7026 lines
212 KiB
C
7026 lines
212 KiB
C
/*-------------------------------------------------------------------------
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*
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* selfuncs.c
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* Selectivity functions and index cost estimation functions for
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* standard operators and index access methods.
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*
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* Selectivity routines are registered in the pg_operator catalog
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* in the "oprrest" and "oprjoin" attributes.
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*
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* Index cost functions are located via the index AM's API struct,
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* which is obtained from the handler function registered in pg_am.
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*
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* Portions Copyright (c) 1996-2019, PostgreSQL Global Development Group
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* Portions Copyright (c) 1994, Regents of the University of California
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*
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*
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* IDENTIFICATION
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* src/backend/utils/adt/selfuncs.c
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*
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*-------------------------------------------------------------------------
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*/
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/*----------
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* Operator selectivity estimation functions are called to estimate the
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* selectivity of WHERE clauses whose top-level operator is their operator.
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* We divide the problem into two cases:
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* Restriction clause estimation: the clause involves vars of just
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* one relation.
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* Join clause estimation: the clause involves vars of multiple rels.
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* Join selectivity estimation is far more difficult and usually less accurate
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* than restriction estimation.
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*
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* When dealing with the inner scan of a nestloop join, we consider the
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* join's joinclauses as restriction clauses for the inner relation, and
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* treat vars of the outer relation as parameters (a/k/a constants of unknown
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* values). So, restriction estimators need to be able to accept an argument
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* telling which relation is to be treated as the variable.
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*
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* The call convention for a restriction estimator (oprrest function) is
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*
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* Selectivity oprrest (PlannerInfo *root,
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* Oid operator,
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* List *args,
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* int varRelid);
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*
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* root: general information about the query (rtable and RelOptInfo lists
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* are particularly important for the estimator).
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* operator: OID of the specific operator in question.
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* args: argument list from the operator clause.
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* varRelid: if not zero, the relid (rtable index) of the relation to
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* be treated as the variable relation. May be zero if the args list
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* is known to contain vars of only one relation.
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*
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* This is represented at the SQL level (in pg_proc) as
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*
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* float8 oprrest (internal, oid, internal, int4);
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*
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* The result is a selectivity, that is, a fraction (0 to 1) of the rows
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* of the relation that are expected to produce a TRUE result for the
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* given operator.
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*
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* The call convention for a join estimator (oprjoin function) is similar
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* except that varRelid is not needed, and instead join information is
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* supplied:
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*
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* Selectivity oprjoin (PlannerInfo *root,
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* Oid operator,
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* List *args,
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* JoinType jointype,
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* SpecialJoinInfo *sjinfo);
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*
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* float8 oprjoin (internal, oid, internal, int2, internal);
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*
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* (Before Postgres 8.4, join estimators had only the first four of these
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* parameters. That signature is still allowed, but deprecated.) The
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* relationship between jointype and sjinfo is explained in the comments for
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* clause_selectivity() --- the short version is that jointype is usually
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* best ignored in favor of examining sjinfo.
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*
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* Join selectivity for regular inner and outer joins is defined as the
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* fraction (0 to 1) of the cross product of the relations that is expected
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* to produce a TRUE result for the given operator. For both semi and anti
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* joins, however, the selectivity is defined as the fraction of the left-hand
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* side relation's rows that are expected to have a match (ie, at least one
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* row with a TRUE result) in the right-hand side.
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*
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* For both oprrest and oprjoin functions, the operator's input collation OID
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* (if any) is passed using the standard fmgr mechanism, so that the estimator
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* function can fetch it with PG_GET_COLLATION(). Note, however, that all
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* statistics in pg_statistic are currently built using the relevant column's
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* collation. Thus, in most cases where we are looking at statistics, we
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* should ignore the operator collation and use the stats entry's collation.
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* We expect that the error induced by doing this is usually not large enough
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* to justify complicating matters. In any case, doing otherwise would yield
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* entirely garbage results for ordered stats data such as histograms.
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*----------
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*/
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#include "postgres.h"
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#include <ctype.h>
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#include <math.h>
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#include "access/brin.h"
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#include "access/gin.h"
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#include "access/table.h"
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#include "access/tableam.h"
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#include "access/visibilitymap.h"
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#include "catalog/pg_am.h"
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#include "catalog/pg_collation.h"
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#include "catalog/pg_operator.h"
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#include "catalog/pg_statistic.h"
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#include "catalog/pg_statistic_ext.h"
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#include "executor/nodeAgg.h"
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#include "miscadmin.h"
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#include "nodes/makefuncs.h"
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#include "nodes/nodeFuncs.h"
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#include "optimizer/clauses.h"
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#include "optimizer/cost.h"
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#include "optimizer/optimizer.h"
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#include "optimizer/pathnode.h"
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#include "optimizer/paths.h"
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#include "optimizer/plancat.h"
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#include "parser/parse_clause.h"
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#include "parser/parsetree.h"
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#include "statistics/statistics.h"
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#include "storage/bufmgr.h"
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#include "utils/builtins.h"
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#include "utils/date.h"
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#include "utils/datum.h"
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#include "utils/fmgroids.h"
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#include "utils/index_selfuncs.h"
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#include "utils/lsyscache.h"
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#include "utils/memutils.h"
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#include "utils/pg_locale.h"
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#include "utils/rel.h"
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#include "utils/selfuncs.h"
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#include "utils/snapmgr.h"
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#include "utils/spccache.h"
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#include "utils/syscache.h"
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#include "utils/timestamp.h"
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#include "utils/typcache.h"
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/* Hooks for plugins to get control when we ask for stats */
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get_relation_stats_hook_type get_relation_stats_hook = NULL;
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get_index_stats_hook_type get_index_stats_hook = NULL;
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static double eqsel_internal(PG_FUNCTION_ARGS, bool negate);
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static double eqjoinsel_inner(Oid opfuncoid,
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VariableStatData *vardata1, VariableStatData *vardata2,
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double nd1, double nd2,
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bool isdefault1, bool isdefault2,
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AttStatsSlot *sslot1, AttStatsSlot *sslot2,
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Form_pg_statistic stats1, Form_pg_statistic stats2,
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bool have_mcvs1, bool have_mcvs2);
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static double eqjoinsel_semi(Oid opfuncoid,
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VariableStatData *vardata1, VariableStatData *vardata2,
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double nd1, double nd2,
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bool isdefault1, bool isdefault2,
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AttStatsSlot *sslot1, AttStatsSlot *sslot2,
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Form_pg_statistic stats1, Form_pg_statistic stats2,
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bool have_mcvs1, bool have_mcvs2,
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RelOptInfo *inner_rel);
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static bool estimate_multivariate_ndistinct(PlannerInfo *root,
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RelOptInfo *rel, List **varinfos, double *ndistinct);
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static bool convert_to_scalar(Datum value, Oid valuetypid, Oid collid,
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double *scaledvalue,
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Datum lobound, Datum hibound, Oid boundstypid,
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double *scaledlobound, double *scaledhibound);
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static double convert_numeric_to_scalar(Datum value, Oid typid, bool *failure);
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static void convert_string_to_scalar(char *value,
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double *scaledvalue,
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char *lobound,
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double *scaledlobound,
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char *hibound,
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double *scaledhibound);
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static void convert_bytea_to_scalar(Datum value,
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double *scaledvalue,
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Datum lobound,
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double *scaledlobound,
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Datum hibound,
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double *scaledhibound);
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static double convert_one_string_to_scalar(char *value,
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int rangelo, int rangehi);
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static double convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
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int rangelo, int rangehi);
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static char *convert_string_datum(Datum value, Oid typid, Oid collid,
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bool *failure);
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static double convert_timevalue_to_scalar(Datum value, Oid typid,
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bool *failure);
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static void examine_simple_variable(PlannerInfo *root, Var *var,
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VariableStatData *vardata);
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static bool get_variable_range(PlannerInfo *root, VariableStatData *vardata,
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Oid sortop, Datum *min, Datum *max);
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static bool get_actual_variable_range(PlannerInfo *root,
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VariableStatData *vardata,
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Oid sortop,
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Datum *min, Datum *max);
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static bool get_actual_variable_endpoint(Relation heapRel,
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Relation indexRel,
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ScanDirection indexscandir,
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ScanKey scankeys,
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int16 typLen,
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bool typByVal,
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TupleTableSlot *tableslot,
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MemoryContext outercontext,
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Datum *endpointDatum);
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static RelOptInfo *find_join_input_rel(PlannerInfo *root, Relids relids);
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/*
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* eqsel - Selectivity of "=" for any data types.
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*
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* Note: this routine is also used to estimate selectivity for some
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* operators that are not "=" but have comparable selectivity behavior,
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* such as "~=" (geometric approximate-match). Even for "=", we must
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* keep in mind that the left and right datatypes may differ.
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*/
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Datum
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eqsel(PG_FUNCTION_ARGS)
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{
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PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, false));
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}
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/*
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* Common code for eqsel() and neqsel()
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*/
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static double
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eqsel_internal(PG_FUNCTION_ARGS, bool negate)
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{
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PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
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Oid operator = PG_GETARG_OID(1);
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List *args = (List *) PG_GETARG_POINTER(2);
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int varRelid = PG_GETARG_INT32(3);
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VariableStatData vardata;
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Node *other;
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bool varonleft;
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double selec;
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/*
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* When asked about <>, we do the estimation using the corresponding =
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* operator, then convert to <> via "1.0 - eq_selectivity - nullfrac".
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*/
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if (negate)
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{
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operator = get_negator(operator);
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if (!OidIsValid(operator))
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{
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/* Use default selectivity (should we raise an error instead?) */
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return 1.0 - DEFAULT_EQ_SEL;
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}
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}
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/*
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* If expression is not variable = something or something = variable, then
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* punt and return a default estimate.
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*/
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if (!get_restriction_variable(root, args, varRelid,
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&vardata, &other, &varonleft))
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return negate ? (1.0 - DEFAULT_EQ_SEL) : DEFAULT_EQ_SEL;
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/*
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* We can do a lot better if the something is a constant. (Note: the
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* Const might result from estimation rather than being a simple constant
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* in the query.)
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*/
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if (IsA(other, Const))
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selec = var_eq_const(&vardata, operator,
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((Const *) other)->constvalue,
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((Const *) other)->constisnull,
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varonleft, negate);
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else
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selec = var_eq_non_const(&vardata, operator, other,
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varonleft, negate);
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ReleaseVariableStats(vardata);
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return selec;
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}
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/*
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* var_eq_const --- eqsel for var = const case
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*
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* This is exported so that some other estimation functions can use it.
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*/
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double
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var_eq_const(VariableStatData *vardata, Oid operator,
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Datum constval, bool constisnull,
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bool varonleft, bool negate)
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{
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double selec;
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double nullfrac = 0.0;
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bool isdefault;
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Oid opfuncoid;
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/*
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* If the constant is NULL, assume operator is strict and return zero, ie,
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* operator will never return TRUE. (It's zero even for a negator op.)
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*/
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if (constisnull)
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return 0.0;
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/*
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* Grab the nullfrac for use below. Note we allow use of nullfrac
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* regardless of security check.
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*/
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if (HeapTupleIsValid(vardata->statsTuple))
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{
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Form_pg_statistic stats;
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stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
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nullfrac = stats->stanullfrac;
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}
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/*
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* If we matched the var to a unique index or DISTINCT clause, assume
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* there is exactly one match regardless of anything else. (This is
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* slightly bogus, since the index or clause's equality operator might be
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* different from ours, but it's much more likely to be right than
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* ignoring the information.)
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*/
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if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
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{
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selec = 1.0 / vardata->rel->tuples;
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}
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else if (HeapTupleIsValid(vardata->statsTuple) &&
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statistic_proc_security_check(vardata,
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(opfuncoid = get_opcode(operator))))
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{
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AttStatsSlot sslot;
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bool match = false;
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int i;
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/*
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* Is the constant "=" to any of the column's most common values?
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* (Although the given operator may not really be "=", we will assume
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* that seeing whether it returns TRUE is an appropriate test. If you
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* don't like this, maybe you shouldn't be using eqsel for your
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* operator...)
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*/
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if (get_attstatsslot(&sslot, vardata->statsTuple,
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STATISTIC_KIND_MCV, InvalidOid,
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ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
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{
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FmgrInfo eqproc;
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fmgr_info(opfuncoid, &eqproc);
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for (i = 0; i < sslot.nvalues; i++)
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{
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/* be careful to apply operator right way 'round */
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if (varonleft)
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match = DatumGetBool(FunctionCall2Coll(&eqproc,
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sslot.stacoll,
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sslot.values[i],
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constval));
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else
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match = DatumGetBool(FunctionCall2Coll(&eqproc,
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sslot.stacoll,
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constval,
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sslot.values[i]));
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if (match)
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break;
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}
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}
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else
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{
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/* no most-common-value info available */
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i = 0; /* keep compiler quiet */
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}
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if (match)
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{
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/*
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* Constant is "=" to this common value. We know selectivity
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* exactly (or as exactly as ANALYZE could calculate it, anyway).
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*/
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selec = sslot.numbers[i];
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}
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else
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{
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/*
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* Comparison is against a constant that is neither NULL nor any
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* of the common values. Its selectivity cannot be more than
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* this:
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*/
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double sumcommon = 0.0;
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double otherdistinct;
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for (i = 0; i < sslot.nnumbers; i++)
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sumcommon += sslot.numbers[i];
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selec = 1.0 - sumcommon - nullfrac;
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CLAMP_PROBABILITY(selec);
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|
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/*
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* and in fact it's probably a good deal less. We approximate that
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* all the not-common values share this remaining fraction
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* equally, so we divide by the number of other distinct values.
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*/
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otherdistinct = get_variable_numdistinct(vardata, &isdefault) -
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sslot.nnumbers;
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if (otherdistinct > 1)
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selec /= otherdistinct;
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|
|
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/*
|
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* Another cross-check: selectivity shouldn't be estimated as more
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* than the least common "most common value".
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*/
|
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if (sslot.nnumbers > 0 && selec > sslot.numbers[sslot.nnumbers - 1])
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selec = sslot.numbers[sslot.nnumbers - 1];
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}
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|
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free_attstatsslot(&sslot);
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}
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else
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{
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/*
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* No ANALYZE stats available, so make a guess using estimated number
|
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* of distinct values and assuming they are equally common. (The guess
|
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* is unlikely to be very good, but we do know a few special cases.)
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*/
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selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
|
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}
|
|
|
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/* now adjust if we wanted <> rather than = */
|
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if (negate)
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selec = 1.0 - selec - nullfrac;
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|
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/* result should be in range, but make sure... */
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CLAMP_PROBABILITY(selec);
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|
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return selec;
|
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}
|
|
|
|
/*
|
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* var_eq_non_const --- eqsel for var = something-other-than-const case
|
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*
|
|
* This is exported so that some other estimation functions can use it.
|
|
*/
|
|
double
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var_eq_non_const(VariableStatData *vardata, Oid operator,
|
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Node *other,
|
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bool varonleft, bool negate)
|
|
{
|
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double selec;
|
|
double nullfrac = 0.0;
|
|
bool isdefault;
|
|
|
|
/*
|
|
* Grab the nullfrac for use below.
|
|
*/
|
|
if (HeapTupleIsValid(vardata->statsTuple))
|
|
{
|
|
Form_pg_statistic stats;
|
|
|
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stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
|
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nullfrac = stats->stanullfrac;
|
|
}
|
|
|
|
/*
|
|
* If we matched the var to a unique index or DISTINCT clause, assume
|
|
* there is exactly one match regardless of anything else. (This is
|
|
* slightly bogus, since the index or clause's equality operator might be
|
|
* different from ours, but it's much more likely to be right than
|
|
* ignoring the information.)
|
|
*/
|
|
if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
|
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{
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selec = 1.0 / vardata->rel->tuples;
|
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}
|
|
else if (HeapTupleIsValid(vardata->statsTuple))
|
|
{
|
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double ndistinct;
|
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AttStatsSlot sslot;
|
|
|
|
/*
|
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* 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
|
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* fraction divided by number of distinct values, so that we get a
|
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* result averaged over all possible values whether common or
|
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* uncommon. (Essentially, we are assuming that the not-yet-known
|
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* comparison value is equally likely to be any of the possible
|
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* values, regardless of their frequency in the table. Is that a good
|
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* idea?)
|
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*/
|
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selec = 1.0 - nullfrac;
|
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ndistinct = get_variable_numdistinct(vardata, &isdefault);
|
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if (ndistinct > 1)
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selec /= ndistinct;
|
|
|
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/*
|
|
* Cross-check: selectivity should never be estimated as more than the
|
|
* most common value's.
|
|
*/
|
|
if (get_attstatsslot(&sslot, vardata->statsTuple,
|
|
STATISTIC_KIND_MCV, InvalidOid,
|
|
ATTSTATSSLOT_NUMBERS))
|
|
{
|
|
if (sslot.nnumbers > 0 && selec > sslot.numbers[0])
|
|
selec = sslot.numbers[0];
|
|
free_attstatsslot(&sslot);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
/*
|
|
* No ANALYZE stats available, so make a guess using estimated number
|
|
* of distinct values and assuming they are equally common. (The guess
|
|
* is unlikely to be very good, but we do know a few special cases.)
|
|
*/
|
|
selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
|
|
}
|
|
|
|
/* now adjust if we wanted <> rather than = */
|
|
if (negate)
|
|
selec = 1.0 - selec - nullfrac;
|
|
|
|
/* result should be in range, but make sure... */
|
|
CLAMP_PROBABILITY(selec);
|
|
|
|
return selec;
|
|
}
|
|
|
|
/*
|
|
* neqsel - Selectivity of "!=" for any data types.
|
|
*
|
|
* This routine is also used for some operators that are not "!="
|
|
* but have comparable selectivity behavior. See above comments
|
|
* for eqsel().
|
|
*/
|
|
Datum
|
|
neqsel(PG_FUNCTION_ARGS)
|
|
{
|
|
PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, true));
|
|
}
|
|
|
|
/*
|
|
* scalarineqsel - Selectivity of "<", "<=", ">", ">=" for scalars.
|
|
*
|
|
* This is the guts of scalarltsel/scalarlesel/scalargtsel/scalargesel.
|
|
* The isgt and iseq flags distinguish which of the four cases apply.
|
|
*
|
|
* The caller has commuted the clause, if necessary, so that we can treat
|
|
* the variable as being on the left. The caller must also make sure that
|
|
* the other side of the clause is a non-null Const, and dissect that into
|
|
* a value and datatype. (This definition simplifies some callers that
|
|
* want to estimate against a computed value instead of a Const node.)
|
|
*
|
|
* This routine works for any datatype (or pair of datatypes) known to
|
|
* convert_to_scalar(). If it is applied to some other datatype,
|
|
* it will return an approximate estimate based on assuming that the constant
|
|
* value falls in the middle of the bin identified by binary search.
|
|
*/
|
|
static double
|
|
scalarineqsel(PlannerInfo *root, Oid operator, bool isgt, bool iseq,
|
|
VariableStatData *vardata, Datum constval, Oid consttype)
|
|
{
|
|
Form_pg_statistic stats;
|
|
FmgrInfo opproc;
|
|
double mcv_selec,
|
|
hist_selec,
|
|
sumcommon;
|
|
double selec;
|
|
|
|
if (!HeapTupleIsValid(vardata->statsTuple))
|
|
{
|
|
/*
|
|
* No stats are available. Typically this means we have to fall back
|
|
* on the default estimate; but if the variable is CTID then we can
|
|
* make an estimate based on comparing the constant to the table size.
|
|
*/
|
|
if (vardata->var && IsA(vardata->var, Var) &&
|
|
((Var *) vardata->var)->varattno == SelfItemPointerAttributeNumber)
|
|
{
|
|
ItemPointer itemptr;
|
|
double block;
|
|
double density;
|
|
|
|
/*
|
|
* If the relation's empty, we're going to include all of it.
|
|
* (This is mostly to avoid divide-by-zero below.)
|
|
*/
|
|
if (vardata->rel->pages == 0)
|
|
return 1.0;
|
|
|
|
itemptr = (ItemPointer) DatumGetPointer(constval);
|
|
block = ItemPointerGetBlockNumberNoCheck(itemptr);
|
|
|
|
/*
|
|
* Determine the average number of tuples per page (density).
|
|
*
|
|
* Since the last page will, on average, be only half full, we can
|
|
* estimate it to have half as many tuples as earlier pages. So
|
|
* give it half the weight of a regular page.
|
|
*/
|
|
density = vardata->rel->tuples / (vardata->rel->pages - 0.5);
|
|
|
|
/* If target is the last page, use half the density. */
|
|
if (block >= vardata->rel->pages - 1)
|
|
density *= 0.5;
|
|
|
|
/*
|
|
* Using the average tuples per page, calculate how far into the
|
|
* page the itemptr is likely to be and adjust block accordingly,
|
|
* by adding that fraction of a whole block (but never more than a
|
|
* whole block, no matter how high the itemptr's offset is). Here
|
|
* we are ignoring the possibility of dead-tuple line pointers,
|
|
* which is fairly bogus, but we lack the info to do better.
|
|
*/
|
|
if (density > 0.0)
|
|
{
|
|
OffsetNumber offset = ItemPointerGetOffsetNumberNoCheck(itemptr);
|
|
|
|
block += Min(offset / density, 1.0);
|
|
}
|
|
|
|
/*
|
|
* Convert relative block number to selectivity. Again, the last
|
|
* page has only half weight.
|
|
*/
|
|
selec = block / (vardata->rel->pages - 0.5);
|
|
|
|
/*
|
|
* The calculation so far gave us a selectivity for the "<=" case.
|
|
* We'll have one less tuple for "<" and one additional tuple for
|
|
* ">=", the latter of which we'll reverse the selectivity for
|
|
* below, so we can simply subtract one tuple for both cases. The
|
|
* cases that need this adjustment can be identified by iseq being
|
|
* equal to isgt.
|
|
*/
|
|
if (iseq == isgt && vardata->rel->tuples >= 1.0)
|
|
selec -= (1.0 / vardata->rel->tuples);
|
|
|
|
/* Finally, reverse the selectivity for the ">", ">=" cases. */
|
|
if (isgt)
|
|
selec = 1.0 - selec;
|
|
|
|
CLAMP_PROBABILITY(selec);
|
|
return selec;
|
|
}
|
|
|
|
/* no stats available, so default result */
|
|
return DEFAULT_INEQ_SEL;
|
|
}
|
|
stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
|
|
|
|
fmgr_info(get_opcode(operator), &opproc);
|
|
|
|
/*
|
|
* If we have most-common-values info, add up the fractions of the MCV
|
|
* entries that satisfy MCV OP CONST. These fractions contribute directly
|
|
* to the result selectivity. Also add up the total fraction represented
|
|
* by MCV entries.
|
|
*/
|
|
mcv_selec = mcv_selectivity(vardata, &opproc, constval, true,
|
|
&sumcommon);
|
|
|
|
/*
|
|
* If there is a histogram, determine which bin the constant falls in, and
|
|
* compute the resulting contribution to selectivity.
|
|
*/
|
|
hist_selec = ineq_histogram_selectivity(root, vardata,
|
|
&opproc, isgt, iseq,
|
|
constval, consttype);
|
|
|
|
/*
|
|
* Now merge the results from the MCV and histogram calculations,
|
|
* realizing that the histogram covers only the non-null values that are
|
|
* not listed in MCV.
|
|
*/
|
|
selec = 1.0 - stats->stanullfrac - sumcommon;
|
|
|
|
if (hist_selec >= 0.0)
|
|
selec *= hist_selec;
|
|
else
|
|
{
|
|
/*
|
|
* If no histogram but there are values not accounted for by MCV,
|
|
* arbitrarily assume half of them will match.
|
|
*/
|
|
selec *= 0.5;
|
|
}
|
|
|
|
selec += mcv_selec;
|
|
|
|
/* result should be in range, but make sure... */
|
|
CLAMP_PROBABILITY(selec);
|
|
|
|
return selec;
|
|
}
|
|
|
|
/*
|
|
* mcv_selectivity - Examine the MCV list for selectivity estimates
|
|
*
|
|
* Determine the fraction of the variable's MCV population that satisfies
|
|
* the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft. Also
|
|
* compute the fraction of the total column population represented by the MCV
|
|
* list. This code will work for any boolean-returning predicate operator.
|
|
*
|
|
* The function result is the MCV selectivity, and the fraction of the
|
|
* total population is returned into *sumcommonp. Zeroes are returned
|
|
* if there is no MCV list.
|
|
*/
|
|
double
|
|
mcv_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
|
|
Datum constval, bool varonleft,
|
|
double *sumcommonp)
|
|
{
|
|
double mcv_selec,
|
|
sumcommon;
|
|
AttStatsSlot sslot;
|
|
int i;
|
|
|
|
mcv_selec = 0.0;
|
|
sumcommon = 0.0;
|
|
|
|
if (HeapTupleIsValid(vardata->statsTuple) &&
|
|
statistic_proc_security_check(vardata, opproc->fn_oid) &&
|
|
get_attstatsslot(&sslot, vardata->statsTuple,
|
|
STATISTIC_KIND_MCV, InvalidOid,
|
|
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
|
|
{
|
|
for (i = 0; i < sslot.nvalues; i++)
|
|
{
|
|
if (varonleft ?
|
|
DatumGetBool(FunctionCall2Coll(opproc,
|
|
sslot.stacoll,
|
|
sslot.values[i],
|
|
constval)) :
|
|
DatumGetBool(FunctionCall2Coll(opproc,
|
|
sslot.stacoll,
|
|
constval,
|
|
sslot.values[i])))
|
|
mcv_selec += sslot.numbers[i];
|
|
sumcommon += sslot.numbers[i];
|
|
}
|
|
free_attstatsslot(&sslot);
|
|
}
|
|
|
|
*sumcommonp = sumcommon;
|
|
return mcv_selec;
|
|
}
|
|
|
|
/*
|
|
* histogram_selectivity - Examine the histogram for selectivity estimates
|
|
*
|
|
* Determine the fraction of the variable's histogram entries that satisfy
|
|
* the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft.
|
|
*
|
|
* This code will work for any boolean-returning predicate operator, whether
|
|
* or not it has anything to do with the histogram sort operator. We are
|
|
* essentially using the histogram just as a representative sample. However,
|
|
* small histograms are unlikely to be all that representative, so the caller
|
|
* should be prepared to fall back on some other estimation approach when the
|
|
* histogram is missing or very small. It may also be prudent to combine this
|
|
* approach with another one when the histogram is small.
|
|
*
|
|
* If the actual histogram size is not at least min_hist_size, we won't bother
|
|
* to do the calculation at all. Also, if the n_skip parameter is > 0, we
|
|
* ignore the first and last n_skip histogram elements, on the grounds that
|
|
* they are outliers and hence not very representative. Typical values for
|
|
* these parameters are 10 and 1.
|
|
*
|
|
* The function result is the selectivity, or -1 if there is no histogram
|
|
* or it's smaller than min_hist_size.
|
|
*
|
|
* The output parameter *hist_size receives the actual histogram size,
|
|
* or zero if no histogram. Callers may use this number to decide how
|
|
* much faith to put in the function result.
|
|
*
|
|
* Note that the result disregards both the most-common-values (if any) and
|
|
* null entries. The caller is expected to combine this result with
|
|
* statistics for those portions of the column population. It may also be
|
|
* prudent to clamp the result range, ie, disbelieve exact 0 or 1 outputs.
|
|
*/
|
|
double
|
|
histogram_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
|
|
Datum constval, bool varonleft,
|
|
int min_hist_size, int n_skip,
|
|
int *hist_size)
|
|
{
|
|
double result;
|
|
AttStatsSlot sslot;
|
|
|
|
/* check sanity of parameters */
|
|
Assert(n_skip >= 0);
|
|
Assert(min_hist_size > 2 * n_skip);
|
|
|
|
if (HeapTupleIsValid(vardata->statsTuple) &&
|
|
statistic_proc_security_check(vardata, opproc->fn_oid) &&
|
|
get_attstatsslot(&sslot, vardata->statsTuple,
|
|
STATISTIC_KIND_HISTOGRAM, InvalidOid,
|
|
ATTSTATSSLOT_VALUES))
|
|
{
|
|
*hist_size = sslot.nvalues;
|
|
if (sslot.nvalues >= min_hist_size)
|
|
{
|
|
int nmatch = 0;
|
|
int i;
|
|
|
|
for (i = n_skip; i < sslot.nvalues - n_skip; i++)
|
|
{
|
|
if (varonleft ?
|
|
DatumGetBool(FunctionCall2Coll(opproc,
|
|
sslot.stacoll,
|
|
sslot.values[i],
|
|
constval)) :
|
|
DatumGetBool(FunctionCall2Coll(opproc,
|
|
sslot.stacoll,
|
|
constval,
|
|
sslot.values[i])))
|
|
nmatch++;
|
|
}
|
|
result = ((double) nmatch) / ((double) (sslot.nvalues - 2 * n_skip));
|
|
}
|
|
else
|
|
result = -1;
|
|
free_attstatsslot(&sslot);
|
|
}
|
|
else
|
|
{
|
|
*hist_size = 0;
|
|
result = -1;
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
/*
|
|
* ineq_histogram_selectivity - Examine the histogram for scalarineqsel
|
|
*
|
|
* Determine the fraction of the variable's histogram population that
|
|
* satisfies the inequality condition, ie, VAR < (or <=, >, >=) CONST.
|
|
* The isgt and iseq flags distinguish which of the four cases apply.
|
|
*
|
|
* Returns -1 if there is no histogram (valid results will always be >= 0).
|
|
*
|
|
* Note that the result disregards both the most-common-values (if any) and
|
|
* null entries. The caller is expected to combine this result with
|
|
* statistics for those portions of the column population.
|
|
*
|
|
* This is exported so that some other estimation functions can use it.
|
|
*/
|
|
double
|
|
ineq_histogram_selectivity(PlannerInfo *root,
|
|
VariableStatData *vardata,
|
|
FmgrInfo *opproc, bool isgt, bool iseq,
|
|
Datum constval, Oid consttype)
|
|
{
|
|
double hist_selec;
|
|
AttStatsSlot sslot;
|
|
|
|
hist_selec = -1.0;
|
|
|
|
/*
|
|
* Someday, ANALYZE might store more than one histogram per rel/att,
|
|
* corresponding to more than one possible sort ordering defined for the
|
|
* column type. However, to make that work we will need to figure out
|
|
* which staop to search for --- it's not necessarily the one we have at
|
|
* hand! (For example, we might have a '<=' operator rather than the '<'
|
|
* operator that will appear in staop.) For now, assume that whatever
|
|
* appears in pg_statistic is sorted the same way our operator sorts, or
|
|
* the reverse way if isgt is true.
|
|
*/
|
|
if (HeapTupleIsValid(vardata->statsTuple) &&
|
|
statistic_proc_security_check(vardata, opproc->fn_oid) &&
|
|
get_attstatsslot(&sslot, vardata->statsTuple,
|
|
STATISTIC_KIND_HISTOGRAM, InvalidOid,
|
|
ATTSTATSSLOT_VALUES))
|
|
{
|
|
if (sslot.nvalues > 1)
|
|
{
|
|
/*
|
|
* Use binary search to find the desired location, namely the
|
|
* right end of the histogram bin containing the comparison value,
|
|
* which is the leftmost entry for which the comparison operator
|
|
* succeeds (if isgt) or fails (if !isgt). (If the given operator
|
|
* isn't actually sort-compatible with the histogram, you'll get
|
|
* garbage results ... but probably not any more garbage-y than
|
|
* you would have from the old linear search.)
|
|
*
|
|
* In this loop, we pay no attention to whether the operator iseq
|
|
* or not; that detail will be mopped up below. (We cannot tell,
|
|
* anyway, whether the operator thinks the values are equal.)
|
|
*
|
|
* If the binary search accesses the first or last histogram
|
|
* entry, we try to replace that endpoint with the true column min
|
|
* or max as found by get_actual_variable_range(). This
|
|
* ameliorates misestimates when the min or max is moving as a
|
|
* result of changes since the last ANALYZE. Note that this could
|
|
* result in effectively including MCVs into the histogram that
|
|
* weren't there before, but we don't try to correct for that.
|
|
*/
|
|
double histfrac;
|
|
int lobound = 0; /* first possible slot to search */
|
|
int hibound = sslot.nvalues; /* last+1 slot to search */
|
|
bool have_end = false;
|
|
|
|
/*
|
|
* If there are only two histogram entries, we'll want up-to-date
|
|
* values for both. (If there are more than two, we need at most
|
|
* one of them to be updated, so we deal with that within the
|
|
* loop.)
|
|
*/
|
|
if (sslot.nvalues == 2)
|
|
have_end = get_actual_variable_range(root,
|
|
vardata,
|
|
sslot.staop,
|
|
&sslot.values[0],
|
|
&sslot.values[1]);
|
|
|
|
while (lobound < hibound)
|
|
{
|
|
int probe = (lobound + hibound) / 2;
|
|
bool ltcmp;
|
|
|
|
/*
|
|
* If we find ourselves about to compare to the first or last
|
|
* histogram entry, first try to replace it with the actual
|
|
* current min or max (unless we already did so above).
