member(opfamily, op_righttype, op_lefttype, BTLessEqualStrategyNumber);
}
break;
case BTGreaterStrategyNumber:
/* descending-order case */
isgt = true;
if (op_lefttype == op_righttype) {
/* easy case */
l_top = get_opfamily_member(opfamily, op_lefttype, op_righttype, BTGreaterStrategyNumber);
l_eop = get_opfamily_member(opfamily, op_lefttype, op_righttype, BTGreaterEqualStrategyNumber);
l_sort_op = l_top;
r_sort_op = l_top;
l_stat_op = get_opfamily_member(opfamily, op_lefttype, op_lefttype, BTLessStrategyNumber);
r_stat_op = l_stat_op;
r_evl_top = l_top;
r_evl_eop = l_eop;
} else {
l_top = get_opfamily_member(opfamily, op_lefttype, op_righttype, BTGreaterStrategyNumber);
l_eop = get_opfamily_member(opfamily, op_lefttype, op_righttype, BTGreaterEqualStrategyNumber);
l_sort_op = get_opfamily_member(opfamily, op_lefttype, op_lefttype, BTGreaterStrategyNumber);
r_sort_op = get_opfamily_member(opfamily, op_righttype, op_righttype, BTGreaterStrategyNumber);
l_stat_op = get_opfamily_member(opfamily, op_lefttype, op_lefttype, BTLessStrategyNumber);
r_stat_op = get_opfamily_member(opfamily, op_righttype, op_righttype, BTLessStrategyNumber);
r_evl_top = get_opfamily_member(opfamily, op_righttype, op_lefttype, BTGreaterStrategyNumber);
r_evl_eop = get_opfamily_member(opfamily, op_righttype, op_lefttype, BTGreaterEqualStrategyNumber);
}
break;
default:
goto fail; /* shouldn't get here */
}
if (!OidIsValid(l_sort_op) || !OidIsValid(r_sort_op) || !OidIsValid(l_stat_op) || !OidIsValid(r_stat_op) ||
!OidIsValid(l_top) || !OidIsValid(l_eop) || !OidIsValid(r_evl_top) || !OidIsValid(r_evl_eop)) {
goto fail; /* insufficient info in catalogs */
}
/* Try to get ranges of both inputs */
if (!isgt) {
if (!get_variable_range(root, &left_var, l_stat_op, &left_min, &left_max)) {
goto fail; /* no range available from stats */
}
if (!get_variable_range(root, &right_var, r_stat_op, &right_min, &right_max)) {
goto fail; /* no range available from stats */
}
} else {
/* need to swap the max and min */
if (!get_variable_range(root, &left_var, l_stat_op, &left_max, &left_min)) {
goto fail; /* no range available from stats */
}
if (!get_variable_range(root, &right_var, r_stat_op, &right_max, &right_min)) {
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, l_eop, isgt, &left_var, right_max, op_righttype);
if (selec != DEFAULT_INEQ_SEL) {
*leftend = selec;
}
/* And similarly for the right variable. */
selec = scalarineqsel(root, r_evl_eop, isgt, &right_var, left_max, 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, l_top, isgt, &left_var, right_min, op_righttype);
if (selec != DEFAULT_INEQ_SEL) {
*leftstart = selec;
}
/* And similarly for the right variable. */
selec = scalarineqsel(root, r_evl_top, isgt, &right_var, left_min, 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;
if (HeapTupleIsValid(left_var.statsTuple)) {
stats = (Form_pg_statistic)GETSTRUCT(left_var.statsTuple);
*leftstart += stats->stanullfrac;
CLAMP_PROBABILITY(*leftstart);
*leftend += stats->stanullfrac;
CLAMP_PROBABILITY(*leftend);
}
if (HeapTupleIsValid(right_var.statsTuple)) {
stats = (Form_pg_statistic)GETSTRUCT(right_var.statsTuple);
*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(left_var);
ReleaseVariableStats(right_var);
}
static List* add_unique_group_var(
PlannerInfo* root, List* var_infos, Node* var, VariableStatData* var_data, STATS_EST_TYPE e_type)
{
GroupVarInfo* var_info = NULL;
double n_distinct;
bool is_default = false;
ListCell* lc = NULL;
n_distinct = get_variable_numdistinct(var_data, &is_default, false, 1.0, NULL, e_type);
/* cannot use foreach here because of possible list_delete */
lc = list_head(var_infos);
while (lc != NULL) {
var_info = (GroupVarInfo*)lfirst(lc);
/* must advance lc before list_delete possibly pfree's it */
lc = lnext(lc);
/* Drop exact duplicates */
if (equal(var, var_info->var)) {
return var_infos;
}
/*
* Drop known-equal vars, but only if they belong to different
* relations (see comments for estimate_num_groups)
*/
if (var_data->rel != var_info->rel && exprs_known_equal(root, var, var_info->var)) {
if (var_info->ndistinct <= n_distinct) {
/* Keep older item, forget new one */
return var_infos;
} else {
/* Delete the older item */
var_infos = list_delete_ptr(var_infos, var_info);
}
}
}
var_info = (GroupVarInfo*)palloc(sizeof(GroupVarInfo));
var_info->var = var;
var_info->rel = var_data->rel;
var_info->ndistinct = n_distinct;
var_info->isdefault = is_default;
var_info->es_is_used = false;
var_info->es_attnums = NULL;
var_infos = lappend(var_infos, var_info);
return var_infos;
}
/*
* 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
*
* 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 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.) Multiplying
* by the restriction selectivity is effectively assuming that the
* restriction clauses are independent of the grouping, which is a crummy
* 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* group_exprs, double input_rows, unsigned int num_datanodes,
STATS_EST_TYPE e_type, List** pgset)
{
List* var_infos = NIL;
double num_distinct;
ListCell* l = NULL;
int i;
ES_SELECTIVITY* es = NULL;
MemoryContext extended_stat;
MemoryContext old_context;
/*
* 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 (group_exprs == 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).
*/
num_distinct = 1.0;
i = 0;
foreach (l, group_exprs) {
Node* groupexpr = (Node*)lfirst(l);
VariableStatData vardata;
List* varshere = NIL;
ListCell* l2 = NULL;
/* is expression in this grouping set? */
if (pgset && !list_member_int(*pgset, i++)) {
continue;
}
/* Short-circuit for expressions returning boolean */
if (exprType(groupexpr) == BOOLOID || IsA(groupexpr, GroupingFunc)) {
num_distinct *= 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(vardata.statsTuple) || vardata.isunique) {
/*
* we don't need possion to estimate distinct because
* we can't estimate accurate for multiple exprs.
*/
vardata.enablePossion = false;
var_infos = add_unique_group_var(root, var_infos, groupexpr, &vardata, e_type);
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_PLACEHOLDERS, PVC_INCLUDE_SPECIAL_EXPR);
if (varshere != NULL) {
varshere = specialExpr_group_num(root, varshere, &num_distinct, input_rows);
}
/*
* 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 clamp_row_est(input_rows);
}
continue;
}
/*
* Else add variables to var_infos list
*/
foreach (l2, varshere) {
Node* var = (Node*)lfirst(l2);
examine_variable(root, var, 0, &vardata);
/*
* we don't need possion to estimate distinct because
* we can't estimate accurate for multiple exprs.
*/
vardata.enablePossion = false;
var_infos = add_unique_group_var(root, var_infos, var, &vardata, e_type);
ReleaseVariableStats(vardata);
}
list_free_ext(varshere);
}
/*
* If now no Vars, we must have an all-constant or all-boolean GROUP BY
* list.
*/
if (var_infos == NIL) {
/* Guard against out-of-range answers */
if (num_distinct > input_rows) {
num_distinct = input_rows;
}
return clamp_row_est(num_distinct);
}
if (list_length(var_infos) >= 2) {
/* initialize es_selectivity class */
extended_stat = AllocSetContextCreate(CurrentMemoryContext,
"extended_stat",
ALLOCSET_DEFAULT_MINSIZE,
ALLOCSET_DEFAULT_INITSIZE,
ALLOCSET_DEFAULT_MAXSIZE);
old_context = MemoryContextSwitchTo(extended_stat);
es = New(extended_stat) ES_SELECTIVITY();
(void)es->calculate_selectivity(root, var_infos, NULL, JOIN_INNER, NULL, ES_GROUPBY, e_type);
(void)MemoryContextSwitchTo(old_context);
var_infos = es->unmatched_clause_group;
}
List* varinfo_orig = var_infos;
/*
* Group Vars by relation and estimate total num_distinct.
*
* For each iteration of the outer loop, we process the frontmost Var in
* var_infos, 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* var_info1 = (GroupVarInfo*)linitial(var_infos);
RelOptInfo* rel = var_info1->rel;
double rel_distinct = var_info1->ndistinct;
double rel_max_ndistinct = rel_distinct;
double rel_tuples =
(e_type == STATS_TYPE_LOCAL)
? get_local_rows(rel->tuples, rel->multiple, IsLocatorReplicated(rel->locator_type), num_datanodes)
: rel->tuples;
double rel_rows =
(e_type == STATS_TYPE_LOCAL)
? get_local_rows(rel->rows, rel->multiple, IsLocatorReplicated(rel->locator_type), num_datanodes)
: rel->rows;
int rel_var_count = 1;
List* new_var_infos = NIL;
bool has_default = var_info1->isdefault;
/*
* Get the product of num_distinct estimates of the Vars for this rel.
* Also, construct new var_infos list of remaining Vars.
*/
for_each_cell(l, lnext(list_head(var_infos))) {
GroupVarInfo* var_info2 = (GroupVarInfo*)lfirst(l);
if (var_info2->rel == var_info1->rel) {
rel_distinct *= var_info2->ndistinct;
has_default = has_default || var_info2->isdefault;
if (rel_max_ndistinct < var_info2->ndistinct)
rel_max_ndistinct = var_info2->ndistinct;
rel_var_count++;
} else {
/* not time to process var_info2 yet */
new_var_infos = lcons(var_info2, new_var_infos);
}
}
/*
* Sanity check --- don't divide by zero if empty relation.
*/
Assert(rel->reloptkind == RELOPT_BASEREL || rel->reloptkind == RELOPT_OTHER_MEMBER_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 (rel_var_count > 1) {
/* if we use default value, so estimate as 1/10 for each dn */
if (has_default) {
clamp *= 0.1;
if (e_type == STATS_TYPE_GLOBAL) {
clamp *= 1 / (num_datanodes * (1 - 0.1));
}
}
if (clamp < rel_max_ndistinct) {
clamp = rel_max_ndistinct;
/* for sanity in case some ndistinct is too large: */
if (clamp > rel_tuples) {
clamp = rel_tuples;
}
}
}
if (rel_distinct > clamp) {
rel_distinct = clamp;
}
/*
* Multiply by restriction selectivity.
* If used extended statistc, use possion model to adjust rel_distinct.
*/
double tmp = rel_distinct;
if (var_info1->es_is_used && rel_tuples > 0 &&
rel_rows / rel_tuples < SELECTIVITY_THRESHOLD_TO_USE_POISSON) {
rel_distinct = NUM_DISTINCT_SELECTIVITY_FOR_POISSON(rel_distinct, rel_tuples, rel_rows / rel_tuples);
ereport(ES_DEBUG_LEVEL,
(errmodule(MOD_OPT),
(errmsg(
"[ES]The origin distinct value is %f. "
"After using possion model with rel_tuples=%f and ration=%e "
"The new distinct value is %f",
tmp,
rel_tuples,
rel_rows / rel_tuples,
rel_distinct))));
} else {
rel_distinct *= rel_rows / rel_tuples;
ereport(ES_DEBUG_LEVEL,
(errmodule(MOD_OPT),
(errmsg("The origin distinct value is %f. After multiplying rel_rows(%e)/rel_tuples(%e), "
"The new distinct value is %f",
tmp,
rel_rows,
rel_tuples,
rel_distinct))));
}
/*
* Update estimate of total distinct groups.
*/
num_distinct *= rel_distinct;
}
var_infos = new_var_infos;
} while (var_infos != NIL);
list_free_deep(varinfo_orig);
/* free space used by extended statistic */
if (es != NULL) {
/* notice that: es->unmatched_clause_group has been
* deleted in list_free_deep(var_infos); So no need to free
* it again.
*/
delete es;
MemoryContextDelete(extended_stat);
}
num_distinct = ceil(num_distinct);
/* Guard against out-of-range answers */
if (num_distinct > input_rows) {
num_distinct = input_rows;
}
if (num_distinct < 1.0) {
num_distinct = 1.0;
}
return clamp_row_est(num_distinct);
}
/*
* Estimate hash bucketsize fraction (ie, number of entries in a bucket
* divided by total tuples in relation) if the specified expression is used
* as a hash key.
*
* XXX This is really pretty bogus since we're effectively assuming that the
* distribution of hash keys will be the same after applying restriction
* clauses as it was in the underlying relation. However, we are not nearly
* smart enough to figure out how the restrict clauses might change the
* distribution, so this will have to do for now.
*
* We are passed the number of buckets the executor will use for the given
* input relation. If the data were perfectly distributed, with the same
* number of tuples going into each available bucket, then the bucketsize
* fraction would be 1/nbuckets. But this happy state of affairs will occur
* only if (a) there are at least nbuckets distinct data values, and (b)
* we have a not-too-skewed data distribution. Otherwise the buckets will
* be nonuniformly occupied. If the other relation in the join has a key
* distribution similar to this one's, then the most-loaded buckets are
* exactly those that will be probed most often. Therefore, the "average"
* bucket size for costing purposes should really be taken as something close
* to the "worst case" bucket size. We try to estimate this by adjusting the
* fraction if there are too few distinct data values, and then scaling up
* by the ratio of the most common value's frequency to the average frequency.
*
* If no statistics are available, use a default estimate of 0.1. This will
* discourage use of a hash rather strongly if the inner relation is large,
* which is what we want. We do not want to hash unless we know that the
* inner rel is well-dispersed (or the alternatives seem much worse).
*/
Selectivity estimate_hash_bucketsize(PlannerInfo* root, Node* hash_key, double n_buckets, Path* inner_path,
SpecialJoinInfo* sj_info, double* distinct_num)
{
VariableStatData var_data;
double est_fract;
double ndistinct;
double global_ndistinct;
double mcv_freq;
double avg_freq;
bool is_default = false;
float4* numbers = NULL;
int n_numbers;
ndistinct = estimate_local_numdistinct(root, hash_key, inner_path, sj_info,
&global_ndistinct, &is_default, &var_data);
/* caller does not need it, we allow for distinct_num == NULL */
if (distinct_num != NULL) {
*distinct_num = ndistinct;
}
/* If ndistinct isn't real, punt and return 0.1, per comments above */
if (is_default) {
ReleaseVariableStats(var_data);
return (Selectivity)1.0 / DEFAULT_NUM_DISTINCT;
}
/* Compute avg freq of all distinct data values in raw relation */
avg_freq = 1.0 / ndistinct;
/*
* Initial estimate of bucketsize fraction is 1/n_buckets as long as the
* number of buckets is less than the expected number of distinct values;
* otherwise it is 1/ndistinct.
*/
if (ndistinct > n_buckets) {
est_fract = 1.0 / n_buckets;
} else {
if (ndistinct < 1.0) {
ndistinct = 1.0;
}
est_fract = 1.0 / ndistinct;
}
/*
* Look up the frequency of the most common value, if available.
*/
mcv_freq = 0.0;
if (HeapTupleIsValid(var_data.statsTuple)) {
if (get_attstatsslot(var_data.statsTuple,
var_data.atttype,
var_data.atttypmod,
STATISTIC_KIND_MCV,
InvalidOid,
NULL,
NULL,
NULL,
&numbers,
&n_numbers)) {
/*
* The first MCV stat is for the most common value.
*/
if (n_numbers > 0) {
if (var_data.rel == NULL) {
ereport(ERROR,
(errcode(ERRCODE_UNEXPECTED_NULL_VALUE),
errmsg("Unexpected null value for the rel of var_data")));
}
/* We should adjust mcv_freq with selectivity because mcv_freq is global. */
mcv_freq = numbers[0] / (var_data.rel->rows / var_data.rel->tuples);
}
free_attstatsslot(var_data.atttype, NULL, 0, numbers, n_numbers);
}
}
/*
* Adjust estimated bucketsize upward to account for skewed distribution.
*/
bool check_freq = avg_freq > 0.0 && mcv_freq > avg_freq;
if (check_freq) {
/* for distribute key, mcv freq should be multiplied by dn number */
double multiple = 1.0;
if (var_data.rel && list_length(var_data.rel->distribute_keys) == 1 &&
equal(hash_key, linitial(var_data.rel->distribute_keys))) {
multiple = u_sess->pgxc_cxt.NumDataNodes;
}
est_fract *= mcv_freq / avg_freq;
/* if adjusted selectivity is larger than mcv_freq, then the estimate is too far off,
take the mcv_freq instead. */
if (est_fract > mcv_freq * multiple) {
est_fract = mcv_freq * multiple;
}
}
ereport(DEBUG1,
(errmodule(MOD_OPT),
errmsg("ndistinct=%.lf, global_ndistinct=%.lf, avg_freq=%.10f, mcv_freq=%.10f, est_fract=%.10f",
ndistinct,
global_ndistinct,
avg_freq,
mcv_freq,
est_fract)));
if (var_data.rel != NULL) {
ereport(DEBUG1,
(errmodule(MOD_OPT),
errmsg("rows=%.lf, tuples=%.lf, multiple=%.lf",
var_data.rel->rows,
var_data.rel->tuples,
var_data.rel->multiple)));
}
/*
* 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.
* We should adjust the lower bound as 1.0e-7 because the distinct value
* may be larger than 1000000 as the increasing of work_mem.
*/
if (est_fract < 1.0e-7) {
est_fract = 1.0e-7;
} else if (est_fract >= 1.0) {
if (mcv_freq > 0.0) {
est_fract = mcv_freq;
} else {
est_fract = 1.0;
}
}
ReleaseVariableStats(var_data);
return (Selectivity)est_fract;
}
/* -------------------------------------------------------------------------
*
* Support routines
*
* -------------------------------------------------------------------------
*/
/*
* 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 by convert_string_to_scalar(),
* which is explained below. The reason why this routine deals with
* three values at a time, not just one, is that we need it for strings.
*
* 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 value_typid, double* scaled_value, Datum lo_bound, Datum hi_bound,
Oid bounds_typid, double* scaled_lo_bound, double* scaled_hi_bound)
{
/*
* Both the value_typid and the bounds_typid should exactly match the
* declared input type(s) of the operator we are invoked for, so we just
* error out if either is not recognized.
*
* 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 (value_typid) {
/*
* 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:
*scaled_value = convert_numeric_to_scalar(value, value_typid);
*scaled_lo_bound = convert_numeric_to_scalar(lo_bound, bounds_typid);
*scaled_hi_bound = convert_numeric_to_scalar(hi_bound, bounds_typid);
return true;
/*
* Built-in string types
*/
case CHAROID:
case BPCHAROID:
case VARCHAROID:
case TEXTOID:
case NAMEOID: {
char* valstr = convert_string_datum(value, value_typid);
char* lostr = convert_string_datum(lo_bound, bounds_typid);
char* histr = convert_string_datum(hi_bound, bounds_typid);
convert_string_to_scalar(valstr, scaled_value, lostr, scaled_lo_bound, histr, scaled_hi_bound);
pfree_ext(valstr);
pfree_ext(lostr);
pfree_ext(histr);
return true;
}
/*
* Built-in bytea type
*/
case BYTEAOID: {
convert_bytea_to_scalar(value, scaled_value, lo_bound, scaled_lo_bound, hi_bound, scaled_hi_bound);
return true;
}
/*
* Built-in time types
*/
case TIMESTAMPOID:
case TIMESTAMPTZOID:
case ABSTIMEOID:
case DATEOID:
case INTERVALOID:
case RELTIMEOID:
case TINTERVALOID:
case TIMEOID:
case TIMETZOID:
*scaled_value = convert_timevalue_to_scalar(value, value_typid);
*scaled_lo_bound = convert_timevalue_to_scalar(lo_bound, bounds_typid);
*scaled_hi_bound = convert_timevalue_to_scalar(hi_bound, bounds_typid);
return true;
/*
* Built-in network types
*/
case INETOID:
case CIDROID:
case MACADDROID:
*scaled_value = convert_network_to_scalar(value, value_typid);
*scaled_lo_bound = convert_network_to_scalar(lo_bound, bounds_typid);
*scaled_hi_bound = convert_network_to_scalar(hi_bound, bounds_typid);
return true;
default:
break;
}
/* Don't know how to convert */
*scaled_value = *scaled_lo_bound = *scaled_hi_bound = 0;
return false;
}
/*
* Do convert_to_scalar()'s work for any numeric data type.
*/
static double convert_numeric_to_scalar(Datum value, Oid typid)
{
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:
/* we can treat OIDs as integers... */
return (double)DatumGetObjectId(value);
default:
break;
}
/*
* Can't get here unless someone tries to use scalarltsel/scalargtsel on
* an operator with one numeric and one non-numeric operand.
*/
ereport(
ERROR, (errmodule(MOD_OPT), (errcode(ERRCODE_UNRECOGNIZED_NODE_TYPE), errmsg("unsupported type: %u", typid))));
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(const char* value, double* scaled_value, char* lo_bound, double* scaled_lo_bound,
char* hi_bound, double* scaled_hi_bound)
{
int range_lo;
int range_hi;
char* sptr = NULL;
range_lo = range_hi = (unsigned char)hi_bound[0];
for (sptr = lo_bound; *sptr; sptr++) {
if (range_lo > (unsigned char)*sptr) {
range_lo = (unsigned char)*sptr;
}
if (range_hi < (unsigned char)*sptr) {
range_hi = (unsigned char)*sptr;
}
}
for (sptr = hi_bound; *sptr; sptr++) {
if (range_lo > (unsigned char)*sptr) {
range_lo = (unsigned char)*sptr;
}
if (range_hi < (unsigned char)*sptr) {
range_hi = (unsigned char)*sptr;
}
}
/* If range includes any upper-case ASCII chars, make it include all */
if (range_lo <= 'Z' && range_hi >= 'A') {
if (range_lo > 'A') {
range_lo = 'A';
}
if (range_hi < 'Z') {
range_hi = 'Z';
}
}
/* Ditto lower-case */
if (range_lo <= 'z' && range_hi >= 'a') {
if (range_lo > 'a') {
range_lo = 'a';
}
if (range_hi < 'z') {
range_hi = 'z';
}
}
/* Ditto digits */
if (range_lo <= '9' && range_hi >= '0') {
if (range_lo > '0') {
range_lo = '0';
}
if (range_hi < '9') {
range_hi = '9';
}
}
/*
* If range includes less than 10 chars, assume we have not got enough
* data, and make it include regular ASCII set.
*/
if (range_hi - range_lo < 9) {
range_lo = ' ';
range_hi = 127;
}
/*
* Now strip any common prefix of the three strings.
*/
while (*lo_bound) {
if (*lo_bound != *hi_bound || *lo_bound != *value) {
break;
}
lo_bound++, hi_bound++, value++;
}
/*
* Now we can do the conversions.
*/
*scaled_value = convert_one_string_to_scalar(value, range_lo, range_hi);
*scaled_lo_bound = convert_one_string_to_scalar(lo_bound, range_lo, range_hi);
*scaled_hi_bound = convert_one_string_to_scalar(hi_bound, range_lo, range_hi);
}
static double convert_one_string_to_scalar(const char* value, int range_lo, int range_hi)
{
int slen = strlen(value);
double num;
double denom;
double base;
if (slen <= 0) {
return 0.0; /* empty string has scalar value 0 */
}
/*
* Since base is at least 10, need not consider more than about 20 chars
*/
if (slen > 20) {
slen = 20;
}
/* Convert initial characters to fraction */
base = range_hi - range_lo + 1;
num = 0.0;
denom = base;
while (slen-- > 0) {
int ch = (unsigned char)*value++;
if (ch < range_lo) {
ch = range_lo - 1;
} else if (ch > range_hi) {
ch = range_hi + 1;
}
num += ((double)(ch - range_lo)) / denom;
denom *= base;
}
return num;
}
/*
* Convert a string-type Datum into a palloc'd, null-terminated string.
*
* 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)
{
char* val = NULL;
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:
/*
* Can't get here unless someone tries to use scalarltsel on an
* operator with one string and one non-string operand.
*/
ereport(ERROR,
(errmodule(MOD_OPT), (errcode(ERRCODE_UNRECOGNIZED_NODE_TYPE), errmsg("unsupported type: %u", typid))));
return NULL;
}
if (!lc_collate_is_c(DEFAULT_COLLATION_OID)) {
char* xfrm_str = NULL;
size_t xfrm_len;
size_t xfrm_len2 PG_USED_FOR_ASSERTS_ONLY;
/*
* Note: originally we guessed at a suitable output buffer size, and
* only needed to call strxfrm twice if our guess was too small.
* However, it seems that some versions of Solaris have buggy strxfrm
* that can write past the specified buffer length in that scenario.
* So, do it the dumb way for portability.
*
* Yet other systems (e.g., glibc) sometimes return a smaller value
* from the second call than the first; thus the Assert must be <= not
* == as you'd expect. Can't any of these people program their way
* out of a paper bag?
*
* 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];
xfrm_len = strxfrm(x, val, 0);
}
#else
xfrm_len = 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 (xfrm_len == INT_MAX) {
return val;
}
#endif
xfrm_str = (char*)palloc(xfrm_len + 1);
xfrm_len2 = strxfrm(xfrm_str, val, xfrm_len + 1);
Assert(xfrm_len2 <= xfrm_len);
pfree_ext(val);
val = xfrm_str;
}
return val;
}
/*
* Do convert_to_scalar()'s work for any bytea data type.
*
* Very similar to 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* scaled_value, Datum lo_bound, double* scaled_lo_bound, Datum hi_bound, double* scaled_hi_bound)
{
int range_lo;
int range_hi;
int value_len = VARSIZE(DatumGetPointer(value)) - VARHDRSZ;
int lo_bound_len = VARSIZE(DatumGetPointer(lo_bound)) - VARHDRSZ;
int hi_bound_len = VARSIZE(DatumGetPointer(hi_bound)) - VARHDRSZ;
int i;
int min_len;
unsigned char *val_str = (unsigned char*)VARDATA(DatumGetPointer(value));
unsigned char *lo_str = (unsigned char*)VARDATA(DatumGetPointer(lo_bound));
unsigned char *hi_str = (unsigned char*)VARDATA(DatumGetPointer(hi_bound));
/*
* Assume bytea data is uniformly distributed across all byte values.
*/
range_lo = 0;
range_hi = 255;
/*
* Now strip any common prefix of the three strings.
*/
min_len = Min(Min(value_len, lo_bound_len), hi_bound_len);
for (i = 0; i < min_len; i++) {
if (*lo_str != *hi_str || *lo_str != *val_str) {
break;
}
lo_str++, hi_str++, val_str++;
lo_bound_len--, hi_bound_len--, value_len--;
}
/*
* Now we can do the conversions.
*/
*scaled_value = convert_one_bytea_to_scalar(val_str, value_len, range_lo, range_hi);
*scaled_lo_bound = convert_one_bytea_to_scalar(lo_str, lo_bound_len, range_lo, range_hi);
*scaled_hi_bound = convert_one_bytea_to_scalar(hi_str, hi_bound_len, range_lo, range_hi);
}
static double convert_one_bytea_to_scalar(unsigned char* value, int value_len, int range_lo, int range_hi)
{
double num;
double denom;
double base;
if (value_len <= 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 (value_len > 10) {
value_len = 10;
}
/* Convert initial characters to fraction */
base = range_hi - range_lo + 1;
num = 0.0;
denom = base;
while (value_len-- > 0) {
int ch = *value++;
if (ch < range_lo) {
ch = range_lo - 1;
} else if (ch > range_hi) {
ch = range_hi + 1;
}
num += ((double)(ch - range_lo)) / denom;
denom *= base;
}
return num;
}
/*
* Do convert_to_scalar()'s work for any timevalue data type.
*/
static double convert_timevalue_to_scalar(Datum value, Oid typid)
{
switch (typid) {
case TIMESTAMPOID:
return DatumGetTimestamp(value);
case TIMESTAMPTZOID:
return DatumGetTimestampTz(value);
case ABSTIMEOID:
return DatumGetTimestamp(DirectFunctionCall1(abstime_timestamp, 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.
*/
#ifdef HAVE_INT64_TIMESTAMP
return interval->time + interval->day * (double)USECS_PER_DAY +
interval->month * ((DAYS_PER_YEAR / (double)MONTHS_PER_YEAR) * USECS_PER_DAY);
#else
return interval->time + interval->day * SECS_PER_DAY +
interval->month * ((DAYS_PER_YEAR / (double)MONTHS_PER_YEAR) * (double)SECS_PER_DAY);
#endif
}
case RELTIMEOID:
#ifdef HAVE_INT64_TIMESTAMP
return (DatumGetRelativeTime(value) * 1000000.0);
#else
return DatumGetRelativeTime(value);
#endif
case TINTERVALOID: {
TimeInterval tinterval = DatumGetTimeInterval(value);
#ifdef HAVE_INT64_TIMESTAMP
if (tinterval->status != 0) {
return ((tinterval->data[1] - tinterval->data[0]) * 1000000.0);
}
#else
if (tinterval->status != 0) {
return tinterval->data[1] - tinterval->data[0];
}
#endif
return 0; /* for lack of a better idea */
}
case TIMEOID:
return DatumGetTimeADT(value);
case TIMETZOID: {
TimeTzADT* timetz = DatumGetTimeTzADTP(value);
/* use GMT-equivalent time */
#ifdef HAVE_INT64_TIMESTAMP
return (double)(timetz->time + (timetz->zone * 1000000.0));
#else
return (double)(timetz->time + timetz->zone);
#endif
}
default:
break;
}
/*
* Can't get here unless someone tries to use scalarltsel/scalargtsel on
* an operator with one timevalue and one non-timevalue operand.
*/
ereport(
ERROR, (errmodule(MOD_OPT), (errcode(ERRCODE_UNRECOGNIZED_NODE_TYPE), errmsg("unsupported type: %u", typid))));
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 var_relid, VariableStatData* var_data, Node** other, bool* var_on_left)
{
Node* left = NULL;
Node* right = NULL;
VariableStatData r_data;
r_data.statsTuple = NULL;
r_data.freefunc = NULL;
r_data.rel = NULL;
r_data.var = NULL;
/* 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 var_relid is nonzero, Vars of other
* relations will be treated as pseudoconstants.
*/
examine_variable(root, left, var_relid, var_data);
examine_variable(root, right, var_relid, &r_data);
/*
* If one side is a variable and the other not, we win.
*/
if (var_data->rel && r_data.rel == NULL) {
*var_on_left = true;
*other = estimate_expression_value(root, r_data.var);
/* Assume we need no ReleaseVariableStats(r_data) here */
return true;
}
if (var_data->rel == NULL && r_data.rel) {
*var_on_left = false;
*other = estimate_expression_value(root, var_data->var);
/* Assume we need no ReleaseVariableStats(*var_data) here */
*var_data = r_data;
return true;
}
/* Ooops, clause has wrong structure (probably var op var) */
ReleaseVariableStats(*var_data);
ReleaseVariableStats(r_data);
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* sj_info, VariableStatData* var_data1,
VariableStatData* var_data2, bool* join_is_reversed)
{
Node* left = NULL;
Node* right = NULL;
if (list_length(args) != 2) {
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_OPERATE_INVALID_PARAM), errmsg("join operator should take two arguments"))));
}
left = (Node*)linitial(args);
right = (Node*)lsecond(args);
examine_variable(root, left, 0, var_data1);
examine_variable(root, right, 0, var_data2);
if (var_data1->rel && bms_is_subset(var_data1->rel->relids, sj_info->syn_righthand)) {
*join_is_reversed = true; /* var1 is on RHS */
} else if (var_data2->rel && bms_is_subset(var_data2->rel->relids, sj_info->syn_lefthand)) {
*join_is_reversed = true; /* var2 is on LHS */
} else {
*join_is_reversed = false;
}
}
/*
* 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: type data to pass to get_attstatsslot(). 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.)
* aclOk: TRUE if current user has permission to read the column(s)
* underlying the pg_statistic entry. This is consulted
* by StatisticProcSecurityCheck.
*
* Caller is responsible for doing ReleaseVariableStats() before exiting.
*/
void examine_variable(PlannerInfo* root, Node* node, int var_relid, VariableStatData* var_data)
{
Node* base_node = NULL;
Relids varnos;
RelOptInfo* one_rel = NULL;
/* Make sure we don't return dangling pointers in var_data */
errno_t rc = memset_s(var_data, sizeof(VariableStatData), 0, sizeof(VariableStatData));
securec_check(rc, "\0", "\0");
/* we enable possion to estimate distinct for default. */
var_data->enablePossion = true;
/* Save the exposed type of the expression */
var_data->vartype = exprType(node);
/* Look inside any binary-compatible relabeling */
if (IsA(node, RelabelType)) {
base_node = (Node*)((RelabelType*)node)->arg;
} else {
base_node = node;
}
/* Fast path for a simple Var */
if (IsA(base_node, Var) && (var_relid == 0 || (uint)var_relid == ((Var*)base_node)->varno)) {
Var* var = (Var*)base_node;
/* Set up result fields other than the stats tuple */
var_data->var = base_node; /* return Var without relabeling */
var_data->rel = find_base_rel(root, var->varno);
var_data->atttype = var->vartype;
var_data->atttypmod = var->vartypmod;
var_data->isunique = has_unique_index(var_data->rel, var->varattno);
/* Try to locate some stats */
examine_simple_variable(root, var, var_data);
return;
}
/*
* Okay, it's a more complicated expression. Determine variable
* membership. Note that when var_relid isn't zero, only vars of that
* relation are considered "real" vars.
*/
varnos = pull_varnos(base_node);
one_rel = NULL;
switch (bms_membership(varnos)) {
case BMS_EMPTY_SET:
/* No Vars at all ... must be pseudo-constant clause */
break;
case BMS_SINGLETON:
if (var_relid == 0 || bms_is_member(var_relid, varnos)) {
one_rel = find_base_rel(root, (var_relid ? var_relid : bms_singleton_member(varnos)));
var_data->rel = one_rel;
node = base_node; /* strip any relabeling */
}
/* else treat it as a constant */
break;
case BMS_MULTIPLE:
if (var_relid == 0) {
/* treat it as a variable of a join relation */
var_data->rel = find_join_rel(root, varnos);
node = base_node; /* strip any relabeling */
} else if (bms_is_member(var_relid, varnos)) {
/* ignore the vars belonging to other relations */
var_data->rel = find_base_rel(root, var_relid);
node = base_node; /* strip any relabeling */
/* note: no point in expressional-index search here */
}
/* else treat it as a constant */
break;
default:
break;
}
bms_free(varnos);
varnos = NULL;
var_data->var = node;
var_data->atttype = exprType(node);
var_data->atttypmod = exprTypmod(node);
if (one_rel != NULL) {
/*
* We have an expression in vars of a single relation. Try to match
* it to expressional index columns, in hopes of finding some
* statistics.
*/
ListCell* ilist = NULL;
foreach (ilist, one_rel->indexlist) {
IndexOptInfo* index = (IndexOptInfo*)lfirst(ilist);
ListCell* indexpr_item = NULL;
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* index_key = NULL;
if (indexpr_item == NULL) {
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_OPERATE_INVALID_PARAM), errmsg("too few entries in indexprs list"))));
}
index_key = (Node*)lfirst(indexpr_item);
if (index_key && IsA(index_key, RelabelType))
index_key = (Node*)((RelabelType*)index_key)->arg;
if (equal(node, index_key)) {
/*
* Found a match ... is it a unique index? Tests here
* should match has_unique_index().
*/
if (index->unique && index->ncolumns == 1 && (index->indpred == NIL || index->predOK))
var_data->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 (index->indpred == NIL) {
/*
* we don't use statistics of partition or index partition
* currently, so if the index is index partition, just use
* the partition index statistic.
*/
char stakind = STARELKIND_CLASS;
Oid indexid = index->indexoid;
char relPersistence = get_rel_persistence(index->indexoid);
if (u_sess->attr.attr_common.upgrade_mode != 0) {
var_data->statsTuple = NULL;
var_data->freefunc = ReleaseSysCache;
} else if (relPersistence == RELPERSISTENCE_GLOBAL_TEMP) {
var_data->statsTuple = get_gtt_att_statistic(index->indexoid,
Int16GetDatum(pos + 1));
var_data->freefunc = release_gtt_statistic_cache;
} else {
var_data->statsTuple = SearchSysCache4(STATRELKINDATTINH,
ObjectIdGetDatum(indexid),
CharGetDatum(stakind),
Int16GetDatum(pos + 1),
BoolGetDatum(false));
var_data->freefunc = ReleaseSysCache;
}
if (HeapTupleIsValid(var_data->statsTuple)) {
/* Get index's table for permission check */
RangeTblEntry *rte;
rte = planner_rt_fetch((int)index->rel->relid, root);
Assert(rte->rtekind == RTE_RELATION);
/*
* For simplicity, we insist on the whole
* table being selectable, rather than trying
* to identify which column(s) the index
* depends on.
*/
var_data->aclOk =
(pg_class_aclcheck(rte->relid, GetUserId(), ACL_SELECT) == ACLCHECK_OK);
} else {
/* suppress leakproofness checks later */
var_data->aclOk = true;
}
}
if (var_data->statsTuple) {
break;
}
}
indexpr_item = lnext(indexpr_item);
}
}
if (var_data->statsTuple) {
break;
}
}
if (!HeapTupleIsValid(var_data->statsTuple) && !contain_var_unsubstitutable_functions(base_node)) {
List* vars = NIL;
vars = pull_var_clause(base_node, PVC_INCLUDE_AGGREGATES, PVC_INCLUDE_PLACEHOLDERS);
if (list_length(vars) == 1) {
base_node = (Node*)linitial(vars);
if (!IsA(node, Aggref) && !IsA(node, PlaceHolderVar) && !IsA(node, GroupingFunc) &&
!IsA(node, GroupingId)) {
Assert(IsA(base_node, Var));
if (var_relid == 0 || (uint)var_relid == ((Var*)base_node)->varno) {
Var* var = (Var*)base_node;
/* Set up result fields other than the stats tuple */
var_data->var = base_node; /* return Var without relabeling */
var_data->rel = find_base_rel(root, var->varno);
var_data->atttype = var->vartype;
var_data->atttypmod = var->vartypmod;
var_data->isunique = has_unique_index(var_data->rel, var->varattno);
/* Try to locate some stats */
examine_simple_variable(root, var, var_data);
}
}
}
}
}
}
/*
* 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* var_data)
{
RangeTblEntry* rte = root->simple_rte_array[var->varno];
Assert(IsA(rte, RangeTblEntry));
if (rte->rtekind == RTE_RELATION) {
char sta_kind = STARELKIND_CLASS;
Oid sta_relid = rte->relid;
/*
* get parent table's statistic for each partition if have no statistic, because
* there is no state info in pg_statistic for each partition, only partition
* table have state info in pg_statistic.
*
* We do not search system cache in upgrading
*/
char relPersistence = get_rel_persistence(rte->relid);
if (u_sess->attr.attr_common.upgrade_mode != 0) {
var_data->statsTuple = NULL;
var_data->freefunc = ReleaseSysCache;
} else if (relPersistence == RELPERSISTENCE_GLOBAL_TEMP) {
var_data->statsTuple = get_gtt_att_statistic(rte->relid, var->varattno);
var_data->freefunc = release_gtt_statistic_cache;
} else {
var_data->statsTuple = SearchSysCache4(STATRELKINDATTINH,
ObjectIdGetDatum(sta_relid),
CharGetDatum(sta_kind),
Int16GetDatum(var->varattno),
BoolGetDatum(rte->inh));
var_data->freefunc = ReleaseSysCache;
}
if (HeapTupleIsValid(var_data->statsTuple)) {
/* check if user has permission to read this column */
var_data->aclOk = (pg_class_aclcheck(rte->relid, GetUserId(), ACL_SELECT) == ACLCHECK_OK) ||
(pg_attribute_aclcheck(rte->relid, var->varattno, GetUserId(), ACL_SELECT) == ACLCHECK_OK);
} else {
/* suppress any possible leakproofness checks later */
var_data->aclOk = true;
}
#ifdef PGXC
if ((sta_relid >= FirstNormalObjectId) && (var->varattno > SelfItemPointerAttributeNumber)) {
if (!HeapTupleIsValid(var_data->statsTuple) && IS_PGXC_COORDINATOR) {
/* Save no analyzed reloid if is coordinator. */
set_noanalyze_rellist(rte->relid, var->varattno);
}
}
#endif
} else if (rte->rtekind == RTE_SUBQUERY && !rte->inh) {
/*
* Plain subquery (not one that was converted to an appendrel).
*/
Query* sub_query = rte->subquery;
RelOptInfo* rel = NULL;
TargetEntry* ste = NULL;
/*
* Punt if it's a whole-row var rather than a plain column reference.