|
|
*/
|
|
if (probe == 0 && sslot.nvalues > 2)
|
|
have_end = get_actual_variable_range(root,
|
|
vardata,
|
|
sslot.staop,
|
|
&sslot.values[0],
|
|
NULL);
|
|
else if (probe == sslot.nvalues - 1 && sslot.nvalues > 2)
|
|
have_end = get_actual_variable_range(root,
|
|
vardata,
|
|
sslot.staop,
|
|
NULL,
|
|
&sslot.values[probe]);
|
|
|
|
ltcmp = DatumGetBool(FunctionCall2Coll(opproc,
|
|
sslot.stacoll,
|
|
sslot.values[probe],
|
|
constval));
|
|
if (isgt)
|
|
ltcmp = !ltcmp;
|
|
if (ltcmp)
|
|
lobound = probe + 1;
|
|
else
|
|
hibound = probe;
|
|
}
|
|
|
|
if (lobound <= 0)
|
|
{
|
|
/*
|
|
* Constant is below lower histogram boundary. More
|
|
* precisely, we have found that no entry in the histogram
|
|
* satisfies the inequality clause (if !isgt) or they all do
|
|
* (if isgt). We estimate that that's true of the entire
|
|
* table, so set histfrac to 0.0 (which we'll flip to 1.0
|
|
* below, if isgt).
|
|
*/
|
|
histfrac = 0.0;
|
|
}
|
|
else if (lobound >= sslot.nvalues)
|
|
{
|
|
/*
|
|
* Inverse case: constant is above upper histogram boundary.
|
|
*/
|
|
histfrac = 1.0;
|
|
}
|
|
else
|
|
{
|
|
/* We have values[i-1] <= constant <= values[i]. */
|
|
int i = lobound;
|
|
double eq_selec = 0;
|
|
double val,
|
|
high,
|
|
low;
|
|
double binfrac;
|
|
|
|
/*
|
|
* In the cases where we'll need it below, obtain an estimate
|
|
* of the selectivity of "x = constval". We use a calculation
|
|
* similar to what var_eq_const() does for a non-MCV constant,
|
|
* ie, estimate that all distinct non-MCV values occur equally
|
|
* often. But multiplication by "1.0 - sumcommon - nullfrac"
|
|
* will be done by our caller, so we shouldn't do that here.
|
|
* Therefore we can't try to clamp the estimate by reference
|
|
* to the least common MCV; the result would be too small.
|
|
*
|
|
* Note: since this is effectively assuming that constval
|
|
* isn't an MCV, it's logically dubious if constval in fact is
|
|
* one. But we have to apply *some* correction for equality,
|
|
* and anyway we cannot tell if constval is an MCV, since we
|
|
* don't have a suitable equality operator at hand.
|
|
*/
|
|
if (i == 1 || isgt == iseq)
|
|
{
|
|
double otherdistinct;
|
|
bool isdefault;
|
|
AttStatsSlot mcvslot;
|
|
|
|
/* Get estimated number of distinct values */
|
|
otherdistinct = get_variable_numdistinct(vardata,
|
|
&isdefault);
|
|
|
|
/* Subtract off the number of known MCVs */
|
|
if (get_attstatsslot(&mcvslot, vardata->statsTuple,
|
|
STATISTIC_KIND_MCV, InvalidOid,
|
|
ATTSTATSSLOT_NUMBERS))
|
|
{
|
|
otherdistinct -= mcvslot.nnumbers;
|
|
free_attstatsslot(&mcvslot);
|
|
}
|
|
|
|
/* If result doesn't seem sane, leave eq_selec at 0 */
|
|
if (otherdistinct > 1)
|
|
eq_selec = 1.0 / otherdistinct;
|
|
}
|
|
|
|
/*
|
|
* Convert the constant and the two nearest bin boundary
|
|
* values to a uniform comparison scale, and do a linear
|
|
* interpolation within this bin.
|
|
*/
|
|
if (convert_to_scalar(constval, consttype, sslot.stacoll,
|
|
&val,
|
|
sslot.values[i - 1], sslot.values[i],
|
|
vardata->vartype,
|
|
&low, &high))
|
|
{
|
|
if (high <= low)
|
|
{
|
|
/* cope if bin boundaries appear identical */
|
|
binfrac = 0.5;
|
|
}
|
|
else if (val <= low)
|
|
binfrac = 0.0;
|
|
else if (val >= high)
|
|
binfrac = 1.0;
|
|
else
|
|
{
|
|
binfrac = (val - low) / (high - low);
|
|
|
|
/*
|
|
* Watch out for the possibility that we got a NaN or
|
|
* Infinity from the division. This can happen
|
|
* despite the previous checks, if for example "low"
|
|
* is -Infinity.
|
|
*/
|
|
if (isnan(binfrac) ||
|
|
binfrac < 0.0 || binfrac > 1.0)
|
|
binfrac = 0.5;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
/*
|
|
* Ideally we'd produce an error here, on the grounds that
|
|
* the given operator shouldn't have scalarXXsel
|
|
* registered as its selectivity func unless we can deal
|
|
* with its operand types. But currently, all manner of
|
|
* stuff is invoking scalarXXsel, so give a default
|
|
* estimate until that can be fixed.
|
|
*/
|
|
binfrac = 0.5;
|
|
}
|
|
|
|
/*
|
|
* Now, compute the overall selectivity across the values
|
|
* represented by the histogram. We have i-1 full bins and
|
|
* binfrac partial bin below the constant.
|
|
*/
|
|
histfrac = (double) (i - 1) + binfrac;
|
|
histfrac /= (double) (sslot.nvalues - 1);
|
|
|
|
/*
|
|
* At this point, histfrac is an estimate of the fraction of
|
|
* the population represented by the histogram that satisfies
|
|
* "x <= constval". Somewhat remarkably, this statement is
|
|
* true regardless of which operator we were doing the probes
|
|
* with, so long as convert_to_scalar() delivers reasonable
|
|
* results. If the probe constant is equal to some histogram
|
|
* entry, we would have considered the bin to the left of that
|
|
* entry if probing with "<" or ">=", or the bin to the right
|
|
* if probing with "<=" or ">"; but binfrac would have come
|
|
* out as 1.0 in the first case and 0.0 in the second, leading
|
|
* to the same histfrac in either case. For probe constants
|
|
* between histogram entries, we find the same bin and get the
|
|
* same estimate with any operator.
|
|
*
|
|
* The fact that the estimate corresponds to "x <= constval"
|
|
* and not "x < constval" is because of the way that ANALYZE
|
|
* constructs the histogram: each entry is, effectively, the
|
|
* rightmost value in its sample bucket. So selectivity
|
|
* values that are exact multiples of 1/(histogram_size-1)
|
|
* should be understood as estimates including a histogram
|
|
* entry plus everything to its left.
|
|
*
|
|
* However, that breaks down for the first histogram entry,
|
|
* which necessarily is the leftmost value in its sample
|
|
* bucket. That means the first histogram bin is slightly
|
|
* narrower than the rest, by an amount equal to eq_selec.
|
|
* Another way to say that is that we want "x <= leftmost" to
|
|
* be estimated as eq_selec not zero. So, if we're dealing
|
|
* with the first bin (i==1), rescale to make that true while
|
|
* adjusting the rest of that bin linearly.
|
|
*/
|
|
if (i == 1)
|
|
histfrac += eq_selec * (1.0 - binfrac);
|
|
|
|
/*
|
|
* "x <= constval" is good if we want an estimate for "<=" or
|
|
* ">", but if we are estimating for "<" or ">=", we now need
|
|
* to decrease the estimate by eq_selec.
|
|
*/
|
|
if (isgt == iseq)
|
|
histfrac -= eq_selec;
|
|
}
|
|
|
|
/*
|
|
* Now the estimate is finished for "<" and "<=" cases. If we are
|
|
* estimating for ">" or ">=", flip it.
|
|
*/
|
|
hist_selec = isgt ? (1.0 - histfrac) : histfrac;
|
|
|
|
/*
|
|
* The histogram boundaries are only approximate to begin with,
|
|
* and may well be out of date anyway. Therefore, don't believe
|
|
* extremely small or large selectivity estimates --- unless we
|
|
* got actual current endpoint values from the table, in which
|
|
* case just do the usual sanity clamp. Somewhat arbitrarily, we
|
|
* set the cutoff for other cases at a hundredth of the histogram
|
|
* resolution.
|
|
*/
|
|
if (have_end)
|
|
CLAMP_PROBABILITY(hist_selec);
|
|
else
|
|
{
|
|
double cutoff = 0.01 / (double) (sslot.nvalues - 1);
|
|
|
|
if (hist_selec < cutoff)
|
|
hist_selec = cutoff;
|
|
else if (hist_selec > 1.0 - cutoff)
|
|
hist_selec = 1.0 - cutoff;
|
|
}
|
|
}
|
|
|
|
free_attstatsslot(&sslot);
|
|
}
|
|
|
|
return hist_selec;
|
|
}
|
|
|
|
/*
|
|
* Common wrapper function for the selectivity estimators that simply
|
|
* invoke scalarineqsel().
|
|
*/
|
|
static Datum
|
|
scalarineqsel_wrapper(PG_FUNCTION_ARGS, bool isgt, bool iseq)
|
|
{
|
|
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
|
|
Oid operator = PG_GETARG_OID(1);
|
|
List *args = (List *) PG_GETARG_POINTER(2);
|
|
int varRelid = PG_GETARG_INT32(3);
|
|
VariableStatData vardata;
|
|
Node *other;
|
|
bool varonleft;
|
|
Datum constval;
|
|
Oid consttype;
|
|
double selec;
|
|
|
|
/*
|
|
* If expression is not variable op something or something op variable,
|
|
* then punt and return a default estimate.
|
|
*/
|
|
if (!get_restriction_variable(root, args, varRelid,
|
|
&vardata, &other, &varonleft))
|
|
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
|
|
|
|
/*
|
|
* Can't do anything useful if the something is not a constant, either.
|
|
*/
|
|
if (!IsA(other, Const))
|
|
{
|
|
ReleaseVariableStats(vardata);
|
|
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
|
|
}
|
|
|
|
/*
|
|
* If the constant is NULL, assume operator is strict and return zero, ie,
|
|
* operator will never return TRUE.
|
|
*/
|
|
if (((Const *) other)->constisnull)
|
|
{
|
|
ReleaseVariableStats(vardata);
|
|
PG_RETURN_FLOAT8(0.0);
|
|
}
|
|
constval = ((Const *) other)->constvalue;
|
|
consttype = ((Const *) other)->consttype;
|
|
|
|
/*
|
|
* Force the var to be on the left to simplify logic in scalarineqsel.
|
|
*/
|
|
if (!varonleft)
|
|
{
|
|
operator = get_commutator(operator);
|
|
if (!operator)
|
|
{
|
|
/* Use default selectivity (should we raise an error instead?) */
|
|
ReleaseVariableStats(vardata);
|
|
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
|
|
}
|
|
isgt = !isgt;
|
|
}
|
|
|
|
/* The rest of the work is done by scalarineqsel(). */
|
|
selec = scalarineqsel(root, operator, isgt, iseq,
|
|
&vardata, constval, consttype);
|
|
|
|
ReleaseVariableStats(vardata);
|
|
|
|
PG_RETURN_FLOAT8((float8) selec);
|
|
}
|
|
|
|
/*
|
|
* scalarltsel - Selectivity of "<" for scalars.
|
|
*/
|
|
Datum
|
|
scalarltsel(PG_FUNCTION_ARGS)
|
|
{
|
|
return scalarineqsel_wrapper(fcinfo, false, false);
|
|
}
|
|
|
|
/*
|
|
* scalarlesel - Selectivity of "<=" for scalars.
|
|
*/
|
|
Datum
|
|
scalarlesel(PG_FUNCTION_ARGS)
|
|
{
|
|
return scalarineqsel_wrapper(fcinfo, false, true);
|
|
}
|
|
|
|
/*
|
|
* scalargtsel - Selectivity of ">" for scalars.
|
|
*/
|
|
Datum
|
|
scalargtsel(PG_FUNCTION_ARGS)
|
|
{
|
|
return scalarineqsel_wrapper(fcinfo, true, false);
|
|
}
|
|
|
|
/*
|
|
* scalargesel - Selectivity of ">=" for scalars.
|
|
*/
|
|
Datum
|
|
scalargesel(PG_FUNCTION_ARGS)
|
|
{
|
|
return scalarineqsel_wrapper(fcinfo, true, true);
|
|
}
|
|
|
|
/*
|
|
* boolvarsel - Selectivity of Boolean variable.
|
|
*
|
|
* This can actually be called on any boolean-valued expression. If it
|
|
* involves only Vars of the specified relation, and if there are statistics
|
|
* about the Var or expression (the latter is possible if it's indexed) then
|
|
* we'll produce a real estimate; otherwise it's just a default.
|
|
*/
|
|
Selectivity
|
|
boolvarsel(PlannerInfo *root, Node *arg, int varRelid)
|
|
{
|
|
VariableStatData vardata;
|
|
double selec;
|
|
|
|
examine_variable(root, arg, varRelid, &vardata);
|
|
if (HeapTupleIsValid(vardata.statsTuple))
|
|
{
|
|
/*
|
|
* A boolean variable V is equivalent to the clause V = 't', so we
|
|
* compute the selectivity as if that is what we have.
|
|
*/
|
|
selec = var_eq_const(&vardata, BooleanEqualOperator,
|
|
BoolGetDatum(true), false, true, false);
|
|
}
|
|
else
|
|
{
|
|
/* Otherwise, the default estimate is 0.5 */
|
|
selec = 0.5;
|
|
}
|
|
ReleaseVariableStats(vardata);
|
|
return selec;
|
|
}
|
|
|
|
/*
|
|
* booltestsel - Selectivity of BooleanTest Node.
|
|
*/
|
|
Selectivity
|
|
booltestsel(PlannerInfo *root, BoolTestType booltesttype, Node *arg,
|
|
int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
|
|
{
|
|
VariableStatData vardata;
|
|
double selec;
|
|
|
|
examine_variable(root, arg, varRelid, &vardata);
|
|
|
|
if (HeapTupleIsValid(vardata.statsTuple))
|
|
{
|
|
Form_pg_statistic stats;
|
|
double freq_null;
|
|
AttStatsSlot sslot;
|
|
|
|
stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
|
|
freq_null = stats->stanullfrac;
|
|
|
|
if (get_attstatsslot(&sslot, vardata.statsTuple,
|
|
STATISTIC_KIND_MCV, InvalidOid,
|
|
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)
|
|
&& sslot.nnumbers > 0)
|
|
{
|
|
double freq_true;
|
|
double freq_false;
|
|
|
|
/*
|
|
* Get first MCV frequency and derive frequency for true.
|
|
*/
|
|
if (DatumGetBool(sslot.values[0]))
|
|
freq_true = sslot.numbers[0];
|
|
else
|
|
freq_true = 1.0 - sslot.numbers[0] - freq_null;
|
|
|
|
/*
|
|
* Next derive frequency for false. Then use these as appropriate
|
|
* to derive frequency for each case.
|
|
*/
|
|
freq_false = 1.0 - freq_true - freq_null;
|
|
|
|
switch (booltesttype)
|
|
{
|
|
case IS_UNKNOWN:
|
|
/* select only NULL values */
|
|
selec = freq_null;
|
|
break;
|
|
case IS_NOT_UNKNOWN:
|
|
/* select non-NULL values */
|
|
selec = 1.0 - freq_null;
|
|
break;
|
|
case IS_TRUE:
|
|
/* select only TRUE values */
|
|
selec = freq_true;
|
|
break;
|
|
case IS_NOT_TRUE:
|
|
/* select non-TRUE values */
|
|
selec = 1.0 - freq_true;
|
|
break;
|
|
case IS_FALSE:
|
|
/* select only FALSE values */
|
|
selec = freq_false;
|
|
break;
|
|
case IS_NOT_FALSE:
|
|
/* select non-FALSE values */
|
|
selec = 1.0 - freq_false;
|
|
break;
|
|
default:
|
|
elog(ERROR, "unrecognized booltesttype: %d",
|
|
(int) booltesttype);
|
|
selec = 0.0; /* Keep compiler quiet */
|
|
break;
|
|
}
|
|
|
|
free_attstatsslot(&sslot);
|
|
}
|
|
else
|
|
{
|
|
/*
|
|
* No most-common-value info available. Still have null fraction
|
|
* information, so use it for IS [NOT] UNKNOWN. Otherwise adjust
|
|
* for null fraction and assume a 50-50 split of TRUE and FALSE.
|
|
*/
|
|
switch (booltesttype)
|
|
{
|
|
case IS_UNKNOWN:
|
|
/* select only NULL values */
|
|
selec = freq_null;
|
|
break;
|
|
case IS_NOT_UNKNOWN:
|
|
/* select non-NULL values */
|
|
selec = 1.0 - freq_null;
|
|
break;
|
|
case IS_TRUE:
|
|
case IS_FALSE:
|
|
/* Assume we select half of the non-NULL values */
|
|
selec = (1.0 - freq_null) / 2.0;
|
|
break;
|
|
case IS_NOT_TRUE:
|
|
case IS_NOT_FALSE:
|
|
/* Assume we select NULLs plus half of the non-NULLs */
|
|
/* equiv. to freq_null + (1.0 - freq_null) / 2.0 */
|
|
selec = (freq_null + 1.0) / 2.0;
|
|
break;
|
|
default:
|
|
elog(ERROR, "unrecognized booltesttype: %d",
|
|
(int) booltesttype);
|
|
selec = 0.0; /* Keep compiler quiet */
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
/*
|
|
* If we can't get variable statistics for the argument, perhaps
|
|
* clause_selectivity can do something with it. We ignore the
|
|
* possibility of a NULL value when using clause_selectivity, and just
|
|
* assume the value is either TRUE or FALSE.
|
|
*/
|
|
switch (booltesttype)
|
|
{
|
|
case IS_UNKNOWN:
|
|
selec = DEFAULT_UNK_SEL;
|
|
break;
|
|
case IS_NOT_UNKNOWN:
|
|
selec = DEFAULT_NOT_UNK_SEL;
|
|
break;
|
|
case IS_TRUE:
|
|
case IS_NOT_FALSE:
|
|
selec = (double) clause_selectivity(root, arg,
|
|
varRelid,
|
|
jointype, sjinfo);
|
|
break;
|
|
case IS_FALSE:
|
|
case IS_NOT_TRUE:
|
|
selec = 1.0 - (double) clause_selectivity(root, arg,
|
|
varRelid,
|
|
jointype, sjinfo);
|
|
break;
|
|
default:
|
|
elog(ERROR, "unrecognized booltesttype: %d",
|
|
(int) booltesttype);
|
|
selec = 0.0; /* Keep compiler quiet */
|
|
break;
|
|
}
|
|
}
|
|
|
|
ReleaseVariableStats(vardata);
|
|
|
|
/* result should be in range, but make sure... */
|
|
CLAMP_PROBABILITY(selec);
|
|
|
|
return (Selectivity) selec;
|
|
}
|
|
|
|
/*
|
|
* nulltestsel - Selectivity of NullTest Node.
|
|
*/
|
|
Selectivity
|
|
nulltestsel(PlannerInfo *root, NullTestType nulltesttype, Node *arg,
|
|
int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
|
|
{
|
|
VariableStatData vardata;
|
|
double selec;
|
|
|
|
examine_variable(root, arg, varRelid, &vardata);
|
|
|
|
if (HeapTupleIsValid(vardata.statsTuple))
|
|
{
|
|
Form_pg_statistic stats;
|
|
double freq_null;
|
|
|
|
stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
|
|
freq_null = stats->stanullfrac;
|
|
|
|
switch (nulltesttype)
|
|
{
|
|
case IS_NULL:
|
|
|
|
/*
|
|
* Use freq_null directly.
|
|
*/
|
|
selec = freq_null;
|
|
break;
|
|
case IS_NOT_NULL:
|
|
|
|
/*
|
|
* Select not unknown (not null) values. Calculate from
|
|
* freq_null.
|
|
*/
|
|
selec = 1.0 - freq_null;
|
|
break;
|
|
default:
|
|
elog(ERROR, "unrecognized nulltesttype: %d",
|
|
(int) nulltesttype);
|
|
return (Selectivity) 0; /* keep compiler quiet */
|
|
}
|
|
}
|
|
else if (vardata.var && IsA(vardata.var, Var) &&
|
|
((Var *) vardata.var)->varattno < 0)
|
|
{
|
|
/*
|
|
* There are no stats for system columns, but we know they are never
|
|
* NULL.
|
|
*/
|
|
selec = (nulltesttype == IS_NULL) ? 0.0 : 1.0;
|
|
}
|
|
else
|
|
{
|
|
/*
|
|
* No ANALYZE stats available, so make a guess
|
|
*/
|
|
switch (nulltesttype)
|
|
{
|
|
case IS_NULL:
|
|
selec = DEFAULT_UNK_SEL;
|
|
break;
|
|
case IS_NOT_NULL:
|
|
selec = DEFAULT_NOT_UNK_SEL;
|
|
break;
|
|
default:
|
|
elog(ERROR, "unrecognized nulltesttype: %d",
|
|
(int) nulltesttype);
|
|
return (Selectivity) 0; /* keep compiler quiet */
|
|
}
|
|
}
|
|
|
|
ReleaseVariableStats(vardata);
|
|
|
|
/* result should be in range, but make sure... */
|
|
CLAMP_PROBABILITY(selec);
|
|
|
|
return (Selectivity) selec;
|
|
}
|
|
|
|
/*
|
|
* strip_array_coercion - strip binary-compatible relabeling from an array expr
|
|
*
|
|
* For array values, the parser normally generates ArrayCoerceExpr conversions,
|
|
* but it seems possible that RelabelType might show up. Also, the planner
|
|
* is not currently tense about collapsing stacked ArrayCoerceExpr nodes,
|
|
* so we need to be ready to deal with more than one level.
|
|
*/
|
|
static Node *
|
|
strip_array_coercion(Node *node)
|
|
{
|
|
for (;;)
|
|
{
|
|
if (node && IsA(node, ArrayCoerceExpr))
|
|
{
|
|
ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node;
|
|
|
|
/*
|
|
* If the per-element expression is just a RelabelType on top of
|
|
* CaseTestExpr, then we know it's a binary-compatible relabeling.
|
|
*/
|
|
if (IsA(acoerce->elemexpr, RelabelType) &&
|
|
IsA(((RelabelType *) acoerce->elemexpr)->arg, CaseTestExpr))
|
|
node = (Node *) acoerce->arg;
|
|
else
|
|
break;
|
|
}
|
|
else if (node && IsA(node, RelabelType))
|
|
{
|
|
/* We don't really expect this case, but may as well cope */
|
|
node = (Node *) ((RelabelType *) node)->arg;
|
|
}
|
|
else
|
|
break;
|
|
}
|
|
return node;
|
|
}
|
|
|
|
/*
|
|
* scalararraysel - Selectivity of ScalarArrayOpExpr Node.
|
|
*/
|
|
Selectivity
|
|
scalararraysel(PlannerInfo *root,
|
|
ScalarArrayOpExpr *clause,
|
|
bool is_join_clause,
|
|
int varRelid,
|
|
JoinType jointype,
|
|
SpecialJoinInfo *sjinfo)
|
|
{
|
|
Oid operator = clause->opno;
|
|
bool useOr = clause->useOr;
|
|
bool isEquality = false;
|
|
bool isInequality = false;
|
|
Node *leftop;
|
|
Node *rightop;
|
|
Oid nominal_element_type;
|
|
Oid nominal_element_collation;
|
|
TypeCacheEntry *typentry;
|
|
RegProcedure oprsel;
|
|
FmgrInfo oprselproc;
|
|
Selectivity s1;
|
|
Selectivity s1disjoint;
|
|
|
|
/* First, deconstruct the expression */
|
|
Assert(list_length(clause->args) == 2);
|
|
leftop = (Node *) linitial(clause->args);
|
|
rightop = (Node *) lsecond(clause->args);
|
|
|
|
/* aggressively reduce both sides to constants */
|
|
leftop = estimate_expression_value(root, leftop);
|
|
rightop = estimate_expression_value(root, rightop);
|
|
|
|
/* get nominal (after relabeling) element type of rightop */
|
|
nominal_element_type = get_base_element_type(exprType(rightop));
|
|
if (!OidIsValid(nominal_element_type))
|
|
return (Selectivity) 0.5; /* probably shouldn't happen */
|
|
/* get nominal collation, too, for generating constants */
|
|
nominal_element_collation = exprCollation(rightop);
|
|
|
|
/* look through any binary-compatible relabeling of rightop */
|
|
rightop = strip_array_coercion(rightop);
|
|
|
|
/*
|
|
* Detect whether the operator is the default equality or inequality
|
|
* operator of the array element type.
|
|
*/
|
|
typentry = lookup_type_cache(nominal_element_type, TYPECACHE_EQ_OPR);
|
|
if (OidIsValid(typentry->eq_opr))
|
|
{
|
|
if (operator == typentry->eq_opr)
|
|
isEquality = true;
|
|
else if (get_negator(operator) == typentry->eq_opr)
|
|
isInequality = true;
|
|
}
|
|
|
|
/*
|
|
* If it is equality or inequality, we might be able to estimate this as a
|
|
* form of array containment; for instance "const = ANY(column)" can be
|
|
* treated as "ARRAY[const] <@ column". scalararraysel_containment tries
|
|
* that, and returns the selectivity estimate if successful, or -1 if not.
|
|
*/
|
|
if ((isEquality || isInequality) && !is_join_clause)
|
|
{
|
|
s1 = scalararraysel_containment(root, leftop, rightop,
|
|
nominal_element_type,
|
|
isEquality, useOr, varRelid);
|
|
if (s1 >= 0.0)
|
|
return s1;
|
|
}
|
|
|
|
/*
|
|
* Look up the underlying operator's selectivity estimator. Punt if it
|
|
* hasn't got one.
|
|
*/
|
|
if (is_join_clause)
|
|
oprsel = get_oprjoin(operator);
|
|
else
|
|
oprsel = get_oprrest(operator);
|
|
if (!oprsel)
|
|
return (Selectivity) 0.5;
|
|
fmgr_info(oprsel, &oprselproc);
|
|
|
|
/*
|
|
* In the array-containment check above, we must only believe that an
|
|
* operator is equality or inequality if it is the default btree equality
|
|
* operator (or its negator) for the element type, since those are the
|
|
* operators that array containment will use. But in what follows, we can
|
|
* be a little laxer, and also believe that any operators using eqsel() or
|
|
* neqsel() as selectivity estimator act like equality or inequality.
|
|
*/
|
|
if (oprsel == F_EQSEL || oprsel == F_EQJOINSEL)
|
|
isEquality = true;
|
|
else if (oprsel == F_NEQSEL || oprsel == F_NEQJOINSEL)
|
|
isInequality = true;
|
|
|
|
/*
|
|
* We consider three cases:
|
|
*
|
|
* 1. rightop is an Array constant: deconstruct the array, apply the
|
|
* operator's selectivity function for each array element, and merge the
|
|
* results in the same way that clausesel.c does for AND/OR combinations.
|
|
*
|
|
* 2. rightop is an ARRAY[] construct: apply the operator's selectivity
|
|
* function for each element of the ARRAY[] construct, and merge.
|
|
*
|
|
* 3. otherwise, make a guess ...
|
|
*/
|
|
if (rightop && IsA(rightop, Const))
|
|
{
|
|
Datum arraydatum = ((Const *) rightop)->constvalue;
|
|
bool arrayisnull = ((Const *) rightop)->constisnull;
|
|
ArrayType *arrayval;
|
|
int16 elmlen;
|
|
bool elmbyval;
|
|
char elmalign;
|
|
int num_elems;
|
|
Datum *elem_values;
|
|
bool *elem_nulls;
|
|
int i;
|
|
|
|
if (arrayisnull) /* qual can't succeed if null array */
|
|
return (Selectivity) 0.0;
|
|
arrayval = DatumGetArrayTypeP(arraydatum);
|
|
get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
|
|
&elmlen, &elmbyval, &elmalign);
|
|
deconstruct_array(arrayval,
|
|
ARR_ELEMTYPE(arrayval),
|
|
elmlen, elmbyval, elmalign,
|
|
&elem_values, &elem_nulls, &num_elems);
|
|
|
|
/*
|
|
* For generic operators, we assume the probability of success is
|
|
* independent for each array element. But for "= ANY" or "<> ALL",
|
|
* if the array elements are distinct (which'd typically be the case)
|
|
* then the probabilities are disjoint, and we should just sum them.
|
|
*
|
|
* If we were being really tense we would try to confirm that the
|
|
* elements are all distinct, but that would be expensive and it
|
|
* doesn't seem to be worth the cycles; it would amount to penalizing
|
|
* well-written queries in favor of poorly-written ones. However, we
|
|
* do protect ourselves a little bit by checking whether the
|
|
* disjointness assumption leads to an impossible (out of range)
|
|
* probability; if so, we fall back to the normal calculation.
|
|
*/
|
|
s1 = s1disjoint = (useOr ? 0.0 : 1.0);
|
|
|
|
for (i = 0; i < num_elems; i++)
|
|
{
|
|
List *args;
|
|
Selectivity s2;
|
|
|
|
args = list_make2(leftop,
|
|
makeConst(nominal_element_type,
|
|
-1,
|
|
nominal_element_collation,
|
|
elmlen,
|
|
elem_values[i],
|
|
elem_nulls[i],
|
|
elmbyval));
|
|
if (is_join_clause)
|
|
s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
|
|
clause->inputcollid,
|
|
PointerGetDatum(root),
|
|
ObjectIdGetDatum(operator),
|
|
PointerGetDatum(args),
|
|
Int16GetDatum(jointype),
|
|
PointerGetDatum(sjinfo)));
|
|
else
|
|
s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
|
|
clause->inputcollid,
|
|
PointerGetDatum(root),
|
|
ObjectIdGetDatum(operator),
|
|
PointerGetDatum(args),
|
|
Int32GetDatum(varRelid)));
|
|
|
|
if (useOr)
|
|
{
|
|
s1 = s1 + s2 - s1 * s2;
|
|
if (isEquality)
|
|
s1disjoint += s2;
|
|
}
|
|
else
|
|
{
|
|
s1 = s1 * s2;
|
|
if (isInequality)
|
|
s1disjoint += s2 - 1.0;
|
|
}
|
|
}
|
|
|
|
/* accept disjoint-probability estimate if in range */
|
|
if ((useOr ? isEquality : isInequality) &&
|
|
s1disjoint >= 0.0 && s1disjoint <= 1.0)
|
|
s1 = s1disjoint;
|
|
}
|
|
else if (rightop && IsA(rightop, ArrayExpr) &&
|
|
!((ArrayExpr *) rightop)->multidims)
|
|
{
|
|
ArrayExpr *arrayexpr = (ArrayExpr *) rightop;
|
|
int16 elmlen;
|
|
bool elmbyval;
|
|
ListCell *l;
|
|
|
|
get_typlenbyval(arrayexpr->element_typeid,
|
|
&elmlen, &elmbyval);
|
|
|
|
/*
|
|
* We use the assumption of disjoint probabilities here too, although
|
|
* the odds of equal array elements are rather higher if the elements
|
|
* are not all constants (which they won't be, else constant folding
|
|
* would have reduced the ArrayExpr to a Const). In this path it's
|
|
* critical to have the sanity check on the s1disjoint estimate.
|
|
*/
|
|
s1 = s1disjoint = (useOr ? 0.0 : 1.0);
|
|
|
|
foreach(l, arrayexpr->elements)
|
|
{
|
|
Node *elem = (Node *) lfirst(l);
|
|
List *args;
|
|
Selectivity s2;
|
|
|
|
/*
|
|
* Theoretically, if elem isn't of nominal_element_type we should
|
|
* insert a RelabelType, but it seems unlikely that any operator
|
|
* estimation function would really care ...
|
|
*/
|
|
args = list_make2(leftop, elem);
|
|
if (is_join_clause)
|
|
s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
|
|
clause->inputcollid,
|
|
PointerGetDatum(root),
|
|
ObjectIdGetDatum(operator),
|
|
PointerGetDatum(args),
|
|
Int16GetDatum(jointype),
|
|
PointerGetDatum(sjinfo)));
|
|
else
|
|
s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
|
|
clause->inputcollid,
|
|
PointerGetDatum(root),
|
|
ObjectIdGetDatum(operator),
|
|
PointerGetDatum(args),
|
|
Int32GetDatum(varRelid)));
|
|
|
|
if (useOr)
|
|
{
|
|
s1 = s1 + s2 - s1 * s2;
|
|
if (isEquality)
|
|
s1disjoint += s2;
|
|
}
|
|
else
|
|
{
|
|
s1 = s1 * s2;
|
|
if (isInequality)
|
|
s1disjoint += s2 - 1.0;
|
|
}
|
|
}
|
|
|
|
/* accept disjoint-probability estimate if in range */
|
|
if ((useOr ? isEquality : isInequality) &&
|
|
s1disjoint >= 0.0 && s1disjoint <= 1.0)
|
|
s1 = s1disjoint;
|
|
}
|
|
else
|
|
{
|
|
CaseTestExpr *dummyexpr;
|
|
List *args;
|
|
Selectivity s2;
|
|
int i;
|
|
|
|
/*
|
|
* We need a dummy rightop to pass to the operator selectivity
|
|
* routine. It can be pretty much anything that doesn't look like a
|
|
* constant; CaseTestExpr is a convenient choice.