*/
if (var->varattno == InvalidAttrNumber) {
return;
}
/*
* Punt if sub_query 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 (sub_query->setOperations || sub_query->groupClause) {
return;
}
/*
* OK, fetch RelOptInfo for sub_query. Note that we don't change the
* rel returned in var_data, 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 sub_query hasn't been planned yet, we have to punt */
if (rel->subroot == NULL) {
return;
}
Assert(IsA(rel->subroot, PlannerInfo));
/*
* Switch our attention to the sub_query 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 sub_query pullup happened during
* planning, Vars in the targetlist might have gotten replaced, and we
* need to see the replacement expressions.
*/
sub_query = rel->subroot->parse;
Assert(IsA(sub_query, Query));
/* Get the sub_query output expression referenced by the upper Var */
ste = get_tle_by_resno(sub_query->targetList, var->varattno);
if (ste == NULL || ste->resjunk) {
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_INVALID_ATTRIBUTE),
errmsg("sub_query %s does not have attribute %d", rte->eref->aliasname, var->varattno))));
}
var = (Var*)ste->expr;
/*
* If sub_query 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 (sub_query->distinctClause) {
if (list_length(sub_query->distinctClause) == 1 &&
targetIsInSortList(ste, InvalidOid, sub_query->distinctClause)) {
var_data->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 sub_query's query level */
if (var && IsA(var, Var) && var->varlevelsup == 0) {
/*
* OK, recurse into the sub_query. Note that the original setting
* of var_data->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 sub_query may have
* joined to other tables in a way that creates duplicates.
*/
examine_simple_variable(rel->subroot, var, var_data);
}
} 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 StatisticProcSecurityCheck(const VariableStatData *vardata, Oid funcOid)
{
if (vardata->aclOk) {
return true;
}
if (!OidIsValid(funcOid)) {
return false;
}
if (get_func_leakproof(funcOid)) {
return true;
}
ereport(DEBUG2,
(errmsg_internal("not using statistics because function \"%s\" is not leak-proof", get_func_name(funcOid))));
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.
*
* relid: adjusting rows for rel after joining with other relation
* all_baserels: identify all baserels include in the local root when sjinfo is null,
* we can use it to judge whether we can use possion or not
*
* 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* var_data, bool* is_default, bool adjust_rows, double join_ratio,
SpecialJoinInfo* sj_info, STATS_EST_TYPE e_type)
{
double sta_distinct;
double sta_null_frac = 0.0;
double ntuples;
double adjust_ratio = 1.0;
double var_ratio = 1.0;
*is_default = false;
if (adjust_rows) {
if (var_data->rel && var_data->rel->rows != 0 && var_data->rel->tuples != 0) {
/* multiply by selectivity */
adjust_ratio = var_data->rel->rows * join_ratio / var_data->rel->tuples;
}
}
/*
* Determine the sta_distinct 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 sta_null_frac too if we can find it
* (otherwise, assume no nulls, for lack of any better idea).
*/
if (HeapTupleIsValid(var_data->statsTuple)) {
/* Use the pg_statistic entry */
Form_pg_statistic stats;
stats = (Form_pg_statistic)GETSTRUCT(var_data->statsTuple);
if (IS_PGXC_COORDINATOR && e_type == STATS_TYPE_LOCAL) {
sta_distinct = get_attstadndistinct(var_data->statsTuple); /* Local distinct */
} else {
sta_distinct = stats->stadistinct;
}
sta_null_frac = stats->stanullfrac;
} else if (var_data->vartype == BOOLOID) {
/*
* Special-case boolean columns: presumably, two distinct values.
*
* Are there any other datatypes we should wire in special estimates
* for?
*/
sta_distinct = 2.0;
adjust_ratio = 1.0;
} else {
/*
* We don't keep statistics for system columns, but in some cases we
* can infer distinctness anyway.
*/
if (var_data->var && IsA(var_data->var, Var)) {
switch (((Var*)var_data->var)->varattno) {
case ObjectIdAttributeNumber:
case SelfItemPointerAttributeNumber:
sta_distinct = -1.0; /* unique */
break;
case TableOidAttributeNumber:
sta_distinct = 1.0; /* only 1 value */
break;
#ifdef PGXC
case XC_NodeIdAttributeNumber:
sta_distinct = 1.0; /* only 1 value */
break;
case BucketIdAttributeNumber:
sta_distinct = 1.0; /* only 1 value */
break;
#endif
default:
sta_distinct = 0.0; /* means "unknown" */
break;
}
} else {
sta_distinct = 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 (var_data->isunique) {
sta_distinct = -1.0 * (1.0 - sta_null_frac);
}
/*
* Otherwise we need to get the relation size; punt if not available.
*/
if (var_data->rel == NULL) {
*is_default = true;
var_data->enablePossion = false;
return DEFAULT_NUM_DISTINCT;
}
if (e_type == STATS_TYPE_LOCAL) {
ntuples = get_local_rows(var_data->rel->tuples,
var_data->rel->multiple,
IsLocatorReplicated(var_data->rel->locator_type),
ng_get_dest_num_data_nodes(var_data->rel));
} else {
ntuples = var_data->rel->tuples;
}
/*
* If we had an absolute estimate, use that.
*/
if (sta_distinct > 0.0) {
/*
* use possion model if satisify condition which the column of join or filter and var_data->var art different,
* but they are belong to the same relation.
* only global stat can use possion because var_ratio computed by global distinct.
*/
if (var_data->enablePossion && (e_type == STATS_TYPE_GLOBAL) && (ntuples > 0.0) &&
can_use_possion(var_data, sj_info, &var_ratio)) {
double tmp = sta_distinct;
sta_distinct = NUM_DISTINCT_SELECTIVITY_FOR_POISSON(sta_distinct, ntuples, var_ratio);
ereport(DEBUG2,
(errmodule(MOD_OPT),
(errmsg("The origin distinct value is %f. After using possion model with ntuples=%f and ration=%f \
The new distinct value is %f",
tmp,
ntuples,
var_ratio,
sta_distinct))));
} else {
/* multiply by selectivity */
double tmp = sta_distinct;
sta_distinct = sta_distinct * adjust_ratio;
var_data->enablePossion = false;
ereport(DEBUG2,
(errmodule(MOD_OPT),
(errmsg("The origin distinct value is %f. After multiply by selectivity with adjust_ratio=%f, \
the new distinct value is %f",
tmp,
adjust_ratio,
sta_distinct))));
}
return clamp_row_est(sta_distinct);
}
if (ntuples <= 0.0) {
*is_default = true;
var_data->enablePossion = false;
return DEFAULT_NUM_DISTINCT;
}
if (sta_distinct < 0.0) {
/*
* use possion model if satisify condition which the column of join or filter and var_data->var art different,
* but they are belong to the same relation.
* only global stat can use possion because var_ratio computed by global distinct.
*/
if (var_data->enablePossion && (e_type == STATS_TYPE_GLOBAL) &&
can_use_possion(var_data, sj_info, &var_ratio)) {
sta_distinct = NUM_DISTINCT_SELECTIVITY_FOR_POISSON(-sta_distinct * ntuples, ntuples, var_ratio);
} else {
/* multiply by selectivity */
sta_distinct = (-sta_distinct * ntuples) * adjust_ratio;
var_data->enablePossion = false;
}
return clamp_row_est(sta_distinct);
}
/*
* 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) {
var_data->enablePossion = false;
return clamp_row_est(ntuples);
}
*is_default = true;
var_data->enablePossion = false;
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* var_data, Oid sort_op, Datum* min, Datum* max)
{
Datum tmin = 0;
Datum tmax = 0;
bool have_data = false;
int16 typ_len;
bool typ_by_val = false;
Oid opfuncoid;
Datum* values = NULL;
int n_values;
int i;
/*
* 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, var_data, sort_op, min, max)) {
return true;
}
#endif
if (!HeapTupleIsValid(var_data->statsTuple)) {
/* 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 (!StatisticProcSecurityCheck(var_data, (opfuncoid = get_opcode(sort_op)))) {
return false;
}
get_typlenbyval(var_data->atttype, &typ_len, &typ_by_val);
/*
* 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(var_data->statsTuple,
var_data->atttype,
var_data->atttypmod,
STATISTIC_KIND_HISTOGRAM,
sort_op,
NULL,
&values,
&n_values,
NULL,
NULL)) {
if (n_values > 0) {
tmin = datumCopy(values[0], typ_by_val, typ_len);
tmax = datumCopy(values[n_values - 1], typ_by_val, typ_len);
have_data = true;
}
free_attstatsslot(var_data->atttype, values, n_values, NULL, 0);
} else if (get_attstatsslot(var_data->statsTuple,
var_data->atttype,
var_data->atttypmod,
STATISTIC_KIND_HISTOGRAM,
InvalidOid,
NULL,
&values,
&n_values,
NULL,
NULL)) {
free_attstatsslot(var_data->atttype, values, n_values, NULL, 0);
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(var_data->statsTuple,
var_data->atttype,
var_data->atttypmod,
STATISTIC_KIND_MCV,
InvalidOid,
NULL,
&values,
&n_values,
NULL,
NULL)) {
bool tmin_is_mcv = false;
bool tmax_is_mcv = false;
FmgrInfo op_proc;
fmgr_info(opfuncoid, &op_proc);
for (i = 0; i < n_values; i++) {
if (!have_data) {
tmin = tmax = values[i];
tmin_is_mcv = tmax_is_mcv = have_data = true;
continue;
}
if (DatumGetBool(FunctionCall2Coll(&op_proc, DEFAULT_COLLATION_OID, values[i], tmin))) {
tmin = values[i];
tmin_is_mcv = true;
}
if (DatumGetBool(FunctionCall2Coll(&op_proc, DEFAULT_COLLATION_OID, tmax, values[i]))) {
tmax = values[i];
tmax_is_mcv = true;
}
}
if (tmin_is_mcv) {
tmin = datumCopy(tmin, typ_by_val, typ_len);
}
if (tmax_is_mcv) {
tmax = datumCopy(tmax, typ_by_val, typ_len);
}
free_attstatsslot(var_data->atttype, values, n_values, NULL, 0);
}
*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* var_data, Oid sort_op,
Datum* min, Datum* max)
{
bool have_data = false;
RelOptInfo* rel = var_data->rel;
RangeTblEntry* rte = NULL;
ListCell* lc = NULL;
/* No hope if no relation or it doesn't have indexes */
if (rel == NULL || rel->indexlist == NIL) {
return false;
}
if (rel->fdwroutine != NULL)
return false;
/* If it has indexes it must be a plain relation */
rte = root->simple_rte_array[rel->relid];
Assert(rte->rtekind == RTE_RELATION);
if (rte->ispartrel || rte->relhasbucket) {
return false;
}
/* Search through the indexes to see if any match our problem */
foreach (lc, rel->indexlist) {
IndexOptInfo* index = (IndexOptInfo*)lfirst(lc);
ScanDirection index_scan_dir;
/* 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(var_data->var, 0, index))
continue;
switch (get_op_opfamily_strategy(sort_op, index->sortopfamily[0])) {
case BTLessStrategyNumber:
if (index->reverse_sort[0]) {
index_scan_dir = BackwardScanDirection;
} else {
index_scan_dir = ForwardScanDirection;
}
break;
case BTGreaterStrategyNumber:
if (index->reverse_sort[0]) {
index_scan_dir = ForwardScanDirection;
} else {
index_scan_dir = BackwardScanDirection;
}
break;
default:
/* index doesn't match the sort_op */
continue;
}
/*
* Found a suitable index to extract data from. We'll need an EState
* and a bunch of other infrastructure.
*/
{
EState* estate = NULL;
ExprContext* econtext = NULL;
MemoryContext tmp_context;
MemoryContext old_context;
Relation heap_rel;
Relation index_rel;
IndexInfo* index_info = NULL;
TupleTableSlot* slot = NULL;
int16 typ_len;
bool typ_by_val = false;
ScanKeyData scan_keys[1];
IndexScanDesc index_scan;
HeapTuple tup;
Datum values[INDEX_MAX_KEYS];
bool is_null[INDEX_MAX_KEYS];
estate = CreateExecutorState();
econtext = GetPerTupleExprContext(estate);
/* Make sure any cruft is generated in the econtext's memory */
tmp_context = econtext->ecxt_per_tuple_memory;
old_context = MemoryContextSwitchTo(tmp_context);
/*
* Open the table and index so we can read from them. We should
* already have at least AccessShareLock on the table, but not
* necessarily on the index.
*/
heap_rel = heap_open(rte->relid, NoLock);
index_rel = index_open(index->indexoid, AccessShareLock);
/* extract index key information from the index's pg_index info */
index_info = BuildIndexInfo(index_rel);
/* some other stuff */
slot = MakeSingleTupleTableSlot(RelationGetDescr(heap_rel));
econtext->ecxt_scantuple = slot;
get_typlenbyval(var_data->atttype, &typ_len, &typ_by_val);
/* set up an IS NOT NULL scan key so that we ignore nulls */
ScanKeyEntryInitialize(&scan_keys[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 != NULL) {
index_scan = index_beginscan(heap_rel, index_rel, SnapshotNow, 1, 0);
index_rescan(index_scan, scan_keys, 1, NULL, 0);
/* Fetch first tuple in sort_op's direction */
if ((tup = index_getnext(index_scan, index_scan_dir)) != NULL) {
/* Extract the index column values from the heap tuple */
(void)ExecStoreTuple(tup, slot, InvalidBuffer, false);
FormIndexDatum(index_info, slot, estate, values, is_null);
/* Shouldn't have got a null, but be careful */
if (is_null[0]) {
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_NULL_VALUE_NOT_ALLOWED),
errmsg("found unexpected null value in index \"%s\"",
RelationGetRelationName(index_rel)))));
}
/* Copy the index column value out to caller's context */
(void)MemoryContextSwitchTo(old_context);
*min = datumCopy(values[0], typ_by_val, typ_len);
(void)MemoryContextSwitchTo(tmp_context);
} else {
have_data = false;
}
index_endscan(index_scan);
}
/* If max is requested, and we didn't find the index is empty */
if ((max != NULL) && have_data) {
index_scan = index_beginscan(heap_rel, index_rel, SnapshotNow, 1, 0);
index_rescan(index_scan, scan_keys, 1, NULL, 0);
/* Fetch first tuple in reverse direction */
if ((tup = index_getnext(index_scan, (ScanDirection)-index_scan_dir)) != NULL) {
/* Extract the index column values from the heap tuple */
(void)ExecStoreTuple(tup, slot, InvalidBuffer, false);
FormIndexDatum(index_info, slot, estate, values, is_null);
/* Shouldn't have got a null, but be careful */
if (is_null[0]) {
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_NULL_VALUE_NOT_ALLOWED),
errmsg("found unexpected null value in index \"%s\"",
RelationGetRelationName(index_rel)))));
}
/* Copy the index column value out to caller's context */
(void)MemoryContextSwitchTo(old_context);
*max = datumCopy(values[0], typ_by_val, typ_len);
(void)MemoryContextSwitchTo(tmp_context);
} else {
have_data = false;
}
index_endscan(index_scan);
}
/* Clean everything up */
ExecDropSingleTupleTableSlot(slot);
index_close(index_rel, AccessShareLock);
heap_close(heap_rel, NoLock);
(void)MemoryContextSwitchTo(old_context);
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.
*/
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;
default:
break;
}
if (rel == NULL) {
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_NULL_VALUE_NOT_ALLOWED), errmsg("could not find RelOptInfo for given relids"))));
}
return rel;
}
/* -------------------------------------------------------------------------
*
* Pattern analysis functions
*
* These routines support analysis of LIKE and regular-expression patterns
* by the planner/optimizer. It's important that they agree with the
* regular-expression code in backend/regex/ and the LIKE code in
* backend/utils/adt/like.c. Also, the computation of the fixed prefix
* must be conservative: if we report a string longer than the true fixed
* prefix, the query may produce actually wrong answers, rather than just
* getting a bad selectivity estimate!
*
* Note that the prefix-analysis functions are called from
* backend/optimizer/path/indxpath.c as well as from routines in this file.
*
* -------------------------------------------------------------------------
*/
/*
* Check whether char is a letter (and, hence, subject to case-folding)
*
* In multibyte character sets, we can't use isalpha, and it does not seem
* worth trying to convert to wchar_t to use iswalpha. Instead, just assume
* any multibyte char is potentially case-varying.
*/
static int pattern_char_isalpha(char c, bool is_multibyte, pg_locale_t locale, bool locale_is_c)
{
if (locale_is_c) {
return (c >= 'A' && c <= 'Z') || (c >= 'a' && c <= 'z');
} else if (is_multibyte && IS_HIGHBIT_SET(c)) {
return true;
#ifdef HAVE_LOCALE_T
} else if (locale) {
return isalpha_l((unsigned char)c, locale);
#endif
} else {
return isalpha((unsigned char)c);
}
}
/*
* Extract the fixed prefix, if any, for a pattern.
*
* *prefix is set to a palloc'd prefix string (in the form of a Const node),
* or to NULL if no fixed prefix exists for the pattern.
* If rest_selec is not NULL, *rest_selec is set to an estimate of the
* selectivity of the remainder of the pattern (without any fixed prefix).
* The prefix Const has the same type (TEXT or BYTEA) as the input pattern.
*
* The return value distinguishes no fixed prefix, a partial prefix,
* or an exact-match-only pattern.
*/
static Pattern_Prefix_Status like_fixed_prefix(
Const* patt_const, bool case_insensitive, Oid collation, Const** prefix_const, Selectivity* rest_selec)
{
char* match = NULL;
char* patt = NULL;
int pattlen;
Oid typeId = patt_const->consttype;
int pos, match_pos;
bool is_multibyte = (pg_database_encoding_max_length() > 1);
pg_locale_t locale = 0;
bool locale_is_c = false;
/* the right-hand const is type text or bytea */
Assert(typeId == BYTEAOID || typeId == TEXTOID);
if (case_insensitive) {
if (typeId == BYTEAOID) {
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("case insensitive matching not supported on type bytea")));
}
/* If case-insensitive, we need locale info */
if (lc_ctype_is_c(collation)) {
locale_is_c = true;
} else if (collation != DEFAULT_COLLATION_OID) {
if (!OidIsValid(collation)) {
/*
* This typically means that the parser could not resolve a
* conflict of implicit collations, so report it that way.
*/
ereport(ERROR,
(errcode(ERRCODE_INDETERMINATE_COLLATION),
errmsg("could not determine which collation to use for ILIKE"),
errhint("Use the COLLATE clause to set the collation explicitly.")));
}
locale = pg_newlocale_from_collation(collation);
}
}
if (typeId != BYTEAOID) {
patt = TextDatumGetCString(patt_const->constvalue);
pattlen = strlen(patt);
} else {
bytea* bstr = DatumGetByteaP(patt_const->constvalue);
pattlen = VARSIZE(bstr) - VARHDRSZ;
patt = (char*)palloc(pattlen);
errno_t rc = memcpy_s(patt, pattlen, VARDATA(bstr), pattlen);
securec_check(rc, "\0", "\0");
if ((Pointer)bstr != DatumGetPointer(patt_const->constvalue)) {
pfree_ext(bstr);
}
}
match = (char*)palloc(pattlen + 1);
match_pos = 0;
pos = 0;
while (pos < pattlen) {
int charlen = pg_mblen(patt + pos);
/* % and _ are wildcard characters in LIKE */
if (patt[pos] == '%' || patt[pos] == '_') {
break;
}
/* Backslash escapes the next character */
if (patt[pos] == '\\') {
pos++;
if (pos >= pattlen) {
break;
}
}
/* Stop if case-varying character (it's sort of a wildcard) */
if (case_insensitive && pattern_char_isalpha(patt[pos], is_multibyte, locale, locale_is_c)) {
break;
}
for (int i = 0; i < charlen; i++) {
match[match_pos++] = patt[pos++];
}
}
match[match_pos] = '\0';
if (typeId != BYTEAOID) {
*prefix_const = string_to_const(match, typeId);
} else {
*prefix_const = string_to_bytea_const(match, match_pos);
}
if (rest_selec != NULL) {
*rest_selec = like_selectivity(&patt[pos], pattlen - pos, case_insensitive);
}
pfree_ext(patt);
pfree_ext(match);
/* in LIKE, an empty pattern is an exact match! */
if (pos == pattlen) {
return Pattern_Prefix_Exact; /* reached end of pattern, so exact */
}
if (match_pos > 0) {
return Pattern_Prefix_Partial;
}
return Pattern_Prefix_None;
}
static Pattern_Prefix_Status regex_fixed_prefix(
Const* patt_const, bool case_insensitive, Oid collation, Const** prefix_const, Selectivity* rest_selec)
{
Oid typeId = patt_const->consttype;
char* prefix = NULL;
bool exact = false;
/*
* Should be unnecessary, there are no bytea regex operators defined. As
* such, it should be noted that the rest of this function has *not* been
* made safe for binary (possibly NULL containing) strings.
*/
if (typeId == BYTEAOID) {
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("regular-expression matching not supported on type bytea")));
}
/* Use the regexp machinery to extract the prefix, if any */
prefix = regexp_fixed_prefix(DatumGetTextPP(patt_const->constvalue), case_insensitive, collation, &exact);
if (prefix == NULL) {
*prefix_const = NULL;
if (rest_selec != NULL) {
char* patt = TextDatumGetCString(patt_const->constvalue);
*rest_selec = regex_selectivity(patt, strlen(patt), case_insensitive, 0);
pfree_ext(patt);
}
return Pattern_Prefix_None;
}
*prefix_const = string_to_const(prefix, typeId);
if (rest_selec != NULL) {
if (exact) {
/* Exact match, so there's no additional selectivity */
*rest_selec = 1.0;
} else {
char* patt = TextDatumGetCString(patt_const->constvalue);
*rest_selec = regex_selectivity(patt, strlen(patt), case_insensitive, strlen(prefix));
pfree_ext(patt);
}
}
pfree_ext(prefix);
if (exact) {
return Pattern_Prefix_Exact; /* pattern specifies exact match */
} else {
return Pattern_Prefix_Partial;
}
}
Pattern_Prefix_Status pattern_fixed_prefix(
Const* patt, Pattern_Type ptype, Oid collation, Const** prefix, Selectivity* rest_selec)
{
Pattern_Prefix_Status result;
switch (ptype) {
case Pattern_Type_Like:
result = like_fixed_prefix(patt, false, collation, prefix, rest_selec);
break;
case Pattern_Type_Like_IC:
result = like_fixed_prefix(patt, true, collation, prefix, rest_selec);
break;
case Pattern_Type_Regex:
result = regex_fixed_prefix(patt, false, collation, prefix, rest_selec);
break;
case Pattern_Type_Regex_IC:
result = regex_fixed_prefix(patt, true, collation, prefix, rest_selec);
break;
default:
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_UNRECOGNIZED_NODE_TYPE), errmsg("unrecognized ptype: %d", (int)ptype))));
result = Pattern_Prefix_None; /* keep compiler quiet */
break;
}
return result;
}
/*
* Estimate the selectivity of a fixed prefix for a pattern match.
*
* A fixed prefix "foo" is estimated as the selectivity of the expression
* "variable >= 'foo' AND variable < 'fop'" (see also indxpath.c).
*
* The selectivity estimate is with respect to the portion of the column
* population represented by the histogram --- the caller must fold this
* together with info about MCVs and NULLs.
*
* We use the >= and < operators from the specified btree opfamily to do the
* estimation. The given variable and Const must be of the associated
* datatype.
*
* XXX Note: we make use of the upper bound to estimate operator selectivity
* even if the locale is such that we cannot rely on the upper-bound string.
* The selectivity only needs to be approximately right anyway, so it seems
* more useful to use the upper-bound code than not.
*/
static Selectivity prefix_selectivity(
PlannerInfo* root, VariableStatData* var_data, Oid var_type, Oid op_family, Const* prefix_con)
{
Selectivity prefix_sel;
Oid cmp_opr;
FmgrInfo op_proc;
Const* greater_str_con = NULL;
Selectivity eq_sel;
cmp_opr = get_opfamily_member(op_family, var_type, var_type, BTGreaterEqualStrategyNumber);
if (cmp_opr == InvalidOid) {
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_OPERATE_NOT_SUPPORTED), errmsg("no >= operator for op_family %u", op_family))));
}
fmgr_info(get_opcode(cmp_opr), &op_proc);
prefix_sel = ineq_histogram_selectivity(root, var_data, &op_proc, true,
prefix_con->constvalue, prefix_con->consttype);
if (prefix_sel < 0.0) {
/* No histogram is present ... return a suitable default estimate */
return DEFAULT_MATCH_SEL;
}
/* -------
* If we can create a string larger than the prefix, say
* "x < greaterstr".
* -------
*/
cmp_opr = get_opfamily_member(op_family, var_type, var_type, BTLessStrategyNumber);
if (cmp_opr == InvalidOid) {
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_OPERATE_NOT_SUPPORTED), errmsg("no < operator for op_family %u", op_family))));
}
fmgr_info(get_opcode(cmp_opr), &op_proc);
greater_str_con = make_greater_string(prefix_con, &op_proc, DEFAULT_COLLATION_OID);
if (greater_str_con != NULL) {
Selectivity topsel;
topsel = ineq_histogram_selectivity(
root, var_data, &op_proc, false, greater_str_con->constvalue, greater_str_con->consttype);
/* ineq_histogram_selectivity worked before, it shouldn't fail now */
Assert(topsel >= 0.0);
/*
* Merge the two selectivities in the same way as for a range query
* (see clauselist_selectivity()). Note that we don't need to worry
* about double-exclusion of nulls, since ineq_histogram_selectivity
* doesn't count those anyway.
*/
prefix_sel = topsel + prefix_sel - 1.0;
}
/*
* If the prefix is long then the two bounding values might be too close
* together for the histogram to distinguish them usefully, resulting in a
* zero estimate (plus or minus roundoff error). To avoid returning a
* ridiculously small estimate, compute the estimated selectivity for
* "variable = 'foo'", and clamp to that. (Obviously, the resultant
* estimate should be at least that.)
*
* We apply this even if we couldn't make a greater string. That case
* suggests that the prefix is near the maximum possible, and thus
* probably off the end of the histogram, and thus we probably got a very
* small estimate from the >= condition; so we still need to clamp.
*/
cmp_opr = get_opfamily_member(op_family, var_type, var_type, BTEqualStrategyNumber);
if (cmp_opr == InvalidOid) {
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_OPERATE_NOT_SUPPORTED), errmsg("no = operator for op_family %u", op_family))));
}
eq_sel = var_eq_const(var_data, cmp_opr, prefix_con->constvalue, false, true);
prefix_sel = Max(prefix_sel, eq_sel);
return prefix_sel;
}
/*
* Estimate the selectivity of a pattern of the specified type.
* Note that any fixed prefix of the pattern will have been removed already,
* so actually we may be looking at just a fragment of the pattern.
*
* For now, we use a very simplistic approach: fixed characters reduce the
* selectivity a good deal, character ranges reduce it a little,
* wildcards (such as % for LIKE or .* for regex) increase it.
*/
#define FIXED_CHAR_SEL 0.20 /* about 1/5 */
#define CHAR_RANGE_SEL 0.25
#define ANY_CHAR_SEL 0.9 /* not 1, since it won't match end-of-string */
#define FULL_WILDCARD_SEL 5.0
#define PARTIAL_WILDCARD_SEL 2.0
static Selectivity like_selectivity(const char* patt, int patt_len, bool case_insensitive)
{
Selectivity sel = 1.0;
int pos = 0;
/* Skip any leading wildcard; it's already factored into initial sel */
for (pos = 0; pos < patt_len; pos++) {
if (patt[pos] != '%' && patt[pos] != '_') {
break;
}
}
while (pos < patt_len) {
/* % and _ are wildcard characters in LIKE */
if (patt[pos] == '%') {
sel *= FULL_WILDCARD_SEL;
} else if (patt[pos] == '_') {
sel *= ANY_CHAR_SEL;
} else if (patt[pos] == '\\') {
/* Backslash quotes the next character */
pos++;
if (pos >= patt_len) {
break;
}
sel *= FIXED_CHAR_SEL;
} else {
sel *= FIXED_CHAR_SEL;
}
pos += pg_mblen(patt + pos);
}
/* Could get sel > 1 if multiple wildcards */
if (sel > 1.0) {
sel = 1.0;
}
return sel;
}
static Selectivity regex_selectivity_sub(const char* patt, int patt_len, bool case_insensitive)
{
Selectivity sel = 1.0;
int paren_depth = 0;
int paren_pos = 0; /* dummy init to keep compiler quiet */
int pos = 0;
while (pos < patt_len) {
if (patt[pos] == '(') {
if (paren_depth == 0) {
paren_pos = pos; /* remember start of parenthesized item */
}
paren_depth++;
} else if (patt[pos] == ')' && paren_depth > 0) {
paren_depth--;
if (paren_depth == 0) {
sel *= regex_selectivity_sub(patt + (paren_pos + 1), pos - (paren_pos + 1), case_insensitive);
}
} else if (patt[pos] == '|' && paren_depth == 0) {
/*
* If unquoted | is present at paren level 0 in pattern, we have
* multiple alternatives; sum their probabilities.
*/
sel += regex_selectivity_sub(patt + (pos + 1), patt_len - (pos + 1), case_insensitive);
break; /* rest of pattern is now processed */
} else if (patt[pos] == '[') {
bool negclass = false;
if (patt[++pos] == '^') {
negclass = true;
pos++;
}
if (patt[pos] == ']') { /* ']' at start of class is not special */
pos++;
}
while (pos < patt_len && patt[pos] != ']') {
pos++;
}
if (paren_depth == 0) {
sel *= (negclass ? (1.0 - CHAR_RANGE_SEL) : CHAR_RANGE_SEL);
}
} else if (patt[pos] == '.') {
if (paren_depth == 0) {
sel *= ANY_CHAR_SEL;
}
} else if (patt[pos] == '*' || patt[pos] == '?' || patt[pos] == '+') {
/* Ought to be smarter about quantifiers... */
if (paren_depth == 0) {
sel *= PARTIAL_WILDCARD_SEL;
}
} else if (patt[pos] == '{') {
while (pos < patt_len && patt[pos] != '}') {
pos++;
}
if (paren_depth == 0) {
sel *= PARTIAL_WILDCARD_SEL;
}
} else if (patt[pos] == '\\') {
/* backslash quotes the next character */
pos++;
if (pos >= patt_len) {
break;
}
if (paren_depth == 0) {
sel *= FIXED_CHAR_SEL;
}
} else {
if (paren_depth == 0) {
sel *= FIXED_CHAR_SEL;
}
}
pos += pg_mblen(patt + pos);
}
/* Could get sel > 1 if multiple wildcards */
if (sel > 1.0) {
sel = 1.0;
}
return sel;
}
static Selectivity regex_selectivity(const char* patt, int patt_len, bool case_insensitive, int fixed_prefix_len)
{
Selectivity sel;
/* If patt doesn't end with $, consider it to have a trailing wildcard */
if (patt_len > 0 && patt[patt_len - 1] == '$' && (patt_len == 1 || patt[patt_len - 2] != '\\')) {
/* has trailing $ */
sel = regex_selectivity_sub(patt, patt_len - 1, case_insensitive);
} else {
/* no trailing $ */
sel = regex_selectivity_sub(patt, patt_len, case_insensitive);
sel *= FULL_WILDCARD_SEL;
}
/* If there's a fixed prefix, discount its selectivity */
if (fixed_prefix_len > 0) {
sel /= pow(FIXED_CHAR_SEL, fixed_prefix_len);
}
/* Make sure result stays in range */
CLAMP_PROBABILITY(sel);
return sel;
}
/*
* For bytea, the increment function need only increment the current byte
* (there are no multibyte characters to worry about).
*/
static bool byte_increment(unsigned char* ptr, int len)
{
if (*ptr >= 255) {
return false;
}
(*ptr)++;
return true;
}
/*
* Try to generate a string greater than the given string or any
* string it is a prefix of. If successful, return a palloc'd string
* in the form of a Const node; else return NULL.
*
* The caller must provide the appropriate "less than" comparison function
* for testing the strings, along with the collation to use.
*
* The key requirement here is that given a prefix string, say "foo",
* we must be able to generate another string "fop" that is greater than
* all strings "foobar" starting with "foo". We can test that we have
* generated a string greater than the prefix string, but in non-C collations
* that is not a bulletproof guarantee that an extension of the string might
* not sort after it; an example is that "foo " is less than "foo!", but it
* is not clear that a "dictionary" sort ordering will consider "foo!" less
* than "foo bar". CAUTION: Therefore, this function should be used only for
* estimation purposes when working in a non-C collation.
*
* To try to catch most cases where an extended string might otherwise sort
* before the result value, we determine which of the strings "Z", "z", "y",
* and "9" is seen as largest by the collation, and append that to the given
* prefix before trying to find a string that compares as larger.
*
* To search for a greater string, we repeatedly "increment" the rightmost
* character, using an encoding-specific character incrementer function.
* When it's no longer possible to increment the last character, we truncate
* off that character and start incrementing the next-to-rightmost.
* For example, if "z" were the last character in the sort order, then we
* could produce "foo" as a string greater than "fonz".
*
* This could be rather slow in the worst case, but in most cases we
* won't have to try more than one or two strings before succeeding.
*
* Note that it's important for the character incrementer not to be too anal
* about producing every possible character code, since in some cases the only
* way to get a larger string is to increment a previous character position.
* So we don't want to spend too much time trying every possible character
* code at the last position. A good rule of thumb is to be sure that we
* don't try more than 256*K values for a K-byte character (and definitely
* not 256^K, which is what an exhaustive search would approach).
*/
Const* make_greater_string(const Const* str_const, FmgrInfo* lt_proc, Oid collation)
{
Oid data_type = str_const->consttype;
char* work_str = NULL;
int len;
Datum cmp_str;
text* cmp_txt = NULL;
mbcharacter_incrementer char_inc;
/*
* Get a modifiable copy of the prefix string in C-string format, and set
* up the string we will compare to as a Datum. In C locale this can just
* be the given prefix string, otherwise we need to add a suffix. Types
* NAME and BYTEA sort bytewise so they don't need a suffix either.
*/
if (data_type == NAMEOID) {
work_str = DatumGetCString(DirectFunctionCall1(nameout, str_const->constvalue));
len = strlen(work_str);
cmp_str = str_const->constvalue;
} else if (data_type == BYTEAOID) {
bytea* bstr = DatumGetByteaP(str_const->constvalue);
len = VARSIZE(bstr) - VARHDRSZ;
work_str = (char*)palloc(len);
errno_t rc = memcpy_s(work_str, len, VARDATA(bstr), len);
securec_check(rc, "\0", "\0");
if ((Pointer)bstr != DatumGetPointer(str_const->constvalue)) {
pfree_ext(bstr);
}
cmp_str = str_const->constvalue;
} else {
work_str = TextDatumGetCString(str_const->constvalue);
len = strlen(work_str);
if (lc_collate_is_c(collation) || len == 0) {
cmp_str = str_const->constvalue;
} else {
/* If first time through, determine the suffix to use */
if (!u_sess->utils_cxt.suffix_char || u_sess->utils_cxt.suffix_collation != collation) {
char* best = NULL;
best = "Z";
if (varstr_cmp(best, 1, "z", 1, collation) < 0) {
best = "z";
}
if (varstr_cmp(best, 1, "y", 1, collation) < 0) {
best = "y";
}
if (varstr_cmp(best, 1, "9", 1, collation) < 0) {
best = "9";
}
u_sess->utils_cxt.suffix_char = *best;
u_sess->utils_cxt.suffix_collation = collation;
}
/* And build the string to compare to */
cmp_txt = (text*)palloc(VARHDRSZ + len + 1);
SET_VARSIZE(cmp_txt, VARHDRSZ + len + 1);
errno_t rc = memcpy_s(VARDATA(cmp_txt), len, work_str, len);
securec_check(rc, "\0", "\0");
*(VARDATA(cmp_txt) + len) = u_sess->utils_cxt.suffix_char;
cmp_str = PointerGetDatum(cmp_txt);
}
}
/* Select appropriate character-incrementer function */
if (data_type == BYTEAOID) {
char_inc = byte_increment;
} else {
char_inc = pg_database_encoding_character_incrementer();
}
/* And search ... */
while (len > 0) {
int char_len;
unsigned char* last_char = NULL;
/* Identify the last character --- for bytea, just the last byte */
if (data_type == BYTEAOID) {
char_len = 1;
} else {
char_len = len - pg_mbcliplen(work_str, len, len - 1);
}
last_char = (unsigned char*)(work_str + len - char_len);
/*
* Try to generate a larger string by incrementing the last character
* (for BYTEA, we treat each byte as a character).
*
* Note: the incrementer function is expected to return true if it's
* generated a valid-per-the-encoding new character, otherwise false.
* The contents of the character on false return are unspecified.
*/
while (char_inc(last_char, char_len)) {
Const* work_str_const = NULL;
if (data_type == BYTEAOID) {
work_str_const = string_to_bytea_const(work_str, len);
} else {
work_str_const = string_to_const(work_str, data_type);
}
if (DatumGetBool(FunctionCall2Coll(lt_proc, collation, cmp_str, work_str_const->constvalue))) {
/* Successfully made a string larger than cmp_str */
if (cmp_txt != NULL) {
pfree_ext(cmp_txt);
}
pfree_ext(work_str);
return work_str_const;
}
/* No good, release unusable value and try again */
pfree(DatumGetPointer(work_str_const->constvalue));
pfree_ext(work_str_const);
}
/*
* No luck here, so truncate off the last character and try to
* increment the next one.
*/
len -= char_len;
work_str[len] = '\0';
}
/* Failed... */
if (cmp_txt != NULL) {
pfree_ext(cmp_txt);
}
pfree_ext(work_str);
return NULL;
}
/*
* Generate a Datum of the appropriate type from a C string.
* Note that all of the supported types are pass-by-ref, so the
* returned value should be pfree'd if no longer needed.
*/
static Datum string_to_datum(const char* str, Oid data_type)
{
Assert(str != NULL);
/*
* We cheat a little by assuming that CStringGetTextDatum() will do for
* bpchar and varchar constants too...
*/
if (data_type == NAMEOID) {
return DirectFunctionCall1(namein, CStringGetDatum(str));
} else if (data_type == BYTEAOID) {
return DirectFunctionCall1(byteain, CStringGetDatum(str));
} else {
return CStringGetTextDatum(str);
}
}
/*
* Generate a Const node of the appropriate type from a C string.
*/
static Const* string_to_const(const char* str, Oid data_type)
{
Datum conval = string_to_datum(str, data_type);
Oid collation;
int constlen;
/*
* We only need to support a few datatypes here, so hard-wire properties
* instead of incurring the expense of catalog lookups.
*/
switch (data_type) {
case TEXTOID:
case VARCHAROID:
case BPCHAROID:
collation = DEFAULT_COLLATION_OID;
constlen = -1;
break;
case NAMEOID:
collation = InvalidOid;
constlen = NAMEDATALEN;
break;
case BYTEAOID:
collation = InvalidOid;
constlen = -1;
break;
default:
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_DATATYPE_MISMATCH),
errmsg("unexpected data_type in string_to_const: %u", data_type))));
return NULL;
}
return makeConst(data_type, -1, collation, constlen, conval, false, false);
}
/*
* Generate a Const node of bytea type from a binary C string and a length.
*/
static Const* string_to_bytea_const(const char* str, size_t str_len)
{
bytea* bstr = (bytea*)palloc(VARHDRSZ + str_len);
Datum con_val;
errno_t rc = memcpy_s(VARDATA(bstr), VARHDRSZ + str_len, str, str_len);
securec_check(rc, "", "");
SET_VARSIZE(bstr, VARHDRSZ + str_len);
con_val = PointerGetDatum(bstr);
return makeConst(BYTEAOID, -1, InvalidOid, -1, con_val, false, false);
}
/* -------------------------------------------------------------------------
*
* Index cost estimation functions
*
* -------------------------------------------------------------------------
*/
/*
* 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.
*/
static List* add_predicate_to_quals(IndexOptInfo* index, List* index_quals)
{
List* pred_extra_quals = NIL;
ListCell* lc = NULL;
if (index->indpred == NIL) {
return index_quals;
}
foreach (lc, index->indpred) {
Node* pred_qual = (Node*)lfirst(lc);
List* one_qual = list_make1(pred_qual);
if (!predicate_implied_by(one_qual, index_quals)) {
pred_extra_quals = list_concat(pred_extra_quals, one_qual);
}
}
/* list_concat avoids modifying the passed-in index_quals list */
return list_concat(pred_extra_quals, index_quals);
}
/*
* generic_cost_estimate is a general-purpose estimator for use when we
* don't have any better idea about how to estimate. Index-type-specific
* knowledge can be incorporated in the type-specific routines.
*
* One bit of index-type-specific knowledge we can relatively easily use
* in generic_cost_estimate is the estimate of the number of index tuples
* visited. If numIndexTuples is not 0 then it is used as the estimate,
* otherwise we compute a generic estimate.
*/
static void generic_cost_estimate(PlannerInfo* root, IndexPath* path, double loop_count, double num_index_tuples,
Cost* index_startup_cost, Cost* index_total_cost, Selectivity* index_selectivity, double* index_correlation)
{
IndexOptInfo* index = path->indexinfo;
List* index_quals = path->indexquals;
List* index_orderbys = path->indexorderbys;
double num_index_pages;
double num_sa_scans;
double num_outer_scans;
double num_scans;
QualCost index_qual_cost;
double qual_op_cost;
double qual_arg_cost;
double spc_random_page_cost;
List* selectivity_quals = NIL;
ListCell* l = NULL;
bool is_partitioned_index = path->indexinfo->rel->isPartitionedTable;
double idx_local_tupls = IDXOPTINFO_LOCAL_FIELD(root, index, tuples);
List* saved_varratios = NIL;
/*
* If the index is partial, AND the index predicate with the explicitly
* given indexquals to produce a more accurate idea of the index
* selectivity.