|
|
*/
|
|
dummyexpr = makeNode(CaseTestExpr);
|
|
dummyexpr->typeId = nominal_element_type;
|
|
dummyexpr->typeMod = -1;
|
|
dummyexpr->collation = clause->inputcollid;
|
|
args = list_make2(leftop, dummyexpr);
|
|
if (is_join_clause)
|
|
s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
|
|
clause->inputcollid,
|
|
PointerGetDatum(root),
|
|
ObjectIdGetDatum(operator),
|
|
PointerGetDatum(args),
|
|
Int16GetDatum(jointype),
|
|
PointerGetDatum(sjinfo)));
|
|
else
|
|
s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
|
|
clause->inputcollid,
|
|
PointerGetDatum(root),
|
|
ObjectIdGetDatum(operator),
|
|
PointerGetDatum(args),
|
|
Int32GetDatum(varRelid)));
|
|
s1 = useOr ? 0.0 : 1.0;
|
|
|
|
/*
|
|
* Arbitrarily assume 10 elements in the eventual array value (see
|
|
* also estimate_array_length). We don't risk an assumption of
|
|
* disjoint probabilities here.
|
|
*/
|
|
for (i = 0; i < 10; i++)
|
|
{
|
|
if (useOr)
|
|
s1 = s1 + s2 - s1 * s2;
|
|
else
|
|
s1 = s1 * s2;
|
|
}
|
|
}
|
|
|
|
/* result should be in range, but make sure... */
|
|
CLAMP_PROBABILITY(s1);
|
|
|
|
return s1;
|
|
}
|
|
|
|
/*
|
|
* Estimate number of elements in the array yielded by an expression.
|
|
*
|
|
* It's important that this agree with scalararraysel.
|
|
*/
|
|
int
|
|
estimate_array_length(Node *arrayexpr)
|
|
{
|
|
/* look through any binary-compatible relabeling of arrayexpr */
|
|
arrayexpr = strip_array_coercion(arrayexpr);
|
|
|
|
if (arrayexpr && IsA(arrayexpr, Const))
|
|
{
|
|
Datum arraydatum = ((Const *) arrayexpr)->constvalue;
|
|
bool arrayisnull = ((Const *) arrayexpr)->constisnull;
|
|
ArrayType *arrayval;
|
|
|
|
if (arrayisnull)
|
|
return 0;
|
|
arrayval = DatumGetArrayTypeP(arraydatum);
|
|
return ArrayGetNItems(ARR_NDIM(arrayval), ARR_DIMS(arrayval));
|
|
}
|
|
else if (arrayexpr && IsA(arrayexpr, ArrayExpr) &&
|
|
!((ArrayExpr *) arrayexpr)->multidims)
|
|
{
|
|
return list_length(((ArrayExpr *) arrayexpr)->elements);
|
|
}
|
|
else
|
|
{
|
|
/* default guess --- see also scalararraysel */
|
|
return 10;
|
|
}
|
|
}
|
|
|
|
/*
|
|
* rowcomparesel - Selectivity of RowCompareExpr Node.
|
|
*
|
|
* We estimate RowCompare selectivity by considering just the first (high
|
|
* order) columns, which makes it equivalent to an ordinary OpExpr. While
|
|
* this estimate could be refined by considering additional columns, it
|
|
* seems unlikely that we could do a lot better without multi-column
|
|
* statistics.
|
|
*/
|
|
Selectivity
|
|
rowcomparesel(PlannerInfo *root,
|
|
RowCompareExpr *clause,
|
|
int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
|
|
{
|
|
Selectivity s1;
|
|
Oid opno = linitial_oid(clause->opnos);
|
|
Oid inputcollid = linitial_oid(clause->inputcollids);
|
|
List *opargs;
|
|
bool is_join_clause;
|
|
|
|
/* Build equivalent arg list for single operator */
|
|
opargs = list_make2(linitial(clause->largs), linitial(clause->rargs));
|
|
|
|
/*
|
|
* Decide if it's a join clause. This should match clausesel.c's
|
|
* treat_as_join_clause(), except that we intentionally consider only the
|
|
* leading columns and not the rest of the clause.
|
|
*/
|
|
if (varRelid != 0)
|
|
{
|
|
/*
|
|
* Caller is forcing restriction mode (eg, because we are examining an
|
|
* inner indexscan qual).
|
|
*/
|
|
is_join_clause = false;
|
|
}
|
|
else if (sjinfo == NULL)
|
|
{
|
|
/*
|
|
* It must be a restriction clause, since it's being evaluated at a
|
|
* scan node.
|
|
*/
|
|
is_join_clause = false;
|
|
}
|
|
else
|
|
{
|
|
/*
|
|
* Otherwise, it's a join if there's more than one relation used.
|
|
*/
|
|
is_join_clause = (NumRelids((Node *) opargs) > 1);
|
|
}
|
|
|
|
if (is_join_clause)
|
|
{
|
|
/* Estimate selectivity for a join clause. */
|
|
s1 = join_selectivity(root, opno,
|
|
opargs,
|
|
inputcollid,
|
|
jointype,
|
|
sjinfo);
|
|
}
|
|
else
|
|
{
|
|
/* Estimate selectivity for a restriction clause. */
|
|
s1 = restriction_selectivity(root, opno,
|
|
opargs,
|
|
inputcollid,
|
|
varRelid);
|
|
}
|
|
|
|
return s1;
|
|
}
|
|
|
|
/*
|
|
* eqjoinsel - Join selectivity of "="
|
|
*/
|
|
Datum
|
|
eqjoinsel(PG_FUNCTION_ARGS)
|
|
{
|
|
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
|
|
Oid operator = PG_GETARG_OID(1);
|
|
List *args = (List *) PG_GETARG_POINTER(2);
|
|
|
|
#ifdef NOT_USED
|
|
JoinType jointype = (JoinType) PG_GETARG_INT16(3);
|
|
#endif
|
|
SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
|
|
double selec;
|
|
double selec_inner;
|
|
VariableStatData vardata1;
|
|
VariableStatData vardata2;
|
|
double nd1;
|
|
double nd2;
|
|
bool isdefault1;
|
|
bool isdefault2;
|
|
Oid opfuncoid;
|
|
AttStatsSlot sslot1;
|
|
AttStatsSlot sslot2;
|
|
Form_pg_statistic stats1 = NULL;
|
|
Form_pg_statistic stats2 = NULL;
|
|
bool have_mcvs1 = false;
|
|
bool have_mcvs2 = false;
|
|
bool join_is_reversed;
|
|
RelOptInfo *inner_rel;
|
|
|
|
get_join_variables(root, args, sjinfo,
|
|
&vardata1, &vardata2, &join_is_reversed);
|
|
|
|
nd1 = get_variable_numdistinct(&vardata1, &isdefault1);
|
|
nd2 = get_variable_numdistinct(&vardata2, &isdefault2);
|
|
|
|
opfuncoid = get_opcode(operator);
|
|
|
|
memset(&sslot1, 0, sizeof(sslot1));
|
|
memset(&sslot2, 0, sizeof(sslot2));
|
|
|
|
if (HeapTupleIsValid(vardata1.statsTuple))
|
|
{
|
|
/* note we allow use of nullfrac regardless of security check */
|
|
stats1 = (Form_pg_statistic) GETSTRUCT(vardata1.statsTuple);
|
|
if (statistic_proc_security_check(&vardata1, opfuncoid))
|
|
have_mcvs1 = get_attstatsslot(&sslot1, vardata1.statsTuple,
|
|
STATISTIC_KIND_MCV, InvalidOid,
|
|
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
|
|
}
|
|
|
|
if (HeapTupleIsValid(vardata2.statsTuple))
|
|
{
|
|
/* note we allow use of nullfrac regardless of security check */
|
|
stats2 = (Form_pg_statistic) GETSTRUCT(vardata2.statsTuple);
|
|
if (statistic_proc_security_check(&vardata2, opfuncoid))
|
|
have_mcvs2 = get_attstatsslot(&sslot2, vardata2.statsTuple,
|
|
STATISTIC_KIND_MCV, InvalidOid,
|
|
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
|
|
}
|
|
|
|
/* We need to compute the inner-join selectivity in all cases */
|
|
selec_inner = eqjoinsel_inner(opfuncoid,
|
|
&vardata1, &vardata2,
|
|
nd1, nd2,
|
|
isdefault1, isdefault2,
|
|
&sslot1, &sslot2,
|
|
stats1, stats2,
|
|
have_mcvs1, have_mcvs2);
|
|
|
|
switch (sjinfo->jointype)
|
|
{
|
|
case JOIN_INNER:
|
|
case JOIN_LEFT:
|
|
case JOIN_FULL:
|
|
selec = selec_inner;
|
|
break;
|
|
case JOIN_SEMI:
|
|
case JOIN_ANTI:
|
|
|
|
/*
|
|
* Look up the join's inner relation. min_righthand is sufficient
|
|
* information because neither SEMI nor ANTI joins permit any
|
|
* reassociation into or out of their RHS, so the righthand will
|
|
* always be exactly that set of rels.
|
|
*/
|
|
inner_rel = find_join_input_rel(root, sjinfo->min_righthand);
|
|
|
|
if (!join_is_reversed)
|
|
selec = eqjoinsel_semi(opfuncoid,
|
|
&vardata1, &vardata2,
|
|
nd1, nd2,
|
|
isdefault1, isdefault2,
|
|
&sslot1, &sslot2,
|
|
stats1, stats2,
|
|
have_mcvs1, have_mcvs2,
|
|
inner_rel);
|
|
else
|
|
{
|
|
Oid commop = get_commutator(operator);
|
|
Oid commopfuncoid = OidIsValid(commop) ? get_opcode(commop) : InvalidOid;
|
|
|
|
selec = eqjoinsel_semi(commopfuncoid,
|
|
&vardata2, &vardata1,
|
|
nd2, nd1,
|
|
isdefault2, isdefault1,
|
|
&sslot2, &sslot1,
|
|
stats2, stats1,
|
|
have_mcvs2, have_mcvs1,
|
|
inner_rel);
|
|
}
|
|
|
|
/*
|
|
* We should never estimate the output of a semijoin to be more
|
|
* rows than we estimate for an inner join with the same input
|
|
* rels and join condition; it's obviously impossible for that to
|
|
* happen. The former estimate is N1 * Ssemi while the latter is
|
|
* N1 * N2 * Sinner, so we may clamp Ssemi <= N2 * Sinner. Doing
|
|
* this is worthwhile because of the shakier estimation rules we
|
|
* use in eqjoinsel_semi, particularly in cases where it has to
|
|
* punt entirely.
|
|
*/
|
|
selec = Min(selec, inner_rel->rows * selec_inner);
|
|
break;
|
|
default:
|
|
/* other values not expected here */
|
|
elog(ERROR, "unrecognized join type: %d",
|
|
(int) sjinfo->jointype);
|
|
selec = 0; /* keep compiler quiet */
|
|
break;
|
|
}
|
|
|
|
free_attstatsslot(&sslot1);
|
|
free_attstatsslot(&sslot2);
|
|
|
|
ReleaseVariableStats(vardata1);
|
|
ReleaseVariableStats(vardata2);
|
|
|
|
CLAMP_PROBABILITY(selec);
|
|
|
|
PG_RETURN_FLOAT8((float8) selec);
|
|
}
|
|
|
|
/*
|
|
* eqjoinsel_inner --- eqjoinsel for normal inner join
|
|
*
|
|
* We also use this for LEFT/FULL outer joins; it's not presently clear
|
|
* that it's worth trying to distinguish them here.
|
|
*/
|
|
static double
|
|
eqjoinsel_inner(Oid opfuncoid,
|
|
VariableStatData *vardata1, VariableStatData *vardata2,
|
|
double nd1, double nd2,
|
|
bool isdefault1, bool isdefault2,
|
|
AttStatsSlot *sslot1, AttStatsSlot *sslot2,
|
|
Form_pg_statistic stats1, Form_pg_statistic stats2,
|
|
bool have_mcvs1, bool have_mcvs2)
|
|
{
|
|
double selec;
|
|
|
|
if (have_mcvs1 && have_mcvs2)
|
|
{
|
|
/*
|
|
* We have most-common-value lists for both relations. Run through
|
|
* the lists to see which MCVs actually join to each other with the
|
|
* given operator. This allows us to determine the exact join
|
|
* selectivity for the portion of the relations represented by the MCV
|
|
* lists. We still have to estimate for the remaining population, but
|
|
* in a skewed distribution this gives us a big leg up in accuracy.
|
|
* For motivation see the analysis in Y. Ioannidis and S.
|
|
* Christodoulakis, "On the propagation of errors in the size of join
|
|
* results", Technical Report 1018, Computer Science Dept., University
|
|
* of Wisconsin, Madison, March 1991 (available from ftp.cs.wisc.edu).
|
|
*/
|
|
FmgrInfo eqproc;
|
|
bool *hasmatch1;
|
|
bool *hasmatch2;
|
|
double nullfrac1 = stats1->stanullfrac;
|
|
double nullfrac2 = stats2->stanullfrac;
|
|
double matchprodfreq,
|
|
matchfreq1,
|
|
matchfreq2,
|
|
unmatchfreq1,
|
|
unmatchfreq2,
|
|
otherfreq1,
|
|
otherfreq2,
|
|
totalsel1,
|
|
totalsel2;
|
|
int i,
|
|
nmatches;
|
|
|
|
fmgr_info(opfuncoid, &eqproc);
|
|
hasmatch1 = (bool *) palloc0(sslot1->nvalues * sizeof(bool));
|
|
hasmatch2 = (bool *) palloc0(sslot2->nvalues * sizeof(bool));
|
|
|
|
/*
|
|
* Note we assume that each MCV will match at most one member of the
|
|
* other MCV list. If the operator isn't really equality, there could
|
|
* be multiple matches --- but we don't look for them, both for speed
|
|
* and because the math wouldn't add up...
|
|
*/
|
|
matchprodfreq = 0.0;
|
|
nmatches = 0;
|
|
for (i = 0; i < sslot1->nvalues; i++)
|
|
{
|
|
int j;
|
|
|
|
for (j = 0; j < sslot2->nvalues; j++)
|
|
{
|
|
if (hasmatch2[j])
|
|
continue;
|
|
if (DatumGetBool(FunctionCall2Coll(&eqproc,
|
|
sslot1->stacoll,
|
|
sslot1->values[i],
|
|
sslot2->values[j])))
|
|
{
|
|
hasmatch1[i] = hasmatch2[j] = true;
|
|
matchprodfreq += sslot1->numbers[i] * sslot2->numbers[j];
|
|
nmatches++;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
CLAMP_PROBABILITY(matchprodfreq);
|
|
/* Sum up frequencies of matched and unmatched MCVs */
|
|
matchfreq1 = unmatchfreq1 = 0.0;
|
|
for (i = 0; i < sslot1->nvalues; i++)
|
|
{
|
|
if (hasmatch1[i])
|
|
matchfreq1 += sslot1->numbers[i];
|
|
else
|
|
unmatchfreq1 += sslot1->numbers[i];
|
|
}
|
|
CLAMP_PROBABILITY(matchfreq1);
|
|
CLAMP_PROBABILITY(unmatchfreq1);
|
|
matchfreq2 = unmatchfreq2 = 0.0;
|
|
for (i = 0; i < sslot2->nvalues; i++)
|
|
{
|
|
if (hasmatch2[i])
|
|
matchfreq2 += sslot2->numbers[i];
|
|
else
|
|
unmatchfreq2 += sslot2->numbers[i];
|
|
}
|
|
CLAMP_PROBABILITY(matchfreq2);
|
|
CLAMP_PROBABILITY(unmatchfreq2);
|
|
pfree(hasmatch1);
|
|
pfree(hasmatch2);
|
|
|
|
/*
|
|
* Compute total frequency of non-null values that are not in the MCV
|
|
* lists.
|
|
*/
|
|
otherfreq1 = 1.0 - nullfrac1 - matchfreq1 - unmatchfreq1;
|
|
otherfreq2 = 1.0 - nullfrac2 - matchfreq2 - unmatchfreq2;
|
|
CLAMP_PROBABILITY(otherfreq1);
|
|
CLAMP_PROBABILITY(otherfreq2);
|
|
|
|
/*
|
|
* We can estimate the total selectivity from the point of view of
|
|
* relation 1 as: the known selectivity for matched MCVs, plus
|
|
* unmatched MCVs that are assumed to match against random members of
|
|
* relation 2's non-MCV population, plus non-MCV values that are
|
|
* assumed to match against random members of relation 2's unmatched
|
|
* MCVs plus non-MCV values.
|
|
*/
|
|
totalsel1 = matchprodfreq;
|
|
if (nd2 > sslot2->nvalues)
|
|
totalsel1 += unmatchfreq1 * otherfreq2 / (nd2 - sslot2->nvalues);
|
|
if (nd2 > nmatches)
|
|
totalsel1 += otherfreq1 * (otherfreq2 + unmatchfreq2) /
|
|
(nd2 - nmatches);
|
|
/* Same estimate from the point of view of relation 2. */
|
|
totalsel2 = matchprodfreq;
|
|
if (nd1 > sslot1->nvalues)
|
|
totalsel2 += unmatchfreq2 * otherfreq1 / (nd1 - sslot1->nvalues);
|
|
if (nd1 > nmatches)
|
|
totalsel2 += otherfreq2 * (otherfreq1 + unmatchfreq1) /
|
|
(nd1 - nmatches);
|
|
|
|
/*
|
|
* Use the smaller of the two estimates. This can be justified in
|
|
* essentially the same terms as given below for the no-stats case: to
|
|
* a first approximation, we are estimating from the point of view of
|
|
* the relation with smaller nd.
|
|
*/
|
|
selec = (totalsel1 < totalsel2) ? totalsel1 : totalsel2;
|
|
}
|
|
else
|
|
{
|
|
/*
|
|
* We do not have MCV lists for both sides. Estimate the join
|
|
* selectivity as MIN(1/nd1,1/nd2)*(1-nullfrac1)*(1-nullfrac2). This
|
|
* is plausible if we assume that the join operator is strict and the
|
|
* non-null values are about equally distributed: a given non-null
|
|
* tuple of rel1 will join to either zero or N2*(1-nullfrac2)/nd2 rows
|
|
* of rel2, so total join rows are at most
|
|
* N1*(1-nullfrac1)*N2*(1-nullfrac2)/nd2 giving a join selectivity of
|
|
* not more than (1-nullfrac1)*(1-nullfrac2)/nd2. By the same logic it
|
|
* is not more than (1-nullfrac1)*(1-nullfrac2)/nd1, so the expression
|
|
* with MIN() is an upper bound. Using the MIN() means we estimate
|
|
* from the point of view of the relation with smaller nd (since the
|
|
* larger nd is determining the MIN). It is reasonable to assume that
|
|
* most tuples in this rel will have join partners, so the bound is
|
|
* probably reasonably tight and should be taken as-is.
|
|
*
|
|
* XXX Can we be smarter if we have an MCV list for just one side? It
|
|
* seems that if we assume equal distribution for the other side, we
|
|
* end up with the same answer anyway.
|
|
*/
|
|
double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
|
|
double nullfrac2 = stats2 ? stats2->stanullfrac : 0.0;
|
|
|
|
selec = (1.0 - nullfrac1) * (1.0 - nullfrac2);
|
|
if (nd1 > nd2)
|
|
selec /= nd1;
|
|
else
|
|
selec /= nd2;
|
|
}
|
|
|
|
return selec;
|
|
}
|
|
|
|
/*
|
|
* eqjoinsel_semi --- eqjoinsel for semi join
|
|
*
|
|
* (Also used for anti join, which we are supposed to estimate the same way.)
|
|
* Caller has ensured that vardata1 is the LHS variable.
|
|
* Unlike eqjoinsel_inner, we have to cope with opfuncoid being InvalidOid.
|
|
*/
|
|
static double
|
|
eqjoinsel_semi(Oid opfuncoid,
|
|
VariableStatData *vardata1, VariableStatData *vardata2,
|
|
double nd1, double nd2,
|
|
bool isdefault1, bool isdefault2,
|
|
AttStatsSlot *sslot1, AttStatsSlot *sslot2,
|
|
Form_pg_statistic stats1, Form_pg_statistic stats2,
|
|
bool have_mcvs1, bool have_mcvs2,
|
|
RelOptInfo *inner_rel)
|
|
{
|
|
double selec;
|
|
|
|
/*
|
|
* We clamp nd2 to be not more than what we estimate the inner relation's
|
|
* size to be. This is intuitively somewhat reasonable since obviously
|
|
* there can't be more than that many distinct values coming from the
|
|
* inner rel. The reason for the asymmetry (ie, that we don't clamp nd1
|
|
* likewise) is that this is the only pathway by which restriction clauses
|
|
* applied to the inner rel will affect the join result size estimate,
|
|
* since set_joinrel_size_estimates will multiply SEMI/ANTI selectivity by
|
|
* only the outer rel's size. If we clamped nd1 we'd be double-counting
|
|
* the selectivity of outer-rel restrictions.
|
|
*
|
|
* We can apply this clamping both with respect to the base relation from
|
|
* which the join variable comes (if there is just one), and to the
|
|
* immediate inner input relation of the current join.
|
|
*
|
|
* If we clamp, we can treat nd2 as being a non-default estimate; it's not
|
|
* great, maybe, but it didn't come out of nowhere either. This is most
|
|
* helpful when the inner relation is empty and consequently has no stats.
|
|
*/
|
|
if (vardata2->rel)
|
|
{
|
|
if (nd2 >= vardata2->rel->rows)
|
|
{
|
|
nd2 = vardata2->rel->rows;
|
|
isdefault2 = false;
|
|
}
|
|
}
|
|
if (nd2 >= inner_rel->rows)
|
|
{
|
|
nd2 = inner_rel->rows;
|
|
isdefault2 = false;
|
|
}
|
|
|
|
if (have_mcvs1 && have_mcvs2 && OidIsValid(opfuncoid))
|
|
{
|
|
/*
|
|
* We have most-common-value lists for both relations. Run through
|
|
* the lists to see which MCVs actually join to each other with the
|
|
* given operator. This allows us to determine the exact join
|
|
* selectivity for the portion of the relations represented by the MCV
|
|
* lists. We still have to estimate for the remaining population, but
|
|
* in a skewed distribution this gives us a big leg up in accuracy.
|
|
*/
|
|
FmgrInfo eqproc;
|
|
bool *hasmatch1;
|
|
bool *hasmatch2;
|
|
double nullfrac1 = stats1->stanullfrac;
|
|
double matchfreq1,
|
|
uncertainfrac,
|
|
uncertain;
|
|
int i,
|
|
nmatches,
|
|
clamped_nvalues2;
|
|
|
|
/*
|
|
* The clamping above could have resulted in nd2 being less than
|
|
* sslot2->nvalues; in which case, we assume that precisely the nd2
|
|
* most common values in the relation will appear in the join input,
|
|
* and so compare to only the first nd2 members of the MCV list. Of
|
|
* course this is frequently wrong, but it's the best bet we can make.
|
|
*/
|
|
clamped_nvalues2 = Min(sslot2->nvalues, nd2);
|
|
|
|
fmgr_info(opfuncoid, &eqproc);
|
|
hasmatch1 = (bool *) palloc0(sslot1->nvalues * sizeof(bool));
|
|
hasmatch2 = (bool *) palloc0(clamped_nvalues2 * sizeof(bool));
|
|
|
|
/*
|
|
* Note we assume that each MCV will match at most one member of the
|
|
* other MCV list. If the operator isn't really equality, there could
|
|
* be multiple matches --- but we don't look for them, both for speed
|
|
* and because the math wouldn't add up...
|
|
*/
|
|
nmatches = 0;
|
|
for (i = 0; i < sslot1->nvalues; i++)
|
|
{
|
|
int j;
|
|
|
|
for (j = 0; j < clamped_nvalues2; j++)
|
|
{
|
|
if (hasmatch2[j])
|
|
continue;
|
|
if (DatumGetBool(FunctionCall2Coll(&eqproc,
|
|
sslot1->stacoll,
|
|
sslot1->values[i],
|
|
sslot2->values[j])))
|
|
{
|
|
hasmatch1[i] = hasmatch2[j] = true;
|
|
nmatches++;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
/* Sum up frequencies of matched MCVs */
|
|
matchfreq1 = 0.0;
|
|
for (i = 0; i < sslot1->nvalues; i++)
|
|
{
|
|
if (hasmatch1[i])
|
|
matchfreq1 += sslot1->numbers[i];
|
|
}
|
|
CLAMP_PROBABILITY(matchfreq1);
|
|
pfree(hasmatch1);
|
|
pfree(hasmatch2);
|
|
|
|
/*
|
|
* Now we need to estimate the fraction of relation 1 that has at
|
|
* least one join partner. We know for certain that the matched MCVs
|
|
* do, so that gives us a lower bound, but we're really in the dark
|
|
* about everything else. Our crude approach is: if nd1 <= nd2 then
|
|
* assume all non-null rel1 rows have join partners, else assume for
|
|
* the uncertain rows that a fraction nd2/nd1 have join partners. We
|
|
* can discount the known-matched MCVs from the distinct-values counts
|
|
* before doing the division.
|
|
*
|
|
* Crude as the above is, it's completely useless if we don't have
|
|
* reliable ndistinct values for both sides. Hence, if either nd1 or
|
|
* nd2 is default, punt and assume half of the uncertain rows have
|
|
* join partners.
|
|
*/
|
|
if (!isdefault1 && !isdefault2)
|
|
{
|
|
nd1 -= nmatches;
|
|
nd2 -= nmatches;
|
|
if (nd1 <= nd2 || nd2 < 0)
|
|
uncertainfrac = 1.0;
|
|
else
|
|
uncertainfrac = nd2 / nd1;
|
|
}
|
|
else
|
|
uncertainfrac = 0.5;
|
|
uncertain = 1.0 - matchfreq1 - nullfrac1;
|
|
CLAMP_PROBABILITY(uncertain);
|
|
selec = matchfreq1 + uncertainfrac * uncertain;
|
|
}
|
|
else
|
|
{
|
|
/*
|
|
* Without MCV lists for both sides, we can only use the heuristic
|
|
* about nd1 vs nd2.
|
|
*/
|
|
double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
|
|
|
|
if (!isdefault1 && !isdefault2)
|
|
{
|
|
if (nd1 <= nd2 || nd2 < 0)
|
|
selec = 1.0 - nullfrac1;
|
|
else
|
|
selec = (nd2 / nd1) * (1.0 - nullfrac1);
|
|
}
|
|
else
|
|
selec = 0.5 * (1.0 - nullfrac1);
|
|
}
|
|
|
|
return selec;
|
|
}
|
|
|
|
/*
|
|
* neqjoinsel - Join selectivity of "!="
|
|
*/
|
|
Datum
|
|
neqjoinsel(PG_FUNCTION_ARGS)
|
|
{
|
|
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
|
|
Oid operator = PG_GETARG_OID(1);
|
|
List *args = (List *) PG_GETARG_POINTER(2);
|
|
JoinType jointype = (JoinType) PG_GETARG_INT16(3);
|
|
SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
|
|
float8 result;
|
|
|
|
if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
|
|
{
|
|
/*
|
|
* For semi-joins, if there is more than one distinct value in the RHS
|
|
* relation then every non-null LHS row must find a row to join since
|
|
* it can only be equal to one of them. We'll assume that there is
|
|
* always more than one distinct RHS value for the sake of stability,
|
|
* though in theory we could have special cases for empty RHS
|
|
* (selectivity = 0) and single-distinct-value RHS (selectivity =
|
|
* fraction of LHS that has the same value as the single RHS value).
|
|
*
|
|
* For anti-joins, if we use the same assumption that there is more
|
|
* than one distinct key in the RHS relation, then every non-null LHS
|
|
* row must be suppressed by the anti-join.
|
|
*
|
|
* So either way, the selectivity estimate should be 1 - nullfrac.
|
|
*/
|
|
VariableStatData leftvar;
|
|
VariableStatData rightvar;
|
|
bool reversed;
|
|
HeapTuple statsTuple;
|
|
double nullfrac;
|
|
|
|
get_join_variables(root, args, sjinfo, &leftvar, &rightvar, &reversed);
|
|
statsTuple = reversed ? rightvar.statsTuple : leftvar.statsTuple;
|
|
if (HeapTupleIsValid(statsTuple))
|
|
nullfrac = ((Form_pg_statistic) GETSTRUCT(statsTuple))->stanullfrac;
|
|
else
|
|
nullfrac = 0.0;
|
|
ReleaseVariableStats(leftvar);
|
|
ReleaseVariableStats(rightvar);
|
|
|
|
result = 1.0 - nullfrac;
|
|
}
|
|
else
|
|
{
|
|
/*
|
|
* We want 1 - eqjoinsel() where the equality operator is the one
|
|
* associated with this != operator, that is, its negator.
|
|
*/
|
|
Oid eqop = get_negator(operator);
|
|
|
|
if (eqop)
|
|
{
|
|
result = DatumGetFloat8(DirectFunctionCall5(eqjoinsel,
|
|
PointerGetDatum(root),
|
|
ObjectIdGetDatum(eqop),
|
|
PointerGetDatum(args),
|
|
Int16GetDatum(jointype),
|
|
PointerGetDatum(sjinfo)));
|
|
}
|
|
else
|
|
{
|
|
/* Use default selectivity (should we raise an error instead?) */
|
|
result = DEFAULT_EQ_SEL;
|
|
}
|
|
result = 1.0 - result;
|
|
}
|
|
|
|
PG_RETURN_FLOAT8(result);
|
|
}
|
|
|
|
/*
|
|
* scalarltjoinsel - Join selectivity of "<" for scalars
|
|
*/
|
|
Datum
|
|
scalarltjoinsel(PG_FUNCTION_ARGS)
|
|
{
|
|
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
|
|
}
|
|
|
|
/*
|
|
* scalarlejoinsel - Join selectivity of "<=" for scalars
|
|
*/
|
|
Datum
|
|
scalarlejoinsel(PG_FUNCTION_ARGS)
|
|
{
|
|
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
|
|
}
|
|
|
|
/*
|
|
* scalargtjoinsel - Join selectivity of ">" for scalars
|
|
*/
|
|
Datum
|
|
scalargtjoinsel(PG_FUNCTION_ARGS)
|
|
{
|
|
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
|
|
}
|
|
|
|
/*
|
|
* scalargejoinsel - Join selectivity of ">=" for scalars
|
|
*/
|
|
Datum
|
|
scalargejoinsel(PG_FUNCTION_ARGS)
|
|
{
|
|
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
|
|
}
|
|
|
|
|
|
/*
|
|
* mergejoinscansel - Scan selectivity of merge join.
|
|
*
|
|
* A merge join will stop as soon as it exhausts either input stream.
|
|
* Therefore, if we can estimate the ranges of both input variables,
|
|
* we can estimate how much of the input will actually be read. This
|
|
* can have a considerable impact on the cost when using indexscans.
|
|
*
|
|
* Also, we can estimate how much of each input has to be read before the
|
|
* first join pair is found, which will affect the join's startup time.
|
|
*
|
|
* clause should be a clause already known to be mergejoinable. opfamily,
|
|
* strategy, and nulls_first specify the sort ordering being used.
|
|
*
|
|
* The outputs are:
|
|
* *leftstart is set to the fraction of the left-hand variable expected
|
|
* to be scanned before the first join pair is found (0 to 1).
|
|
* *leftend is set to the fraction of the left-hand variable expected
|
|
* to be scanned before the join terminates (0 to 1).
|
|
* *rightstart, *rightend similarly for the right-hand variable.
|
|
*/
|
|
void
|
|
mergejoinscansel(PlannerInfo *root, Node *clause,
|
|
Oid opfamily, int strategy, bool nulls_first,
|
|
Selectivity *leftstart, Selectivity *leftend,
|
|
Selectivity *rightstart, Selectivity *rightend)
|
|
{
|
|
Node *left,
|
|
*right;
|
|
VariableStatData leftvar,
|
|
rightvar;
|
|
int op_strategy;
|
|
Oid op_lefttype;
|
|
Oid op_righttype;
|
|
Oid opno,
|
|
lsortop,
|
|
rsortop,
|
|
lstatop,
|
|
rstatop,
|
|
ltop,
|
|
leop,
|
|
revltop,
|
|
revleop;
|
|
bool isgt;
|
|
Datum leftmin,
|
|
leftmax,
|
|
rightmin,
|
|
rightmax;
|
|
double selec;
|
|
|
|
/* Set default results if we can't figure anything out. */
|
|
/* XXX should default "start" fraction be a bit more than 0? */
|
|
*leftstart = *rightstart = 0.0;
|
|
*leftend = *rightend = 1.0;
|
|
|
|
/* Deconstruct the merge clause */
|
|
if (!is_opclause(clause))
|
|
return; /* shouldn't happen */
|
|
opno = ((OpExpr *) clause)->opno;
|
|
left = get_leftop((Expr *) clause);
|
|
right = get_rightop((Expr *) clause);
|
|
if (!right)
|
|
return; /* shouldn't happen */
|
|
|
|
/* Look for stats for the inputs */
|
|
examine_variable(root, left, 0, &leftvar);
|
|
examine_variable(root, right, 0, &rightvar);
|
|
|
|
/* Extract the operator's declared left/right datatypes */
|
|
get_op_opfamily_properties(opno, opfamily, false,
|
|
&op_strategy,
|
|
&op_lefttype,
|
|
&op_righttype);
|
|
Assert(op_strategy == BTEqualStrategyNumber);
|
|
|
|
/*
|
|
* Look up the various operators we need. If we don't find them all, it
|
|
* probably means the opfamily is broken, but we just fail silently.
|
|
*
|
|
* Note: we expect that pg_statistic histograms will be sorted by the '<'
|
|
* operator, regardless of which sort direction we are considering.