*/
selectivity_quals = add_predicate_to_quals(index, index_quals);
/*
* Check for ScalarArrayOpExpr index quals, and estimate the number of
* index scans that will be performed.
*/
num_sa_scans = 1;
foreach (l, index_quals) {
RestrictInfo* r_info = (RestrictInfo*)lfirst(l);
if (IsA(r_info->clause, ScalarArrayOpExpr)) {
ScalarArrayOpExpr* sa_op = (ScalarArrayOpExpr*)r_info->clause;
int a_length = estimate_array_length((Node*)lsecond(sa_op->args));
if (a_length > 1) {
num_sa_scans *= a_length;
}
}
}
saved_varratios = index->rel->varratio;
index->rel->varratio = NULL;
/* Estimate the fraction of main-table tuples that will be visited */
*index_selectivity = clauselist_selectivity(root, selectivity_quals, index->rel->relid, JOIN_INNER, NULL, false);
list_free_deep(index->rel->varratio);
index->rel->varratio = saved_varratios;
/*
* 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.
*/
if (num_index_tuples <= 0.0) {
num_index_tuples = *index_selectivity * RELOPTINFO_LOCAL_FIELD(root, 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.)
*/
num_index_tuples = rint(num_index_tuples / 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
* index_selectivity estimate is tiny.
*/
if (num_index_tuples > idx_local_tupls) {
num_index_tuples = idx_local_tupls;
}
if (num_index_tuples < 1.0) {
num_index_tuples = 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
* this seems a better approximation than charging for access to the upper
* levels, perhaps because those tend to stay in cache under load.
*/
if (index->pages > 1 && idx_local_tupls > 1) {
num_index_pages = ceil(num_index_tuples * index->pages / idx_local_tupls);
} else {
num_index_pages = 1.0;
}
/* fetch estimated page cost for schema 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 = num_index_pages * 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, is_partitioned_index);
/*
* 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.)
*/
*index_total_cost = (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.
*/
*index_total_cost = num_index_pages * spc_random_page_cost;
}
/*
* A difficulty with the leaf-pages-only cost approach is that for small
* selectivities (eg, single index tuple fetched) all indexes will look
* equally attractive because we will estimate exactly 1 leaf page to be
* fetched. All else being equal, we should prefer physically smaller
* indexes over larger ones. (An index might be smaller because it is
* partial or because it contains fewer columns; presumably the other
* columns in the larger index aren't useful to the query, or the larger
* index would have better selectivity.)
*
* We can deal with this by adding a very small "fudge factor" that
* depends on the index size. The fudge factor used here is one
* spc_random_page_cost per 100000 index pages, which should be small
* enough to not alter index-vs-seqscan decisions, but will prevent
* indexes of different sizes from looking exactly equally attractive.
*/
*index_total_cost += index->pages * spc_random_page_cost / 100000.0;
/*
* 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 num_index_tuples 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
* and OR-clause expressions. Detecting that that might be needed seems
* more expensive than it's worth, though, considering all the other
* inaccuracies here ...
*/
cost_qual_eval(&index_qual_cost, index_quals, root);
qual_arg_cost = index_qual_cost.startup + index_qual_cost.per_tuple;
cost_qual_eval(&index_qual_cost, index_orderbys, root);
qual_arg_cost += index_qual_cost.startup + index_qual_cost.per_tuple;
qual_op_cost = u_sess->attr.attr_sql.cpu_operator_cost * (list_length(index_quals) + list_length(index_orderbys));
qual_arg_cost -= qual_op_cost;
if (qual_arg_cost < 0) { /* just in case... */
qual_arg_cost = 0;
}
*index_startup_cost = qual_arg_cost;
*index_total_cost += qual_arg_cost;
*index_total_cost += num_index_tuples * num_sa_scans * (u_sess->attr.attr_sql.cpu_index_tuple_cost + qual_op_cost);
/*
* We also add a CPU-cost component to represent the general costs of
* starting an indexscan, such as analysis of btree index keys and initial
* tree descent. This is estimated at 100x cpu_operator_cost, which is a
* bit arbitrary but seems the right order of magnitude. (As noted above,
* we don't charge any I/O for touching upper tree levels, but charging
* nothing at all has been found too optimistic.)
*
* Although this is startup cost with respect to any one scan, we add it
* to the "total" cost component because it's only very interesting in the
* many-ScalarArrayOpExpr-scan case, and there it will be paid over the
* life of the scan node.
*/
*index_total_cost += num_sa_scans * 100.0 * u_sess->attr.attr_sql.cpu_operator_cost;
/*
* Generic assumption about index correlation: there isn't any.
*/
*index_correlation = 0.0;
}
Datum btcostestimate(PG_FUNCTION_ARGS)
{
PlannerInfo* root = (PlannerInfo*)PG_GETARG_POINTER(0);
IndexPath* path = (IndexPath*)PG_GETARG_POINTER(1);
double loop_count = PG_GETARG_FLOAT8(2);
Cost* index_startup_cost = (Cost*)PG_GETARG_POINTER(3);
Cost* index_total_cost = (Cost*)PG_GETARG_POINTER(4);
Selectivity* index_selectivity = (Selectivity*)PG_GETARG_POINTER(5);
double* index_correlation = (double*)PG_GETARG_POINTER(6);
IndexOptInfo* index = path->indexinfo;
Oid relid;
AttrNumber col_num;
VariableStatData var_data;
double num_index_tuples;
List* index_bound_quals = NIL;
int index_col;
bool eq_qual_here = false;
bool found_saop = false;
bool found_is_null_op = false;
double num_sa_scans;
ListCell* lcc = NULL;
ListCell* lci = NULL;
/*
* 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 index_selectivity, but they should not count
* for estimating num_index_tuples. 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.
*/
index_bound_quals = NIL;
index_col = 0;
eq_qual_here = false;
found_saop = false;
found_is_null_op = false;
num_sa_scans = 1;
forboth(lcc, path->indexquals, lci, path->indexqualcols)
{
RestrictInfo* rinfo = (RestrictInfo*)lfirst(lcc);
Expr* clause = NULL;
Node* left_op = NULL;
Node PG_USED_FOR_ASSERTS_ONLY* right_op = NULL;
Oid clause_op;
int op_strategy;
bool is_null_op = false;
if (index_col != lfirst_int(lci)) {
/* Beginning of a new column's quals */
if (!eq_qual_here) {
break; /* done if no '=' qual for index_col */
}
eq_qual_here = false;
index_col++;
if (index_col != lfirst_int(lci)) {
break; /* no quals at all for index_col */
}
}
Assert(IsA(rinfo, RestrictInfo));
clause = rinfo->clause;
if (IsA(clause, OpExpr)) {
left_op = get_leftop(clause);
right_op = get_rightop(clause);
clause_op = ((OpExpr*)clause)->opno;
} else if (IsA(clause, RowCompareExpr)) {
RowCompareExpr* rc = (RowCompareExpr*)clause;
left_op = (Node*)linitial(rc->largs);
right_op = (Node*)linitial(rc->rargs);
clause_op = linitial_oid(rc->opnos);
} else if (IsA(clause, ScalarArrayOpExpr)) {
ScalarArrayOpExpr* saop = (ScalarArrayOpExpr*)clause;
left_op = (Node*)linitial(saop->args);
right_op = (Node*)lsecond(saop->args);
clause_op = saop->opno;
found_saop = true;
} else if (IsA(clause, NullTest)) {
NullTest* nt = (NullTest*)clause;
left_op = (Node*)nt->arg;
right_op = NULL;
clause_op = InvalidOid;
if (nt->nulltesttype == IS_NULL) {
found_is_null_op = true;
is_null_op = true;
}
} else {
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("unsupported indexqual type: %d", (int)nodeTag(clause)))));
continue; /* keep compiler quiet */
}
if (match_index_to_operand(left_op, index_col, index)) {
/* clause_op is correct */
} else {
Assert(match_index_to_operand(right_op, index_col, index));
/* Must flip operator to get the opfamily member */
clause_op = get_commutator(clause_op);
}
/* check for equality operator */
if (OidIsValid(clause_op)) {
op_strategy = get_op_opfamily_strategy(clause_op, index->opfamily[index_col]);
Assert(op_strategy != 0); /* not a member of opfamily?? */
if (op_strategy == BTEqualStrategyNumber) {
eq_qual_here = true;
}
} else if (is_null_op) {
/* IS NULL is like = for purposes of selectivity determination */
eq_qual_here = true;
}
/* count up number of SA scans induced by index_bound_quals only */
if (IsA(clause, ScalarArrayOpExpr)) {
ScalarArrayOpExpr* saop = (ScalarArrayOpExpr*)clause;
int alength = estimate_array_length((Node*)lsecond(saop->args));
if (alength > 1) {
num_sa_scans *= alength;
}
}
index_bound_quals = lappend(index_bound_quals, rinfo);
}
/*
* If index is unique and we found an '=' clause for each column, we can
* just assume num_index_tuples = 1 and skip the expensive
* clauselist_selectivity calculations. However, a ScalarArrayOp or
* NullTest invalidates that theory, even though it sets eq_qual_here.
*/
if (index->unique && index_col == index->ncolumns - 1 && eq_qual_here && !found_saop && !found_is_null_op)
num_index_tuples = 1.0;
else {
List* selectivity_quals = NIL;
Selectivity btree_selectivity;
List* saved_varratios = NIL;
/*
* 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.
*/
selectivity_quals = add_predicate_to_quals(index, index_bound_quals);
saved_varratios = index->rel->varratio;
index->rel->varratio = NULL;
btree_selectivity = clauselist_selectivity(root, selectivity_quals, index->rel->relid, JOIN_INNER, NULL, false);
list_free_deep(index->rel->varratio);
index->rel->varratio = saved_varratios;
num_index_tuples = btree_selectivity * RELOPTINFO_LOCAL_FIELD(root, index->rel, tuples);
/*
* As in generic_cost_estimate(), we have to adjust for any
* ScalarArrayOpExpr quals included in index_bound_quals, and then round
* to integer.
*/
num_index_tuples = rint(num_index_tuples / num_sa_scans);
}
generic_cost_estimate(root, path, loop_count, num_index_tuples, index_startup_cost,
index_total_cost, index_selectivity, index_correlation);
/*
* 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.)
*/
errno_t rc = memset_s(&var_data, sizeof(var_data), 0, sizeof(var_data));
securec_check(rc, "\0", "\0");
if (index->indexkeys[0] != 0) {
/* Simple variable --- look to stats for the underlying table */
RangeTblEntry* rte = planner_rt_fetch(index->rel->relid, root);
char relPersistence = get_rel_persistence(rte->relid);
Assert(rte->rtekind == RTE_RELATION);
relid = rte->relid;
Assert(relid != InvalidOid);
col_num = index->indexkeys[0];
char stakind = STARELKIND_CLASS;
Oid staoid = relid;
if (OidIsValid(rte->partitionOid)) {
Assert(rte->isContainPartition && rte->ispartrel);
stakind = STARELKIND_PARTITION;
staoid = rte->partitionOid;
}
if (u_sess->attr.attr_common.upgrade_mode != 0) {
var_data.statsTuple = NULL;
var_data.freefunc = ReleaseSysCache;
} else if (relPersistence == RELPERSISTENCE_GLOBAL_TEMP) {
var_data.statsTuple = get_gtt_att_statistic(rte->relid, col_num);
var_data.freefunc = release_gtt_statistic_cache;
} else {
var_data.statsTuple = SearchSysCache4(STATRELKINDATTINH,
ObjectIdGetDatum(staoid),
CharGetDatum(stakind),
Int16GetDatum(col_num),
BoolGetDatum(rte->inh));
var_data.freefunc = ReleaseSysCache;
}
} else {
/* Expression --- maybe there are stats for the index itself */
char relPersistence = get_rel_persistence(index->indexoid);
relid = index->indexoid;
col_num = 1;
char stakind = STARELKIND_CLASS;
Oid staoid = relid;
if (OidIsValid(index->partitionindex)) {
Assert(index->ispartitionedindex);
stakind = STARELKIND_PARTITION;
staoid = index->partitionindex;
}
if (u_sess->attr.attr_common.upgrade_mode != 0) {
var_data.statsTuple = NULL;
var_data.freefunc = ReleaseSysCache;
} else if (relPersistence == RELPERSISTENCE_GLOBAL_TEMP) {
var_data.statsTuple = get_gtt_att_statistic(relid, col_num);
var_data.freefunc = release_gtt_statistic_cache;
} else {
var_data.statsTuple = SearchSysCache4(STATRELKINDATTINH,
ObjectIdGetDatum(staoid),
CharGetDatum(stakind),
Int16GetDatum(col_num),
BoolGetDatum(false));
var_data.freefunc = ReleaseSysCache;
}
}
if (HeapTupleIsValid(var_data.statsTuple)) {
Oid sort_op;
float4* numbers = NULL;
int nnumbers;
sort_op =
get_opfamily_member(index->opfamily[0], index->opcintype[0], index->opcintype[0], BTLessStrategyNumber);
if (OidIsValid(sort_op) &&
get_attstatsslot(var_data.statsTuple,
InvalidOid,
0,
STATISTIC_KIND_CORRELATION,
sort_op,
NULL,
NULL,
NULL,
&numbers,
&nnumbers)) {
double var_correlation;
Assert(nnumbers == 1);
var_correlation = numbers[0];
if (index->reverse_sort[0]) {
var_correlation = -var_correlation;
}
if (index->ncolumns > 1) {
*index_correlation = var_correlation * 0.75;
} else {
*index_correlation = var_correlation;
}
free_attstatsslot(InvalidOid, NULL, 0, numbers, nnumbers);
}
}
ReleaseVariableStats(var_data);
PG_RETURN_VOID();
}
Datum hashcostestimate(PG_FUNCTION_ARGS)
{
PlannerInfo* root = (PlannerInfo*)PG_GETARG_POINTER(0);
IndexPath* path = (IndexPath*)PG_GETARG_POINTER(1);
double loop_count = PG_GETARG_FLOAT8(2);
Cost* index_startup_cost = (Cost*)PG_GETARG_POINTER(3);
Cost* index_total_cost = (Cost*)PG_GETARG_POINTER(4);
Selectivity* index_selectivity = (Selectivity*)PG_GETARG_POINTER(5);
double* index_correlation = (double*)PG_GETARG_POINTER(6);
generic_cost_estimate(
root, path, loop_count, 0.0, index_startup_cost, index_total_cost, index_selectivity, index_correlation);
PG_RETURN_VOID();
}
Datum gistcostestimate(PG_FUNCTION_ARGS)
{
PlannerInfo* root = (PlannerInfo*)PG_GETARG_POINTER(0);
IndexPath* path = (IndexPath*)PG_GETARG_POINTER(1);
double loop_count = PG_GETARG_FLOAT8(2);
Cost* index_startup_cost = (Cost*)PG_GETARG_POINTER(3);
Cost* index_total_cost = (Cost*)PG_GETARG_POINTER(4);
Selectivity* index_selectivity = (Selectivity*)PG_GETARG_POINTER(5);
double* index_correlation = (double*)PG_GETARG_POINTER(6);
generic_cost_estimate(
root, path, loop_count, 0.0, index_startup_cost, index_total_cost, index_selectivity, index_correlation);
PG_RETURN_VOID();
}
Datum spgcostestimate(PG_FUNCTION_ARGS)
{
PlannerInfo* root = (PlannerInfo*)PG_GETARG_POINTER(0);
IndexPath* path = (IndexPath*)PG_GETARG_POINTER(1);
double loop_count = PG_GETARG_FLOAT8(2);
Cost* indexStartupCost = (Cost*)PG_GETARG_POINTER(3);
Cost* indexTotalCost = (Cost*)PG_GETARG_POINTER(4);
Selectivity* indexSelectivity = (Selectivity*)PG_GETARG_POINTER(5);
double* indexCorrelation = (double*)PG_GETARG_POINTER(6);
generic_cost_estimate(
root, path, loop_count, 0.0, indexStartupCost, indexTotalCost, indexSelectivity, indexCorrelation);
PG_RETURN_VOID();
}
#define DFS_INDEX_SELECTIVITY_THRESHOLD 0.001
Datum psortcostestimate(PG_FUNCTION_ARGS)
{
PlannerInfo* root = (PlannerInfo*)PG_GETARG_POINTER(0);
IndexPath* path = (IndexPath*)PG_GETARG_POINTER(1);
double loop_count = PG_GETARG_FLOAT8(2);
Cost* indexStartupCost = (Cost*)PG_GETARG_POINTER(3);
Cost* indexTotalCost = (Cost*)PG_GETARG_POINTER(4);
Selectivity* indexSelectivity = (Selectivity*)PG_GETARG_POINTER(5);
double* indexCorrelation = (double*)PG_GETARG_POINTER(6);
IndexOptInfo* index = path->indexinfo;
RelOptInfo* baserel = index->rel;
/* selectedTuple - selected tuple in scan, decided by all entry num and selectiviry */
double selectedTuple = 0.0;
/* cuNum is the num of cu loaded for scan */
double cuNum = 0.0;
/* estimated cost for each tuple */
Cost cpu_per_tuple = 0.0;
/* indicate the multiplier cost and the default rows in a cu*/
const int col_tuple_multiplier_cost = 10;
int per_cu_itemnum = DefaultFullCUSize;
generic_cost_estimate(
root, path, loop_count, 0.0, indexStartupCost, indexTotalCost, indexSelectivity, indexCorrelation);
/*
* psort get_tid cost is 10 times as btree, we estimate the psort tid cost based
* on the former indexTotalCost by generic_cost_estimate
*/
(*indexTotalCost) *= 100;
/*
* calculate the selected tuple num according to the the num of all tuples and
* the selectivity
*/
*indexSelectivity = (*indexSelectivity <= 1) ? *indexSelectivity : 1;
selectedTuple = (*indexSelectivity) * RELOPTINFO_LOCAL_FIELD(root, baserel, tuples);
selectedTuple = ceil(selectedTuple);
/*
* calculate how many cu need to be scanned for cost estimaing,
* the ratio 0.02 adopted for cu_num estimate, which can be adjusted later
*/
cuNum = selectedTuple * 0.02;
/*
* when selectivity and selectedTuple are very small, set the smallest cu = 1,
* ensure smallest cost of index scan
*/
cuNum = ceil(cuNum);
/*
* estimate the tuple scan cost in selected cu(cpu + memory), ignore dop
* the index only scan cost are treated the same as indexscan now
*/
cpu_per_tuple =
u_sess->attr.attr_sql.cpu_tuple_cost / col_tuple_multiplier_cost + baserel->baserestrictcost.per_tuple;
*indexTotalCost += cpu_per_tuple * per_cu_itemnum * cuNum;
/* For dfs table, index scan is used when the selectivity is smaller than 0.001. */
elog(DEBUG1, "indexSelectivity: %f.", *indexSelectivity);
if (path->indexinfo->rel->orientation == REL_PAX_ORIENTED && *indexSelectivity > DFS_INDEX_SELECTIVITY_THRESHOLD) {
*indexTotalCost += g_instance.cost_cxt.disable_cost * 0.5;
}
*indexTotalCost -= 100.0 * u_sess->attr.attr_sql.cpu_operator_cost;
*indexCorrelation = 1.0;
PG_RETURN_VOID();
}
Datum cbtreecostestimate(PG_FUNCTION_ARGS)
{
PlannerInfo* root = (PlannerInfo*)PG_GETARG_POINTER(0);
IndexPath* path = (IndexPath*)PG_GETARG_POINTER(1);
double loop_count = PG_GETARG_FLOAT8(2);
Cost* indexStartupCost = (Cost*)PG_GETARG_POINTER(3);
Cost* indexTotalCost = (Cost*)PG_GETARG_POINTER(4);
Selectivity* indexSelectivity = (Selectivity*)PG_GETARG_POINTER(5);
double* indexCorrelation = (double*)PG_GETARG_POINTER(6);
IndexOptInfo* index = path->indexinfo;
RelOptInfo* baserel = index->rel;
/* selectedTuple - selected tuple in scan, decided by all entry num and selectiviry */
double selectedTuple = 0.0;
/* cuNum is the num of cu loaded for scan */
double cuNum = 0.0;
/* estimated cost for each tuple */
Cost cpu_per_tuple = 0.0;
/* indicate the multiplier cost and the default rows in a cu*/
const int col_tuple_multiplier_cost = 10;
int per_cu_itemnum = DefaultFullCUSize;
generic_cost_estimate(
root, path, loop_count, 0.0, indexStartupCost, indexTotalCost, indexSelectivity, indexCorrelation);
/* estimate the btree tid cost based on the former indexTotalCost */
(*indexTotalCost) *= 10;
/*
* calculate the selected tuple num according to the the num of all tuples and
* the selectivity
*/
*indexSelectivity = (*indexSelectivity <= 1) ? *indexSelectivity : 1;
selectedTuple = (*indexSelectivity) * RELOPTINFO_LOCAL_FIELD(root, baserel, tuples);
selectedTuple = ceil(selectedTuple);
/*
* calculate how many cu need to be scanned for cost estimaing,
* the ratio 0.002 adopted for cu_num estimate, which can be adjusted later
*/
cuNum = selectedTuple * 0.002;
/*
* when selectivity and selectedTuple are very small, set the smallest cu = 1,
* ensure smallest cost of index scan
*/
cuNum = ceil(cuNum);
/*
* estimate the tuple scan cost in selected cu(cpu + memory), ignore dop
* the index only scan cost are treated the same as indexscan now
*/
cpu_per_tuple =
u_sess->attr.attr_sql.cpu_tuple_cost / col_tuple_multiplier_cost + baserel->baserestrictcost.per_tuple;
*indexTotalCost += cpu_per_tuple * per_cu_itemnum * cuNum;
/* For dfs table, index scan is used when the selectivity is smaller than 0.001. */
elog(DEBUG1, "indexSelectivity: %f.", *indexSelectivity);
if (*indexSelectivity > DFS_INDEX_SELECTIVITY_THRESHOLD) {
*indexTotalCost += g_instance.cost_cxt.disable_cost * 0.5;
}
*indexTotalCost -= 100.0 * u_sess->attr.attr_sql.cpu_operator_cost;
/* cbtree is a bit better than psort for search now. */
*indexTotalCost *= 0.9;
*indexCorrelation = 1.0;
PG_RETURN_VOID();
}
/*
* Support routines for gincostestimate
*/
typedef struct {
bool haveFullScan;
double partialEntries;
double exactEntries;
double searchEntries;
double arrayScans;
} GinQualCounts;
/* Find the index column matching "op"; return its index, or -1 if no match */
static int find_index_column(Node* op, IndexOptInfo* index)
{
int i;
for (i = 0; i < index->ncolumns; i++) {
if (match_index_to_operand(op, i, index))
return i;
}
return -1;
}
/*
* 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;
int strategy_op;
Oid left_type;
Oid right_type;
int32 nentries = 0;
bool* partial_matches = NULL;
Pointer* extra_data = NULL;
bool* null_flags = NULL;
int32 search_mode = GIN_SEARCH_MODE_DEFAULT;
int32 i;
/*
* 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, &left_type, &right_type);
/*
* GIN always uses the "default" support functions, which are those with
* left_type == right_type == the opclass' opcintype (see
* index_support_initialize 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 */
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_NO_FUNCTION_PROVIDED),
errmsg("missing support function %d for attribute %d of index \"%s\"",
GIN_EXTRACTQUERY_PROC,
indexcol + 1,
get_rel_name(index->indexoid)))));
}
OidFunctionCall7(extractProcOid,
query,
PointerGetDatum(&nentries),
UInt16GetDatum(strategy_op),
PointerGetDatum(&partial_matches),
PointerGetDatum(&extra_data),
PointerGetDatum(&null_flags),
PointerGetDatum(&search_mode));
if (nentries <= 0 && search_mode == 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 != NULL) && partial_matches[i]) {
counts->partialEntries += 100;
} else {
counts->exactEntries++;
}
counts->searchEntries++;
}
if (search_mode == GIN_SEARCH_MODE_INCLUDE_EMPTY) {
/* Treat "include empty" like an exact-match item */
counts->exactEntries++;
counts->searchEntries++;
} else if (search_mode != 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(IndexOptInfo* index, OpExpr* clause, GinQualCounts* counts)
{
Node* leftop = get_leftop((Expr*)clause);
Node* rightop = get_rightop((Expr*)clause);
Oid clause_op = clause->opno;
int index_col;
Node* operand = NULL;
/* Locate the operand being compared to the index column */
if ((index_col = find_index_column(leftop, index)) >= 0) {
operand = rightop;
if (operand == NULL) {
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_OPERATE_RESULT_NOT_EXPECTED),
errmsg("the right operand should not be null in gincost_opexpr"))));
}
} else if ((index_col = find_index_column(rightop, index)) >= 0) {
operand = leftop;
clause_op = get_commutator(clause_op);
} else {
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_INDEX_OPERATOR_MISMATCH), errmsg("could not match index to operand"))));
operand = NULL; /* keep compiler quiet */
}
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, index_col, 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(
IndexOptInfo* index, ScalarArrayOpExpr* clause, double num_index_entries, GinQualCounts* counts)
{
Node* left_op = (Node*)linitial(clause->args);
Node* right_op = (Node*)lsecond(clause->args);
Oid clause_op = clause->opno;
int index_col;
ArrayType* array_val = NULL;
int16 elm_len;
bool elm_by_val = false;
char elm_align;
int num_elems;
Datum* elem_values = NULL;
bool* elem_nulls = NULL;
GinQualCounts array_counts;
int num_possible = 0;
int i;
Assert(clause->useOr);
/* index column must be on the left */
if ((index_col = find_index_column(left_op, index)) < 0) {
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_INDEX_OPERATOR_MISMATCH), errmsg("could not match index to operand"))));
}
if (IsA(right_op, RelabelType)) {
right_op = (Node*)((RelabelType*)right_op)->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(right_op, Const)) {
counts->exactEntries++;
counts->searchEntries++;
counts->arrayScans *= estimate_array_length(right_op);
return true;
}
/* If Const is null, there can be no matches */
if (((Const*)right_op)->constisnull) {
return false;
}
/* Otherwise, extract the array elements and iterate over them */
array_val = DatumGetArrayTypeP(((Const*)right_op)->constvalue);
get_typlenbyvalalign(ARR_ELEMTYPE(array_val), &elm_len, &elm_by_val, &elm_align);
deconstruct_array(array_val, ARR_ELEMTYPE(array_val), elm_len, elm_by_val, elm_align,
&elem_values, &elem_nulls, &num_elems);
errno_t rc = memset_s(&array_counts, sizeof(array_counts), 0, sizeof(array_counts));
securec_check(rc, "\0", "\0");
for (i = 0; i < num_elems; i++) {
GinQualCounts elemcounts;
/* NULL can't match anything, so ignore, as the executor will */
if (elem_nulls[i]) {
continue;
}
/* Otherwise, apply extractQuery and get the actual term counts */
errno_t rc = memset_s(&elemcounts, sizeof(elemcounts), 0, sizeof(elemcounts));
securec_check(rc, "\0", "\0");
if (gincost_pattern(index, index_col, clause_op, elem_values[i], &elemcounts)) {
/* We ignore array elements that are unsatisfiable patterns */
num_possible++;
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 = num_index_entries;
elemcounts.searchEntries = num_index_entries;
}
array_counts.partialEntries += elemcounts.partialEntries;
array_counts.exactEntries += elemcounts.exactEntries;
array_counts.searchEntries += elemcounts.searchEntries;
}
}
if (num_possible == 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 += array_counts.partialEntries / num_possible;
counts->exactEntries += array_counts.exactEntries / num_possible;
counts->searchEntries += array_counts.searchEntries / num_possible;
counts->arrayScans *= num_possible;
/* free the unused buffer */
pfree_ext(elem_nulls);
return true;
}
/*
* GIN has search behavior completely different from other index types
*/
Datum gincostestimate(PG_FUNCTION_ARGS)
{
PlannerInfo* root = (PlannerInfo*)PG_GETARG_POINTER(0);
IndexPath* path = (IndexPath*)PG_GETARG_POINTER(1);
double loop_count = PG_GETARG_FLOAT8(2);
Cost* index_startup_cost = (Cost*)PG_GETARG_POINTER(3);
Cost* index_total_cost = (Cost*)PG_GETARG_POINTER(4);
Selectivity* index_selectivity = (Selectivity*)PG_GETARG_POINTER(5);
double* index_correlation = (double*)PG_GETARG_POINTER(6);
IndexOptInfo* index = path->indexinfo;
List* index_quals = path->indexquals;
List* index_orderbys = path->indexorderbys;
ListCell* l = NULL;
List* selectivity_quals = NIL;
double num_pages = index->pages;
double num_tuples = IDXOPTINFO_LOCAL_FIELD(root, index, tuples);
double num_entry_pages;
double num_data_pages;
double num_pending_pages;
double num_entries;
GinQualCounts counts;
bool match_possible = false;
double entry_pages_fetched;
double data_pages_fetched;
double data_pages_fetched_by_sel;
double qual_op_cost;
double qual_arg_cost;
double spc_random_page_cost;
double outer_scans;
QualCost index_qual_cost;
Relation index_rel;
GinStatsData gin_stats;
bool is_partitioned_index = path->indexinfo->rel->isPartitionedTable;
List* saved_varratios = NIL;
/*
* Obtain statistic information from the meta page
*/
index_rel = index_open(index->indexoid, AccessShareLock);
ginGetStats(index_rel, &gin_stats);
index_close(index_rel, AccessShareLock);
num_entry_pages = gin_stats.nEntryPages;
num_data_pages = gin_stats.nDataPages;
num_pending_pages = gin_stats.nPendingPages;
num_entries = gin_stats.nEntries;
/*
* nPendingPages can be trusted, but the other fields are as of the last
* VACUUM. Scale them by the ratio num_pages / nTotalPages to account for
* growth since then. If the fields are zero (implying no VACUUM at all,
* and an index created pre-9.1), assume all pages are entry pages.
*/
if (gin_stats.nTotalPages == 0 || gin_stats.nEntryPages == 0) {
num_entry_pages = num_pages;
num_data_pages = 0;
num_entries = num_tuples; /* bogus, but no other info available */
} else {
double scale = num_pages / gin_stats.nTotalPages;
num_entry_pages = ceil(num_entry_pages * scale);
num_data_pages = ceil(num_data_pages * scale);
num_entries = ceil(num_entries * scale);
/* ensure we didn't round up too much */
num_entry_pages = Min(num_entry_pages, num_pages);
num_data_pages = Min(num_data_pages, num_pages - num_entry_pages);
}
/* In an empty index, num_entries could be zero. Avoid divide-by-zero */
if (num_entries < 1) {
num_entries = 1;
}
/*
* Include predicate in selectivity_quals (should match
* generic_cost_estimate)
*/
if (index->indpred != NIL) {
List* predExtraQuals = NIL;
foreach (l, index->indpred) {
Node* predQual = (Node*)lfirst(l);
List* oneQual = list_make1(predQual);
if (!predicate_implied_by(oneQual, index_quals)) {
predExtraQuals = list_concat(predExtraQuals, oneQual);
}
}
/* list_concat avoids modifying the passed-in index_quals list */
selectivity_quals = list_concat(predExtraQuals, index_quals);
} else {
selectivity_quals = index_quals;
}
saved_varratios = index->rel->varratio;
index->rel->varratio = NULL;
/* Estimate the fraction of main-table tuples that will be visited */
*index_selectivity = clauselist_selectivity(root, selectivity_quals, index->rel->relid, JOIN_INNER, NULL, false);
list_free_deep(index->rel->varratio);
index->rel->varratio = saved_varratios;
/* fetch estimated page cost for schema containing index */
get_tablespace_page_costs(index->reltablespace, &spc_random_page_cost, NULL);
/*
* Generic assumption about index correlation: there isn't any.
*/
*index_correlation = 0.0;
/*
* Examine quals to estimate number of search entries & partial matches
*/
errno_t rc = memset_s(&counts, sizeof(counts), 0, sizeof(counts));
securec_check(rc, "", "");
counts.arrayScans = 1;
match_possible = true;
foreach (l, index_quals) {
RestrictInfo* rinfo = (RestrictInfo*)lfirst(l);
Expr* clause = NULL;
Assert(IsA(rinfo, RestrictInfo));
clause = rinfo->clause;
if (IsA(clause, OpExpr)) {
match_possible = gincost_opexpr(index, (OpExpr*)clause, &counts);
if (!match_possible) {
break;
}
} else if (IsA(clause, ScalarArrayOpExpr)) {
match_possible = gincost_scalararrayopexpr(index, (ScalarArrayOpExpr*)clause, num_entries, &counts);
if (!match_possible) {
break;
}
} else {
/* shouldn't be anything else for a GIN index */
ereport(ERROR,
(errmodule(MOD_OPT),
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("unsupported GIN indexqual type: %d", (int)nodeTag(clause)))));
}
}
/* Fall out if there were any provably-unsatisfiable quals */
if (!match_possible) {
*index_startup_cost = 0;
*index_total_cost = 0;
*index_selectivity = 0;
PG_RETURN_VOID();
}
if (counts.haveFullScan || index_quals == 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 = num_entries;
counts.searchEntries = num_entries;
}
/* 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.
*/
entry_pages_fetched = num_pending_pages;
/*
* 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
*/
entry_pages_fetched += ceil(counts.searchEntries * rint(pow(num_entry_pages, 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.
*/
entry_pages_fetched += ceil(num_entry_pages * counts.partialEntries / num_entries);
/*
* 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
*/
data_pages_fetched = ceil(num_data_pages * counts.partialEntries / num_entries);
/*
* 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 generic_cost_estimate).
*/
if (outer_scans > 1 || counts.arrayScans > 1) {
entry_pages_fetched *= outer_scans * counts.arrayScans;
entry_pages_fetched = index_pages_fetched(entry_pages_fetched, (BlockNumber)num_entry_pages, num_entry_pages,
root, is_partitioned_index);
entry_pages_fetched /= outer_scans;
data_pages_fetched *= outer_scans * counts.arrayScans;
data_pages_fetched = index_pages_fetched(data_pages_fetched, (BlockNumber)num_data_pages, num_data_pages,
root, is_partitioned_index);
data_pages_fetched /= outer_scans;
}
/*
* Here we use random page cost because logically-close pages could be far
* apart on disk.
*/
*index_startup_cost = (entry_pages_fetched + data_pages_fetched) * spc_random_page_cost;
/*
* Now we compute the number of data pages fetched while the scan
* proceeds.
*/
/* data pages scanned for each exact (non-partial) matched entry */
data_pages_fetched = ceil(num_data_pages * counts.exactEntries / num_entries);
/*
* Estimate number of data pages read, using selectivity estimation and
* capacity of data page.
*/
data_pages_fetched_by_sel = ceil(*index_selectivity * (num_tuples / (BLCKSZ / SizeOfIptrData)));
if (data_pages_fetched_by_sel > data_pages_fetched) {
/*
* At least one of entries is very frequent and, unfortunately, we
* couldn't get statistic about entries (only tsvector has such
* statistics). So, we obviously have too small estimation of pages
* fetched from data tree. Re-estimate it from known capacity of data
* pages
*/
data_pages_fetched = data_pages_fetched_by_sel;
}
/* Account for cache effects, the same as above */
if (outer_scans > 1 || counts.arrayScans > 1) {
data_pages_fetched *= outer_scans * counts.arrayScans;
data_pages_fetched = index_pages_fetched(data_pages_fetched, (BlockNumber)num_data_pages, num_data_pages,
root, is_partitioned_index);
data_pages_fetched /= outer_scans;
}
/* And apply random_page_cost as the cost per page */
*index_total_cost = *index_startup_cost + data_pages_fetched * spc_random_page_cost;
/*
* Add on index qual eval costs, much as in generic_cost_estimate
*/
cost_qual_eval(&index_qual_cost, index_quals, root);
qual_arg_cost = index_qual_cost.startup + index_qual_cost.per_tuple;
cost_qual_eval(&index_qual_cost, index_orderbys, root);
qual_arg_cost += index_qual_cost.startup + index_qual_cost.per_tuple;
qual_op_cost = u_sess->attr.attr_sql.cpu_operator_cost * (list_length(index_quals) + list_length(index_orderbys));
qual_arg_cost -= qual_op_cost;
if (qual_arg_cost < 0) { /* just in case... */
qual_arg_cost = 0;
}
*index_startup_cost += qual_arg_cost;
*index_total_cost += qual_arg_cost;
*index_total_cost += ((num_tuples * *index_selectivity) *
(u_sess->attr.attr_sql.cpu_index_tuple_cost + qual_op_cost));
PG_RETURN_VOID();
}
bool is_func_distinct_unshippable(Oid funcid)
{
for (uint i = 0; i < lengthof(distinct_unshippable_func); i++) {
if (funcid == distinct_unshippable_func[i]) {
return true;
}
}
return false;
}
static List* specialExpr_group_num(PlannerInfo* root, List* node_list, double* num_distinct, double rows)
{
List* var_list = NIL;
List* all_var_list = NIL;
List* result_list = NIL;
ListCell* lc = NULL;
double local_distinct = 1.0;
double agg_distinct = 1.0;
double var_distinct;
/*
* For case when, substr func or agg, now we can not exact get their estimated value,
* give them a tiny default value. we may be alter here code when we can
* exact estimate.
* With more complicated case, multiple such expressions share the same var. We should
* distinguish such circumstance and only count expression with non-duplicate vars. Finally,
* we should also compare the estimation with var estimation to make it better.
*/
foreach (lc, node_list) {
Node* expr = (Node*)lfirst(lc);
if (IsA(expr, Var)) {
var_list = lappend(var_list, expr);
}
}
foreach (lc, node_list) {
Node* expr = (Node*)lfirst(lc);
EstSPNode* sp = (EstSPNode*)expr;
if (IsA(sp, EstSPNode)) {
if (IsA(sp->expr, Aggref)) {
agg_distinct *= DEFAULT_SPECIAL_EXPR_DISTINCT;
} else {
/* For no overlap vars, we should count it */
if ((result_list = list_intersection(var_list, sp->varlist)) == NIL) {
if (exprType(sp->expr) == BOOLOID) {
local_distinct *= 2;
} else {
local_distinct *= DEFAULT_SPECIAL_EXPR_DISTINCT;
}
}
list_free_ext(result_list);
all_var_list = list_concat_unique(all_var_list, sp->varlist);
}
list_free_ext(sp->varlist);
pfree_ext(sp);
}
}
var_distinct = estimate_num_groups(root, all_var_list, rows, u_sess->pgxc_cxt.NumDataNodes);
*num_distinct *= Min(Min(local_distinct, var_distinct) * agg_distinct, rows);
list_free_ext(all_var_list);
list_free_ext(node_list);
return var_list;
}
/*
* set_local_rel_size
* Set local size of rel according to the global size
* Parameters:
* root: query info of current query level
* rel: rel to calculate
*/
void set_local_rel_size(PlannerInfo* root, RelOptInfo* rel)
{
if (rel->distribute_keys) {
/*
* determine if table is skewed for the distribute key. Since multiple is related to final
* rows, we should use final tuples to do estimation, as if there's no filter
*/
if (rel->multiple == 0.0) {
double saved_rows = rel->rows;
rel->rows = rel->tuples;
rel->multiple = get_multiple_by_distkey(root, rel->distribute_keys, rel->tuples);
rel->rows = saved_rows;
}
} else { /* The local is even for joinrel and RROBIN */
RangeTblEntry* rte = NULL;
rte = planner_rt_fetch(rel->relid, root);
if (IS_EC_FUNC(rte)) {
rel->multiple = (double)u_sess->pgxc_cxt.NumDataNodes;
} else {
rel->multiple = 1.0;
}
}
RangeTblEntry* rte = planner_rt_fetch(rel->relid, root);
if (rte->relkind != RELKIND_FOREIGN_TABLE) {
rel->pages = RELOPTINFO_LOCAL_FIELD(root, rel, pages);
}
/* Set baserel index tuples and pages. */
if (rel->indexlist) {
ListCell* lc = NULL;
foreach (lc, rel->indexlist) {
IndexOptInfo* idx = (IndexOptInfo*)lfirst(lc);
idx->pages = IDXOPTINFO_LOCAL_FIELD(root, idx, pages);
}
}
}
/* Get hybrd multiple between skew and bias by distribute key. */
double get_multiple_by_distkey(PlannerInfo* root, List* dist_key, double rows)
{
bool use_skew_multiple = true;
double result_multiple = 1.0;
double bias_multiple = 0.0;
double skew_multiple = 0.0;
if ((root == NULL) || (dist_key == NULL)) {
return result_multiple;
}
get_multiple_from_exprlist(root, dist_key, rows, &use_skew_multiple, true, &skew_multiple, &bias_multiple);
if ((skew_multiple == 1) && (bias_multiple < 1)) {
result_multiple = 1;
} else {
result_multiple = Max(bias_multiple, skew_multiple);
}
Assert(result_multiple >= 1);
return result_multiple;
}
/*
* estimate_agg_num_distinct: Reestimate local distinct value for hashagg.