|
|
*/
|
|
switch (strategy)
|
|
{
|
|
case BTLessStrategyNumber:
|
|
isgt = false;
|
|
if (op_lefttype == op_righttype)
|
|
{
|
|
/* easy case */
|
|
ltop = get_opfamily_member(opfamily,
|
|
op_lefttype, op_righttype,
|
|
BTLessStrategyNumber);
|
|
leop = get_opfamily_member(opfamily,
|
|
op_lefttype, op_righttype,
|
|
BTLessEqualStrategyNumber);
|
|
lsortop = ltop;
|
|
rsortop = ltop;
|
|
lstatop = lsortop;
|
|
rstatop = rsortop;
|
|
revltop = ltop;
|
|
revleop = leop;
|
|
}
|
|
else
|
|
{
|
|
ltop = get_opfamily_member(opfamily,
|
|
op_lefttype, op_righttype,
|
|
BTLessStrategyNumber);
|
|
leop = get_opfamily_member(opfamily,
|
|
op_lefttype, op_righttype,
|
|
BTLessEqualStrategyNumber);
|
|
lsortop = get_opfamily_member(opfamily,
|
|
op_lefttype, op_lefttype,
|
|
BTLessStrategyNumber);
|
|
rsortop = get_opfamily_member(opfamily,
|
|
op_righttype, op_righttype,
|
|
BTLessStrategyNumber);
|
|
lstatop = lsortop;
|
|
rstatop = rsortop;
|
|
revltop = get_opfamily_member(opfamily,
|
|
op_righttype, op_lefttype,
|
|
BTLessStrategyNumber);
|
|
revleop = get_opfamily_member(opfamily,
|
|
op_righttype, op_lefttype,
|
|
BTLessEqualStrategyNumber);
|
|
}
|
|
break;
|
|
case BTGreaterStrategyNumber:
|
|
/* descending-order case */
|
|
isgt = true;
|
|
if (op_lefttype == op_righttype)
|
|
{
|
|
/* easy case */
|
|
ltop = get_opfamily_member(opfamily,
|
|
op_lefttype, op_righttype,
|
|
BTGreaterStrategyNumber);
|
|
leop = get_opfamily_member(opfamily,
|
|
op_lefttype, op_righttype,
|
|
BTGreaterEqualStrategyNumber);
|
|
lsortop = ltop;
|
|
rsortop = ltop;
|
|
lstatop = get_opfamily_member(opfamily,
|
|
op_lefttype, op_lefttype,
|
|
BTLessStrategyNumber);
|
|
rstatop = lstatop;
|
|
revltop = ltop;
|
|
revleop = leop;
|
|
}
|
|
else
|
|
{
|
|
ltop = get_opfamily_member(opfamily,
|
|
op_lefttype, op_righttype,
|
|
BTGreaterStrategyNumber);
|
|
leop = get_opfamily_member(opfamily,
|
|
op_lefttype, op_righttype,
|
|
BTGreaterEqualStrategyNumber);
|
|
lsortop = get_opfamily_member(opfamily,
|
|
op_lefttype, op_lefttype,
|
|
BTGreaterStrategyNumber);
|
|
rsortop = get_opfamily_member(opfamily,
|
|
op_righttype, op_righttype,
|
|
BTGreaterStrategyNumber);
|
|
lstatop = get_opfamily_member(opfamily,
|
|
op_lefttype, op_lefttype,
|
|
BTLessStrategyNumber);
|
|
rstatop = get_opfamily_member(opfamily,
|
|
op_righttype, op_righttype,
|
|
BTLessStrategyNumber);
|
|
revltop = get_opfamily_member(opfamily,
|
|
op_righttype, op_lefttype,
|
|
BTGreaterStrategyNumber);
|
|
revleop = get_opfamily_member(opfamily,
|
|
op_righttype, op_lefttype,
|
|
BTGreaterEqualStrategyNumber);
|
|
}
|
|
break;
|
|
default:
|
|
goto fail; /* shouldn't get here */
|
|
}
|
|
|
|
if (!OidIsValid(lsortop) ||
|
|
!OidIsValid(rsortop) ||
|
|
!OidIsValid(lstatop) ||
|
|
!OidIsValid(rstatop) ||
|
|
!OidIsValid(ltop) ||
|
|
!OidIsValid(leop) ||
|
|
!OidIsValid(revltop) ||
|
|
!OidIsValid(revleop))
|
|
goto fail; /* insufficient info in catalogs */
|
|
|
|
/* Try to get ranges of both inputs */
|
|
if (!isgt)
|
|
{
|
|
if (!get_variable_range(root, &leftvar, lstatop,
|
|
&leftmin, &leftmax))
|
|
goto fail; /* no range available from stats */
|
|
if (!get_variable_range(root, &rightvar, rstatop,
|
|
&rightmin, &rightmax))
|
|
goto fail; /* no range available from stats */
|
|
}
|
|
else
|
|
{
|
|
/* need to swap the max and min */
|
|
if (!get_variable_range(root, &leftvar, lstatop,
|
|
&leftmax, &leftmin))
|
|
goto fail; /* no range available from stats */
|
|
if (!get_variable_range(root, &rightvar, rstatop,
|
|
&rightmax, &rightmin))
|
|
goto fail; /* no range available from stats */
|
|
}
|
|
|
|
/*
|
|
* Now, the fraction of the left variable that will be scanned is the
|
|
* fraction that's <= the right-side maximum value. But only believe
|
|
* non-default estimates, else stick with our 1.0.
|
|
*/
|
|
selec = scalarineqsel(root, leop, isgt, true, &leftvar,
|
|
rightmax, op_righttype);
|
|
if (selec != DEFAULT_INEQ_SEL)
|
|
*leftend = selec;
|
|
|
|
/* And similarly for the right variable. */
|
|
selec = scalarineqsel(root, revleop, isgt, true, &rightvar,
|
|
leftmax, op_lefttype);
|
|
if (selec != DEFAULT_INEQ_SEL)
|
|
*rightend = selec;
|
|
|
|
/*
|
|
* Only one of the two "end" fractions can really be less than 1.0;
|
|
* believe the smaller estimate and reset the other one to exactly 1.0. If
|
|
* we get exactly equal estimates (as can easily happen with self-joins),
|
|
* believe neither.
|
|
*/
|
|
if (*leftend > *rightend)
|
|
*leftend = 1.0;
|
|
else if (*leftend < *rightend)
|
|
*rightend = 1.0;
|
|
else
|
|
*leftend = *rightend = 1.0;
|
|
|
|
/*
|
|
* Also, the fraction of the left variable that will be scanned before the
|
|
* first join pair is found is the fraction that's < the right-side
|
|
* minimum value. But only believe non-default estimates, else stick with
|
|
* our own default.
|
|
*/
|
|
selec = scalarineqsel(root, ltop, isgt, false, &leftvar,
|
|
rightmin, op_righttype);
|
|
if (selec != DEFAULT_INEQ_SEL)
|
|
*leftstart = selec;
|
|
|
|
/* And similarly for the right variable. */
|
|
selec = scalarineqsel(root, revltop, isgt, false, &rightvar,
|
|
leftmin, op_lefttype);
|
|
if (selec != DEFAULT_INEQ_SEL)
|
|
*rightstart = selec;
|
|
|
|
/*
|
|
* Only one of the two "start" fractions can really be more than zero;
|
|
* believe the larger estimate and reset the other one to exactly 0.0. If
|
|
* we get exactly equal estimates (as can easily happen with self-joins),
|
|
* believe neither.
|
|
*/
|
|
if (*leftstart < *rightstart)
|
|
*leftstart = 0.0;
|
|
else if (*leftstart > *rightstart)
|
|
*rightstart = 0.0;
|
|
else
|
|
*leftstart = *rightstart = 0.0;
|
|
|
|
/*
|
|
* If the sort order is nulls-first, we're going to have to skip over any
|
|
* nulls too. These would not have been counted by scalarineqsel, and we
|
|
* can safely add in this fraction regardless of whether we believe
|
|
* scalarineqsel's results or not. But be sure to clamp the sum to 1.0!
|
|
*/
|
|
if (nulls_first)
|
|
{
|
|
Form_pg_statistic stats;
|
|
|
|
if (HeapTupleIsValid(leftvar.statsTuple))
|
|
{
|
|
stats = (Form_pg_statistic) GETSTRUCT(leftvar.statsTuple);
|
|
*leftstart += stats->stanullfrac;
|
|
CLAMP_PROBABILITY(*leftstart);
|
|
*leftend += stats->stanullfrac;
|
|
CLAMP_PROBABILITY(*leftend);
|
|
}
|
|
if (HeapTupleIsValid(rightvar.statsTuple))
|
|
{
|
|
stats = (Form_pg_statistic) GETSTRUCT(rightvar.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(leftvar);
|
|
ReleaseVariableStats(rightvar);
|
|
}
|
|
|
|
|
|
/*
|
|
* Helper routine for estimate_num_groups: add an item to a list of
|
|
* GroupVarInfos, but only if it's not known equal to any of the existing
|
|
* entries.
|
|
*/
|
|
typedef struct
|
|
{
|
|
Node *var; /* might be an expression, not just a Var */
|
|
RelOptInfo *rel; /* relation it belongs to */
|
|
double ndistinct; /* # distinct values */
|
|
} GroupVarInfo;
|
|
|
|
static List *
|
|
add_unique_group_var(PlannerInfo *root, List *varinfos,
|
|
Node *var, VariableStatData *vardata)
|
|
{
|
|
GroupVarInfo *varinfo;
|
|
double ndistinct;
|
|
bool isdefault;
|
|
ListCell *lc;
|
|
|
|
ndistinct = get_variable_numdistinct(vardata, &isdefault);
|
|
|
|
foreach(lc, varinfos)
|
|
{
|
|
varinfo = (GroupVarInfo *) lfirst(lc);
|
|
|
|
/* Drop exact duplicates */
|
|
if (equal(var, varinfo->var))
|
|
return varinfos;
|
|
|
|
/*
|
|
* Drop known-equal vars, but only if they belong to different
|
|
* relations (see comments for estimate_num_groups)
|
|
*/
|
|
if (vardata->rel != varinfo->rel &&
|
|
exprs_known_equal(root, var, varinfo->var))
|
|
{
|
|
if (varinfo->ndistinct <= ndistinct)
|
|
{
|
|
/* Keep older item, forget new one */
|
|
return varinfos;
|
|
}
|
|
else
|
|
{
|
|
/* Delete the older item */
|
|
varinfos = foreach_delete_current(varinfos, lc);
|
|
}
|
|
}
|
|
}
|
|
|
|
varinfo = (GroupVarInfo *) palloc(sizeof(GroupVarInfo));
|
|
|
|
varinfo->var = var;
|
|
varinfo->rel = vardata->rel;
|
|
varinfo->ndistinct = ndistinct;
|
|
varinfos = lappend(varinfos, varinfo);
|
|
return varinfos;
|
|
}
|
|
|
|
/*
|
|
* estimate_num_groups - Estimate number of groups in a grouped query
|
|
*
|
|
* Given a query having a GROUP BY clause, estimate how many groups there
|
|
* will be --- ie, the number of distinct combinations of the GROUP BY
|
|
* expressions.
|
|
*
|
|
* This routine is also used to estimate the number of rows emitted by
|
|
* a DISTINCT filtering step; that is an isomorphic problem. (Note:
|
|
* actually, we only use it for DISTINCT when there's no grouping or
|
|
* aggregation ahead of the DISTINCT.)
|
|
*
|
|
* Inputs:
|
|
* root - the query
|
|
* groupExprs - list of expressions being grouped by
|
|
* input_rows - number of rows estimated to arrive at the group/unique
|
|
* filter step
|
|
* pgset - NULL, or a List** pointing to a grouping set to filter the
|
|
* groupExprs against
|
|
*
|
|
* Given the lack of any cross-correlation statistics in the system, it's
|
|
* impossible to do anything really trustworthy with GROUP BY conditions
|
|
* involving multiple Vars. We should however avoid assuming the worst
|
|
* case (all possible cross-product terms actually appear as groups) since
|
|
* very often the grouped-by Vars are highly correlated. Our current approach
|
|
* is as follows:
|
|
* 1. Expressions yielding boolean are assumed to contribute two groups,
|
|
* independently of their content, and are ignored in the subsequent
|
|
* steps. This is mainly because tests like "col IS NULL" break the
|
|
* heuristic used in step 2 especially badly.
|
|
* 2. Reduce the given expressions to a list of unique Vars used. For
|
|
* example, GROUP BY a, a + b is treated the same as GROUP BY a, b.
|
|
* It is clearly correct not to count the same Var more than once.
|
|
* It is also reasonable to treat f(x) the same as x: f() cannot
|
|
* increase the number of distinct values (unless it is volatile,
|
|
* which we consider unlikely for grouping), but it probably won't
|
|
* reduce the number of distinct values much either.
|
|
* As a special case, if a GROUP BY expression can be matched to an
|
|
* expressional index for which we have statistics, then we treat the
|
|
* whole expression as though it were just a Var.
|
|
* 3. If the list contains Vars of different relations that are known equal
|
|
* due to equivalence classes, then drop all but one of the Vars from each
|
|
* known-equal set, keeping the one with smallest estimated # of values
|
|
* (since the extra values of the others can't appear in joined rows).
|
|
* Note the reason we only consider Vars of different relations is that
|
|
* if we considered ones of the same rel, we'd be double-counting the
|
|
* restriction selectivity of the equality in the next step.
|
|
* 4. For Vars within a single source rel, we multiply together the numbers
|
|
* of values, clamp to the number of rows in the rel (divided by 10 if
|
|
* more than one Var), and then multiply by a factor based on the
|
|
* selectivity of the restriction clauses for that rel. When there's
|
|
* more than one Var, the initial product is probably too high (it's the
|
|
* worst case) but clamping to a fraction of the rel's rows seems to be a
|
|
* helpful heuristic for not letting the estimate get out of hand. (The
|
|
* factor of 10 is derived from pre-Postgres-7.4 practice.) The factor
|
|
* we multiply by to adjust for the restriction selectivity assumes that
|
|
* the restriction clauses are independent of the grouping, which may not
|
|
* be a valid assumption, but it's hard to do better.
|
|
* 5. If there are Vars from multiple rels, we repeat step 4 for each such
|
|
* rel, and multiply the results together.
|
|
* Note that rels not containing grouped Vars are ignored completely, as are
|
|
* join clauses. Such rels cannot increase the number of groups, and we
|
|
* assume such clauses do not reduce the number either (somewhat bogus,
|
|
* but we don't have the info to do better).
|
|
*/
|
|
double
|
|
estimate_num_groups(PlannerInfo *root, List *groupExprs, double input_rows,
|
|
List **pgset)
|
|
{
|
|
List *varinfos = NIL;
|
|
double srf_multiplier = 1.0;
|
|
double numdistinct;
|
|
ListCell *l;
|
|
int i;
|
|
|
|
/*
|
|
* We don't ever want to return an estimate of zero groups, as that tends
|
|
* to lead to division-by-zero and other unpleasantness. The input_rows
|
|
* estimate is usually already at least 1, but clamp it just in case it
|
|
* isn't.
|
|
*/
|
|
input_rows = clamp_row_est(input_rows);
|
|
|
|
/*
|
|
* If no grouping columns, there's exactly one group. (This can't happen
|
|
* for normal cases with GROUP BY or DISTINCT, but it is possible for
|
|
* corner cases with set operations.)
|
|
*/
|
|
if (groupExprs == NIL || (pgset && list_length(*pgset) < 1))
|
|
return 1.0;
|
|
|
|
/*
|
|
* Count groups derived from boolean grouping expressions. For other
|
|
* expressions, find the unique Vars used, treating an expression as a Var
|
|
* if we can find stats for it. For each one, record the statistical
|
|
* estimate of number of distinct values (total in its table, without
|
|
* regard for filtering).
|
|
*/
|
|
numdistinct = 1.0;
|
|
|
|
i = 0;
|
|
foreach(l, groupExprs)
|
|
{
|
|
Node *groupexpr = (Node *) lfirst(l);
|
|
double this_srf_multiplier;
|
|
VariableStatData vardata;
|
|
List *varshere;
|
|
ListCell *l2;
|
|
|
|
/* is expression in this grouping set? */
|
|
if (pgset && !list_member_int(*pgset, i++))
|
|
continue;
|
|
|
|
/*
|
|
* Set-returning functions in grouping columns are a bit problematic.
|
|
* The code below will effectively ignore their SRF nature and come up
|
|
* with a numdistinct estimate as though they were scalar functions.
|
|
* We compensate by scaling up the end result by the largest SRF
|
|
* rowcount estimate. (This will be an overestimate if the SRF
|
|
* produces multiple copies of any output value, but it seems best to
|
|
* assume the SRF's outputs are distinct. In any case, it's probably
|
|
* pointless to worry too much about this without much better
|
|
* estimates for SRF output rowcounts than we have today.)
|
|
*/
|
|
this_srf_multiplier = expression_returns_set_rows(root, groupexpr);
|
|
if (srf_multiplier < this_srf_multiplier)
|
|
srf_multiplier = this_srf_multiplier;
|
|
|
|
/* Short-circuit for expressions returning boolean */
|
|
if (exprType(groupexpr) == BOOLOID)
|
|
{
|
|
numdistinct *= 2.0;
|
|
continue;
|
|
}
|
|
|
|
/*
|
|
* If examine_variable is able to deduce anything about the GROUP BY
|
|
* expression, treat it as a single variable even if it's really more
|
|
* complicated.
|
|
*/
|
|
examine_variable(root, groupexpr, 0, &vardata);
|
|
if (HeapTupleIsValid(vardata.statsTuple) || vardata.isunique)
|
|
{
|
|
varinfos = add_unique_group_var(root, varinfos,
|
|
groupexpr, &vardata);
|
|
ReleaseVariableStats(vardata);
|
|
continue;
|
|
}
|
|
ReleaseVariableStats(vardata);
|
|
|
|
/*
|
|
* Else pull out the component Vars. Handle PlaceHolderVars by
|
|
* recursing into their arguments (effectively assuming that the
|
|
* PlaceHolderVar doesn't change the number of groups, which boils
|
|
* down to ignoring the possible addition of nulls to the result set).
|
|
*/
|
|
varshere = pull_var_clause(groupexpr,
|
|
PVC_RECURSE_AGGREGATES |
|
|
PVC_RECURSE_WINDOWFUNCS |
|
|
PVC_RECURSE_PLACEHOLDERS);
|
|
|
|
/*
|
|
* If we find any variable-free GROUP BY item, then either it is a
|
|
* constant (and we can ignore it) or it contains a volatile function;
|
|
* in the latter case we punt and assume that each input row will
|
|
* yield a distinct group.
|
|
*/
|
|
if (varshere == NIL)
|
|
{
|
|
if (contain_volatile_functions(groupexpr))
|
|
return input_rows;
|
|
continue;
|
|
}
|
|
|
|
/*
|
|
* Else add variables to varinfos list
|
|
*/
|
|
foreach(l2, varshere)
|
|
{
|
|
Node *var = (Node *) lfirst(l2);
|
|
|
|
examine_variable(root, var, 0, &vardata);
|
|
varinfos = add_unique_group_var(root, varinfos, var, &vardata);
|
|
ReleaseVariableStats(vardata);
|
|
}
|
|
}
|
|
|
|
/*
|
|
* If now no Vars, we must have an all-constant or all-boolean GROUP BY
|
|
* list.
|
|
*/
|
|
if (varinfos == NIL)
|
|
{
|
|
/* Apply SRF multiplier as we would do in the long path */
|
|
numdistinct *= srf_multiplier;
|
|
/* Round off */
|
|
numdistinct = ceil(numdistinct);
|
|
/* Guard against out-of-range answers */
|
|
if (numdistinct > input_rows)
|
|
numdistinct = input_rows;
|
|
if (numdistinct < 1.0)
|
|
numdistinct = 1.0;
|
|
return numdistinct;
|
|
}
|
|
|
|
/*
|
|
* Group Vars by relation and estimate total numdistinct.
|
|
*
|
|
* For each iteration of the outer loop, we process the frontmost Var in
|
|
* varinfos, plus all other Vars in the same relation. We remove these
|
|
* Vars from the newvarinfos list for the next iteration. This is the
|
|
* easiest way to group Vars of same rel together.
|
|
*/
|
|
do
|
|
{
|
|
GroupVarInfo *varinfo1 = (GroupVarInfo *) linitial(varinfos);
|
|
RelOptInfo *rel = varinfo1->rel;
|
|
double reldistinct = 1;
|
|
double relmaxndistinct = reldistinct;
|
|
int relvarcount = 0;
|
|
List *newvarinfos = NIL;
|
|
List *relvarinfos = NIL;
|
|
|
|
/*
|
|
* Split the list of varinfos in two - one for the current rel, one
|
|
* for remaining Vars on other rels.
|
|
*/
|
|
relvarinfos = lappend(relvarinfos, varinfo1);
|
|
for_each_cell(l, varinfos, list_second_cell(varinfos))
|
|
{
|
|
GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
|
|
|
|
if (varinfo2->rel == varinfo1->rel)
|
|
{
|
|
/* varinfos on current rel */
|
|
relvarinfos = lappend(relvarinfos, varinfo2);
|
|
}
|
|
else
|
|
{
|
|
/* not time to process varinfo2 yet */
|
|
newvarinfos = lappend(newvarinfos, varinfo2);
|
|
}
|
|
}
|
|
|
|
/*
|
|
* Get the numdistinct estimate for the Vars of this rel. We
|
|
* iteratively search for multivariate n-distinct with maximum number
|
|
* of vars; assuming that each var group is independent of the others,
|
|
* we multiply them together. Any remaining relvarinfos after no more
|
|
* multivariate matches are found are assumed independent too, so
|
|
* their individual ndistinct estimates are multiplied also.
|
|
*
|
|
* While iterating, count how many separate numdistinct values we
|
|
* apply. We apply a fudge factor below, but only if we multiplied
|
|
* more than one such values.
|
|
*/
|
|
while (relvarinfos)
|
|
{
|
|
double mvndistinct;
|
|
|
|
if (estimate_multivariate_ndistinct(root, rel, &relvarinfos,
|
|
&mvndistinct))
|
|
{
|
|
reldistinct *= mvndistinct;
|
|
if (relmaxndistinct < mvndistinct)
|
|
relmaxndistinct = mvndistinct;
|
|
relvarcount++;
|
|
}
|
|
else
|
|
{
|
|
foreach(l, relvarinfos)
|
|
{
|
|
GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
|
|
|
|
reldistinct *= varinfo2->ndistinct;
|
|
if (relmaxndistinct < varinfo2->ndistinct)
|
|
relmaxndistinct = varinfo2->ndistinct;
|
|
relvarcount++;
|
|
}
|
|
|
|
/* we're done with this relation */
|
|
relvarinfos = NIL;
|
|
}
|
|
}
|
|
|
|
/*
|
|
* Sanity check --- don't divide by zero if empty relation.
|
|
*/
|
|
Assert(IS_SIMPLE_REL(rel));
|
|
if (rel->tuples > 0)
|
|
{
|
|
/*
|
|
* Clamp to size of rel, or size of rel / 10 if multiple Vars. The
|
|
* fudge factor is because the Vars are probably correlated but we
|
|
* don't know by how much. We should never clamp to less than the
|
|
* largest ndistinct value for any of the Vars, though, since
|
|
* there will surely be at least that many groups.
|
|
*/
|
|
double clamp = rel->tuples;
|
|
|
|
if (relvarcount > 1)
|
|
{
|
|
clamp *= 0.1;
|
|
if (clamp < relmaxndistinct)
|
|
{
|
|
clamp = relmaxndistinct;
|
|
/* for sanity in case some ndistinct is too large: */
|
|
if (clamp > rel->tuples)
|
|
clamp = rel->tuples;
|
|
}
|
|
}
|
|
if (reldistinct > clamp)
|
|
reldistinct = clamp;
|
|
|
|
/*
|
|
* Update the estimate based on the restriction selectivity,
|
|
* guarding against division by zero when reldistinct is zero.
|
|
* Also skip this if we know that we are returning all rows.
|
|
*/
|
|
if (reldistinct > 0 && rel->rows < rel->tuples)
|
|
{
|
|
/*
|
|
* Given a table containing N rows with n distinct values in a
|
|
* uniform distribution, if we select p rows at random then
|
|
* the expected number of distinct values selected is
|
|
*
|
|
* n * (1 - product((N-N/n-i)/(N-i), i=0..p-1))
|
|
*
|
|
* = n * (1 - (N-N/n)! / (N-N/n-p)! * (N-p)! / N!)
|
|
*
|
|
* See "Approximating block accesses in database
|
|
* organizations", S. B. Yao, Communications of the ACM,
|
|
* Volume 20 Issue 4, April 1977 Pages 260-261.
|
|
*
|
|
* Alternatively, re-arranging the terms from the factorials,
|
|
* this may be written as
|
|
*
|
|
* n * (1 - product((N-p-i)/(N-i), i=0..N/n-1))
|
|
*
|
|
* This form of the formula is more efficient to compute in
|
|
* the common case where p is larger than N/n. Additionally,
|
|
* as pointed out by Dell'Era, if i << N for all terms in the
|
|
* product, it can be approximated by
|
|
*
|
|
* n * (1 - ((N-p)/N)^(N/n))
|
|
*
|
|
* See "Expected distinct values when selecting from a bag
|
|
* without replacement", Alberto Dell'Era,
|
|
* http://www.adellera.it/investigations/distinct_balls/.
|
|
*
|
|
* The condition i << N is equivalent to n >> 1, so this is a
|
|
* good approximation when the number of distinct values in
|
|
* the table is large. It turns out that this formula also
|
|
* works well even when n is small.
|
|
*/
|
|
reldistinct *=
|
|
(1 - pow((rel->tuples - rel->rows) / rel->tuples,
|
|
rel->tuples / reldistinct));
|
|
}
|
|
reldistinct = clamp_row_est(reldistinct);
|
|
|
|
/*
|
|
* Update estimate of total distinct groups.
|
|
*/
|
|
numdistinct *= reldistinct;
|
|
}
|
|
|
|
varinfos = newvarinfos;
|
|
} while (varinfos != NIL);
|
|
|
|
/* Now we can account for the effects of any SRFs */
|
|
numdistinct *= srf_multiplier;
|
|
|
|
/* Round off */
|
|
numdistinct = ceil(numdistinct);
|
|
|
|
/* Guard against out-of-range answers */
|
|
if (numdistinct > input_rows)
|
|
numdistinct = input_rows;
|
|
if (numdistinct < 1.0)
|
|
numdistinct = 1.0;
|
|
|
|
return numdistinct;
|
|
}
|
|
|
|
/*
|
|
* Estimate hash bucket statistics when the specified expression is used
|
|
* as a hash key for the given number of buckets.
|
|
*
|
|
* This attempts to determine two values:
|
|
*
|
|
* 1. The frequency of the most common value of the expression (returns
|
|
* zero into *mcv_freq if we can't get that).
|
|
*
|
|
* 2. The "bucketsize fraction", ie, average number of entries in a bucket
|
|
* divided by total tuples in relation.
|
|
*
|
|
* XXX This is really pretty bogus since we're effectively assuming that the
|
|
* distribution of hash keys will be the same after applying restriction
|
|
* clauses as it was in the underlying relation. However, we are not nearly
|
|
* smart enough to figure out how the restrict clauses might change the
|
|
* distribution, so this will have to do for now.
|
|
*
|
|
* We are passed the number of buckets the executor will use for the given
|
|
* input relation. If the data were perfectly distributed, with the same
|
|
* number of tuples going into each available bucket, then the bucketsize
|
|
* fraction would be 1/nbuckets. But this happy state of affairs will occur
|
|
* only if (a) there are at least nbuckets distinct data values, and (b)
|
|
* we have a not-too-skewed data distribution. Otherwise the buckets will
|
|
* be nonuniformly occupied. If the other relation in the join has a key
|
|
* distribution similar to this one's, then the most-loaded buckets are
|
|
* exactly those that will be probed most often. Therefore, the "average"
|
|
* bucket size for costing purposes should really be taken as something close
|
|
* to the "worst case" bucket size. We try to estimate this by adjusting the
|
|
* fraction if there are too few distinct data values, and then scaling up
|
|
* by the ratio of the most common value's frequency to the average frequency.
|
|
*
|
|
* If no statistics are available, use a default estimate of 0.1. This will
|
|
* discourage use of a hash rather strongly if the inner relation is large,
|
|
* which is what we want. We do not want to hash unless we know that the
|
|
* inner rel is well-dispersed (or the alternatives seem much worse).
|
|
*
|
|
* The caller should also check that the mcv_freq is not so large that the
|
|
* most common value would by itself require an impractically large bucket.
|
|
* In a hash join, the executor can split buckets if they get too big, but
|
|
* obviously that doesn't help for a bucket that contains many duplicates of
|
|
* the same value.
|
|
*/
|
|
void
|
|
estimate_hash_bucket_stats(PlannerInfo *root, Node *hashkey, double nbuckets,
|
|
Selectivity *mcv_freq,
|
|
Selectivity *bucketsize_frac)
|
|
{
|
|
VariableStatData vardata;
|
|
double estfract,
|
|
ndistinct,
|
|
stanullfrac,
|
|
avgfreq;
|
|
bool isdefault;
|
|
AttStatsSlot sslot;
|
|
|
|
examine_variable(root, hashkey, 0, &vardata);
|
|
|
|
/* Look up the frequency of the most common value, if available */
|
|
*mcv_freq = 0.0;
|
|
|
|
if (HeapTupleIsValid(vardata.statsTuple))
|
|
{
|
|
if (get_attstatsslot(&sslot, vardata.statsTuple,
|
|
STATISTIC_KIND_MCV, InvalidOid,
|
|
ATTSTATSSLOT_NUMBERS))
|
|
{
|
|
/*
|
|
* The first MCV stat is for the most common value.
|
|
*/
|
|
if (sslot.nnumbers > 0)
|
|
*mcv_freq = sslot.numbers[0];
|
|
free_attstatsslot(&sslot);
|
|
}
|
|
}
|
|
|
|
/* Get number of distinct values */
|
|
ndistinct = get_variable_numdistinct(&vardata, &isdefault);
|
|
|
|
/*
|
|
* If ndistinct isn't real, punt. We normally return 0.1, but if the
|
|
* mcv_freq is known to be even higher than that, use it instead.
|
|
*/
|
|
if (isdefault)
|
|
{
|
|
*bucketsize_frac = (Selectivity) Max(0.1, *mcv_freq);
|
|
ReleaseVariableStats(vardata);
|
|
return;
|
|
}
|
|
|
|
/* Get fraction that are null */
|
|
if (HeapTupleIsValid(vardata.statsTuple))
|
|
{
|
|
Form_pg_statistic stats;
|
|
|
|
stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
|
|
stanullfrac = stats->stanullfrac;
|
|
}
|
|
else
|
|
stanullfrac = 0.0;
|
|
|
|
/* Compute avg freq of all distinct data values in raw relation */
|
|
avgfreq = (1.0 - stanullfrac) / ndistinct;
|
|
|
|
/*
|
|
* Adjust ndistinct to account for restriction clauses. Observe we are
|
|
* assuming that the data distribution is affected uniformly by the
|
|
* restriction clauses!
|
|
*
|
|
* XXX Possibly better way, but much more expensive: multiply by
|
|
* selectivity of rel's restriction clauses that mention the target Var.
|
|
*/
|
|
if (vardata.rel && vardata.rel->tuples > 0)
|
|
{
|
|
ndistinct *= vardata.rel->rows / vardata.rel->tuples;
|
|
ndistinct = clamp_row_est(ndistinct);
|
|
}
|
|
|
|
/*
|
|
* Initial estimate of bucketsize fraction is 1/nbuckets as long as the
|
|
* number of buckets is less than the expected number of distinct values;
|
|
* otherwise it is 1/ndistinct.
|
|
*/
|
|
if (ndistinct > nbuckets)
|
|
estfract = 1.0 / nbuckets;
|
|
else
|
|
estfract = 1.0 / ndistinct;
|
|
|
|
/*
|
|
* Adjust estimated bucketsize upward to account for skewed distribution.
|
|
*/
|
|
if (avgfreq > 0.0 && *mcv_freq > avgfreq)
|
|
estfract *= *mcv_freq / avgfreq;
|
|
|
|
/*
|
|
* Clamp bucketsize to sane range (the above adjustment could easily
|
|
* produce an out-of-range result). We set the lower bound a little above
|
|
* zero, since zero isn't a very sane result.
|
|
*/
|
|
if (estfract < 1.0e-6)
|
|
estfract = 1.0e-6;
|
|
else if (estfract > 1.0)
|
|
estfract = 1.0;
|
|
|
|
*bucketsize_frac = (Selectivity) estfract;
|
|
|
|
ReleaseVariableStats(vardata);
|
|
}
|
|
|
|
/*
|
|
* estimate_hashagg_tablesize
|
|
* estimate the number of bytes that a hash aggregate hashtable will
|
|
* require based on the agg_costs, path width and number of groups.
|
|
*
|
|
* We return the result as "double" to forestall any possible overflow
|
|
* problem in the multiplication by dNumGroups.
|
|
*
|
|
* XXX this may be over-estimating the size now that hashagg knows to omit
|
|
* unneeded columns from the hashtable. Also for mixed-mode grouping sets,
|
|
* grouping columns not in the hashed set are counted here even though hashagg
|
|
* won't store them. Is this a problem?
|
|
*/
|
|
double
|
|
estimate_hashagg_tablesize(Path *path, const AggClauseCosts *agg_costs,
|
|
double dNumGroups)
|
|
{
|
|
Size hashentrysize;
|
|
|
|
/* Estimate per-hash-entry space at tuple width... */
|
|
hashentrysize = MAXALIGN(path->pathtarget->width) +
|
|
MAXALIGN(SizeofMinimalTupleHeader);
|
|
|
|
/* plus space for pass-by-ref transition values... */
|
|
hashentrysize += agg_costs->transitionSpace;
|
|
/* plus the per-hash-entry overhead */
|
|
hashentrysize += hash_agg_entry_size(agg_costs->numAggs);
|
|
|
|
/*
|
|
* Note that this disregards the effect of fill-factor and growth policy
|
|
* of the hash table. That's probably ok, given that the default
|
|
* fill-factor is relatively high. It'd be hard to meaningfully factor in
|
|
* "double-in-size" growth policies here.
|
|
*/
|
|
return hashentrysize * dNumGroups;
|
|
}
|
|
|
|
|
|
/*-------------------------------------------------------------------------
|
|
*
|
|
* Support routines
|
|
*
|
|
*-------------------------------------------------------------------------
|
|
*/
|
|
|
|
/*
|
|
* Find applicable ndistinct statistics for the given list of VarInfos (which
|
|
* must all belong to the given rel), and update *ndistinct to the estimate of
|
|
* the MVNDistinctItem that best matches. If a match it found, *varinfos is
|
|
* updated to remove the list of matched varinfos.
|
|
*
|
|
* Varinfos that aren't for simple Vars are ignored.
|
|
*
|
|
* Return true if we're able to find a match, false otherwise.
|
|
*/
|
|
static bool
|
|
estimate_multivariate_ndistinct(PlannerInfo *root, RelOptInfo *rel,
|
|
List **varinfos, double *ndistinct)
|
|
{
|
|
ListCell *lc;
|
|
Bitmapset *attnums = NULL;
|
|
int nmatches;
|
|
Oid statOid = InvalidOid;
|
|
MVNDistinct *stats;
|
|
Bitmapset *matched = NULL;
|
|
|
|
/* bail out immediately if the table has no extended statistics */
|
|
if (!rel->statlist)
|
|
return false;
|
|
|
|
/* Determine the attnums we're looking for */
|
|
foreach(lc, *varinfos)
|
|
{
|
|
GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc);
|
|
|
|
Assert(varinfo->rel == rel);
|
|
|
|
if (IsA(varinfo->var, Var))
|
|
{
|
|
attnums = bms_add_member(attnums,
|
|
((Var *) varinfo->var)->varattno);
|
|
}
|
|
}
|
|
|
|
/* look for the ndistinct statistics matching the most vars */
|
|
nmatches = 1; /* we require at least two matches */
|
|
foreach(lc, rel->statlist)
|
|
{
|
|
StatisticExtInfo *info = (StatisticExtInfo *) lfirst(lc);
|
|
Bitmapset *shared;
|
|
int nshared;
|
|
|
|
/* skip statistics of other kinds */
|
|
if (info->kind != STATS_EXT_NDISTINCT)
|
|
continue;
|
|
|
|
/* compute attnums shared by the vars and the statistics object */
|
|
shared = bms_intersect(info->keys, attnums);
|
|
nshared = bms_num_members(shared);
|
|
|
|
/*
|
|
* Does this statistics object match more columns than the currently
|
|
* best object? If so, use this one instead.
|
|
*
|
|
* XXX This should break ties using name of the object, or something
|
|
* like that, to make the outcome stable.
|
|
*/
|
|
if (nshared > nmatches)
|
|
{
|
|
statOid = info->statOid;
|
|
nmatches = nshared;
|
|
matched = shared;
|
|
}
|
|
}
|
|
|
|
/* No match? */
|
|
if (statOid == InvalidOid)
|
|
return false;
|
|
Assert(nmatches > 1 && matched != NULL);
|
|
|
|
stats = statext_ndistinct_load(statOid);
|
|
|
|
/*
|
|
* If we have a match, search it for the specific item that matches (there
|
|
* must be one), and construct the output values.
|
|
*/
|
|
if (stats)
|
|
{
|
|
int i;
|
|
List *newlist = NIL;
|
|
MVNDistinctItem *item = NULL;
|
|
|
|
/* Find the specific item that exactly matches the combination */
|
|
for (i = 0; i < stats->nitems; i++)
|
|
{
|
|
MVNDistinctItem *tmpitem = &stats->items[i];
|
|
|
|
if (bms_subset_compare(tmpitem->attrs, matched) == BMS_EQUAL)
|
|
{
|
|
item = tmpitem;
|
|
break;
|
|
}
|
|
}
|
|
|
|
/* make sure we found an item */
|
|
if (!item)
|
|
elog(ERROR, "corrupt MVNDistinct entry");
|
|
|
|
/* Form the output varinfo list, keeping only unmatched ones */
|
|
foreach(lc, *varinfos)
|
|
{
|
|
GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc);
|
|
AttrNumber attnum;
|
|
|
|
if (!IsA(varinfo->var, Var))
|
|
{
|
|
newlist = lappend(newlist, varinfo);
|
|
continue;
|
|
}
|
|
|
|
attnum = ((Var *) varinfo->var)->varattno;
|
|
if (!bms_is_member(attnum, matched))
|
|
newlist = lappend(newlist, varinfo);
|
|
}
|
|
|
|
*varinfos = newlist;
|
|
*ndistinct = item->ndistinct;
|
|
return true;
|
|
}
|
|
|
|
return false;
|
|
}
|
|
|
|
/*
|
|
* convert_to_scalar
|
|
* Convert non-NULL values of the indicated types to the comparison
|
|
* scale needed by scalarineqsel().
|
|
* Returns "true" if successful.
|
|
*
|
|
* XXX this routine is a hack: ideally we should look up the conversion
|
|
* subroutines in pg_type.
|
|
*
|
|
* All numeric datatypes are simply converted to their equivalent
|
|
* "double" values. (NUMERIC values that are outside the range of "double"
|
|
* are clamped to +/- HUGE_VAL.)
|
|
*
|
|
* String datatypes are converted 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 valuetypid, Oid collid, double *scaledvalue,
|
|
Datum lobound, Datum hibound, Oid boundstypid,
|
|
double *scaledlobound, double *scaledhibound)
|
|
{
|
|
bool failure = false;
|
|
|
|
/*
|
|
* Both the valuetypid and the boundstypid should exactly match the
|
|
* declared input type(s) of the operator we are invoked for. However,
|
|
* extensions might try to use scalarineqsel as estimator for operators
|
|
* with input type(s) we don't handle here; in such cases, we want to
|
|
* return false, not fail. In any case, we mustn't assume that valuetypid
|
|
* and boundstypid are identical.
|
|
*
|
|
* XXX The histogram we are interpolating between points of could belong
|
|
* to a column that's only binary-compatible with the declared type. In
|
|
* essence we are assuming that the semantics of binary-compatible types
|
|
* are enough alike that we can use a histogram generated with one type's
|
|
* operators to estimate selectivity for the other's. This is outright
|
|
* wrong in some cases --- in particular signed versus unsigned
|
|
* interpretation could trip us up. But it's useful enough in the
|
|
* majority of cases that we do it anyway. Should think about more
|
|
* rigorous ways to do it.
|
|
*/
|
|
switch (valuetypid)
|
|
{
|
|
/*
|
|
* Built-in numeric types
|
|
*/
|
|
case BOOLOID:
|
|
case INT2OID:
|
|
case INT4OID:
|
|
case INT8OID:
|
|
case FLOAT4OID:
|
|
case FLOAT8OID:
|
|
case NUMERICOID:
|
|
case OIDOID:
|
|
case REGPROCOID:
|
|
case REGPROCEDUREOID:
|
|
case REGOPEROID:
|
|
case REGOPERATOROID:
|
|
case REGCLASSOID:
|
|
case REGTYPEOID:
|
|
case REGCONFIGOID:
|
|
case REGDICTIONARYOID:
|
|
case REGROLEOID:
|
|
case REGNAMESPACEOID:
|
|
*scaledvalue = convert_numeric_to_scalar(value, valuetypid,
|
|
&failure);
|
|
*scaledlobound = convert_numeric_to_scalar(lobound, boundstypid,
|
|
&failure);
|
|
*scaledhibound = convert_numeric_to_scalar(hibound, boundstypid,
|
|
&failure);
|
|
return !failure;
|
|
|
|
/*
|
|
* Built-in string types
|
|
*/
|
|
case CHAROID:
|
|
case BPCHAROID:
|
|
case VARCHAROID:
|
|
case TEXTOID:
|
|
case NAMEOID:
|
|
{
|
|
char *valstr = convert_string_datum(value, valuetypid,
|
|
collid, &failure);
|
|
char *lostr = convert_string_datum(lobound, boundstypid,
|
|
collid, &failure);
|
|
char *histr = convert_string_datum(hibound, boundstypid,
|
|
collid, &failure);
|
|
|
|
/*
|
|
* Bail out if any of the values is not of string type. We
|
|
* might leak converted strings for the other value(s), but
|
|
* that's not worth troubling over.
|
|
*/
|
|
if (failure)
|
|
return false;
|
|
|
|
convert_string_to_scalar(valstr, scaledvalue,
|
|
lostr, scaledlobound,
|
|
histr, scaledhibound);
|
|
pfree(valstr);
|
|
pfree(lostr);
|
|
pfree(histr);
|
|
return true;
|
|
}
|
|
|
|
/*
|
|
* Built-in bytea type
|
|
*/
|
|
case BYTEAOID:
|
|
{
|
|
/* We only support bytea vs bytea comparison */
|
|
if (boundstypid != BYTEAOID)
|
|
return false;
|
|
convert_bytea_to_scalar(value, scaledvalue,
|
|
lobound, scaledlobound,
|
|
hibound, scaledhibound);
|
|
return true;
|
|
}
|
|
|
|
/*
|
|
* Built-in time types
|
|
*/
|
|
case TIMESTAMPOID:
|
|
case TIMESTAMPTZOID:
|
|
case DATEOID:
|
|
case INTERVALOID:
|
|
case TIMEOID:
|
|
case TIMETZOID:
|
|
*scaledvalue = convert_timevalue_to_scalar(value, valuetypid,
|
|
&failure);
|
|
*scaledlobound = convert_timevalue_to_scalar(lobound, boundstypid,
|
|
&failure);
|
|
*scaledhibound = convert_timevalue_to_scalar(hibound, boundstypid,
|
|
&failure);
|
|
return !failure;
|
|
|
|
/*
|
|
* Built-in network types
|
|
*/
|
|
case INETOID:
|
|
case CIDROID:
|
|
case MACADDROID:
|
|
case MACADDR8OID:
|
|
*scaledvalue = convert_network_to_scalar(value, valuetypid,
|
|
&failure);
|
|
*scaledlobound = convert_network_to_scalar(lobound, boundstypid,
|
|
&failure);
|
|
*scaledhibound = convert_network_to_scalar(hibound, boundstypid,
|
|
&failure);
|
|
return !failure;
|
|
}
|
|
/* Don't know how to convert */
|
|
*scaledvalue = *scaledlobound = *scaledhibound = 0;
|
|
return false;
|
|
}
|
|
|
|
/*
|
|
* Do convert_to_scalar()'s work for any numeric data type.
|
|
*
|
|
* On failure (e.g., unsupported typid), set *failure to true;
|
|
* otherwise, that variable is not changed.
|
|
*/
|
|
static double
|
|
convert_numeric_to_scalar(Datum value, Oid typid, bool *failure)
|
|
{
|
|
switch (typid)
|
|
{
|
|
case BOOLOID:
|
|
return (double) DatumGetBool(value);
|
|
case INT2OID:
|
|
return (double) DatumGetInt16(value);
|
|
case INT4OID:
|
|
return (double) DatumGetInt32(value);
|
|
case INT8OID:
|
|
return (double) DatumGetInt64(value);
|
|
case FLOAT4OID:
|
|
return (double) DatumGetFloat4(value);
|
|
case FLOAT8OID:
|
|
return (double) DatumGetFloat8(value);
|
|
case NUMERICOID:
|
|
/* Note: out-of-range values will be clamped to +-HUGE_VAL */
|
|
return (double)
|
|
DatumGetFloat8(DirectFunctionCall1(numeric_float8_no_overflow,
|
|
value));
|
|
case OIDOID:
|
|
case REGPROCOID:
|
|
case REGPROCEDUREOID:
|
|
case REGOPEROID:
|
|
case REGOPERATOROID:
|
|
case REGCLASSOID:
|
|
case REGTYPEOID:
|
|
case REGCONFIGOID:
|
|
case REGDICTIONARYOID:
|
|
case REGROLEOID:
|
|
case REGNAMESPACEOID:
|
|
/* we can treat OIDs as integers... */
|
|
return (double) DatumGetObjectId(value);
|
|
}
|
|
|
|
*failure = true;
|
|
return 0;
|
|
}
|
|
|
|
/*
|
|
* Do convert_to_scalar()'s work for any character-string data type.
|
|
*
|
|
* String datatypes are converted to a scale that ranges from 0 to 1,
|
|
* where we visualize the bytes of the string as fractional digits.
|
|
*
|
|
* We do not want the base to be 256, however, since that tends to
|
|
* generate inflated selectivity estimates; few databases will have
|
|
* occurrences of all 256 possible byte values at each position.
|
|
* Instead, use the smallest and largest byte values seen in the bounds
|
|
* as the estimated range for each byte, after some fudging to deal with
|
|
* the fact that we probably aren't going to see the full range that way.
|
|
*
|
|
* An additional refinement is that we discard any common prefix of the
|
|
* three strings before computing the scaled values. This allows us to
|
|
* "zoom in" when we encounter a narrow data range. An example is a phone
|
|
* number database where all the values begin with the same area code.
|
|
* (Actually, the bounds will be adjacent histogram-bin-boundary values,
|
|
* so this is more likely to happen than you might think.)
|
|
*/
|
|
static void
|
|
convert_string_to_scalar(char *value,
|
|
double *scaledvalue,
|
|
char *lobound,
|
|
double *scaledlobound,
|
|
char *hibound,
|
|
double *scaledhibound)
|
|
{
|
|
int rangelo,
|
|
rangehi;
|
|
char *sptr;
|
|
|
|
rangelo = rangehi = (unsigned char) hibound[0];
|
|
for (sptr = lobound; *sptr; sptr++)
|
|
{
|
|
if (rangelo > (unsigned char) *sptr)
|
|
rangelo = (unsigned char) *sptr;
|
|
if (rangehi < (unsigned char) *sptr)
|
|
rangehi = (unsigned char) *sptr;
|
|
}
|
|
for (sptr = hibound; *sptr; sptr++)
|
|
{
|
|
if (rangelo > (unsigned char) *sptr)
|
|
rangelo = (unsigned char) *sptr;
|
|
if (rangehi < (unsigned char) *sptr)
|
|
rangehi = (unsigned char) *sptr;
|
|
}
|
|
/* If range includes any upper-case ASCII chars, make it include all */
|
|
if (rangelo <= 'Z' && rangehi >= 'A')
|
|
{
|
|
if (rangelo > 'A')
|
|
rangelo = 'A';
|
|
if (rangehi < 'Z')
|
|
rangehi = 'Z';
|
|
}
|
|
/* Ditto lower-case */
|
|
if (rangelo <= 'z' && rangehi >= 'a')
|
|
{
|
|
if (rangelo > 'a')
|
|
rangelo = 'a';
|
|
if (rangehi < 'z')
|
|
rangehi = 'z';
|
|
}
|
|
/* Ditto digits */
|
|
if (rangelo <= '9' && rangehi >= '0')
|
|
{
|
|
if (rangelo > '0')
|
|
rangelo = '0';
|
|
if (rangehi < '9')
|
|
rangehi = '9';
|
|
}
|
|
|
|
/*
|
|
* If range includes less than 10 chars, assume we have not got enough
|
|
* data, and make it include regular ASCII set.
|
|
*/
|
|
if (rangehi - rangelo < 9)
|
|
{
|
|
rangelo = ' ';
|
|
rangehi = 127;
|
|
}
|
|
|
|
/*
|
|
* Now strip any common prefix of the three strings.
|
|
*/
|
|
while (*lobound)
|
|
{
|
|
if (*lobound != *hibound || *lobound != *value)
|
|
break;
|
|
lobound++, hibound++, value++;
|
|
}
|
|
|
|
/*
|
|
* Now we can do the conversions.
|
|
*/
|
|
*scaledvalue = convert_one_string_to_scalar(value, rangelo, rangehi);
|
|
*scaledlobound = convert_one_string_to_scalar(lobound, rangelo, rangehi);
|
|
*scaledhibound = convert_one_string_to_scalar(hibound, rangelo, rangehi);
|
|
}
|
|
|
|
static double
|
|
convert_one_string_to_scalar(char *value, int rangelo, int rangehi)
|
|
{
|
|
int slen = strlen(value);
|
|
double num,
|
|
denom,
|
|
base;
|
|
|
|
if (slen <= 0)
|
|
return 0.0; /* empty string has scalar value 0 */
|
|
|
|
/*
|
|
* There seems little point in considering more than a dozen bytes from
|
|
* the string. Since base is at least 10, that will give us nominal
|
|
* resolution of at least 12 decimal digits, which is surely far more
|
|
* precision than this estimation technique has got anyway (especially in
|
|
* non-C locales). Also, even with the maximum possible base of 256, this
|
|
* ensures denom cannot grow larger than 256^13 = 2.03e31, which will not
|
|
* overflow on any known machine.
|
|
*/
|
|
if (slen > 12)
|
|
slen = 12;
|
|
|
|
/* Convert initial characters to fraction */
|
|
base = rangehi - rangelo + 1;
|
|
num = 0.0;
|
|
denom = base;
|
|
while (slen-- > 0)
|
|
{
|
|
int ch = (unsigned char) *value++;
|
|
|
|
if (ch < rangelo)
|
|
ch = rangelo - 1;
|
|
else if (ch > rangehi)
|
|
ch = rangehi + 1;
|
|
num += ((double) (ch - rangelo)) / denom;
|
|
denom *= base;
|
|
}
|
|
|
|
return num;
|
|
}
|
|
|
|
/*
|
|
* Convert a string-type Datum into a palloc'd, null-terminated string.
|
|
*
|
|
* On failure (e.g., unsupported typid), set *failure to true;
|
|
* otherwise, that variable is not changed. (We'll return NULL on failure.)
|
|
*
|
|
* When using a non-C locale, we must pass the string through strxfrm()
|
|
* before continuing, so as to generate correct locale-specific results.
|
|
*/
|
|
static char *
|
|
convert_string_datum(Datum value, Oid typid, Oid collid, bool *failure)
|
|
{
|
|
char *val;
|
|
|
|
switch (typid)
|
|
{
|
|
case CHAROID:
|
|
val = (char *) palloc(2);
|
|
val[0] = DatumGetChar(value);
|
|
val[1] = '\0';
|
|
break;
|
|
case BPCHAROID:
|
|
case VARCHAROID:
|
|
case TEXTOID:
|
|
val = TextDatumGetCString(value);
|
|
break;
|
|
case NAMEOID:
|
|
{
|
|
NameData *nm = (NameData *) DatumGetPointer(value);
|
|
|
|
val = pstrdup(NameStr(*nm));
|
|
break;
|
|
}
|
|
default:
|
|
*failure = true;
|
|
return NULL;
|
|
}
|
|
|
|
if (!lc_collate_is_c(collid))
|
|
{
|
|
char *xfrmstr;
|
|
size_t xfrmlen;
|
|
size_t xfrmlen2 PG_USED_FOR_ASSERTS_ONLY;
|
|
|
|
/*
|
|
* XXX: We could guess at a suitable output buffer size and only call
|
|
* strxfrm twice if our guess is too small.
|
|
*
|
|
* XXX: strxfrm doesn't support UTF-8 encoding on Win32, it can return
|
|
* bogus data or set an error. This is not really a problem unless it
|
|
* crashes since it will only give an estimation error and nothing
|
|
* fatal.
|
|
*/
|
|
xfrmlen = strxfrm(NULL, val, 0);
|
|
#ifdef WIN32
|
|
|
|
/*
|
|
* On Windows, strxfrm returns INT_MAX when an error occurs. Instead
|
|
* of trying to allocate this much memory (and fail), just return the
|
|
* original string unmodified as if we were in the C locale.
|
|
*/
|
|
if (xfrmlen == INT_MAX)
|
|
return val;
|
|
#endif
|
|
xfrmstr = (char *) palloc(xfrmlen + 1);
|
|
xfrmlen2 = strxfrm(xfrmstr, val, xfrmlen + 1);
|
|
|
|
/*
|
|
* Some systems (e.g., glibc) can return a smaller value from the
|
|
* second call than the first; thus the Assert must be <= not ==.
|
|
*/
|
|
Assert(xfrmlen2 <= xfrmlen);
|
|
pfree(val);
|
|
val = xfrmstr;
|
|
}
|
|
|
|
return val;
|
|
}
|
|
|
|
/*
|
|
* Do convert_to_scalar()'s work for any bytea data type.
|
|
*
|
|
* Very similar to convert_string_to_scalar except we can't assume
|
|
* null-termination and therefore pass explicit lengths around.
|
|
*
|
|
* Also, assumptions about likely "normal" ranges of characters have been
|
|
* removed - a data range of 0..255 is always used, for now. (Perhaps
|
|
* someday we will add information about actual byte data range to
|
|
* pg_statistic.)
|
|
*/
|
|
static void
|
|
convert_bytea_to_scalar(Datum value,
|
|
double *scaledvalue,
|
|
Datum lobound,
|
|
double *scaledlobound,
|
|
Datum hibound,
|
|
double *scaledhibound)
|
|
{
|
|
bytea *valuep = DatumGetByteaPP(value);
|
|
bytea *loboundp = DatumGetByteaPP(lobound);
|
|
bytea *hiboundp = DatumGetByteaPP(hibound);
|
|
int rangelo,
|
|
rangehi,
|
|
valuelen = VARSIZE_ANY_EXHDR(valuep),
|
|
loboundlen = VARSIZE_ANY_EXHDR(loboundp),
|
|
hiboundlen = VARSIZE_ANY_EXHDR(hiboundp),
|
|
i,
|
|
minlen;
|
|
unsigned char *valstr = (unsigned char *) VARDATA_ANY(valuep);
|
|
unsigned char *lostr = (unsigned char *) VARDATA_ANY(loboundp);
|
|
unsigned char *histr = (unsigned char *) VARDATA_ANY(hiboundp);
|
|
|
|
/*
|
|
* Assume bytea data is uniformly distributed across all byte values.
|
|
*/
|
|
rangelo = 0;
|
|
rangehi = 255;
|
|
|
|
/*
|
|
* Now strip any common prefix of the three strings.
|
|
*/
|
|
minlen = Min(Min(valuelen, loboundlen), hiboundlen);
|
|
for (i = 0; i < minlen; i++)
|
|
{
|
|
if (*lostr != *histr || *lostr != *valstr)
|
|
break;
|
|
lostr++, histr++, valstr++;
|
|
loboundlen--, hiboundlen--, valuelen--;
|
|
}
|
|
|
|
/*
|
|
* Now we can do the conversions.
|
|
*/
|
|
*scaledvalue = convert_one_bytea_to_scalar(valstr, valuelen, rangelo, rangehi);
|
|
*scaledlobound = convert_one_bytea_to_scalar(lostr, loboundlen, rangelo, rangehi);
|
|
*scaledhibound = convert_one_bytea_to_scalar(histr, hiboundlen, rangelo, rangehi);
|
|
}
|
|
|
|
static double
|
|
convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
|
|
int rangelo, int rangehi)
|
|
{
|
|
double num,
|
|
denom,
|
|
base;
|
|
|
|
if (valuelen <= 0)
|
|
return 0.0; /* empty string has scalar value 0 */
|
|
|
|
/*
|
|
* Since base is 256, need not consider more than about 10 chars (even
|
|
* this many seems like overkill)
|
|
*/
|
|
if (valuelen > 10)
|
|
valuelen = 10;
|
|
|
|
/* Convert initial characters to fraction */
|
|
base = rangehi - rangelo + 1;
|
|
num = 0.0;
|
|
denom = base;
|
|
while (valuelen-- > 0)
|
|
{
|
|
int ch = *value++;
|
|
|
|
if (ch < rangelo)
|
|
ch = rangelo - 1;
|
|
else if (ch > rangehi)
|
|
ch = rangehi + 1;
|
|
num += ((double) (ch - rangelo)) / denom;
|
|
denom *= base;
|
|
}
|
|
|
|
return num;
|
|
}
|
|
|
|
/*
|
|
* Do convert_to_scalar()'s work for any timevalue data type.
|
|
*
|
|
* On failure (e.g., unsupported typid), set *failure to true;
|
|
* otherwise, that variable is not changed.
|
|
*/
|
|
static double
|
|
convert_timevalue_to_scalar(Datum value, Oid typid, bool *failure)
|
|
{
|
|
switch (typid)
|
|
{
|
|
case TIMESTAMPOID:
|
|
return DatumGetTimestamp(value);
|
|
case TIMESTAMPTZOID:
|
|
return DatumGetTimestampTz(value);
|
|
case DATEOID:
|
|
return date2timestamp_no_overflow(DatumGetDateADT(value));
|
|
case INTERVALOID:
|
|
{
|
|
Interval *interval = DatumGetIntervalP(value);
|
|
|
|
/*
|
|
* Convert the month part of Interval to days using assumed
|
|
* average month length of 365.25/12.0 days. Not too
|
|
* accurate, but plenty good enough for our purposes.
|
|
*/
|
|
return interval->time + interval->day * (double) USECS_PER_DAY +
|
|
interval->month * ((DAYS_PER_YEAR / (double) MONTHS_PER_YEAR) * USECS_PER_DAY);
|
|
}
|
|
case TIMEOID:
|
|
return DatumGetTimeADT(value);
|
|
case TIMETZOID:
|
|
{
|
|
TimeTzADT *timetz = DatumGetTimeTzADTP(value);
|
|
|
|
/* use GMT-equivalent time */
|
|
return (double) (timetz->time + (timetz->zone * 1000000.0));
|
|
}
|
|
}
|
|
|
|
*failure = true;
|
|
return 0;
|
|
}
|
|
|
|
|
|
/*
|
|
* get_restriction_variable
|
|
* Examine the args of a restriction clause to see if it's of the
|
|
* form (variable op pseudoconstant) or (pseudoconstant op variable),
|
|
* where "variable" could be either a Var or an expression in vars of a
|
|
* single relation. If so, extract information about the variable,
|
|
* and also indicate which side it was on and the other argument.
|
|
*
|
|
* Inputs:
|
|
* root: the planner info
|
|
* args: clause argument list
|
|
* varRelid: see specs for restriction selectivity functions
|
|
*
|
|
* Outputs: (these are valid only if true is returned)
|
|
* *vardata: gets information about variable (see examine_variable)
|
|
* *other: gets other clause argument, aggressively reduced to a constant
|
|
* *varonleft: set true if variable is on the left, false if on the right
|
|
*
|
|
* Returns true if a variable is identified, otherwise false.
|
|
*
|
|
* Note: if there are Vars on both sides of the clause, we must fail, because
|
|
* callers are expecting that the other side will act like a pseudoconstant.
|
|
*/
|
|
bool
|
|
get_restriction_variable(PlannerInfo *root, List *args, int varRelid,
|
|
VariableStatData *vardata, Node **other,
|
|
bool *varonleft)
|
|
{
|
|
Node *left,
|
|
*right;
|
|
VariableStatData rdata;
|
|
|
|
/* Fail if not a binary opclause (probably shouldn't happen) */
|
|
if (list_length(args) != 2)
|
|
return false;
|
|
|
|
left = (Node *) linitial(args);
|
|
right = (Node *) lsecond(args);
|
|
|
|
/*
|
|
* Examine both sides. Note that when varRelid is nonzero, Vars of other
|
|
* relations will be treated as pseudoconstants.
|
|
*/
|
|
examine_variable(root, left, varRelid, vardata);
|
|
examine_variable(root, right, varRelid, &rdata);
|
|
|
|
/*
|
|
* If one side is a variable and the other not, we win.
|
|
*/
|
|
if (vardata->rel && rdata.rel == NULL)
|
|
{
|
|
*varonleft = true;
|
|
*other = estimate_expression_value(root, rdata.var);
|
|
/* Assume we need no ReleaseVariableStats(rdata) here */
|
|
return true;
|
|
}
|
|
|
|
if (vardata->rel == NULL && rdata.rel)
|
|
{
|
|
*varonleft = false;
|
|
*other = estimate_expression_value(root, vardata->var);
|
|
/* Assume we need no ReleaseVariableStats(*vardata) here */
|
|
*vardata = rdata;
|
|
return true;
|
|
}
|
|
|
|
/* Oops, clause has wrong structure (probably var op var) */
|
|
ReleaseVariableStats(*vardata);
|
|
ReleaseVariableStats(rdata);
|
|
|
|
return false;
|
|
}
|
|
|
|
/*
|
|
* get_join_variables
|
|
* Apply examine_variable() to each side of a join clause.
|
|
* Also, attempt to identify whether the join clause has the same
|
|
* or reversed sense compared to the SpecialJoinInfo.
|
|
*
|
|
* We consider the join clause "normal" if it is "lhs_var OP rhs_var",
|
|
* or "reversed" if it is "rhs_var OP lhs_var". In complicated cases
|
|
* where we can't tell for sure, we default to assuming it's normal.
|
|
*/
|
|
void
|
|
get_join_variables(PlannerInfo *root, List *args, SpecialJoinInfo *sjinfo,
|
|
VariableStatData *vardata1, VariableStatData *vardata2,
|
|
bool *join_is_reversed)
|
|
{
|
|
Node *left,
|
|
*right;
|
|
|
|
if (list_length(args) != 2)
|
|
elog(ERROR, "join operator should take two arguments");
|
|
|
|
left = (Node *) linitial(args);
|
|
right = (Node *) lsecond(args);
|
|
|
|
examine_variable(root, left, 0, vardata1);
|
|
examine_variable(root, right, 0, vardata2);
|
|
|
|
if (vardata1->rel &&
|
|
bms_is_subset(vardata1->rel->relids, sjinfo->syn_righthand))
|
|
*join_is_reversed = true; /* var1 is on RHS */
|
|
else if (vardata2->rel &&
|
|
bms_is_subset(vardata2->rel->relids, sjinfo->syn_lefthand))
|
|
*join_is_reversed = true; /* var2 is on LHS */
|
|
else
|
|
*join_is_reversed = false;
|
|
}
|
|
|
|
/*
|
|
* examine_variable
|
|
* Try to look up statistical data about an expression.
|
|
* Fill in a VariableStatData struct to describe the expression.
|
|
*
|
|
* Inputs:
|
|
* root: the planner info
|
|
* node: the expression tree to examine
|
|
* varRelid: see specs for restriction selectivity functions
|
|
*
|
|
* Outputs: *vardata is filled as follows:
|
|
* var: the input expression (with any binary relabeling stripped, if
|
|
* it is or contains a variable; but otherwise the type is preserved)
|
|
* rel: RelOptInfo for relation containing variable; NULL if expression
|
|
* contains no Vars (NOTE this could point to a RelOptInfo of a
|
|
* subquery, not one in the current query).
|
|
* statsTuple: the pg_statistic entry for the variable, if one exists;
|
|
* otherwise NULL.
|
|
* freefunc: pointer to a function to release statsTuple with.
|
|
* vartype: exposed type of the expression; this should always match
|
|
* the declared input type of the operator we are estimating for.
|
|
* atttype, atttypmod: actual type/typmod of the "var" expression. This is
|
|
* commonly the same as the exposed type of the variable argument,
|
|
* but can be different in binary-compatible-type cases.
|
|
* isunique: true if we were able to match the var to a unique index or a
|
|
* single-column DISTINCT clause, implying its values are unique for
|
|
* this query. (Caution: this should be trusted for statistical
|
|
* purposes only, since we do not check indimmediate nor verify that
|
|
* the exact same definition of equality applies.)
|
|
* acl_ok: true if current user has permission to read the column(s)
|
|
* underlying the pg_statistic entry. This is consulted by
|
|
* statistic_proc_security_check().
|
|
*
|
|
* Caller is responsible for doing ReleaseVariableStats() before exiting.
|
|
*/
|
|
void
|
|
examine_variable(PlannerInfo *root, Node *node, int varRelid,
|
|
VariableStatData *vardata)
|
|
{
|
|
Node *basenode;
|
|
Relids varnos;
|
|
RelOptInfo *onerel;
|
|
|
|
/* Make sure we don't return dangling pointers in vardata */
|
|
MemSet(vardata, 0, sizeof(VariableStatData));
|
|
|
|
/* Save the exposed type of the expression */
|
|
vardata->vartype = exprType(node);
|
|
|
|
/* Look inside any binary-compatible relabeling */
|
|
|
|
if (IsA(node, RelabelType))
|
|
basenode = (Node *) ((RelabelType *) node)->arg;
|
|
else
|
|
basenode = node;
|
|
|
|
/* Fast path for a simple Var */
|
|
|
|
if (IsA(basenode, Var) &&
|
|
(varRelid == 0 || varRelid == ((Var *) basenode)->varno))
|
|
{
|
|
Var *var = (Var *) basenode;
|
|
|
|
/* Set up result fields other than the stats tuple */
|
|
vardata->var = basenode; /* return Var without relabeling */
|
|
vardata->rel = find_base_rel(root, var->varno);
|
|
vardata->atttype = var->vartype;
|
|
vardata->atttypmod = var->vartypmod;
|
|
vardata->isunique = has_unique_index(vardata->rel, var->varattno);
|
|
|
|
/* Try to locate some stats */
|
|
examine_simple_variable(root, var, vardata);
|
|
|
|
return;
|
|
}
|
|
|
|
/*
|
|
* Okay, it's a more complicated expression. Determine variable
|
|
* membership. Note that when varRelid isn't zero, only vars of that
|
|
* relation are considered "real" vars.