* Before, we estimate local distinct value of group exprs for the final aggregation,
* but if it needs a local aggregation sometimes, then we should reestimate it for
* the accurate cost estimation of first local aggreation.
*
* Parameters:
* @In root: PlannerInfo strunct for current query level
* @In group_exprs: group exprs whose distinct value needs to be estimated
* @In Plan: current plan node to do aggregation
* @In numGroups: local and global distinct value already estimated
* Returns: adjusted local distinct value
*/
double estimate_agg_num_distinct(PlannerInfo* root, List* group_exprs, Plan* plan, const double* num_groups)
{
if (plan == NULL) {
ereport(ERROR,
(errcode(ERRCODE_UNEXPECTED_NULL_VALUE), errmsg("Unexpected null value for the plan")));
}
int dop = SET_DOP(plan->dop);
unsigned int num_datanodes = ng_get_dest_num_data_nodes(plan);
double rel_multiple = (double)num_datanodes * dop;
bool is_groupexprs_contain_plandistkey = false;
bool plandistkey_is_rel_diskey = false;
double local_distinct;
double plan_rows = PLAN_LOCAL_ROWS(plan);
List* sub_distribute_keys = NIL;
/* we should use global distinct for replication or query execute on datanode. */
if (!IS_PGXC_COORDINATOR || (plan->exec_nodes && IsLocatorReplicated(plan->exec_nodes->baselocatortype))) {
return num_groups[1];
}
sub_distribute_keys = find_skew_join_distribute_keys(plan);
/*
* judge if plan's distribute key is base rel's, or we can possibly use local distinct estimation
* if there's no skew on base rel.
* we should use local distinct if there's only one rel in group exprs and plan distkey is that rel distkey.
*/
if (sub_distribute_keys != NIL) {
Relids group_varnos = pull_varnos((Node*)group_exprs);
int varno = 0;
/* if there are multiple rels involved, try to remove const vars */
if (bms_num_members(group_varnos) > 1) {
List* group_exprs_copy = list_copy(group_exprs);
ListCell* lc = NULL;
ListCell* lc2 = NULL;
foreach (lc, root->eq_classes) {
EquivalenceClass* ec = (EquivalenceClass*)lfirst(lc);
if (ec->ec_has_const) {
foreach (lc2, ec->ec_members) {
EquivalenceMember* em = (EquivalenceMember*)lfirst(lc2);
if (list_member(group_exprs_copy, em->em_expr)) {
group_exprs_copy = list_delete(group_exprs_copy, em->em_expr);
}
}
}
}
bms_free(group_varnos);
group_varnos = pull_varnos((Node*)group_exprs_copy);
if (bms_num_members(group_varnos) == 1) {
varno = bms_first_member(group_varnos);
}
list_free_ext(group_exprs_copy);
} else {
varno = bms_first_member(group_varnos);
}
/* Locate the base rel, and judge if the distribute key is equal */
if (varno > 0) {
RelOptInfo* rel = find_base_rel(root, varno);
if (equal_distributekey(root, sub_distribute_keys, rel->distribute_keys)) {
rel_multiple = get_multiple_by_distkey(root, rel->distribute_keys, rel->tuples);
plandistkey_is_rel_diskey = true;
}
}
bms_free(group_varnos);
group_varnos = NULL;
}
is_groupexprs_contain_plandistkey = !(needs_agg_stream(root, group_exprs, sub_distribute_keys));
/*
* Estimate local distinct according to global.
* group_exprs include plan->distribute_keys.
*/
if (is_groupexprs_contain_plandistkey) {
/* group_exprs include plan->distribute_keys and plan->distributed_keys include baserel distkey. */
if (plandistkey_is_rel_diskey) {
local_distinct =
(rel_multiple > 1) ? get_local_rows(num_groups[1], 1.0, false, num_datanodes) : num_groups[0];
} else { /* group_exprs include plan->distribute_keys and plan->distributed_keys not include baserel distkey. */
local_distinct = get_local_rows(num_groups[1], 1.0, false, num_datanodes);
}
/*
* In agg+local_redistribute + agg situation case,
* the local distinct value will increase. So, recalculate it.
*/
if (dop > 1 && is_local_redistribute_needed(plan)) {
double numLocalGroup = local_distinct;
local_distinct = NUM_PARALLEL_DISTINCT_GTL_FOR_POISSON(numLocalGroup, plan_rows, dop) * dop;
local_distinct = Min(clamp_row_est(local_distinct), plan_rows);
local_distinct = Min(local_distinct, numLocalGroup * dop);
return local_distinct;
}
} else { /* group_exprs not include plan->distribute_keys. */
double plan_multiple = get_multiple_by_distkey(root, sub_distribute_keys, plan->plan_rows);
/*
* plan->distributed_keys include baserel distkey, or
* plan->distributed_keys not include baserel distkey and group_exprs not include baserel distkey.
*/
if (plandistkey_is_rel_diskey) {
if (rel_multiple > 1) {
local_distinct =
(NUM_DISTINCT_GTL_FOR_POISSON(num_groups[1], plan->plan_rows, num_datanodes, dop) * plan_multiple) *
dop;
} else {
if (dop > 1) {
local_distinct = NUM_PARALLEL_DISTINCT_GTL_FOR_POISSON(num_groups[0], plan_rows, dop) * dop;
} else {
local_distinct = num_groups[0];
}
}
} else { /* plan->distributed_keys not include baserel distkey and group_exprs include baserel distkey. */
local_distinct =
NUM_DISTINCT_GTL_FOR_POISSON(num_groups[1], plan->plan_rows, num_datanodes, dop) * plan_multiple * dop;
}
}
/*
* The local_distinct refer to distinct in one DN, when parallel,
* the local_distinct must be lower than num_groups[1] * dop.
*/
local_distinct = Min(clamp_row_est(local_distinct), plan_rows);
local_distinct = Min(local_distinct, num_groups[1] * dop);
return local_distinct;
}
/*
* estimate_agg_num_distinct: Reestimate local distinct value for hashagg.
* Before, we estimate local distinct value of group exprs for the final aggregation,
* but if it needs a local aggregation sometimes, then we should reestimate it for
* the accurate cost estimation of first local aggreation.
*
* Parameters:
* @In root: PlannerInfo strunct for current query level
* @In group_exprs: group exprs whose distinct value needs to be estimated
* @In path: current path node to do aggregation
* @In numGroups: local and global distinct value already estimated
* Returns: adjusted local distinct value
*/
double estimate_agg_num_distinct(PlannerInfo* root, List* group_exprs, Path* path, const double* num_groups)
{
if (path == NULL) {
ereport(ERROR,
(errcode(ERRCODE_UNEXPECTED_NULL_VALUE), errmsg("Unexpected null value for the path")));
}
int dop = SET_DOP(path->dop);
unsigned int num_datanodes = ng_get_dest_num_data_nodes(path);
double rel_multiple = (double)num_datanodes * dop;
bool is_groupexprs_contain_plandistkey = false;
bool plandistkey_is_rel_diskey = false;
double local_distinct;
double plan_rows = PATH_LOCAL_ROWS(path);
/* we should use global distinct for replication or query execute on datanode. */
if (!IS_PGXC_COORDINATOR || (path && IsLocatorReplicated(path->locator_type))) {
return num_groups[1];
}
/*
* judge if path's distribute key is base rel's, or we can possibly use local distinct estimation
* if there's no skew on base rel.
* we should use local distinct if there's only one rel in group exprs and plan distkey is that rel distkey.
*/
if (path->distribute_keys != NIL) {
Relids group_varnos = pull_varnos((Node*)group_exprs);
int varno = 0;
/* if there are multiple rels involved, try to remove const vars */
if (bms_num_members(group_varnos) > 1) {
List* group_exprs_copy = list_copy(group_exprs);
ListCell* lc = NULL;
ListCell* lc2 = NULL;
foreach (lc, root->eq_classes) {
EquivalenceClass* ec = (EquivalenceClass*)lfirst(lc);
if (ec->ec_has_const) {
foreach (lc2, ec->ec_members) {
EquivalenceMember* em = (EquivalenceMember*)lfirst(lc2);
if (list_member(group_exprs_copy, em->em_expr))
group_exprs_copy = list_delete(group_exprs_copy, em->em_expr);
}
}
}
bms_free(group_varnos);
group_varnos = pull_varnos((Node*)group_exprs_copy);
if (bms_num_members(group_varnos) == 1)
varno = bms_first_member(group_varnos);
list_free_ext(group_exprs_copy);
} else {
varno = bms_first_member(group_varnos);
}
/* Locate the base rel, and judge if the distribute key is equal */
if (varno > 0) {
RelOptInfo* rel = find_base_rel(root, varno);
if (equal_distributekey(root, path->distribute_keys, rel->distribute_keys)) {
rel_multiple = get_multiple_by_distkey(root, rel->distribute_keys, rel->tuples);
plandistkey_is_rel_diskey = true;
}
}
bms_free(group_varnos);
group_varnos = NULL;
}
is_groupexprs_contain_plandistkey = !(needs_agg_stream(root, group_exprs, path->distribute_keys));
/*
* Estimate local distinct according to global.
* group_exprs include path->distribute_keys.
*/
if (is_groupexprs_contain_plandistkey) {
/* group_exprs include plan->distribute_keys and plan->distributed_keys include baserel distkey. */
if (plandistkey_is_rel_diskey) {
local_distinct =
(rel_multiple > 1) ? get_local_rows(num_groups[1], 1.0, false, num_datanodes) : num_groups[0];
} else { /* group_exprs include plan->distribute_keys and plan->distributed_keys not include baserel distkey. */
local_distinct = get_local_rows(num_groups[1], 1.0, false, num_datanodes);
}
/*
* In agg+local_redistribute + agg situation case,
* the local distinct value will increase. So, recalculate it.
*/
if (dop > 1) {
double num_local_group = local_distinct;
local_distinct = NUM_PARALLEL_DISTINCT_GTL_FOR_POISSON(num_local_group, plan_rows, dop) * dop;
local_distinct = Min(clamp_row_est(local_distinct), plan_rows);
local_distinct = Min(local_distinct, num_local_group * dop);
return local_distinct;
}
} else {
double plan_multiple = get_multiple_by_distkey(root, path->distribute_keys, path->rows);
/*
* path->distributed_keys include baserel distkey, or
* path->distributed_keys not include baserel distkey and group_exprs not include baserel distkey.
*/
if (plandistkey_is_rel_diskey) {
if (rel_multiple > 1) {
local_distinct =
(NUM_DISTINCT_GTL_FOR_POISSON(num_groups[1], path->rows, num_datanodes, dop) * plan_multiple) * dop;
} else {
if (dop > 1) {
local_distinct = NUM_PARALLEL_DISTINCT_GTL_FOR_POISSON(num_groups[0], plan_rows, dop) * dop;
} else {
local_distinct = num_groups[0];
}
}
} else { /* plan->distributed_keys not include baserel distkey and group_exprs include baserel distkey. */
local_distinct =
NUM_DISTINCT_GTL_FOR_POISSON(num_groups[1], path->rows, num_datanodes, dop) * plan_multiple * dop;
}
}
/*
* The local_distinct refer to distinct in one DN, when parallel,
* the local_distinct must be lower than num_groups[1] * dop.
*/
local_distinct = Min(clamp_row_est(local_distinct), plan_rows);
local_distinct = Min(local_distinct, num_groups[1] * dop);
return local_distinct;
}
/*
* estimate_hash_num_distinct
* adjust local dn inner hash distinct value
*
* Parameters:
* @in root: plannerinfo struct for current query level
* @in hashclauses: hash join clause of current join
* @in hashkey: current hash join node
* @in inner_path: inner side path of join
* @in vardata: statistic data of hashkey
* @in local_ndistinct: calculated local distinct from stats
* @in global_ndisitnct: calculated global distinct from stats
* @out usesinglestats: if we use single dn stats
* Returns:
* adjusted local inner distinct value
*/
double estimate_hash_num_distinct(PlannerInfo* root, List* hash_key, Path* inner_path, VariableStatData* var_data,
double local_ndistinct, double global_ndistinct, bool* use_single_stats)
{
int dop = inner_path->dop > 1 ? inner_path->dop : 1;
double ndistinct = local_ndistinct;
if (!IS_PGXC_COORDINATOR) {
return global_ndistinct;
}
Assert(IsA(inner_path, StreamPath) || IsA(inner_path, HashPath) || IsLocatorReplicated(inner_path->locator_type));
if (IsLocatorReplicated(inner_path->locator_type)) {
/* Inner is broadcast or replication, we should use global distinct. */
ndistinct = global_ndistinct;
*use_single_stats = false;
} else {
unsigned int num_datanodes = ng_get_dest_num_data_nodes(inner_path);
double rel_multiple = (double)num_datanodes * dop;
double plan_multiple = get_multiple_by_distkey(root, inner_path->distribute_keys, inner_path->rows);
bool is_join_clauses_contain_inner_key = false;
bool is_inner_distkey_contain_base_rel_distkey = false;
/*
* It means that distkey is not in joinclauses if distribute on joinclauses,
* Other wise, it means distkey is in joinclauses.
*/
if (inner_path->distribute_keys) {
if (list_length(hash_key) >= list_length(inner_path->distribute_keys)) {
is_join_clauses_contain_inner_key = list_is_subset(inner_path->distribute_keys, hash_key);
}
if (var_data->rel->reloptkind == RELOPT_BASEREL &&
list_length(inner_path->distribute_keys) == list_length(var_data->rel->distribute_keys) &&
!(needs_agg_stream(root, inner_path->distribute_keys, var_data->rel->distribute_keys))) {
is_inner_distkey_contain_base_rel_distkey = true;
rel_multiple = get_multiple_by_distkey(root, var_data->rel->distribute_keys, var_data->rel->tuples);
}
}
if (is_join_clauses_contain_inner_key && is_inner_distkey_contain_base_rel_distkey) {
ndistinct =
(rel_multiple > 1) ? get_local_rows(global_ndistinct, 1.0, false, num_datanodes) : local_ndistinct;
ereport(DEBUG2,
(errmodule(MOD_OPT),
(errmsg("is_join_clauses_contain_inner_key && is_inner_distkey_contain_base_rel_distkey: \
ndistinct (%.0f) = global_ndistinct(%.0f) / num_datanodes(%u) or \
= local_ndistinct(%.0f) depending on rel_multiple(%f) > 1 or not",
ndistinct,
global_ndistinct,
num_datanodes,
local_ndistinct,
rel_multiple))));
} else if (is_join_clauses_contain_inner_key && !is_inner_distkey_contain_base_rel_distkey) {
ndistinct = get_local_rows(global_ndistinct, 1.0, false, num_datanodes);
ereport(DEBUG2,
(errmodule(MOD_OPT),
(errmsg("is_join_clauses_contain_inner_key && !is_inner_distkey_contain_base_rel_distkey: \
ndistinct (%.0f) = global_ndistinct(%.0f) / num_datanodes(%u) ",
ndistinct,
global_ndistinct,
num_datanodes))));
} else if (!is_join_clauses_contain_inner_key && is_inner_distkey_contain_base_rel_distkey) {
/*
* if biase more than 1, we consider all the mcv is even in all dn, so it should use the distinct on dn1.
* other wise, it should use formular to estimate single dn according to global.
*/
ndistinct = (rel_multiple > 1)
? NUM_DISTINCT_GTL_FOR_POISSON(global_ndistinct, var_data->rel->rows, num_datanodes, dop) *
plan_multiple
: local_ndistinct;
ereport(DEBUG2,
(errmodule(MOD_OPT),
(errmsg("!is_join_clauses_contain_inner_key && is_inner_distkey_contain_base_rel_distkey: \
ndistinct = %.0f \
using poisson model with global_ndistinct(%.0f) , var_data->rel->rows(%e), \
num_datanodes(%u), dop(%d), plan_multiple(%f) \
or just equals local_ndistinct(%.0f) depending on rel_multiple(%f) > 1 or not",
ndistinct,
global_ndistinct,
var_data->rel->rows,
num_datanodes,
dop,
plan_multiple,
local_ndistinct,
rel_multiple))));
} else {
bool is_join_clauses_contain_rel_distkey = false;
if (list_length(hash_key) >= list_length(var_data->rel->distribute_keys)) {
is_join_clauses_contain_rel_distkey = list_is_subset(var_data->rel->distribute_keys, hash_key);
}
/* if skew, join contains rel diskey, we should use poisson to calculate local distinct */
if (is_join_clauses_contain_rel_distkey || (rel_multiple > 1)) {
ndistinct = NUM_DISTINCT_GTL_FOR_POISSON(global_ndistinct, var_data->rel->rows, num_datanodes, dop) *
plan_multiple;
}
ereport(DEBUG2,
(errmodule(MOD_OPT),
(errmsg("other situation: \
ndistinct = %.0f \
using poisson model with global_ndistinct(%.0f) , var_data->rel->rows(%e), \
num_datanodes(%u), dop(%d), plan_multiple(%f) ",
ndistinct,
global_ndistinct,
var_data->rel->rows,
num_datanodes,
dop,
plan_multiple))));
}
}
return ndistinct;
}
/*
* estimate_local_numdistinct
* estimate local dn distinct value of a path
*
* Parameters:
* @in root: plannerinfo struct for current query level
* @in hashkey: current hash join node
* @in path: the path that hashkey belongs to
* @in sjinfo: special join info that path takes part in
* @out global_distinct: estimated global_distinct
* @out isdefault: if there's no real distinct estimated
* @out vardata: statistic data of hashkeyts
* Returns:
* estimated distinct value
*/
double estimate_local_numdistinct(PlannerInfo* root, Node* hash_key, Path* path, SpecialJoinInfo* sj_info,
double* global_distinct, bool* isdefault, VariableStatData* var_data)
{
double ndistinct;
bool use_single_stats = true;
examine_variable(root, hash_key, 0, var_data);
/* Get number of distinct values */
ndistinct = get_variable_numdistinct(var_data, isdefault, false, 1.0, sj_info, STATS_TYPE_LOCAL);
*global_distinct = get_variable_numdistinct(var_data, isdefault, false, 1.0, sj_info, STATS_TYPE_GLOBAL);
/* If ndistinct isn't real, punt and return 0.1, per comments above */
if (*isdefault) {
ndistinct = DEFAULT_NUM_DISTINCT;
} else {
unsigned int num_datanodes = (path != NULL) ? ng_get_dest_num_data_nodes(path) : u_sess->pgxc_cxt.NumDataNodes;
/* we should adjust local distinct if there is no tuples in dn1 for global stats. */
if ((ndistinct * num_datanodes) < *global_distinct) {
if (var_data->rel == NULL) {
ereport(ERROR,
(errcode(ERRCODE_UNEXPECTED_NULL_VALUE), errmsg("Unexpected null value for the rel of var_data")));
}
ndistinct = get_local_rows(*global_distinct, var_data->rel->multiple, false, num_datanodes);
}
/* Adjust global distinct values for STREAM_BROADCAST and STREAM_REDISTRIBUTE. */
if (path != NULL) {
ereport(DEBUG2,
(errmodule(MOD_OPT),
(errmsg("Adjust global distinct values(%.0f) for STREAM_BROADCAST and STREAM_REDISTRIBUTE.",
*global_distinct))));
List* hash_keys = list_make1(hash_key);
ndistinct = estimate_hash_num_distinct(root, hash_keys, path, var_data, ndistinct,
*global_distinct, &use_single_stats);
list_free_ext(hash_keys);
}
/*
* 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.
*
* Only single stat need multiple the ratio as rows/tuples, because
* possion for global stat.
* Else if we have use possion, we don't need multiple the ratio.
*/
if (var_data->rel) {
if (use_single_stats || !var_data->enablePossion) {
ndistinct *= var_data->rel->rows / var_data->rel->tuples;
ereport(DEBUG2,
(errmodule(MOD_OPT),
(errmsg(
"To estimate local numdistinct, \
the distinct value is multiplied by relrows(%e)/reltuples(%e) \
The new distinct value is %.0f",
var_data->rel->rows,
var_data->rel->tuples,
ndistinct))));
}
}
}
return ndistinct;
}
/* get_num_distinct
* get local and global distinct by local and global rows
*
* Parameters:
* @in root: plannerinfo struct of current query level
* @in groupExprs: group expr to calculate distinct
* @in local_rows: single dn rows of current plan
* @in global_rows: global dn rows of current plan
* @out numdistinct: estimated local and global distinct value
* @in pgset: grouping set info
*/
void get_num_distinct(PlannerInfo* root, List* group_exprs, double local_rows, double global_rows,
unsigned int num_datanodes, double* num_distinct, List** pgset)
{
/* get local distinct */
num_distinct[0] = estimate_num_groups(root, group_exprs, local_rows, num_datanodes, STATS_TYPE_LOCAL, pgset);
/* get global distinct */
num_distinct[1] = estimate_num_groups(root, group_exprs, global_rows, num_datanodes, STATS_TYPE_GLOBAL, pgset);
/* we should adjust local distinct if there is no tuples in dn1 for global stats. */
if ((num_distinct[0] * num_datanodes) < num_distinct[1]) {
num_distinct[0] = get_local_rows(num_distinct[1], 1.0, false, num_datanodes);
}
num_distinct[0] = Min(num_distinct[0], num_distinct[1]);
}
/*
* Description: Check whether the current relation analyzed or not.