|
|
*/
|
|
varnos = pull_varnos(basenode);
|
|
|
|
onerel = NULL;
|
|
|
|
switch (bms_membership(varnos))
|
|
{
|
|
case BMS_EMPTY_SET:
|
|
/* No Vars at all ... must be pseudo-constant clause */
|
|
break;
|
|
case BMS_SINGLETON:
|
|
if (varRelid == 0 || bms_is_member(varRelid, varnos))
|
|
{
|
|
onerel = find_base_rel(root,
|
|
(varRelid ? varRelid : bms_singleton_member(varnos)));
|
|
vardata->rel = onerel;
|
|
node = basenode; /* strip any relabeling */
|
|
}
|
|
/* else treat it as a constant */
|
|
break;
|
|
case BMS_MULTIPLE:
|
|
if (varRelid == 0)
|
|
{
|
|
/* treat it as a variable of a join relation */
|
|
vardata->rel = find_join_rel(root, varnos);
|
|
node = basenode; /* strip any relabeling */
|
|
}
|
|
else if (bms_is_member(varRelid, varnos))
|
|
{
|
|
/* ignore the vars belonging to other relations */
|
|
vardata->rel = find_base_rel(root, varRelid);
|
|
node = basenode; /* strip any relabeling */
|
|
/* note: no point in expressional-index search here */
|
|
}
|
|
/* else treat it as a constant */
|
|
break;
|
|
}
|
|
|
|
bms_free(varnos);
|
|
|
|
vardata->var = node;
|
|
vardata->atttype = exprType(node);
|
|
vardata->atttypmod = exprTypmod(node);
|
|
|
|
if (onerel)
|
|
{
|
|
/*
|
|
* We have an expression in vars of a single relation. Try to match
|
|
* it to expressional index columns, in hopes of finding some
|
|
* statistics.
|
|
*
|
|
* Note that we consider all index columns including INCLUDE columns,
|
|
* since there could be stats for such columns. But the test for
|
|
* uniqueness needs to be warier.
|
|
*
|
|
* XXX it's conceivable that there are multiple matches with different
|
|
* index opfamilies; if so, we need to pick one that matches the
|
|
* operator we are estimating for. FIXME later.
|
|
*/
|
|
ListCell *ilist;
|
|
|
|
foreach(ilist, onerel->indexlist)
|
|
{
|
|
IndexOptInfo *index = (IndexOptInfo *) lfirst(ilist);
|
|
ListCell *indexpr_item;
|
|
int pos;
|
|
|
|
indexpr_item = list_head(index->indexprs);
|
|
if (indexpr_item == NULL)
|
|
continue; /* no expressions here... */
|
|
|
|
for (pos = 0; pos < index->ncolumns; pos++)
|
|
{
|
|
if (index->indexkeys[pos] == 0)
|
|
{
|
|
Node *indexkey;
|
|
|
|
if (indexpr_item == NULL)
|
|
elog(ERROR, "too few entries in indexprs list");
|
|
indexkey = (Node *) lfirst(indexpr_item);
|
|
if (indexkey && IsA(indexkey, RelabelType))
|
|
indexkey = (Node *) ((RelabelType *) indexkey)->arg;
|
|
if (equal(node, indexkey))
|
|
{
|
|
/*
|
|
* Found a match ... is it a unique index? Tests here
|
|
* should match has_unique_index().
|
|
*/
|
|
if (index->unique &&
|
|
index->nkeycolumns == 1 &&
|
|
pos == 0 &&
|
|
(index->indpred == NIL || index->predOK))
|
|
vardata->isunique = true;
|
|
|
|
/*
|
|
* Has it got stats? We only consider stats for
|
|
* non-partial indexes, since partial indexes probably
|
|
* don't reflect whole-relation statistics; the above
|
|
* check for uniqueness is the only info we take from
|
|
* a partial index.
|
|
*
|
|
* An index stats hook, however, must make its own
|
|
* decisions about what to do with partial indexes.
|
|
*/
|
|
if (get_index_stats_hook &&
|
|
(*get_index_stats_hook) (root, index->indexoid,
|
|
pos + 1, vardata))
|
|
{
|
|
/*
|
|
* The hook took control of acquiring a stats
|
|
* tuple. If it did supply a tuple, it'd better
|
|
* have supplied a freefunc.
|
|
*/
|
|
if (HeapTupleIsValid(vardata->statsTuple) &&
|
|
!vardata->freefunc)
|
|
elog(ERROR, "no function provided to release variable stats with");
|
|
}
|
|
else if (index->indpred == NIL)
|
|
{
|
|
vardata->statsTuple =
|
|
SearchSysCache3(STATRELATTINH,
|
|
ObjectIdGetDatum(index->indexoid),
|
|
Int16GetDatum(pos + 1),
|
|
BoolGetDatum(false));
|
|
vardata->freefunc = ReleaseSysCache;
|
|
|
|
if (HeapTupleIsValid(vardata->statsTuple))
|
|
{
|
|
/* Get index's table for permission check */
|
|
RangeTblEntry *rte;
|
|
Oid userid;
|
|
|
|
rte = planner_rt_fetch(index->rel->relid, root);
|
|
Assert(rte->rtekind == RTE_RELATION);
|
|
|
|
/*
|
|
* Use checkAsUser if it's set, in case we're
|
|
* accessing the table via a view.
|
|
*/
|
|
userid = rte->checkAsUser ? rte->checkAsUser : GetUserId();
|
|
|
|
/*
|
|
* For simplicity, we insist on the whole
|
|
* table being selectable, rather than trying
|
|
* to identify which column(s) the index
|
|
* depends on. Also require all rows to be
|
|
* selectable --- there must be no
|
|
* securityQuals from security barrier views
|
|
* or RLS policies.
|
|
*/
|
|
vardata->acl_ok =
|
|
rte->securityQuals == NIL &&
|
|
(pg_class_aclcheck(rte->relid, userid,
|
|
ACL_SELECT) == ACLCHECK_OK);
|
|
}
|
|
else
|
|
{
|
|
/* suppress leakproofness checks later */
|
|
vardata->acl_ok = true;
|
|
}
|
|
}
|
|
if (vardata->statsTuple)
|
|
break;
|
|
}
|
|
indexpr_item = lnext(index->indexprs, indexpr_item);
|
|
}
|
|
}
|
|
if (vardata->statsTuple)
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
/*
|
|
* examine_simple_variable
|
|
* Handle a simple Var for examine_variable
|
|
*
|
|
* This is split out as a subroutine so that we can recurse to deal with
|
|
* Vars referencing subqueries.
|
|
*
|
|
* We already filled in all the fields of *vardata except for the stats tuple.
|
|
*/
|
|
static void
|
|
examine_simple_variable(PlannerInfo *root, Var *var,
|
|
VariableStatData *vardata)
|
|
{
|
|
RangeTblEntry *rte = root->simple_rte_array[var->varno];
|
|
|
|
Assert(IsA(rte, RangeTblEntry));
|
|
|
|
if (get_relation_stats_hook &&
|
|
(*get_relation_stats_hook) (root, rte, var->varattno, vardata))
|
|
{
|
|
/*
|
|
* The hook took control of acquiring a stats tuple. If it did supply
|
|
* a tuple, it'd better have supplied a freefunc.
|
|
*/
|
|
if (HeapTupleIsValid(vardata->statsTuple) &&
|
|
!vardata->freefunc)
|
|
elog(ERROR, "no function provided to release variable stats with");
|
|
}
|
|
else if (rte->rtekind == RTE_RELATION)
|
|
{
|
|
/*
|
|
* Plain table or parent of an inheritance appendrel, so look up the
|
|
* column in pg_statistic
|
|
*/
|
|
vardata->statsTuple = SearchSysCache3(STATRELATTINH,
|
|
ObjectIdGetDatum(rte->relid),
|
|
Int16GetDatum(var->varattno),
|
|
BoolGetDatum(rte->inh));
|
|
vardata->freefunc = ReleaseSysCache;
|
|
|
|
if (HeapTupleIsValid(vardata->statsTuple))
|
|
{
|
|
Oid userid;
|
|
|
|
/*
|
|
* Check if user has permission to read this column. We require
|
|
* all rows to be accessible, so there must be no securityQuals
|
|
* from security barrier views or RLS policies. Use checkAsUser
|
|
* if it's set, in case we're accessing the table via a view.
|
|
*/
|
|
userid = rte->checkAsUser ? rte->checkAsUser : GetUserId();
|
|
|
|
vardata->acl_ok =
|
|
rte->securityQuals == NIL &&
|
|
((pg_class_aclcheck(rte->relid, userid,
|
|
ACL_SELECT) == ACLCHECK_OK) ||
|
|
(pg_attribute_aclcheck(rte->relid, var->varattno, userid,
|
|
ACL_SELECT) == ACLCHECK_OK));
|
|
}
|
|
else
|
|
{
|
|
/* suppress any possible leakproofness checks later */
|
|
vardata->acl_ok = true;
|
|
}
|
|
}
|
|
else if (rte->rtekind == RTE_SUBQUERY && !rte->inh)
|
|
{
|
|
/*
|
|
* Plain subquery (not one that was converted to an appendrel).
|
|
*/
|
|
Query *subquery = rte->subquery;
|
|
RelOptInfo *rel;
|
|
TargetEntry *ste;
|
|
|
|
/*
|
|
* Punt if it's a whole-row var rather than a plain column reference.
|
|
*/
|
|
if (var->varattno == InvalidAttrNumber)
|
|
return;
|
|
|
|
/*
|
|
* Punt if subquery uses set operations or GROUP BY, as these will
|
|
* mash underlying columns' stats beyond recognition. (Set ops are
|
|
* particularly nasty; if we forged ahead, we would return stats
|
|
* relevant to only the leftmost subselect...) DISTINCT is also
|
|
* problematic, but we check that later because there is a possibility
|
|
* of learning something even with it.
|
|
*/
|
|
if (subquery->setOperations ||
|
|
subquery->groupClause)
|
|
return;
|
|
|
|
/*
|
|
* OK, fetch RelOptInfo for subquery. Note that we don't change the
|
|
* rel returned in vardata, since caller expects it to be a rel of the
|
|
* caller's query level. Because we might already be recursing, we
|
|
* can't use that rel pointer either, but have to look up the Var's
|
|
* rel afresh.
|
|
*/
|
|
rel = find_base_rel(root, var->varno);
|
|
|
|
/* If the subquery hasn't been planned yet, we have to punt */
|
|
if (rel->subroot == NULL)
|
|
return;
|
|
Assert(IsA(rel->subroot, PlannerInfo));
|
|
|
|
/*
|
|
* Switch our attention to the subquery as mangled by the planner. It
|
|
* was okay to look at the pre-planning version for the tests above,
|
|
* but now we need a Var that will refer to the subroot's live
|
|
* RelOptInfos. For instance, if any subquery pullup happened during
|
|
* planning, Vars in the targetlist might have gotten replaced, and we
|
|
* need to see the replacement expressions.
|
|
*/
|
|
subquery = rel->subroot->parse;
|
|
Assert(IsA(subquery, Query));
|
|
|
|
/* Get the subquery output expression referenced by the upper Var */
|
|
ste = get_tle_by_resno(subquery->targetList, var->varattno);
|
|
if (ste == NULL || ste->resjunk)
|
|
elog(ERROR, "subquery %s does not have attribute %d",
|
|
rte->eref->aliasname, var->varattno);
|
|
var = (Var *) ste->expr;
|
|
|
|
/*
|
|
* If subquery uses DISTINCT, we can't make use of any stats for the
|
|
* variable ... but, if it's the only DISTINCT column, we are entitled
|
|
* to consider it unique. We do the test this way so that it works
|
|
* for cases involving DISTINCT ON.
|
|
*/
|
|
if (subquery->distinctClause)
|
|
{
|
|
if (list_length(subquery->distinctClause) == 1 &&
|
|
targetIsInSortList(ste, InvalidOid, subquery->distinctClause))
|
|
vardata->isunique = true;
|
|
/* cannot go further */
|
|
return;
|
|
}
|
|
|
|
/*
|
|
* If the sub-query originated from a view with the security_barrier
|
|
* attribute, we must not look at the variable's statistics, though it
|
|
* seems all right to notice the existence of a DISTINCT clause. So
|
|
* stop here.
|
|
*
|
|
* This is probably a harsher restriction than necessary; it's
|
|
* certainly OK for the selectivity estimator (which is a C function,
|
|
* and therefore omnipotent anyway) to look at the statistics. But
|
|
* many selectivity estimators will happily *invoke the operator
|
|
* function* to try to work out a good estimate - and that's not OK.
|
|
* So for now, don't dig down for stats.
|
|
*/
|
|
if (rte->security_barrier)
|
|
return;
|
|
|
|
/* Can only handle a simple Var of subquery's query level */
|
|
if (var && IsA(var, Var) &&
|
|
var->varlevelsup == 0)
|
|
{
|
|
/*
|
|
* OK, recurse into the subquery. Note that the original setting
|
|
* of vardata->isunique (which will surely be false) is left
|
|
* unchanged in this situation. That's what we want, since even
|
|
* if the underlying column is unique, the subquery may have
|
|
* joined to other tables in a way that creates duplicates.
|
|
*/
|
|
examine_simple_variable(rel->subroot, var, vardata);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
/*
|
|
* Otherwise, the Var comes from a FUNCTION, VALUES, or CTE RTE. (We
|
|
* won't see RTE_JOIN here because join alias Vars have already been
|
|
* flattened.) There's not much we can do with function outputs, but
|
|
* maybe someday try to be smarter about VALUES and/or CTEs.
|
|
*/
|
|
}
|
|
}
|
|
|
|
/*
|
|
* Check whether it is permitted to call func_oid passing some of the
|
|
* pg_statistic data in vardata. We allow this either if the user has SELECT
|
|
* privileges on the table or column underlying the pg_statistic data or if
|
|
* the function is marked leak-proof.
|
|
*/
|
|
bool
|
|
statistic_proc_security_check(VariableStatData *vardata, Oid func_oid)
|
|
{
|
|
if (vardata->acl_ok)
|
|
return true;
|
|
|
|
if (!OidIsValid(func_oid))
|
|
return false;
|
|
|
|
if (get_func_leakproof(func_oid))
|
|
return true;
|
|
|
|
ereport(DEBUG2,
|
|
(errmsg_internal("not using statistics because function \"%s\" is not leak-proof",
|
|
get_func_name(func_oid))));
|
|
return false;
|
|
}
|
|
|
|
/*
|
|
* get_variable_numdistinct
|
|
* Estimate the number of distinct values of a variable.
|
|
*
|
|
* vardata: results of examine_variable
|
|
* *isdefault: set to true if the result is a default rather than based on
|
|
* anything meaningful.
|
|
*
|
|
* NB: be careful to produce a positive integral result, since callers may
|
|
* compare the result to exact integer counts, or might divide by it.
|
|
*/
|
|
double
|
|
get_variable_numdistinct(VariableStatData *vardata, bool *isdefault)
|
|
{
|
|
double stadistinct;
|
|
double stanullfrac = 0.0;
|
|
double ntuples;
|
|
|
|
*isdefault = false;
|
|
|
|
/*
|
|
* Determine the stadistinct value to use. There are cases where we can
|
|
* get an estimate even without a pg_statistic entry, or can get a better
|
|
* value than is in pg_statistic. Grab stanullfrac too if we can find it
|
|
* (otherwise, assume no nulls, for lack of any better idea).
|
|
*/
|
|
if (HeapTupleIsValid(vardata->statsTuple))
|
|
{
|
|
/* Use the pg_statistic entry */
|
|
Form_pg_statistic stats;
|
|
|
|
stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
|
|
stadistinct = stats->stadistinct;
|
|
stanullfrac = stats->stanullfrac;
|
|
}
|
|
else if (vardata->vartype == BOOLOID)
|
|
{
|
|
/*
|
|
* Special-case boolean columns: presumably, two distinct values.
|
|
*
|
|
* Are there any other datatypes we should wire in special estimates
|
|
* for?
|
|
*/
|
|
stadistinct = 2.0;
|
|
}
|
|
else if (vardata->rel && vardata->rel->rtekind == RTE_VALUES)
|
|
{
|
|
/*
|
|
* If the Var represents a column of a VALUES RTE, assume it's unique.
|
|
* This could of course be very wrong, but it should tend to be true
|
|
* in well-written queries. We could consider examining the VALUES'
|
|
* contents to get some real statistics; but that only works if the
|
|
* entries are all constants, and it would be pretty expensive anyway.
|
|
*/
|
|
stadistinct = -1.0; /* unique (and all non null) */
|
|
}
|
|
else
|
|
{
|
|
/*
|
|
* We don't keep statistics for system columns, but in some cases we
|
|
* can infer distinctness anyway.
|
|
*/
|
|
if (vardata->var && IsA(vardata->var, Var))
|
|
{
|
|
switch (((Var *) vardata->var)->varattno)
|
|
{
|
|
case SelfItemPointerAttributeNumber:
|
|
stadistinct = -1.0; /* unique (and all non null) */
|
|
break;
|
|
case TableOidAttributeNumber:
|
|
stadistinct = 1.0; /* only 1 value */
|
|
break;
|
|
default:
|
|
stadistinct = 0.0; /* means "unknown" */
|
|
break;
|
|
}
|
|
}
|
|
else
|
|
stadistinct = 0.0; /* means "unknown" */
|
|
|
|
/*
|
|
* XXX consider using estimate_num_groups on expressions?
|
|
*/
|
|
}
|
|
|
|
/*
|
|
* If there is a unique index or DISTINCT clause for the variable, assume
|
|
* it is unique no matter what pg_statistic says; the statistics could be
|
|
* out of date, or we might have found a partial unique index that proves
|
|
* the var is unique for this query. However, we'd better still believe
|
|
* the null-fraction statistic.
|
|
*/
|
|
if (vardata->isunique)
|
|
stadistinct = -1.0 * (1.0 - stanullfrac);
|
|
|
|
/*
|
|
* If we had an absolute estimate, use that.
|
|
*/
|
|
if (stadistinct > 0.0)
|
|
return clamp_row_est(stadistinct);
|
|
|
|
/*
|
|
* Otherwise we need to get the relation size; punt if not available.
|
|
*/
|
|
if (vardata->rel == NULL)
|
|
{
|
|
*isdefault = true;
|
|
return DEFAULT_NUM_DISTINCT;
|
|
}
|
|
ntuples = vardata->rel->tuples;
|
|
if (ntuples <= 0.0)
|
|
{
|
|
*isdefault = true;
|
|
return DEFAULT_NUM_DISTINCT;
|
|
}
|
|
|
|
/*
|
|
* If we had a relative estimate, use that.
|
|
*/
|
|
if (stadistinct < 0.0)
|
|
return clamp_row_est(-stadistinct * ntuples);
|
|
|
|
/*
|
|
* With no data, estimate ndistinct = ntuples if the table is small, else
|
|
* use default. We use DEFAULT_NUM_DISTINCT as the cutoff for "small" so
|
|
* that the behavior isn't discontinuous.
|
|
*/
|
|
if (ntuples < DEFAULT_NUM_DISTINCT)
|
|
return clamp_row_est(ntuples);
|
|
|
|
*isdefault = true;
|
|
return DEFAULT_NUM_DISTINCT;
|
|
}
|
|
|
|
/*
|
|
* get_variable_range
|
|
* Estimate the minimum and maximum value of the specified variable.
|
|
* If successful, store values in *min and *max, and return true.
|
|
* If no data available, return false.
|
|
*
|
|
* sortop is the "<" comparison operator to use. This should generally
|
|
* be "<" not ">", as only the former is likely to be found in pg_statistic.
|
|
*/
|
|
static bool
|
|
get_variable_range(PlannerInfo *root, VariableStatData *vardata, Oid sortop,
|
|
Datum *min, Datum *max)
|
|
{
|
|
Datum tmin = 0;
|
|
Datum tmax = 0;
|
|
bool have_data = false;
|
|
int16 typLen;
|
|
bool typByVal;
|
|
Oid opfuncoid;
|
|
AttStatsSlot sslot;
|
|
int i;
|
|
|
|
/*
|
|
* XXX It's very tempting to try to use the actual column min and max, if
|
|
* we can get them relatively-cheaply with an index probe. However, since
|
|
* this function is called many times during join planning, that could
|
|
* have unpleasant effects on planning speed. Need more investigation
|
|
* before enabling this.
|
|
*/
|
|
#ifdef NOT_USED
|
|
if (get_actual_variable_range(root, vardata, sortop, min, max))
|
|
return true;
|
|
#endif
|
|
|
|
if (!HeapTupleIsValid(vardata->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 (!statistic_proc_security_check(vardata,
|
|
(opfuncoid = get_opcode(sortop))))
|
|
return false;
|
|
|
|
get_typlenbyval(vardata->atttype, &typLen, &typByVal);
|
|
|
|
/*
|
|
* If there is a histogram, grab the first and last values.
|
|
*
|
|
* If there is a histogram that is sorted with some other operator than
|
|
* the one we want, fail --- this suggests that there is data we can't
|
|
* use.
|
|
*/
|
|
if (get_attstatsslot(&sslot, vardata->statsTuple,
|
|
STATISTIC_KIND_HISTOGRAM, sortop,
|
|
ATTSTATSSLOT_VALUES))
|
|
{
|
|
if (sslot.nvalues > 0)
|
|
{
|
|
tmin = datumCopy(sslot.values[0], typByVal, typLen);
|
|
tmax = datumCopy(sslot.values[sslot.nvalues - 1], typByVal, typLen);
|
|
have_data = true;
|
|
}
|
|
free_attstatsslot(&sslot);
|
|
}
|
|
else if (get_attstatsslot(&sslot, vardata->statsTuple,
|
|
STATISTIC_KIND_HISTOGRAM, InvalidOid,
|
|
0))
|
|
{
|
|
free_attstatsslot(&sslot);
|
|
return false;
|
|
}
|
|
|
|
/*
|
|
* If we have most-common-values info, look for extreme MCVs. This is
|
|
* needed even if we also have a histogram, since the histogram excludes
|
|
* the MCVs. However, usually the MCVs will not be the extreme values, so
|
|
* avoid unnecessary data copying.
|
|
*/
|
|
if (get_attstatsslot(&sslot, vardata->statsTuple,
|
|
STATISTIC_KIND_MCV, InvalidOid,
|
|
ATTSTATSSLOT_VALUES))
|
|
{
|
|
bool tmin_is_mcv = false;
|
|
bool tmax_is_mcv = false;
|
|
FmgrInfo opproc;
|
|
|
|
fmgr_info(opfuncoid, &opproc);
|
|
|
|
for (i = 0; i < sslot.nvalues; i++)
|
|
{
|
|
if (!have_data)
|
|
{
|
|
tmin = tmax = sslot.values[i];
|
|
tmin_is_mcv = tmax_is_mcv = have_data = true;
|
|
continue;
|
|
}
|
|
if (DatumGetBool(FunctionCall2Coll(&opproc,
|
|
sslot.stacoll,
|
|
sslot.values[i], tmin)))
|
|
{
|
|
tmin = sslot.values[i];
|
|
tmin_is_mcv = true;
|
|
}
|
|
if (DatumGetBool(FunctionCall2Coll(&opproc,
|
|
sslot.stacoll,
|
|
tmax, sslot.values[i])))
|
|
{
|
|
tmax = sslot.values[i];
|
|
tmax_is_mcv = true;
|
|
}
|
|
}
|
|
if (tmin_is_mcv)
|
|
tmin = datumCopy(tmin, typByVal, typLen);
|
|
if (tmax_is_mcv)
|
|
tmax = datumCopy(tmax, typByVal, typLen);
|
|
free_attstatsslot(&sslot);
|
|
}
|
|
|
|
*min = tmin;
|
|
*max = tmax;
|
|
return have_data;
|
|
}
|
|
|
|
|
|
/*
|
|
* get_actual_variable_range
|
|
* Attempt to identify the current *actual* minimum and/or maximum
|
|
* of the specified variable, by looking for a suitable btree index
|
|
* and fetching its low and/or high values.
|
|
* If successful, store values in *min and *max, and return true.
|
|
* (Either pointer can be NULL if that endpoint isn't needed.)
|
|
* If no data available, return false.
|
|
*
|
|
* sortop is the "<" comparison operator to use.
|
|
*/
|
|
static bool
|
|
get_actual_variable_range(PlannerInfo *root, VariableStatData *vardata,
|
|
Oid sortop,
|
|
Datum *min, Datum *max)
|
|
{
|
|
bool have_data = false;
|
|
RelOptInfo *rel = vardata->rel;
|
|
RangeTblEntry *rte;
|
|
ListCell *lc;
|
|
|
|
/* No hope if no relation or it doesn't have indexes */
|
|
if (rel == NULL || rel->indexlist == NIL)
|
|
return false;
|
|
/* If it has indexes it must be a plain relation */
|
|
rte = root->simple_rte_array[rel->relid];
|
|
Assert(rte->rtekind == RTE_RELATION);
|
|
|
|
/* Search through the indexes to see if any match our problem */
|
|
foreach(lc, rel->indexlist)
|
|
{
|
|
IndexOptInfo *index = (IndexOptInfo *) lfirst(lc);
|
|
ScanDirection indexscandir;
|
|
|
|
/* Ignore non-btree indexes */
|
|
if (index->relam != BTREE_AM_OID)
|
|
continue;
|
|
|
|
/*
|
|
* Ignore partial indexes --- we only want stats that cover the entire
|
|
* relation.
|
|
*/
|
|
if (index->indpred != NIL)
|
|
continue;
|
|
|
|
/*
|
|
* The index list might include hypothetical indexes inserted by a
|
|
* get_relation_info hook --- don't try to access them.
|
|
*/
|
|
if (index->hypothetical)
|
|
continue;
|
|
|
|
/*
|
|
* The first index column must match the desired variable and sort
|
|
* operator --- but we can use a descending-order index.
|
|
*/
|
|
if (!match_index_to_operand(vardata->var, 0, index))
|
|
continue;
|
|
switch (get_op_opfamily_strategy(sortop, index->sortopfamily[0]))
|
|
{
|
|
case BTLessStrategyNumber:
|
|
if (index->reverse_sort[0])
|
|
indexscandir = BackwardScanDirection;
|
|
else
|
|
indexscandir = ForwardScanDirection;
|
|
break;
|
|
case BTGreaterStrategyNumber:
|
|
if (index->reverse_sort[0])
|
|
indexscandir = ForwardScanDirection;
|
|
else
|
|
indexscandir = BackwardScanDirection;
|
|
break;
|
|
default:
|
|
/* index doesn't match the sortop */
|
|
continue;
|
|
}
|
|
|
|
/*
|
|
* Found a suitable index to extract data from. Set up some data that
|
|
* can be used by both invocations of get_actual_variable_endpoint.
|
|
*/
|
|
{
|
|
MemoryContext tmpcontext;
|
|
MemoryContext oldcontext;
|
|
Relation heapRel;
|
|
Relation indexRel;
|
|
TupleTableSlot *slot;
|
|
int16 typLen;
|
|
bool typByVal;
|
|
ScanKeyData scankeys[1];
|
|
|
|
/* Make sure any cruft gets recycled when we're done */
|
|
tmpcontext = AllocSetContextCreate(CurrentMemoryContext,
|
|
"get_actual_variable_range workspace",
|
|
ALLOCSET_DEFAULT_SIZES);
|
|
oldcontext = MemoryContextSwitchTo(tmpcontext);
|
|
|
|
/*
|
|
* Open the table and index so we can read from them. We should
|
|
* already have some type of lock on each.
|
|
*/
|
|
heapRel = table_open(rte->relid, NoLock);
|
|
indexRel = index_open(index->indexoid, NoLock);
|
|
|
|
/* build some stuff needed for indexscan execution */
|
|
slot = table_slot_create(heapRel, NULL);
|
|
get_typlenbyval(vardata->atttype, &typLen, &typByVal);
|
|
|
|
/* set up an IS NOT NULL scan key so that we ignore nulls */
|
|
ScanKeyEntryInitialize(&scankeys[0],
|
|
SK_ISNULL | SK_SEARCHNOTNULL,
|
|
1, /* index col to scan */
|
|
InvalidStrategy, /* no strategy */
|
|
InvalidOid, /* no strategy subtype */
|
|
InvalidOid, /* no collation */
|
|
InvalidOid, /* no reg proc for this */
|
|
(Datum) 0); /* constant */
|
|
|
|
/* If min is requested ... */
|
|
if (min)
|
|
{
|
|
have_data = get_actual_variable_endpoint(heapRel,
|
|
indexRel,
|
|
indexscandir,
|
|
scankeys,
|
|
typLen,
|
|
typByVal,
|
|
slot,
|
|
oldcontext,
|
|
min);
|
|
}
|
|
else
|
|
{
|
|
/* If min not requested, assume index is nonempty */
|
|
have_data = true;
|
|
}
|
|
|
|
/* If max is requested, and we didn't find the index is empty */
|
|
if (max && have_data)
|
|
{
|
|
/* scan in the opposite direction; all else is the same */
|
|
have_data = get_actual_variable_endpoint(heapRel,
|
|
indexRel,
|
|
-indexscandir,
|
|
scankeys,
|
|
typLen,
|
|
typByVal,
|
|
slot,
|
|
oldcontext,
|
|
max);
|
|
}
|
|
|
|
/* Clean everything up */
|
|
ExecDropSingleTupleTableSlot(slot);
|
|
|
|
index_close(indexRel, NoLock);
|
|
table_close(heapRel, NoLock);
|
|
|
|
MemoryContextSwitchTo(oldcontext);
|
|
MemoryContextDelete(tmpcontext);
|
|
|
|
/* And we're done */
|
|
break;
|
|
}
|
|
}
|
|
|
|
return have_data;
|
|
}
|
|
|
|
/*
|
|
* Get one endpoint datum (min or max depending on indexscandir) from the
|
|
* specified index. Return true if successful, false if index is empty.
|
|
* On success, endpoint value is stored to *endpointDatum (and copied into
|
|
* outercontext).
|
|
*
|
|
* scankeys is a 1-element scankey array set up to reject nulls.
|
|
* typLen/typByVal describe the datatype of the index's first column.
|
|
* tableslot is a slot suitable to hold table tuples, in case we need
|
|
* to probe the heap.
|
|
* (We could compute these values locally, but that would mean computing them
|
|
* twice when get_actual_variable_range needs both the min and the max.)
|
|
*/
|
|
static bool
|
|
get_actual_variable_endpoint(Relation heapRel,
|
|
Relation indexRel,
|
|
ScanDirection indexscandir,
|
|
ScanKey scankeys,
|
|
int16 typLen,
|
|
bool typByVal,
|
|
TupleTableSlot *tableslot,
|
|
MemoryContext outercontext,
|
|
Datum *endpointDatum)
|
|
{
|
|
bool have_data = false;
|
|
SnapshotData SnapshotNonVacuumable;
|
|
IndexScanDesc index_scan;
|
|
Buffer vmbuffer = InvalidBuffer;
|
|
ItemPointer tid;
|
|
Datum values[INDEX_MAX_KEYS];
|
|
bool isnull[INDEX_MAX_KEYS];
|
|
MemoryContext oldcontext;
|
|
|
|
/*
|
|
* We use the index-only-scan machinery for this. With mostly-static
|
|
* tables that's a win because it avoids a heap visit. It's also a win
|
|
* for dynamic data, but the reason is less obvious; read on for details.
|
|
*
|
|
* In principle, we should scan the index with our current active
|
|
* snapshot, which is the best approximation we've got to what the query
|
|
* will see when executed. But that won't be exact if a new snap is taken
|
|
* before running the query, and it can be very expensive if a lot of
|
|
* recently-dead or uncommitted rows exist at the beginning or end of the
|
|
* index (because we'll laboriously fetch each one and reject it).
|
|
* Instead, we use SnapshotNonVacuumable. That will accept recently-dead
|
|
* and uncommitted rows as well as normal visible rows. On the other
|
|
* hand, it will reject known-dead rows, and thus not give a bogus answer
|
|
* when the extreme value has been deleted (unless the deletion was quite
|
|
* recent); that case motivates not using SnapshotAny here.
|
|
*
|
|
* A crucial point here is that SnapshotNonVacuumable, with
|
|
* RecentGlobalXmin as horizon, yields the inverse of the condition that
|
|
* the indexscan will use to decide that index entries are killable (see
|
|
* heap_hot_search_buffer()). Therefore, if the snapshot rejects a tuple
|
|
* (or more precisely, all tuples of a HOT chain) and we have to continue
|
|
* scanning past it, we know that the indexscan will mark that index entry
|
|
* killed. That means that the next get_actual_variable_endpoint() call
|
|
* will not have to re-consider that index entry. In this way we avoid
|
|
* repetitive work when this function is used a lot during planning.
|
|
*
|
|
* But using SnapshotNonVacuumable creates a hazard of its own. In a
|
|
* recently-created index, some index entries may point at "broken" HOT
|
|
* chains in which not all the tuple versions contain data matching the
|
|
* index entry. The live tuple version(s) certainly do match the index,
|
|
* but SnapshotNonVacuumable can accept recently-dead tuple versions that
|
|
* don't match. Hence, if we took data from the selected heap tuple, we
|
|
* might get a bogus answer that's not close to the index extremal value,
|
|
* or could even be NULL. We avoid this hazard because we take the data
|
|
* from the index entry not the heap.
|
|
*/
|
|
InitNonVacuumableSnapshot(SnapshotNonVacuumable, RecentGlobalXmin);
|
|
|
|
index_scan = index_beginscan(heapRel, indexRel,
|
|
&SnapshotNonVacuumable,
|
|
1, 0);
|
|
/* Set it up for index-only scan */
|
|
index_scan->xs_want_itup = true;
|
|
index_rescan(index_scan, scankeys, 1, NULL, 0);
|
|
|
|
/* Fetch first/next tuple in specified direction */
|
|
while ((tid = index_getnext_tid(index_scan, indexscandir)) != NULL)
|
|
{
|
|
if (!VM_ALL_VISIBLE(heapRel,
|
|
ItemPointerGetBlockNumber(tid),
|
|
&vmbuffer))
|
|
{
|
|
/* Rats, we have to visit the heap to check visibility */
|
|
if (!index_fetch_heap(index_scan, tableslot))
|
|
continue; /* no visible tuple, try next index entry */
|
|
|
|
/* We don't actually need the heap tuple for anything */
|
|
ExecClearTuple(tableslot);
|
|
|
|
/*
|
|
* We don't care whether there's more than one visible tuple in
|
|
* the HOT chain; if any are visible, that's good enough.
|
|
*/
|
|
}
|
|
|
|
/*
|
|
* We expect that btree will return data in IndexTuple not HeapTuple
|
|
* format. It's not lossy either.
|
|
*/
|
|
if (!index_scan->xs_itup)
|
|
elog(ERROR, "no data returned for index-only scan");
|
|
if (index_scan->xs_recheck)
|
|
elog(ERROR, "unexpected recheck indication from btree");
|
|
|
|
/* OK to deconstruct the index tuple */
|
|
index_deform_tuple(index_scan->xs_itup,
|
|
index_scan->xs_itupdesc,
|
|
values, isnull);
|
|
|
|
/* Shouldn't have got a null, but be careful */
|
|
if (isnull[0])
|
|
elog(ERROR, "found unexpected null value in index \"%s\"",
|
|
RelationGetRelationName(indexRel));
|
|
|
|
/* Copy the index column value out to caller's context */
|
|
oldcontext = MemoryContextSwitchTo(outercontext);
|
|
*endpointDatum = datumCopy(values[0], typByVal, typLen);
|
|
MemoryContextSwitchTo(oldcontext);
|
|
have_data = true;
|
|
break;
|
|
}
|
|
|
|
if (vmbuffer != InvalidBuffer)
|
|
ReleaseBuffer(vmbuffer);
|
|
index_endscan(index_scan);
|
|
|
|
return have_data;
|
|
}
|
|
|
|
/*
|
|
* find_join_input_rel
|
|
* Look up the input relation for a join.
|
|
*
|
|
* We assume that the input relation's RelOptInfo must have been constructed
|
|
* already.
|
|
*/
|
|
static RelOptInfo *
|
|
find_join_input_rel(PlannerInfo *root, Relids relids)
|
|
{
|
|
RelOptInfo *rel = NULL;
|
|
|
|
switch (bms_membership(relids))
|
|
{
|
|
case BMS_EMPTY_SET:
|
|
/* should not happen */
|
|
break;
|
|
case BMS_SINGLETON:
|
|
rel = find_base_rel(root, bms_singleton_member(relids));
|
|
break;
|
|
case BMS_MULTIPLE:
|
|
rel = find_join_rel(root, relids);
|
|
break;
|
|
}
|
|
|
|
if (rel == NULL)
|
|
elog(ERROR, "could not find RelOptInfo for given relids");
|
|
|
|
return rel;
|
|
}
|
|
|
|
|
|
/*-------------------------------------------------------------------------
|
|
*
|
|
* Index cost estimation functions
|
|
*
|
|
*-------------------------------------------------------------------------
|
|
*/
|
|
|
|
/*
|
|
* Extract the actual indexquals (as RestrictInfos) from an IndexClause list
|
|
*/
|
|
List *
|
|
get_quals_from_indexclauses(List *indexclauses)
|
|
{
|
|
List *result = NIL;
|
|
ListCell *lc;
|
|
|
|
foreach(lc, indexclauses)
|
|
{
|
|
IndexClause *iclause = lfirst_node(IndexClause, lc);
|
|
ListCell *lc2;
|
|
|
|
foreach(lc2, iclause->indexquals)
|
|
{
|
|
RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
|
|
|
|
result = lappend(result, rinfo);
|
|
}
|
|
}
|
|
return result;
|
|
}
|
|
|
|
/*
|
|
* Compute the total evaluation cost of the comparison operands in a list
|
|
* of index qual expressions. Since we know these will be evaluated just
|
|
* once per scan, there's no need to distinguish startup from per-row cost.
|
|
*
|
|
* This can be used either on the result of get_quals_from_indexclauses(),
|
|
* or directly on an indexorderbys list. In both cases, we expect that the
|
|
* index key expression is on the left side of binary clauses.
|
|
*/
|
|
Cost
|
|
index_other_operands_eval_cost(PlannerInfo *root, List *indexquals)
|
|
{
|
|
Cost qual_arg_cost = 0;
|
|
ListCell *lc;
|
|
|
|
foreach(lc, indexquals)
|
|
{
|
|
Expr *clause = (Expr *) lfirst(lc);
|
|
Node *other_operand;
|
|
QualCost index_qual_cost;
|
|
|
|
/*
|
|
* Index quals will have RestrictInfos, indexorderbys won't. Look
|
|
* through RestrictInfo if present.
|
|
*/
|
|
if (IsA(clause, RestrictInfo))
|
|
clause = ((RestrictInfo *) clause)->clause;
|
|
|
|
if (IsA(clause, OpExpr))
|
|
{
|
|
OpExpr *op = (OpExpr *) clause;
|
|
|
|
other_operand = (Node *) lsecond(op->args);
|
|
}
|
|
else if (IsA(clause, RowCompareExpr))
|
|
{
|
|
RowCompareExpr *rc = (RowCompareExpr *) clause;
|
|
|
|
other_operand = (Node *) rc->rargs;
|
|
}
|
|
else if (IsA(clause, ScalarArrayOpExpr))
|
|
{
|
|
ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
|
|
|
|
other_operand = (Node *) lsecond(saop->args);
|
|
}
|
|
else if (IsA(clause, NullTest))
|
|
{
|
|
other_operand = NULL;
|
|
}
|
|
else
|
|
{
|
|
elog(ERROR, "unsupported indexqual type: %d",
|
|
(int) nodeTag(clause));
|
|
other_operand = NULL; /* keep compiler quiet */
|
|
}
|
|
|
|
cost_qual_eval_node(&index_qual_cost, other_operand, root);
|
|
qual_arg_cost += index_qual_cost.startup + index_qual_cost.per_tuple;
|
|
}
|
|
return qual_arg_cost;
|
|
}
|
|
|
|
void
|
|
genericcostestimate(PlannerInfo *root,
|
|
IndexPath *path,
|
|
double loop_count,
|
|
GenericCosts *costs)
|
|
{
|
|
IndexOptInfo *index = path->indexinfo;
|
|
List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
|
|
List *indexOrderBys = path->indexorderbys;
|
|
Cost indexStartupCost;
|
|
Cost indexTotalCost;
|
|
Selectivity indexSelectivity;
|
|
double indexCorrelation;
|
|
double numIndexPages;
|
|
double numIndexTuples;
|
|
double spc_random_page_cost;
|
|
double num_sa_scans;
|
|
double num_outer_scans;
|
|
double num_scans;
|
|
double qual_op_cost;
|
|
double qual_arg_cost;
|
|
List *selectivityQuals;
|
|
ListCell *l;
|
|
|
|
/*
|
|
* If the index is partial, AND the index predicate with the explicitly
|
|
* given indexquals to produce a more accurate idea of the index
|
|
* selectivity.
|
|
*/
|
|
selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
|
|
|
|
/*
|
|
* Check for ScalarArrayOpExpr index quals, and estimate the number of
|
|
* index scans that will be performed.
|
|
*/
|
|
num_sa_scans = 1;
|
|
foreach(l, indexQuals)
|
|
{
|
|
RestrictInfo *rinfo = (RestrictInfo *) lfirst(l);
|
|
|
|
if (IsA(rinfo->clause, ScalarArrayOpExpr))
|
|
{
|
|
ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) rinfo->clause;
|
|
int alength = estimate_array_length(lsecond(saop->args));
|
|
|
|
if (alength > 1)
|
|
num_sa_scans *= alength;
|
|
}
|
|
}
|
|
|
|
/* Estimate the fraction of main-table tuples that will be visited */
|
|
indexSelectivity = clauselist_selectivity(root, selectivityQuals,
|
|
index->rel->relid,
|
|
JOIN_INNER,
|
|
NULL);
|
|
|
|
/*
|
|
* If caller didn't give us an estimate, estimate the number of index
|
|
* tuples that will be visited. We do it in this rather peculiar-looking
|
|
* way in order to get the right answer for partial indexes.
|
|
*/
|
|
numIndexTuples = costs->numIndexTuples;
|
|
if (numIndexTuples <= 0.0)
|
|
{
|
|
numIndexTuples = indexSelectivity * index->rel->tuples;
|
|
|
|
/*
|
|
* The above calculation counts all the tuples visited across all
|
|
* scans induced by ScalarArrayOpExpr nodes. We want to consider the
|
|
* average per-indexscan number, so adjust. This is a handy place to
|
|
* round to integer, too. (If caller supplied tuple estimate, it's
|
|
* responsible for handling these considerations.)
|
|
*/
|
|
numIndexTuples = rint(numIndexTuples / num_sa_scans);
|
|
}
|
|
|
|
/*
|
|
* We can bound the number of tuples by the index size in any case. Also,
|
|
* always estimate at least one tuple is touched, even when
|
|
* indexSelectivity estimate is tiny.
|
|
*/
|
|
if (numIndexTuples > index->tuples)
|
|
numIndexTuples = index->tuples;
|
|
if (numIndexTuples < 1.0)
|
|
numIndexTuples = 1.0;
|
|
|
|
/*
|
|
* Estimate the number of index pages that will be retrieved.
|
|
*
|
|
* We use the simplistic method of taking a pro-rata fraction of the total
|
|
* number of index pages. In effect, this counts only leaf pages and not
|
|
* any overhead such as index metapage or upper tree levels.
|
|
*
|
|
* In practice access to upper index levels is often nearly free because
|
|
* those tend to stay in cache under load; moreover, the cost involved is
|
|
* highly dependent on index type. We therefore ignore such costs here
|
|
* and leave it to the caller to add a suitable charge if needed.
|
|
*/
|
|
if (index->pages > 1 && index->tuples > 1)
|
|
numIndexPages = ceil(numIndexTuples * index->pages / index->tuples);
|
|
else
|
|
numIndexPages = 1.0;
|
|
|
|
/* fetch estimated page cost for tablespace containing index */
|
|
get_tablespace_page_costs(index->reltablespace,
|
|
&spc_random_page_cost,
|
|
NULL);
|
|
|
|
/*
|
|
* Now compute the disk access costs.
|
|
*
|
|
* The above calculations are all per-index-scan. However, if we are in a
|
|
* nestloop inner scan, we can expect the scan to be repeated (with
|
|
* different search keys) for each row of the outer relation. Likewise,
|
|
* ScalarArrayOpExpr quals result in multiple index scans. This creates
|
|
* the potential for cache effects to reduce the number of disk page
|
|
* fetches needed. We want to estimate the average per-scan I/O cost in
|
|
* the presence of caching.
|
|
*
|
|
* We use the Mackert-Lohman formula (see costsize.c for details) to
|
|
* estimate the total number of page fetches that occur. While this
|
|
* wasn't what it was designed for, it seems a reasonable model anyway.
|
|
* Note that we are counting pages not tuples anymore, so we take N = T =
|
|
* index size, as if there were one "tuple" per page.
|
|
*/
|
|
num_outer_scans = loop_count;
|
|
num_scans = num_sa_scans * num_outer_scans;
|
|
|
|
if (num_scans > 1)
|
|
{
|
|
double pages_fetched;
|
|
|
|
/* total page fetches ignoring cache effects */
|
|
pages_fetched = numIndexPages * num_scans;
|
|
|
|
/* use Mackert and Lohman formula to adjust for cache effects */
|
|
pages_fetched = index_pages_fetched(pages_fetched,
|
|
index->pages,
|
|
(double) index->pages,
|
|
root);
|
|
|
|
/*
|
|
* Now compute the total disk access cost, and then report a pro-rated
|
|
* share for each outer scan. (Don't pro-rate for ScalarArrayOpExpr,
|
|
* since that's internal to the indexscan.)
|
|
*/
|
|
indexTotalCost = (pages_fetched * spc_random_page_cost)
|
|
/ num_outer_scans;
|
|
}
|
|
else
|
|
{
|
|
/*
|
|
* For a single index scan, we just charge spc_random_page_cost per
|
|
* page touched.
|
|
*/
|
|
indexTotalCost = numIndexPages * spc_random_page_cost;
|
|
}
|
|
|
|
/*
|
|
* CPU cost: any complex expressions in the indexquals will need to be
|
|
* evaluated once at the start of the scan to reduce them to runtime keys
|
|
* to pass to the index AM (see nodeIndexscan.c). We model the per-tuple
|
|
* CPU costs as cpu_index_tuple_cost plus one cpu_operator_cost per
|
|
* indexqual operator. Because we have numIndexTuples as a per-scan
|
|
* number, we have to multiply by num_sa_scans to get the correct result
|
|
* for ScalarArrayOpExpr cases. Similarly add in costs for any index
|
|
* ORDER BY expressions.
|
|
*
|
|
* Note: this neglects the possible costs of rechecking lossy operators.
|
|
* Detecting that that might be needed seems more expensive than it's
|
|
* worth, though, considering all the other inaccuracies here ...
|
|
*/
|
|
qual_arg_cost = index_other_operands_eval_cost(root, indexQuals) +
|
|
index_other_operands_eval_cost(root, indexOrderBys);
|
|
qual_op_cost = cpu_operator_cost *
|
|
(list_length(indexQuals) + list_length(indexOrderBys));
|
|
|
|
indexStartupCost = qual_arg_cost;
|
|
indexTotalCost += qual_arg_cost;
|
|
indexTotalCost += numIndexTuples * num_sa_scans * (cpu_index_tuple_cost + qual_op_cost);
|
|
|
|
/*
|
|
* Generic assumption about index correlation: there isn't any.
|
|
*/
|
|
indexCorrelation = 0.0;
|
|
|
|
/*
|
|
* Return everything to caller.
|
|
*/
|
|
costs->indexStartupCost = indexStartupCost;
|
|
costs->indexTotalCost = indexTotalCost;
|
|
costs->indexSelectivity = indexSelectivity;
|
|
costs->indexCorrelation = indexCorrelation;
|
|
costs->numIndexPages = numIndexPages;
|
|
costs->numIndexTuples = numIndexTuples;
|
|
costs->spc_random_page_cost = spc_random_page_cost;
|
|
costs->num_sa_scans = num_sa_scans;
|
|
}
|
|
|
|
/*
|
|
* If the index is partial, add its predicate to the given qual list.
|
|
*
|
|
* ANDing the index predicate with the explicitly given indexquals produces
|
|
* a more accurate idea of the index's selectivity. However, we need to be
|
|
* careful not to insert redundant clauses, because clauselist_selectivity()
|
|
* is easily fooled into computing a too-low selectivity estimate. Our
|
|
* approach is to add only the predicate clause(s) that cannot be proven to
|
|
* be implied by the given indexquals. This successfully handles cases such
|
|
* as a qual "x = 42" used with a partial index "WHERE x >= 40 AND x < 50".
|
|
* There are many other cases where we won't detect redundancy, leading to a
|
|
* too-low selectivity estimate, which will bias the system in favor of using
|
|
* partial indexes where possible. That is not necessarily bad though.
|
|
*
|
|
* Note that indexQuals contains RestrictInfo nodes while the indpred
|
|
* does not, so the output list will be mixed. This is OK for both
|
|
* predicate_implied_by() and clauselist_selectivity(), but might be
|
|
* problematic if the result were passed to other things.
|
|
*/
|
|
List *
|
|
add_predicate_to_index_quals(IndexOptInfo *index, List *indexQuals)
|
|
{
|
|
List *predExtraQuals = NIL;
|
|
ListCell *lc;
|
|
|
|
if (index->indpred == NIL)
|
|
return indexQuals;
|
|
|
|
foreach(lc, index->indpred)
|
|
{
|
|
Node *predQual = (Node *) lfirst(lc);
|
|
List *oneQual = list_make1(predQual);
|
|
|
|
if (!predicate_implied_by(oneQual, indexQuals, false))
|
|
predExtraQuals = list_concat(predExtraQuals, oneQual);
|
|
}
|
|
return list_concat(predExtraQuals, indexQuals);
|
|
}
|
|
|
|
|
|
void
|
|
btcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
|
|
Cost *indexStartupCost, Cost *indexTotalCost,
|
|
Selectivity *indexSelectivity, double *indexCorrelation,
|
|
double *indexPages)
|
|
{
|
|
IndexOptInfo *index = path->indexinfo;
|
|
GenericCosts costs;
|
|
Oid relid;
|
|
AttrNumber colnum;
|
|
VariableStatData vardata;
|
|
double numIndexTuples;
|
|
Cost descentCost;
|
|
List *indexBoundQuals;
|
|
int indexcol;
|
|
bool eqQualHere;
|
|
bool found_saop;
|
|
bool found_is_null_op;
|
|
double num_sa_scans;
|
|
ListCell *lc;
|
|
|
|
/*
|
|
* For a btree scan, only leading '=' quals plus inequality quals for the
|
|
* immediately next attribute contribute to index selectivity (these are
|
|
* the "boundary quals" that determine the starting and stopping points of
|
|
* the index scan). Additional quals can suppress visits to the heap, so
|
|
* it's OK to count them in indexSelectivity, but they should not count
|
|
* for estimating numIndexTuples. So we must examine the given indexquals
|
|
* to find out which ones count as boundary quals. We rely on the
|
|
* knowledge that they are given in index column order.
|
|
*
|
|
* For a RowCompareExpr, we consider only the first column, just as
|
|
* rowcomparesel() does.
|
|
*
|
|
* If there's a ScalarArrayOpExpr in the quals, we'll actually perform N
|
|
* index scans not one, but the ScalarArrayOpExpr's operator can be
|
|
* considered to act the same as it normally does.
|
|
*/
|
|
indexBoundQuals = NIL;
|
|
indexcol = 0;
|
|
eqQualHere = false;
|
|
found_saop = false;
|
|
found_is_null_op = false;
|
|
num_sa_scans = 1;
|
|
foreach(lc, path->indexclauses)
|
|
{
|
|
IndexClause *iclause = lfirst_node(IndexClause, lc);
|
|
ListCell *lc2;
|
|
|
|
if (indexcol != iclause->indexcol)
|
|
{
|
|
/* Beginning of a new column's quals */
|
|
if (!eqQualHere)
|
|
break; /* done if no '=' qual for indexcol */
|
|
eqQualHere = false;
|
|
indexcol++;
|
|
if (indexcol != iclause->indexcol)
|
|
break; /* no quals at all for indexcol */
|
|
}
|
|
|
|
/* Examine each indexqual associated with this index clause */
|
|
foreach(lc2, iclause->indexquals)
|
|
{
|
|
RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
|
|
Expr *clause = rinfo->clause;
|
|
Oid clause_op = InvalidOid;
|
|
int op_strategy;
|
|
|
|
if (IsA(clause, OpExpr))
|
|
{
|
|
OpExpr *op = (OpExpr *) clause;
|
|
|
|
clause_op = op->opno;
|
|
}
|
|
else if (IsA(clause, RowCompareExpr))
|
|
{
|
|
RowCompareExpr *rc = (RowCompareExpr *) clause;
|
|
|
|
clause_op = linitial_oid(rc->opnos);
|
|
}
|
|
else if (IsA(clause, ScalarArrayOpExpr))
|
|
{
|
|
ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
|
|
Node *other_operand = (Node *) lsecond(saop->args);
|
|
int alength = estimate_array_length(other_operand);
|
|
|
|
clause_op = saop->opno;
|
|
found_saop = true;
|
|
/* count number of SA scans induced by indexBoundQuals only */
|
|
if (alength > 1)
|
|
num_sa_scans *= alength;
|
|
}
|
|
else if (IsA(clause, NullTest))
|
|
{
|
|
NullTest *nt = (NullTest *) clause;
|
|
|
|
if (nt->nulltesttype == IS_NULL)
|
|
{
|
|
found_is_null_op = true;
|
|
/* IS NULL is like = for selectivity purposes */
|
|
eqQualHere = true;
|
|
}
|
|
}
|
|
else
|
|
elog(ERROR, "unsupported indexqual type: %d",
|
|
(int) nodeTag(clause));
|
|
|
|
/* check for equality operator */
|
|
if (OidIsValid(clause_op))
|
|
{
|
|
op_strategy = get_op_opfamily_strategy(clause_op,
|
|
index->opfamily[indexcol]);
|
|
Assert(op_strategy != 0); /* not a member of opfamily?? */
|
|
if (op_strategy == BTEqualStrategyNumber)
|
|
eqQualHere = true;
|
|
}
|
|
|
|
indexBoundQuals = lappend(indexBoundQuals, rinfo);
|
|
}
|
|
}
|
|
|
|
/*
|
|
* If index is unique and we found an '=' clause for each column, we can
|
|
* just assume numIndexTuples = 1 and skip the expensive
|
|
* clauselist_selectivity calculations. However, a ScalarArrayOp or
|
|
* NullTest invalidates that theory, even though it sets eqQualHere.
|
|
*/
|
|
if (index->unique &&
|
|
indexcol == index->nkeycolumns - 1 &&
|
|
eqQualHere &&
|
|
!found_saop &&
|
|
!found_is_null_op)
|
|
numIndexTuples = 1.0;
|
|
else
|
|
{
|
|
List *selectivityQuals;
|
|
Selectivity btreeSelectivity;
|
|
|
|
/*
|
|
* If the index is partial, AND the index predicate with the
|
|
* index-bound quals to produce a more accurate idea of the number of
|
|
* rows covered by the bound conditions.
|
|
*/
|
|
selectivityQuals = add_predicate_to_index_quals(index, indexBoundQuals);
|
|
|
|
btreeSelectivity = clauselist_selectivity(root, selectivityQuals,
|
|
index->rel->relid,
|
|
JOIN_INNER,
|
|
NULL);
|
|
numIndexTuples = btreeSelectivity * index->rel->tuples;
|
|
|
|
/*
|
|
* As in genericcostestimate(), we have to adjust for any
|
|
* ScalarArrayOpExpr quals included in indexBoundQuals, and then round
|
|
* to integer.
|
|
*/
|
|
numIndexTuples = rint(numIndexTuples / num_sa_scans);
|
|
}
|
|
|
|
/*
|
|
* Now do generic index cost estimation.
|
|
*/
|
|
MemSet(&costs, 0, sizeof(costs));
|
|
costs.numIndexTuples = numIndexTuples;
|
|
|
|
genericcostestimate(root, path, loop_count, &costs);
|
|
|
|
/*
|
|
* Add a CPU-cost component to represent the costs of initial btree
|
|
* descent. We don't charge any I/O cost for touching upper btree levels,
|
|
* since they tend to stay in cache, but we still have to do about log2(N)
|
|
* comparisons to descend a btree of N leaf tuples. We charge one
|
|
* cpu_operator_cost per comparison.
|
|
*
|
|
* If there are ScalarArrayOpExprs, charge this once per SA scan. The
|
|
* ones after the first one are not startup cost so far as the overall
|
|
* plan is concerned, so add them only to "total" cost.
|
|
*/
|
|
if (index->tuples > 1) /* avoid computing log(0) */
|
|
{
|
|
descentCost = ceil(log(index->tuples) / log(2.0)) * cpu_operator_cost;
|
|
costs.indexStartupCost += descentCost;
|
|
costs.indexTotalCost += costs.num_sa_scans * descentCost;
|
|
}
|
|
|
|
/*
|
|
* Even though we're not charging I/O cost for touching upper btree pages,
|
|
* it's still reasonable to charge some CPU cost per page descended
|
|
* through. Moreover, if we had no such charge at all, bloated indexes
|
|
* would appear to have the same search cost as unbloated ones, at least
|
|
* in cases where only a single leaf page is expected to be visited. This
|
|
* cost is somewhat arbitrarily set at 50x cpu_operator_cost per page
|
|
* touched. The number of such pages is btree tree height plus one (ie,
|
|
* we charge for the leaf page too). As above, charge once per SA scan.
|
|
*/
|
|
descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost;
|
|
costs.indexStartupCost += descentCost;
|
|
costs.indexTotalCost += costs.num_sa_scans * descentCost;
|
|
|
|
/*
|
|
* If we can get an estimate of the first column's ordering correlation C
|
|
* from pg_statistic, estimate the index correlation as C for a
|
|
* single-column index, or C * 0.75 for multiple columns. (The idea here
|
|
* is that multiple columns dilute the importance of the first column's
|
|
* ordering, but don't negate it entirely. Before 8.0 we divided the
|
|
* correlation by the number of columns, but that seems too strong.)
|
|
*/
|
|
MemSet(&vardata, 0, sizeof(vardata));
|
|
|
|
if (index->indexkeys[0] != 0)
|
|
{
|
|
/* Simple variable --- look to stats for the underlying table */
|
|
RangeTblEntry *rte = planner_rt_fetch(index->rel->relid, root);
|
|
|
|
Assert(rte->rtekind == RTE_RELATION);
|
|
relid = rte->relid;
|
|
Assert(relid != InvalidOid);
|
|
colnum = index->indexkeys[0];
|
|
|
|
if (get_relation_stats_hook &&
|
|
(*get_relation_stats_hook) (root, rte, colnum, &vardata))
|
|
{
|
|
/*
|
|
* The hook took control of acquiring a stats tuple. If it did
|
|
* supply a tuple, it'd better have supplied a freefunc.
|
|
*/
|
|
if (HeapTupleIsValid(vardata.statsTuple) &&
|
|
!vardata.freefunc)
|
|
elog(ERROR, "no function provided to release variable stats with");
|
|
}
|
|
else
|
|
{
|
|
vardata.statsTuple = SearchSysCache3(STATRELATTINH,
|
|
ObjectIdGetDatum(relid),
|
|
Int16GetDatum(colnum),
|
|
BoolGetDatum(rte->inh));
|
|
vardata.freefunc = ReleaseSysCache;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
/* Expression --- maybe there are stats for the index itself */
|
|
relid = index->indexoid;
|
|
colnum = 1;
|
|
|
|
if (get_index_stats_hook &&
|
|
(*get_index_stats_hook) (root, relid, colnum, &vardata))
|
|
{
|
|
/*
|
|
* The hook took control of acquiring a stats tuple. If it did
|
|
* supply a tuple, it'd better have supplied a freefunc.
|
|
*/
|
|
if (HeapTupleIsValid(vardata.statsTuple) &&
|
|
!vardata.freefunc)
|
|
elog(ERROR, "no function provided to release variable stats with");
|
|
}
|
|
else
|
|
{
|
|
vardata.statsTuple = SearchSysCache3(STATRELATTINH,
|
|
ObjectIdGetDatum(relid),
|
|
Int16GetDatum(colnum),
|
|
BoolGetDatum(false));
|
|
vardata.freefunc = ReleaseSysCache;
|
|
}
|
|
}
|
|
|
|
if (HeapTupleIsValid(vardata.statsTuple))
|
|
{
|
|
Oid sortop;
|
|
AttStatsSlot sslot;
|
|
|
|
sortop = get_opfamily_member(index->opfamily[0],
|
|
index->opcintype[0],
|
|
index->opcintype[0],
|
|
BTLessStrategyNumber);
|
|
if (OidIsValid(sortop) &&
|
|
get_attstatsslot(&sslot, vardata.statsTuple,
|
|
STATISTIC_KIND_CORRELATION, sortop,
|
|
ATTSTATSSLOT_NUMBERS))
|
|
{
|
|
double varCorrelation;
|
|
|
|
Assert(sslot.nnumbers == 1);
|
|
varCorrelation = sslot.numbers[0];
|
|
|
|
if (index->reverse_sort[0])
|
|
varCorrelation = -varCorrelation;
|
|
|
|
if (index->nkeycolumns > 1)
|
|
costs.indexCorrelation = varCorrelation * 0.75;
|
|
else
|
|
costs.indexCorrelation = varCorrelation;
|
|
|
|
free_attstatsslot(&sslot);
|
|
}
|
|
}
|
|
|
|
ReleaseVariableStats(vardata);
|
|
|
|
*indexStartupCost = costs.indexStartupCost;
|
|
*indexTotalCost = costs.indexTotalCost;
|
|
*indexSelectivity = costs.indexSelectivity;
|
|
*indexCorrelation = costs.indexCorrelation;
|
|
*indexPages = costs.numIndexPages;
|
|
}
|
|
|
|
void
|
|
hashcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
|
|
Cost *indexStartupCost, Cost *indexTotalCost,
|
|
Selectivity *indexSelectivity, double *indexCorrelation,
|
|
double *indexPages)
|
|
{
|
|
GenericCosts costs;
|
|
|
|
MemSet(&costs, 0, sizeof(costs));
|
|
|
|
genericcostestimate(root, path, loop_count, &costs);
|
|
|
|
/*
|
|
* A hash index has no descent costs as such, since the index AM can go
|
|
* directly to the target bucket after computing the hash value. There
|
|
* are a couple of other hash-specific costs that we could conceivably add
|
|
* here, though:
|
|
*
|
|
* Ideally we'd charge spc_random_page_cost for each page in the target
|
|
* bucket, not just the numIndexPages pages that genericcostestimate
|
|
* thought we'd visit. However in most cases we don't know which bucket
|
|
* that will be. There's no point in considering the average bucket size
|
|
* because the hash AM makes sure that's always one page.
|
|
*
|
|
* Likewise, we could consider charging some CPU for each index tuple in
|
|
* the bucket, if we knew how many there were. But the per-tuple cost is
|
|
* just a hash value comparison, not a general datatype-dependent
|
|
* comparison, so any such charge ought to be quite a bit less than
|
|
* cpu_operator_cost; which makes it probably not worth worrying about.
|
|
*
|
|
* A bigger issue is that chance hash-value collisions will result in
|
|
* wasted probes into the heap. We don't currently attempt to model this
|
|
* cost on the grounds that it's rare, but maybe it's not rare enough.
|
|
* (Any fix for this ought to consider the generic lossy-operator problem,
|
|
* though; it's not entirely hash-specific.)
|
|
*/
|
|
|
|
*indexStartupCost = costs.indexStartupCost;
|
|
*indexTotalCost = costs.indexTotalCost;
|
|
*indexSelectivity = costs.indexSelectivity;
|
|
*indexCorrelation = costs.indexCorrelation;
|
|
*indexPages = costs.numIndexPages;
|
|
}
|
|
|
|
void
|
|
gistcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
|
|
Cost *indexStartupCost, Cost *indexTotalCost,
|
|
Selectivity *indexSelectivity, double *indexCorrelation,
|
|
double *indexPages)
|
|
{
|
|
IndexOptInfo *index = path->indexinfo;
|
|
GenericCosts costs;
|
|
Cost descentCost;
|
|
|
|
MemSet(&costs, 0, sizeof(costs));
|
|
|
|
genericcostestimate(root, path, loop_count, &costs);
|
|
|
|
/*
|
|
* We model index descent costs similarly to those for btree, but to do
|
|
* that we first need an idea of the tree height. We somewhat arbitrarily
|
|
* assume that the fanout is 100, meaning the tree height is at most
|
|
* log100(index->pages).
|
|
*
|
|
* Although this computation isn't really expensive enough to require
|
|
* caching, we might as well use index->tree_height to cache it.
|
|
*/
|
|
if (index->tree_height < 0) /* unknown? */
|
|
{
|
|
if (index->pages > 1) /* avoid computing log(0) */
|
|
index->tree_height = (int) (log(index->pages) / log(100.0));
|
|
else
|
|
index->tree_height = 0;
|
|
}
|
|
|
|
/*
|
|
* Add a CPU-cost component to represent the costs of initial descent. We
|
|
* just use log(N) here not log2(N) since the branching factor isn't
|
|
* necessarily two anyway. As for btree, charge once per SA scan.
|
|
*/
|
|
if (index->tuples > 1) /* avoid computing log(0) */
|
|
{
|
|
descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
|
|
costs.indexStartupCost += descentCost;
|
|
costs.indexTotalCost += costs.num_sa_scans * descentCost;
|
|
}
|
|
|
|
/*
|
|
* Likewise add a per-page charge, calculated the same as for btrees.