*
* Parameters:
* @in relid: relation oid
*
* Returns: bool
*/
bool check_relation_analyzed(Oid relid)
{
bool is_analyzed = false;
PgStat_AnaCheckEntry* tab_entry = NULL;
/* if set not check, just return false */
if (!u_sess->attr.attr_sql.enable_analyze_check)
return false;
/* load analyzed info from statfile */
if (u_sess->stat_cxt.analyzeCheckHash == NULL) {
pgstat_read_analyzed();
}
/* load failed */
if (u_sess->stat_cxt.analyzeCheckHash == NULL)
return false;
tab_entry = (PgStat_AnaCheckEntry*)hash_search(u_sess->stat_cxt.analyzeCheckHash, (void*)&relid, HASH_FIND, NULL);
if (tab_entry != NULL) {
is_analyzed = tab_entry->is_analyzed;
}
return is_analyzed;
}
/*
* set_noanalyze_rellist
* Append no analyze relation or attribute to g_NoAnalyzeRelNameList.
*
* Paramters:
* @in relid: oid of current relation
* @in attid: attnum of current column, or 0 for whole relation
*/
void set_noanalyze_rellist(Oid relid, AttrNumber attid)
{
/*
* We should not save the relation to non-analyze list if is under analyzing,
* because it will create temp table and execute some query, the temp table
* don't be analyzed when 2% analyzing.
*/
if (u_sess->analyze_cxt.is_under_analyze) {
return;
}
if (check_relation_analyzed(relid)) {
return;
}
List* tmp_rel_list = NIL;
List* tmp_att_list = NIL;
List* tmp_record_list = NIL;
bool is_obs_ft = false;
/*
* As for OBS foreign table, we do not suggest analyze obs forign table,
* so only check the statistics of foreign table. do not check column statistic.
*/
Relation rel = relation_open(relid, AccessShareLock);
if (rel->rd_rel->relkind == RELKIND_FOREIGN_TABLE && isSpecifiedSrvTypeFromRelId(relid, OBS_SERVER)) {
is_obs_ft = true;
}
/* as for column statistics of obs foreign table, we will return. */
if (attid != 0 && is_obs_ft) {
relation_close(rel, AccessShareLock);
return;
}
/* If the rel is DFS table or gds foreign table, don't add it to list */
if (RelationIsPAXFormat(rel) || RelationGetNamespace(rel) == CSTORE_NAMESPACE ||
((rel->rd_rel->relkind == RELKIND_FOREIGN_TABLE) && IsSpecifiedFDWFromRelid(relid, DIST_FDW))) {
relation_close(rel, AccessShareLock);
return;
}
relation_close(rel, AccessShareLock);
MemoryContext old_context = MemoryContextSwitchTo(t_thrd.mem_cxt.msg_mem_cxt);
if (t_thrd.postgres_cxt.g_NoAnalyzeRelNameList == NIL) {
tmp_rel_list = lappend_oid(tmp_rel_list, relid);
/* Add a new rel list into g_NoAnalyzeRelNameList. */
tmp_record_list = lappend(tmp_record_list, tmp_rel_list);
/*
* If attid = 0, the att list mark the whole rel has no statistics.
* Once a new list with attid = !0 be added, the list with attid = 0 will be deleted.
*/
tmp_att_list = lappend_int(tmp_att_list, attid);
tmp_record_list = lappend(tmp_record_list, tmp_att_list);
t_thrd.postgres_cxt.g_NoAnalyzeRelNameList =
lappend(t_thrd.postgres_cxt.g_NoAnalyzeRelNameList, tmp_record_list);
} else {
ListCell *lc1 = NULL;
ListCell *lc2 = NULL;
bool is_rel_exist = false;
foreach (lc1, t_thrd.postgres_cxt.g_NoAnalyzeRelNameList) {
List* record = (List*)lfirst(lc1);
/* Get the reloid */
Oid tmp_relid = linitial_oid((List*)linitial(record));
if (relid == tmp_relid) {
if (attid == 0) {
/* The rel has no statistic. */
is_rel_exist = true;
break;
} else {
bool is_attr_exist = false;
/* the 1st list is reloid, skip it */
ListCell* prev = list_head(record);
if (prev == NULL) {
ereport(ERROR,
(errcode(ERRCODE_UNEXPECTED_NULL_VALUE),
errmsg("Unexpected null value for the list record's head")));
}
lc2 = prev->next;
while (lc2 != NULL) {
List* rel_or_att = (List*)lfirst(lc2);
/* skip muti-col that has no statistic */
if (list_length(rel_or_att) > 1) {
prev = lc2;
lc2 = lnext(lc2);
continue;
}
/* Whether the attrid exist in rellist or not. */
if (attid == linitial_int(rel_or_att)) {
is_attr_exist = true;
break;
} else if (0 == linitial_int(rel_or_att)) {
/* Delete cell that 'attid = 0' when new single-col attid will be added */
record = list_delete_cell(record, lc2, prev);
break;
}
prev = lc2;
lc2 = lnext(lc2);
}
if (!is_attr_exist) {
/* The rel exist and the attr of rel not exist, add the attr in the rellist. */
tmp_att_list = lappend_int(tmp_att_list, attid);
record = lappend(record, tmp_att_list);
} else {
break;
}
}
}
}
if (attid == 0 && !is_rel_exist) {
/* The rel does not exist in g_NoAnalyzeRelNameList, add a new rellist. */
tmp_rel_list = lappend_oid(tmp_rel_list, relid);
tmp_record_list = lappend(tmp_record_list, tmp_rel_list);
tmp_att_list = lappend_int(tmp_att_list, 0);
tmp_record_list = lappend(tmp_record_list, tmp_att_list);
t_thrd.postgres_cxt.g_NoAnalyzeRelNameList =
lappend(t_thrd.postgres_cxt.g_NoAnalyzeRelNameList, tmp_record_list);
}
}
(void)MemoryContextSwitchTo(old_context);
}
/*
* output_noanalyze_rellist_to_log
* set warning that no-analyzed relation name to log
*
* @in lev: on which log level should we emit the message
*/
void output_noanalyze_rellist_to_log(int lev)
{
/*
* We should not save the relation to non-analyze list if is under analyzing,
* because it will create temp table and execute some query, the temp table
* don't be analyzed when 2% analyzing.
*/
if (u_sess->analyze_cxt.is_under_analyze) {
return;
}
ListCell *lc1 = NULL;
ListCell *lc2 = NULL;
StringInfoData buf;
StringInfoData obs_ft_buf;
bool need_log = false;
MemoryContext old_context = MemoryContextSwitchTo(t_thrd.mem_cxt.msg_mem_cxt);
if (t_thrd.postgres_cxt.g_NoAnalyzeRelNameList == NIL) {
(void)MemoryContextSwitchTo(old_context);
return;
}
initStringInfo(&buf);
initStringInfo(&obs_ft_buf);
foreach (lc1, t_thrd.postgres_cxt.g_NoAnalyzeRelNameList) {
List* record = (List*)lfirst(lc1);
Assert(list_length(record) > 1);
Oid relid = linitial_oid((List*)linitial(record));
Relation rel = relation_open(relid, AccessShareLock);
if ((0 == rel->rd_rel->reltuples && check_relation_analyzed(relid)) ||
((RELKIND_FOREIGN_TABLE == rel->rd_rel->relkind) && isMOTFromTblOid(relid))) {
relation_close(rel, AccessShareLock);
continue;
}
/* the 1st cell is the list of rel , so skip it, and get the att id */
lc2 = lnext(list_head(record));
while (lc2 != NULL) {
List* tmp_record_list = (List*)lfirst(lc2);
/* only single-col that has no statistics will be handle */
if (list_length(tmp_record_list) == 1) {
int attid = linitial_int(tmp_record_list);
/* 'attid = 0' means the whole rel has no statistics */
if (attid == 0) {
if (rel->rd_rel->relkind == RELKIND_FOREIGN_TABLE &&
isSpecifiedSrvTypeFromRelId(relid, OBS_SERVER)) {
if (obs_ft_buf.len > 0) {
appendStringInfoString(&obs_ft_buf, ", ");
}
appendStringInfo(
&obs_ft_buf, "%s.%s", get_namespace_name(RelationGetNamespace(rel), true), get_rel_name(relid));
} else {
if (buf.len > 0) {
appendStringInfoString(&buf, ", ");
}
appendStringInfo(
&buf, "%s.%s", get_namespace_name(RelationGetNamespace(rel), true), get_rel_name(relid));
}
break;
}
if (buf.len > 0) {
appendStringInfoString(&buf, ", ");
}
appendStringInfo(&buf,
"%s.%s.%s",
get_namespace_name(RelationGetNamespace(rel), true),
get_rel_name(relid),
(char*)attnumAttName(rel, attid));
}
lc2 = lnext(lc2);
}
relation_close(rel, AccessShareLock);
need_log = true;
}
/* Need output log/warning if there are rel having no analyze. */
if (need_log) {
/* We should output warning if it is scene of cloud. */
if (isSecurityMode && lev == LOG) {
lev = WARNING;
}
if (buf.len > 0) {
ereport(lev,
(errmsg("Statistics in some tables or columns(%s) are not collected.", buf.data),
errhint("Do analyze for them in order to generate optimized plan.")));
}
if (obs_ft_buf.len > 0) {
ereport(lev,
(errmsg("Statistics in some tables(%s) are not collected.", obs_ft_buf.data),
errhint("Do set totalrows option for them in order to generate optimized plan.")));
}
}
pfree_ext(buf.data);
buf.data = NULL;
/* deep free g_NoAnalyzeRelNameList */
foreach (lc1, t_thrd.postgres_cxt.g_NoAnalyzeRelNameList) {
List* record = (List*)lfirst(lc1);
foreach (lc2, record) {
List* rel_or_att = (List*)lfirst(lc2);
list_free_ext(rel_or_att);
}
list_free_ext(record);
}
list_free_ext(t_thrd.postgres_cxt.g_NoAnalyzeRelNameList);
t_thrd.postgres_cxt.g_NoAnalyzeRelNameList = NULL;
(void)MemoryContextSwitchTo(old_context);
}
/*
* contain_single_col_stat
* if stat_list contains single-col info(including the whole rel), return true, else retrun false.
* Parameters
* @in stat_list: the list will be checked, with the same structure to g_NoAnalyzeRelNameList
*/
bool contain_single_col_stat(List* stat_list)
{
if (stat_list == NIL) {
return false;
}
ListCell* lc1 = NULL;
ListCell* lc2 = NULL;
foreach (lc1, stat_list) {
List* record = (List*)lfirst(lc1);
Assert(list_length(record) > 1);
/* the 1st cell is the list of rel , so skip it, and get the att id */
lc2 = lnext(list_head(record));
while (lc2 != NULL) {
List* tmp_record_list = (List*)lfirst(lc2);
if (list_length(tmp_record_list) == 1) {
return true;
}
lc2 = lnext(lc2);
}
}
return false;
}
/*
* get_global_rows: set global rows from local rows
*
* Parameters:
* @in local_rows: number of local rows
* @in multiple: skew multiple
*
* Returns: global rows
*/
double get_global_rows(double local_rows, double multiple, unsigned int num_data_nodes)
{
if (IS_STREAM_PLAN) {
return ((double)clamp_row_est((local_rows / Max(multiple, 1.0) * num_data_nodes)));
} else {
return ((double)clamp_row_est(local_rows));
}
}
/*
* get_local_rows: set local rows from global rows
*
* Parameters:
* @in global_rows: number of global rows
* @in multiple: skew multiple
*
* Returns: local rows
*/
double get_local_rows(double global_rows, double multiple, bool replicate, unsigned int num_data_nodes)
{
if (IS_STREAM_PLAN && !replicate) {
Assert(num_data_nodes > 0);
return ((double)clamp_row_est((global_rows / num_data_nodes) * Max(multiple, 1.0)));
} else {
return ((double)clamp_row_est(global_rows));
}
}
/*
* set_varratio_after_calc_selectivity
* set the var ratio after join other rel and filter by self
* in order to used for join with other rel and estimate distinct use possion.
*
* Parameters:
* @in vardata: var data info
* @in type: joinratio or filterratio
* @in ratio: join ratio according to estimation
* @in sjinfo: the join info for current relation join with others
*/
void set_varratio_after_calc_selectivity(
VariableStatData* var_data, RatioType type, double ratio, SpecialJoinInfo* sj_info)
{
VarRatio* vr = NULL;
MemoryContext cxt;
ListCell* lc = NULL;
Relids join_relids = NULL;
Node* var = NULL;
if (var_data == NULL || var_data->rel == NULL || ratio == 1.0 || ratio <= 0.0) {
return;
}
/*
* we don't need cache the ratio for the temparary of compute the selectivity
* of inner join when compute semi and anti join factor.
*/
if (sj_info && !sj_info->varratio_cached) {
return;
}
var = var_data->var;
if (!IsA(var_data->var, Var)) {
List* vars = NIL;
vars = pull_var_clause(var_data->var, PVC_REJECT_AGGREGATES, PVC_RECURSE_PLACEHOLDERS);
/* we don't process var_data have more var. */
if (vars == NULL || (list_length(vars) > 1)) {
list_free_ext(vars);
return;
}
var = (Node*)linitial(vars);
list_free_ext(vars);
}
if (RatioType_Join == type) {
if (sj_info == NULL) {
return;
}
/* construct relids set that identifies the joinrel. */
join_relids = bms_union(sj_info->min_lefthand, sj_info->min_righthand);
foreach (lc, var_data->rel->varratio) {
vr = (VarRatio*)lfirst(lc);
if (RatioType_Filter == vr->ratiotype) {
continue;
}
/* we need not save if it have the same joinrel and joinratio, otherwise, we only save the minimum
* joinratio. */
if (bms_equal(vr->joinrelids, join_relids) && _equalSimpleVar(var, vr->var)) {
/* save the minimue ratio. */
if (ratio < vr->ratio) {
vr->ratio = ratio;
}
bms_free(join_relids);
join_relids = NULL;
return;
}
}
bms_free(join_relids);
join_relids = NULL;
}
cxt = MemoryContextSwitchTo(MemoryContextOriginal((char*)var_data->rel));
vr = (VarRatio*)palloc0(sizeof(VarRatio));
vr->var = var;
vr->ratio = ratio;
vr->ratiotype = type;
if (RatioType_Join == type) {
vr->joinrelids = bms_union(sj_info->min_lefthand, sj_info->min_righthand);
} else {
vr->joinrelids = NULL;
}
var_data->rel->varratio = lappend(var_data->rel->varratio, vr);
(void)MemoryContextSwitchTo(cxt);
}
/*
* get_join_ratio
* acquire join ratio when joining to relation with relid
*
* Parameters:
* @in vardata: var data info
* @in sjinfo: the join info for current relation join with others
*
* Return:
* minimum join ratio with specific relation
*/
static double get_join_ratio(VariableStatData* var_data, SpecialJoinInfo* sj_info)
{
double ratio = 1.0;
ListCell* lc = NULL;
RelOptInfo* rel = var_data->rel;
if ((rel == NULL) || (sj_info == NULL)) {
return 1.0;
}
foreach (lc, rel->varratio) {
VarRatio* jr = (VarRatio*)lfirst(lc);
if (RatioType_Filter == jr->ratiotype) {
continue;
}
/*
* only get the joinratio with the var.
* example: we have joinratio(t1 inner join t2 on t1.a=t2.a), we can save the joinratio with
* t1(t1t2, a, ratio1) and t2(t1t2, a, ratio2).
* when we compute three table join ratio as (t1 inner join t2 on t1.a=t2.a inner join t3 on t2.b=t3.b),
* we can use above joinratio ((t1*t2)*t3 (t2.b=t3.b)) with t2 when t1 join with t2 using t2.a.
*/
if (VARNEQ(var_data->var, jr->var)) {
continue;
}
/*
* we can use the ratio under two condition:
* 1. leftrelids include the cached joinrelids if local rel is left in joinrel.
* 2. rightrelids include the cached joinrelids if local rel is right in joinrel.
*/
if (((bms_is_subset(rel->relids, sj_info->min_lefthand) && bms_equal(jr->joinrelids, sj_info->min_lefthand)) ||
(bms_is_subset(rel->relids, sj_info->min_righthand) &&
bms_equal(jr->joinrelids, sj_info->min_righthand))) &&
jr->ratio < ratio) {
ratio = jr->ratio;
}
}
return ratio;
}
/*
* can_use_possion
* decide whether we can use possion or not according to cached ratio.
*
* Parameters:
* @in vardata: the var we want to get distinct
* @in sjinfo: the join info for current relation join with others
* @in all_baserels: identify all baserels include in the local root when sjinfo is null,
* we can use it to judge whether we can use possion or not
* @out ratio: the final selectivity include filter and join if we can use possion
*
* Return:
* whether we can use possion or not
*/
bool can_use_possion(VariableStatData* var_data, SpecialJoinInfo* sj_info, double* ratio)
{
ListCell* lc = NULL;
double filter_ratio = 1.0;
double join_ratio = 1.0;
bool use_possion = false;
if ((var_data->rel == NULL) || (var_data->rel->varratio == NULL) || (var_data->var == NULL)) {
return false;
}
/*
* comput result ratio according to filter and join qual.
* constrain: the column of join or filter and var_data->var art different,
* but they are belong to the same relation.
* thinking: we should get each ratio from varratio list according to ratio type respective
* method: if ratio type is filter and all the ratio var meat constrain above, we should multiple all the ratio.
* if ratio type is join ratio, we should find the ratio by get_join_ratio
*/
foreach (lc, var_data->rel->varratio) {
VarRatio* vr = (VarRatio*)lfirst(lc);
if (VARNEQ(var_data->var, vr->var)) {
if (RatioType_Filter == vr->ratiotype) {
filter_ratio = Min(filter_ratio, vr->ratio);
use_possion = true;
} else {
/*
* we can use the ratio under two condition for have join info:
* 1. leftrelids include the cached joinrelids if local rel is left in joinrel.
* 2. rightrelids include the cached joinrelids if local rel is right in joinrel.
* others, we can use the varratio if the all baserelids is equal to vr->joinrelids.
* for example: select t1.a from t1,t2 where t1.b=t2.b;
* we can estimate the distinct of t1.a using the joinratio of t1.b join with t2.b.
*/
if (sj_info && ((bms_is_subset(var_data->rel->relids, sj_info->min_lefthand) &&
bms_equal(vr->joinrelids, sj_info->min_lefthand)) ||
(bms_is_subset(var_data->rel->relids, sj_info->min_righthand) &&
bms_equal(vr->joinrelids, sj_info->min_righthand)))) {
join_ratio = Min(join_ratio, vr->ratio);
use_possion = true;
}
}
}
}
if ((unsigned int)u_sess->attr.attr_sql.cost_param & COST_ALTERNATIVE_CONJUNCT) {
*ratio = Min(filter_ratio, join_ratio);
} else {
*ratio = filter_ratio * join_ratio;
}
return use_possion;
}
/*
* @Description: Compute less or equal selectivity.
* @in root: Per-query information for planning/optimization.
* @in wc: window function clause.
* @in partitionExprs: partition by expr.
* @in constval: const value, less than or less equal parameter.
* @in tuples: plan all tuples number.
* @return: winfun less than constval selectivity.
*/
double get_windowagg_selectivity(PlannerInfo* root, WindowClause* wc, WindowFunc* wfunc, List* partition_exprs,
int32 const_val, double tuples, unsigned int num_datanodes)
{
Assert(wfunc->winfnoid == ROWNUMBERFUNCOID || wfunc->winfnoid == RANKFUNCOID);
double selec = 0.0;
if (const_val <= 0) {
return 0.0;
}
/* If runk() partition by and order by expr is same, runk() value must be 1.*/
if (wfunc->winfnoid == RANKFUNCOID && equal(wc->partitionClause, wc->orderClause) && const_val >= 1) {
selec = 1.0;
} else {
double numdistinct = estimate_num_groups(root, partition_exprs, tuples, num_datanodes, STATS_TYPE_LOCAL);
/* Estimate less selectivity, group num * c say select rows. */
selec = Min(numdistinct * const_val, tuples) / tuples;
}
return selec;
}
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