|
|
*/
|
|
descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost;
|
|
costs.indexStartupCost += descentCost;
|
|
costs.indexTotalCost += costs.num_sa_scans * descentCost;
|
|
|
|
*indexStartupCost = costs.indexStartupCost;
|
|
*indexTotalCost = costs.indexTotalCost;
|
|
*indexSelectivity = costs.indexSelectivity;
|
|
*indexCorrelation = costs.indexCorrelation;
|
|
*indexPages = costs.numIndexPages;
|
|
}
|
|
|
|
void
|
|
spgcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
|
|
Cost *indexStartupCost, Cost *indexTotalCost,
|
|
Selectivity *indexSelectivity, double *indexCorrelation,
|
|
double *indexPages)
|
|
{
|
|
IndexOptInfo *index = path->indexinfo;
|
|
GenericCosts costs;
|
|
Cost descentCost;
|
|
|
|
MemSet(&costs, 0, sizeof(costs));
|
|
|
|
genericcostestimate(root, path, loop_count, &costs);
|
|
|
|
/*
|
|
* We model index descent costs similarly to those for btree, but to do
|
|
* that we first need an idea of the tree height. We somewhat arbitrarily
|
|
* assume that the fanout is 100, meaning the tree height is at most
|
|
* log100(index->pages).
|
|
*
|
|
* Although this computation isn't really expensive enough to require
|
|
* caching, we might as well use index->tree_height to cache it.
|
|
*/
|
|
if (index->tree_height < 0) /* unknown? */
|
|
{
|
|
if (index->pages > 1) /* avoid computing log(0) */
|
|
index->tree_height = (int) (log(index->pages) / log(100.0));
|
|
else
|
|
index->tree_height = 0;
|
|
}
|
|
|
|
/*
|
|
* Add a CPU-cost component to represent the costs of initial descent. We
|
|
* just use log(N) here not log2(N) since the branching factor isn't
|
|
* necessarily two anyway. As for btree, charge once per SA scan.
|
|
*/
|
|
if (index->tuples > 1) /* avoid computing log(0) */
|
|
{
|
|
descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
|
|
costs.indexStartupCost += descentCost;
|
|
costs.indexTotalCost += costs.num_sa_scans * descentCost;
|
|
}
|
|
|
|
/*
|
|
* Likewise add a per-page charge, calculated the same as for btrees.
|
|
*/
|
|
descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost;
|
|
costs.indexStartupCost += descentCost;
|
|
costs.indexTotalCost += costs.num_sa_scans * descentCost;
|
|
|
|
*indexStartupCost = costs.indexStartupCost;
|
|
*indexTotalCost = costs.indexTotalCost;
|
|
*indexSelectivity = costs.indexSelectivity;
|
|
*indexCorrelation = costs.indexCorrelation;
|
|
*indexPages = costs.numIndexPages;
|
|
}
|
|
|
|
|
|
/*
|
|
* Support routines for gincostestimate
|
|
*/
|
|
|
|
typedef struct
|
|
{
|
|
bool haveFullScan;
|
|
double partialEntries;
|
|
double exactEntries;
|
|
double searchEntries;
|
|
double arrayScans;
|
|
} GinQualCounts;
|
|
|
|
/*
|
|
* Estimate the number of index terms that need to be searched for while
|
|
* testing the given GIN query, and increment the counts in *counts
|
|
* appropriately. If the query is unsatisfiable, return false.
|
|
*/
|
|
static bool
|
|
gincost_pattern(IndexOptInfo *index, int indexcol,
|
|
Oid clause_op, Datum query,
|
|
GinQualCounts *counts)
|
|
{
|
|
Oid extractProcOid;
|
|
Oid collation;
|
|
int strategy_op;
|
|
Oid lefttype,
|
|
righttype;
|
|
int32 nentries = 0;
|
|
bool *partial_matches = NULL;
|
|
Pointer *extra_data = NULL;
|
|
bool *nullFlags = NULL;
|
|
int32 searchMode = GIN_SEARCH_MODE_DEFAULT;
|
|
int32 i;
|
|
|
|
Assert(indexcol < index->nkeycolumns);
|
|
|
|
/*
|
|
* Get the operator's strategy number and declared input data types within
|
|
* the index opfamily. (We don't need the latter, but we use
|
|
* get_op_opfamily_properties because it will throw error if it fails to
|
|
* find a matching pg_amop entry.)
|
|
*/
|
|
get_op_opfamily_properties(clause_op, index->opfamily[indexcol], false,
|
|
&strategy_op, &lefttype, &righttype);
|
|
|
|
/*
|
|
* GIN always uses the "default" support functions, which are those with
|
|
* lefttype == righttype == the opclass' opcintype (see
|
|
* IndexSupportInitialize in relcache.c).
|
|
*/
|
|
extractProcOid = get_opfamily_proc(index->opfamily[indexcol],
|
|
index->opcintype[indexcol],
|
|
index->opcintype[indexcol],
|
|
GIN_EXTRACTQUERY_PROC);
|
|
|
|
if (!OidIsValid(extractProcOid))
|
|
{
|
|
/* should not happen; throw same error as index_getprocinfo */
|
|
elog(ERROR, "missing support function %d for attribute %d of index \"%s\"",
|
|
GIN_EXTRACTQUERY_PROC, indexcol + 1,
|
|
get_rel_name(index->indexoid));
|
|
}
|
|
|
|
/*
|
|
* Choose collation to pass to extractProc (should match initGinState).
|
|
*/
|
|
if (OidIsValid(index->indexcollations[indexcol]))
|
|
collation = index->indexcollations[indexcol];
|
|
else
|
|
collation = DEFAULT_COLLATION_OID;
|
|
|
|
OidFunctionCall7Coll(extractProcOid,
|
|
collation,
|
|
query,
|
|
PointerGetDatum(&nentries),
|
|
UInt16GetDatum(strategy_op),
|
|
PointerGetDatum(&partial_matches),
|
|
PointerGetDatum(&extra_data),
|
|
PointerGetDatum(&nullFlags),
|
|
PointerGetDatum(&searchMode));
|
|
|
|
if (nentries <= 0 && searchMode == GIN_SEARCH_MODE_DEFAULT)
|
|
{
|
|
/* No match is possible */
|
|
return false;
|
|
}
|
|
|
|
for (i = 0; i < nentries; i++)
|
|
{
|
|
/*
|
|
* For partial match we haven't any information to estimate number of
|
|
* matched entries in index, so, we just estimate it as 100
|
|
*/
|
|
if (partial_matches && partial_matches[i])
|
|
counts->partialEntries += 100;
|
|
else
|
|
counts->exactEntries++;
|
|
|
|
counts->searchEntries++;
|
|
}
|
|
|
|
if (searchMode == GIN_SEARCH_MODE_INCLUDE_EMPTY)
|
|
{
|
|
/* Treat "include empty" like an exact-match item */
|
|
counts->exactEntries++;
|
|
counts->searchEntries++;
|
|
}
|
|
else if (searchMode != GIN_SEARCH_MODE_DEFAULT)
|
|
{
|
|
/* It's GIN_SEARCH_MODE_ALL */
|
|
counts->haveFullScan = true;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
/*
|
|
* Estimate the number of index terms that need to be searched for while
|
|
* testing the given GIN index clause, and increment the counts in *counts
|
|
* appropriately. If the query is unsatisfiable, return false.
|
|
*/
|
|
static bool
|
|
gincost_opexpr(PlannerInfo *root,
|
|
IndexOptInfo *index,
|
|
int indexcol,
|
|
OpExpr *clause,
|
|
GinQualCounts *counts)
|
|
{
|
|
Oid clause_op = clause->opno;
|
|
Node *operand = (Node *) lsecond(clause->args);
|
|
|
|
/* aggressively reduce to a constant, and look through relabeling */
|
|
operand = estimate_expression_value(root, operand);
|
|
|
|
if (IsA(operand, RelabelType))
|
|
operand = (Node *) ((RelabelType *) operand)->arg;
|
|
|
|
/*
|
|
* It's impossible to call extractQuery method for unknown operand. So
|
|
* unless operand is a Const we can't do much; just assume there will be
|
|
* one ordinary search entry from the operand at runtime.
|
|
*/
|
|
if (!IsA(operand, Const))
|
|
{
|
|
counts->exactEntries++;
|
|
counts->searchEntries++;
|
|
return true;
|
|
}
|
|
|
|
/* If Const is null, there can be no matches */
|
|
if (((Const *) operand)->constisnull)
|
|
return false;
|
|
|
|
/* Otherwise, apply extractQuery and get the actual term counts */
|
|
return gincost_pattern(index, indexcol, clause_op,
|
|
((Const *) operand)->constvalue,
|
|
counts);
|
|
}
|
|
|
|
/*
|
|
* Estimate the number of index terms that need to be searched for while
|
|
* testing the given GIN index clause, and increment the counts in *counts
|
|
* appropriately. If the query is unsatisfiable, return false.
|
|
*
|
|
* A ScalarArrayOpExpr will give rise to N separate indexscans at runtime,
|
|
* each of which involves one value from the RHS array, plus all the
|
|
* non-array quals (if any). To model this, we average the counts across
|
|
* the RHS elements, and add the averages to the counts in *counts (which
|
|
* correspond to per-indexscan costs). We also multiply counts->arrayScans
|
|
* by N, causing gincostestimate to scale up its estimates accordingly.
|
|
*/
|
|
static bool
|
|
gincost_scalararrayopexpr(PlannerInfo *root,
|
|
IndexOptInfo *index,
|
|
int indexcol,
|
|
ScalarArrayOpExpr *clause,
|
|
double numIndexEntries,
|
|
GinQualCounts *counts)
|
|
{
|
|
Oid clause_op = clause->opno;
|
|
Node *rightop = (Node *) lsecond(clause->args);
|
|
ArrayType *arrayval;
|
|
int16 elmlen;
|
|
bool elmbyval;
|
|
char elmalign;
|
|
int numElems;
|
|
Datum *elemValues;
|
|
bool *elemNulls;
|
|
GinQualCounts arraycounts;
|
|
int numPossible = 0;
|
|
int i;
|
|
|
|
Assert(clause->useOr);
|
|
|
|
/* aggressively reduce to a constant, and look through relabeling */
|
|
rightop = estimate_expression_value(root, rightop);
|
|
|
|
if (IsA(rightop, RelabelType))
|
|
rightop = (Node *) ((RelabelType *) rightop)->arg;
|
|
|
|
/*
|
|
* It's impossible to call extractQuery method for unknown operand. So
|
|
* unless operand is a Const we can't do much; just assume there will be
|
|
* one ordinary search entry from each array entry at runtime, and fall
|
|
* back on a probably-bad estimate of the number of array entries.
|
|
*/
|
|
if (!IsA(rightop, Const))
|
|
{
|
|
counts->exactEntries++;
|
|
counts->searchEntries++;
|
|
counts->arrayScans *= estimate_array_length(rightop);
|
|
return true;
|
|
}
|
|
|
|
/* If Const is null, there can be no matches */
|
|
if (((Const *) rightop)->constisnull)
|
|
return false;
|
|
|
|
/* Otherwise, extract the array elements and iterate over them */
|
|
arrayval = DatumGetArrayTypeP(((Const *) rightop)->constvalue);
|
|
get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
|
|
&elmlen, &elmbyval, &elmalign);
|
|
deconstruct_array(arrayval,
|
|
ARR_ELEMTYPE(arrayval),
|
|
elmlen, elmbyval, elmalign,
|
|
&elemValues, &elemNulls, &numElems);
|
|
|
|
memset(&arraycounts, 0, sizeof(arraycounts));
|
|
|
|
for (i = 0; i < numElems; i++)
|
|
{
|
|
GinQualCounts elemcounts;
|
|
|
|
/* NULL can't match anything, so ignore, as the executor will */
|
|
if (elemNulls[i])
|
|
continue;
|
|
|
|
/* Otherwise, apply extractQuery and get the actual term counts */
|
|
memset(&elemcounts, 0, sizeof(elemcounts));
|
|
|
|
if (gincost_pattern(index, indexcol, clause_op, elemValues[i],
|
|
&elemcounts))
|
|
{
|
|
/* We ignore array elements that are unsatisfiable patterns */
|
|
numPossible++;
|
|
|
|
if (elemcounts.haveFullScan)
|
|
{
|
|
/*
|
|
* Full index scan will be required. We treat this as if
|
|
* every key in the index had been listed in the query; is
|
|
* that reasonable?
|
|
*/
|
|
elemcounts.partialEntries = 0;
|
|
elemcounts.exactEntries = numIndexEntries;
|
|
elemcounts.searchEntries = numIndexEntries;
|
|
}
|
|
arraycounts.partialEntries += elemcounts.partialEntries;
|
|
arraycounts.exactEntries += elemcounts.exactEntries;
|
|
arraycounts.searchEntries += elemcounts.searchEntries;
|
|
}
|
|
}
|
|
|
|
if (numPossible == 0)
|
|
{
|
|
/* No satisfiable patterns in the array */
|
|
return false;
|
|
}
|
|
|
|
/*
|
|
* Now add the averages to the global counts. This will give us an
|
|
* estimate of the average number of terms searched for in each indexscan,
|
|
* including contributions from both array and non-array quals.
|
|
*/
|
|
counts->partialEntries += arraycounts.partialEntries / numPossible;
|
|
counts->exactEntries += arraycounts.exactEntries / numPossible;
|
|
counts->searchEntries += arraycounts.searchEntries / numPossible;
|
|
|
|
counts->arrayScans *= numPossible;
|
|
|
|
return true;
|
|
}
|
|
|
|
/*
|
|
* GIN has search behavior completely different from other index types
|
|
*/
|
|
void
|
|
gincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
|
|
Cost *indexStartupCost, Cost *indexTotalCost,
|
|
Selectivity *indexSelectivity, double *indexCorrelation,
|
|
double *indexPages)
|
|
{
|
|
IndexOptInfo *index = path->indexinfo;
|
|
List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
|
|
List *selectivityQuals;
|
|
double numPages = index->pages,
|
|
numTuples = index->tuples;
|
|
double numEntryPages,
|
|
numDataPages,
|
|
numPendingPages,
|
|
numEntries;
|
|
GinQualCounts counts;
|
|
bool matchPossible;
|
|
double partialScale;
|
|
double entryPagesFetched,
|
|
dataPagesFetched,
|
|
dataPagesFetchedBySel;
|
|
double qual_op_cost,
|
|
qual_arg_cost,
|
|
spc_random_page_cost,
|
|
outer_scans;
|
|
Relation indexRel;
|
|
GinStatsData ginStats;
|
|
ListCell *lc;
|
|
|
|
/*
|
|
* Obtain statistical information from the meta page, if possible. Else
|
|
* set ginStats to zeroes, and we'll cope below.
|
|
*/
|
|
if (!index->hypothetical)
|
|
{
|
|
/* Lock should have already been obtained in plancat.c */
|
|
indexRel = index_open(index->indexoid, NoLock);
|
|
ginGetStats(indexRel, &ginStats);
|
|
index_close(indexRel, NoLock);
|
|
}
|
|
else
|
|
{
|
|
memset(&ginStats, 0, sizeof(ginStats));
|
|
}
|
|
|
|
/*
|
|
* Assuming we got valid (nonzero) stats at all, nPendingPages can be
|
|
* trusted, but the other fields are data as of the last VACUUM. We can
|
|
* scale them up to account for growth since then, but that method only
|
|
* goes so far; in the worst case, the stats might be for a completely
|
|
* empty index, and scaling them will produce pretty bogus numbers.
|
|
* Somewhat arbitrarily, set the cutoff for doing scaling at 4X growth; if
|
|
* it's grown more than that, fall back to estimating things only from the
|
|
* assumed-accurate index size. But we'll trust nPendingPages in any case
|
|
* so long as it's not clearly insane, ie, more than the index size.
|
|
*/
|
|
if (ginStats.nPendingPages < numPages)
|
|
numPendingPages = ginStats.nPendingPages;
|
|
else
|
|
numPendingPages = 0;
|
|
|
|
if (numPages > 0 && ginStats.nTotalPages <= numPages &&
|
|
ginStats.nTotalPages > numPages / 4 &&
|
|
ginStats.nEntryPages > 0 && ginStats.nEntries > 0)
|
|
{
|
|
/*
|
|
* OK, the stats seem close enough to sane to be trusted. But we
|
|
* still need to scale them by the ratio numPages / nTotalPages to
|
|
* account for growth since the last VACUUM.
|
|
*/
|
|
double scale = numPages / ginStats.nTotalPages;
|
|
|
|
numEntryPages = ceil(ginStats.nEntryPages * scale);
|
|
numDataPages = ceil(ginStats.nDataPages * scale);
|
|
numEntries = ceil(ginStats.nEntries * scale);
|
|
/* ensure we didn't round up too much */
|
|
numEntryPages = Min(numEntryPages, numPages - numPendingPages);
|
|
numDataPages = Min(numDataPages,
|
|
numPages - numPendingPages - numEntryPages);
|
|
}
|
|
else
|
|
{
|
|
/*
|
|
* We might get here because it's a hypothetical index, or an index
|
|
* created pre-9.1 and never vacuumed since upgrading (in which case
|
|
* its stats would read as zeroes), or just because it's grown too
|
|
* much since the last VACUUM for us to put our faith in scaling.
|
|
*
|
|
* Invent some plausible internal statistics based on the index page
|
|
* count (and clamp that to at least 10 pages, just in case). We
|
|
* estimate that 90% of the index is entry pages, and the rest is data
|
|
* pages. Estimate 100 entries per entry page; this is rather bogus
|
|
* since it'll depend on the size of the keys, but it's more robust
|
|
* than trying to predict the number of entries per heap tuple.
|
|
*/
|
|
numPages = Max(numPages, 10);
|
|
numEntryPages = floor((numPages - numPendingPages) * 0.90);
|
|
numDataPages = numPages - numPendingPages - numEntryPages;
|
|
numEntries = floor(numEntryPages * 100);
|
|
}
|
|
|
|
/* In an empty index, numEntries could be zero. Avoid divide-by-zero */
|
|
if (numEntries < 1)
|
|
numEntries = 1;
|
|
|
|
/*
|
|
* If the index is partial, AND the index predicate with the index-bound
|
|
* quals to produce a more accurate idea of the number of rows covered by
|
|
* the bound conditions.
|
|
*/
|
|
selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
|
|
|
|
/* Estimate the fraction of main-table tuples that will be visited */
|
|
*indexSelectivity = clauselist_selectivity(root, selectivityQuals,
|
|
index->rel->relid,
|
|
JOIN_INNER,
|
|
NULL);
|
|
|
|
/* fetch estimated page cost for tablespace containing index */
|
|
get_tablespace_page_costs(index->reltablespace,
|
|
&spc_random_page_cost,
|
|
NULL);
|
|
|
|
/*
|
|
* Generic assumption about index correlation: there isn't any.
|
|
*/
|
|
*indexCorrelation = 0.0;
|
|
|
|
/*
|
|
* Examine quals to estimate number of search entries & partial matches
|
|
*/
|
|
memset(&counts, 0, sizeof(counts));
|
|
counts.arrayScans = 1;
|
|
matchPossible = true;
|
|
|
|
foreach(lc, path->indexclauses)
|
|
{
|
|
IndexClause *iclause = lfirst_node(IndexClause, lc);
|
|
ListCell *lc2;
|
|
|
|
foreach(lc2, iclause->indexquals)
|
|
{
|
|
RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
|
|
Expr *clause = rinfo->clause;
|
|
|
|
if (IsA(clause, OpExpr))
|
|
{
|
|
matchPossible = gincost_opexpr(root,
|
|
index,
|
|
iclause->indexcol,
|
|
(OpExpr *) clause,
|
|
&counts);
|
|
if (!matchPossible)
|
|
break;
|
|
}
|
|
else if (IsA(clause, ScalarArrayOpExpr))
|
|
{
|
|
matchPossible = gincost_scalararrayopexpr(root,
|
|
index,
|
|
iclause->indexcol,
|
|
(ScalarArrayOpExpr *) clause,
|
|
numEntries,
|
|
&counts);
|
|
if (!matchPossible)
|
|
break;
|
|
}
|
|
else
|
|
{
|
|
/* shouldn't be anything else for a GIN index */
|
|
elog(ERROR, "unsupported GIN indexqual type: %d",
|
|
(int) nodeTag(clause));
|
|
}
|
|
}
|
|
}
|
|
|
|
/* Fall out if there were any provably-unsatisfiable quals */
|
|
if (!matchPossible)
|
|
{
|
|
*indexStartupCost = 0;
|
|
*indexTotalCost = 0;
|
|
*indexSelectivity = 0;
|
|
return;
|
|
}
|
|
|
|
if (counts.haveFullScan || indexQuals == NIL)
|
|
{
|
|
/*
|
|
* Full index scan will be required. We treat this as if every key in
|
|
* the index had been listed in the query; is that reasonable?
|
|
*/
|
|
counts.partialEntries = 0;
|
|
counts.exactEntries = numEntries;
|
|
counts.searchEntries = numEntries;
|
|
}
|
|
|
|
/* Will we have more than one iteration of a nestloop scan? */
|
|
outer_scans = loop_count;
|
|
|
|
/*
|
|
* Compute cost to begin scan, first of all, pay attention to pending
|
|
* list.
|
|
*/
|
|
entryPagesFetched = numPendingPages;
|
|
|
|
/*
|
|
* Estimate number of entry pages read. We need to do
|
|
* counts.searchEntries searches. Use a power function as it should be,
|
|
* but tuples on leaf pages usually is much greater. Here we include all
|
|
* searches in entry tree, including search of first entry in partial
|
|
* match algorithm
|
|
*/
|
|
entryPagesFetched += ceil(counts.searchEntries * rint(pow(numEntryPages, 0.15)));
|
|
|
|
/*
|
|
* Add an estimate of entry pages read by partial match algorithm. It's a
|
|
* scan over leaf pages in entry tree. We haven't any useful stats here,
|
|
* so estimate it as proportion. Because counts.partialEntries is really
|
|
* pretty bogus (see code above), it's possible that it is more than
|
|
* numEntries; clamp the proportion to ensure sanity.
|
|
*/
|
|
partialScale = counts.partialEntries / numEntries;
|
|
partialScale = Min(partialScale, 1.0);
|
|
|
|
entryPagesFetched += ceil(numEntryPages * partialScale);
|
|
|
|
/*
|
|
* Partial match algorithm reads all data pages before doing actual scan,
|
|
* so it's a startup cost. Again, we haven't any useful stats here, so
|
|
* estimate it as proportion.
|
|
*/
|
|
dataPagesFetched = ceil(numDataPages * partialScale);
|
|
|
|
/*
|
|
* Calculate cache effects if more than one scan due to nestloops or array
|
|
* quals. The result is pro-rated per nestloop scan, but the array qual
|
|
* factor shouldn't be pro-rated (compare genericcostestimate).
|
|
*/
|
|
if (outer_scans > 1 || counts.arrayScans > 1)
|
|
{
|
|
entryPagesFetched *= outer_scans * counts.arrayScans;
|
|
entryPagesFetched = index_pages_fetched(entryPagesFetched,
|
|
(BlockNumber) numEntryPages,
|
|
numEntryPages, root);
|
|
entryPagesFetched /= outer_scans;
|
|
dataPagesFetched *= outer_scans * counts.arrayScans;
|
|
dataPagesFetched = index_pages_fetched(dataPagesFetched,
|
|
(BlockNumber) numDataPages,
|
|
numDataPages, root);
|
|
dataPagesFetched /= outer_scans;
|
|
}
|
|
|
|
/*
|
|
* Here we use random page cost because logically-close pages could be far
|
|
* apart on disk.
|
|
*/
|
|
*indexStartupCost = (entryPagesFetched + dataPagesFetched) * spc_random_page_cost;
|
|
|
|
/*
|
|
* Now compute the number of data pages fetched during the scan.
|
|
*
|
|
* We assume every entry to have the same number of items, and that there
|
|
* is no overlap between them. (XXX: tsvector and array opclasses collect
|
|
* statistics on the frequency of individual keys; it would be nice to use
|
|
* those here.)
|
|
*/
|
|
dataPagesFetched = ceil(numDataPages * counts.exactEntries / numEntries);
|
|
|
|
/*
|
|
* If there is a lot of overlap among the entries, in particular if one of
|
|
* the entries is very frequent, the above calculation can grossly
|
|
* under-estimate. As a simple cross-check, calculate a lower bound based
|
|
* on the overall selectivity of the quals. At a minimum, we must read
|
|
* one item pointer for each matching entry.
|
|
*
|
|
* The width of each item pointer varies, based on the level of
|
|
* compression. We don't have statistics on that, but an average of
|
|
* around 3 bytes per item is fairly typical.
|
|
*/
|
|
dataPagesFetchedBySel = ceil(*indexSelectivity *
|
|
(numTuples / (BLCKSZ / 3)));
|
|
if (dataPagesFetchedBySel > dataPagesFetched)
|
|
dataPagesFetched = dataPagesFetchedBySel;
|
|
|
|
/* Account for cache effects, the same as above */
|
|
if (outer_scans > 1 || counts.arrayScans > 1)
|
|
{
|
|
dataPagesFetched *= outer_scans * counts.arrayScans;
|
|
dataPagesFetched = index_pages_fetched(dataPagesFetched,
|
|
(BlockNumber) numDataPages,
|
|
numDataPages, root);
|
|
dataPagesFetched /= outer_scans;
|
|
}
|
|
|
|
/* And apply random_page_cost as the cost per page */
|
|
*indexTotalCost = *indexStartupCost +
|
|
dataPagesFetched * spc_random_page_cost;
|
|
|
|
/*
|
|
* Add on index qual eval costs, much as in genericcostestimate. But we
|
|
* can disregard indexorderbys, since GIN doesn't support those.
|
|
*/
|
|
qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
|
|
qual_op_cost = cpu_operator_cost * list_length(indexQuals);
|
|
|
|
*indexStartupCost += qual_arg_cost;
|
|
*indexTotalCost += qual_arg_cost;
|
|
*indexTotalCost += (numTuples * *indexSelectivity) * (cpu_index_tuple_cost + qual_op_cost);
|
|
*indexPages = dataPagesFetched;
|
|
}
|
|
|
|
/*
|
|
* BRIN has search behavior completely different from other index types
|
|
*/
|
|
void
|
|
brincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
|
|
Cost *indexStartupCost, Cost *indexTotalCost,
|
|
Selectivity *indexSelectivity, double *indexCorrelation,
|
|
double *indexPages)
|
|
{
|
|
IndexOptInfo *index = path->indexinfo;
|
|
List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
|
|
double numPages = index->pages;
|
|
RelOptInfo *baserel = index->rel;
|
|
RangeTblEntry *rte = planner_rt_fetch(baserel->relid, root);
|
|
Cost spc_seq_page_cost;
|
|
Cost spc_random_page_cost;
|
|
double qual_arg_cost;
|
|
double qualSelectivity;
|
|
BrinStatsData statsData;
|
|
double indexRanges;
|
|
double minimalRanges;
|
|
double estimatedRanges;
|
|
double selec;
|
|
Relation indexRel;
|
|
ListCell *l;
|
|
VariableStatData vardata;
|
|
|
|
Assert(rte->rtekind == RTE_RELATION);
|
|
|
|
/* fetch estimated page cost for the tablespace containing the index */
|
|
get_tablespace_page_costs(index->reltablespace,
|
|
&spc_random_page_cost,
|
|
&spc_seq_page_cost);
|
|
|
|
/*
|
|
* Obtain some data from the index itself. A lock should have already
|
|
* been obtained on the index in plancat.c.
|
|
*/
|
|
indexRel = index_open(index->indexoid, NoLock);
|
|
brinGetStats(indexRel, &statsData);
|
|
index_close(indexRel, NoLock);
|
|
|
|
/*
|
|
* Compute index correlation
|
|
*
|
|
* Because we can use all index quals equally when scanning, we can use
|
|
* the largest correlation (in absolute value) among columns used by the
|
|
* query. Start at zero, the worst possible case. If we cannot find any
|
|
* correlation statistics, we will keep it as 0.
|
|
*/
|
|
*indexCorrelation = 0;
|
|
|
|
foreach(l, path->indexclauses)
|
|
{
|
|
IndexClause *iclause = lfirst_node(IndexClause, l);
|
|
AttrNumber attnum = index->indexkeys[iclause->indexcol];
|
|
|
|
/* attempt to lookup stats in relation for this index column */
|
|
if (attnum != 0)
|
|
{
|
|
/* Simple variable -- look to stats for the underlying table */
|
|
if (get_relation_stats_hook &&
|
|
(*get_relation_stats_hook) (root, rte, attnum, &vardata))
|
|
{
|
|
/*
|
|
* The hook took control of acquiring a stats tuple. If it
|
|
* did supply a tuple, it'd better have supplied a freefunc.
|
|
*/
|
|
if (HeapTupleIsValid(vardata.statsTuple) && !vardata.freefunc)
|
|
elog(ERROR,
|
|
"no function provided to release variable stats with");
|
|
}
|
|
else
|
|
{
|
|
vardata.statsTuple =
|
|
SearchSysCache3(STATRELATTINH,
|
|
ObjectIdGetDatum(rte->relid),
|
|
Int16GetDatum(attnum),
|
|
BoolGetDatum(false));
|
|
vardata.freefunc = ReleaseSysCache;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
/*
|
|
* Looks like we've found an expression column in the index. Let's
|
|
* see if there's any stats for it.
|
|
*/
|
|
|
|
/* get the attnum from the 0-based index. */
|
|
attnum = iclause->indexcol + 1;
|
|
|
|
if (get_index_stats_hook &&
|
|
(*get_index_stats_hook) (root, index->indexoid, attnum, &vardata))
|
|
{
|
|
/*
|
|
* The hook took control of acquiring a stats tuple. If it
|
|
* did supply a tuple, it'd better have supplied a freefunc.
|
|
*/
|
|
if (HeapTupleIsValid(vardata.statsTuple) &&
|
|
!vardata.freefunc)
|
|
elog(ERROR, "no function provided to release variable stats with");
|
|
}
|
|
else
|
|
{
|
|
vardata.statsTuple = SearchSysCache3(STATRELATTINH,
|
|
ObjectIdGetDatum(index->indexoid),
|
|
Int16GetDatum(attnum),
|
|
BoolGetDatum(false));
|
|
vardata.freefunc = ReleaseSysCache;
|
|
}
|
|
}
|
|
|
|
if (HeapTupleIsValid(vardata.statsTuple))
|
|
{
|
|
AttStatsSlot sslot;
|
|
|
|
if (get_attstatsslot(&sslot, vardata.statsTuple,
|
|
STATISTIC_KIND_CORRELATION, InvalidOid,
|
|
ATTSTATSSLOT_NUMBERS))
|
|
{
|
|
double varCorrelation = 0.0;
|
|
|
|
if (sslot.nnumbers > 0)
|
|
varCorrelation = Abs(sslot.numbers[0]);
|
|
|
|
if (varCorrelation > *indexCorrelation)
|
|
*indexCorrelation = varCorrelation;
|
|
|
|
free_attstatsslot(&sslot);
|
|
}
|
|
}
|
|
|
|
ReleaseVariableStats(vardata);
|
|
}
|
|
|
|
qualSelectivity = clauselist_selectivity(root, indexQuals,
|
|
baserel->relid,
|
|
JOIN_INNER, NULL);
|
|
|
|
/* work out the actual number of ranges in the index */
|
|
indexRanges = Max(ceil((double) baserel->pages / statsData.pagesPerRange),
|
|
1.0);
|
|
|
|
/*
|
|
* Now calculate the minimum possible ranges we could match with if all of
|
|
* the rows were in the perfect order in the table's heap.
|
|
*/
|
|
minimalRanges = ceil(indexRanges * qualSelectivity);
|
|
|
|
/*
|
|
* Now estimate the number of ranges that we'll touch by using the
|
|
* indexCorrelation from the stats. Careful not to divide by zero (note
|
|
* we're using the absolute value of the correlation).
|
|
*/
|
|
if (*indexCorrelation < 1.0e-10)
|
|
estimatedRanges = indexRanges;
|
|
else
|
|
estimatedRanges = Min(minimalRanges / *indexCorrelation, indexRanges);
|
|
|
|
/* we expect to visit this portion of the table */
|
|
selec = estimatedRanges / indexRanges;
|
|
|
|
CLAMP_PROBABILITY(selec);
|
|
|
|
*indexSelectivity = selec;
|
|
|
|
/*
|
|
* Compute the index qual costs, much as in genericcostestimate, to add to
|
|
* the index costs. We can disregard indexorderbys, since BRIN doesn't
|
|
* support those.
|
|
*/
|
|
qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
|
|
|
|
/*
|
|
* Compute the startup cost as the cost to read the whole revmap
|
|
* sequentially, including the cost to execute the index quals.
|
|
*/
|
|
*indexStartupCost =
|
|
spc_seq_page_cost * statsData.revmapNumPages * loop_count;
|
|
*indexStartupCost += qual_arg_cost;
|
|
|
|
/*
|
|
* To read a BRIN index there might be a bit of back and forth over
|
|
* regular pages, as revmap might point to them out of sequential order;
|
|
* calculate the total cost as reading the whole index in random order.
|
|
*/
|
|
*indexTotalCost = *indexStartupCost +
|
|
spc_random_page_cost * (numPages - statsData.revmapNumPages) * loop_count;
|
|
|
|
/*
|
|
* Charge a small amount per range tuple which we expect to match to. This
|
|
* is meant to reflect the costs of manipulating the bitmap. The BRIN scan
|
|
* will set a bit for each page in the range when we find a matching
|
|
* range, so we must multiply the charge by the number of pages in the
|
|
* range.
|
|
*/
|
|
*indexTotalCost += 0.1 * cpu_operator_cost * estimatedRanges *
|
|
statsData.pagesPerRange;
|
|
|
|
*indexPages = index->pages;
|
|
}
|