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1767 lines
50 KiB
C
1767 lines
50 KiB
C
/*-------------------------------------------------------------------------
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*
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* analyze.c
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* the postgres statistics generator
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*
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* Portions Copyright (c) 1996-2001, 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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* $Header: /cvsroot/pgsql/src/backend/commands/analyze.c,v 1.25 2002/01/06 00:37:44 tgl Exp $
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*
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*-------------------------------------------------------------------------
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*/
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#include "postgres.h"
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#include <math.h>
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#include "access/heapam.h"
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#include "access/tuptoaster.h"
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#include "catalog/catname.h"
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#include "catalog/indexing.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_type.h"
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#include "commands/vacuum.h"
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#include "miscadmin.h"
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#include "parser/parse_oper.h"
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#include "utils/acl.h"
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#include "utils/builtins.h"
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#include "utils/datum.h"
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#include "utils/fmgroids.h"
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#include "utils/syscache.h"
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#include "utils/tuplesort.h"
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/*
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* Analysis algorithms supported
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*/
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typedef enum
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{
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ALG_MINIMAL = 1, /* Compute only most-common-values */
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ALG_SCALAR /* Compute MCV, histogram, sort
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* correlation */
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} AlgCode;
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/*
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* To avoid consuming too much memory during analysis and/or too much space
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* in the resulting pg_statistic rows, we ignore varlena datums that are wider
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* than WIDTH_THRESHOLD (after detoasting!). This is legitimate for MCV
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* and distinct-value calculations since a wide value is unlikely to be
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* duplicated at all, much less be a most-common value. For the same reason,
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* ignoring wide values will not affect our estimates of histogram bin
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* boundaries very much.
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*/
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#define WIDTH_THRESHOLD 256
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/*
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* We build one of these structs for each attribute (column) that is to be
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* analyzed. The struct and subsidiary data are in TransactionCommandContext,
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* so they live until the end of the ANALYZE operation.
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*/
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typedef struct
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{
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/* These fields are set up by examine_attribute */
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int attnum; /* attribute number */
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AlgCode algcode; /* Which algorithm to use for this column */
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int minrows; /* Minimum # of rows wanted for stats */
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Form_pg_attribute attr; /* copy of pg_attribute row for column */
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Form_pg_type attrtype; /* copy of pg_type row for column */
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Oid eqopr; /* '=' operator for datatype, if any */
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Oid eqfunc; /* and associated function */
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Oid ltopr; /* '<' operator for datatype, if any */
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/*
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* These fields are filled in by the actual statistics-gathering
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* routine
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*/
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bool stats_valid;
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float4 stanullfrac; /* fraction of entries that are NULL */
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int4 stawidth; /* average width */
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float4 stadistinct; /* # distinct values */
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int2 stakind[STATISTIC_NUM_SLOTS];
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Oid staop[STATISTIC_NUM_SLOTS];
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int numnumbers[STATISTIC_NUM_SLOTS];
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float4 *stanumbers[STATISTIC_NUM_SLOTS];
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int numvalues[STATISTIC_NUM_SLOTS];
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Datum *stavalues[STATISTIC_NUM_SLOTS];
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} VacAttrStats;
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typedef struct
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{
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Datum value; /* a data value */
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int tupno; /* position index for tuple it came from */
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} ScalarItem;
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typedef struct
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{
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int count; /* # of duplicates */
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int first; /* values[] index of first occurrence */
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} ScalarMCVItem;
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#define swapInt(a,b) do {int _tmp; _tmp=a; a=b; b=_tmp;} while(0)
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#define swapDatum(a,b) do {Datum _tmp; _tmp=a; a=b; b=_tmp;} while(0)
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static int MESSAGE_LEVEL;
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/* context information for compare_scalars() */
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static FmgrInfo *datumCmpFn;
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static SortFunctionKind datumCmpFnKind;
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static int *datumCmpTupnoLink;
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static VacAttrStats *examine_attribute(Relation onerel, int attnum);
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static int acquire_sample_rows(Relation onerel, HeapTuple *rows,
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int targrows, double *totalrows);
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static double random_fract(void);
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static double init_selection_state(int n);
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static double select_next_random_record(double t, int n, double *stateptr);
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static int compare_rows(const void *a, const void *b);
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static int compare_scalars(const void *a, const void *b);
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static int compare_mcvs(const void *a, const void *b);
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static void compute_minimal_stats(VacAttrStats *stats,
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TupleDesc tupDesc, double totalrows,
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HeapTuple *rows, int numrows);
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static void compute_scalar_stats(VacAttrStats *stats,
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TupleDesc tupDesc, double totalrows,
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HeapTuple *rows, int numrows);
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static void update_attstats(Oid relid, int natts, VacAttrStats **vacattrstats);
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/*
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* analyze_rel() -- analyze one relation
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*/
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void
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analyze_rel(Oid relid, VacuumStmt *vacstmt)
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{
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Relation onerel;
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Form_pg_attribute *attr;
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int attr_cnt,
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tcnt,
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i;
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VacAttrStats **vacattrstats;
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int targrows,
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numrows;
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double totalrows;
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HeapTuple *rows;
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HeapTuple tuple;
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if (vacstmt->verbose)
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MESSAGE_LEVEL = NOTICE;
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else
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MESSAGE_LEVEL = DEBUG;
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/*
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* Begin a transaction for analyzing this relation.
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*
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* Note: All memory allocated during ANALYZE will live in
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* TransactionCommandContext or a subcontext thereof, so it will all
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* be released by transaction commit at the end of this routine.
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*/
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StartTransactionCommand();
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/*
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* Check for user-requested abort. Note we want this to be inside a
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* transaction, so xact.c doesn't issue useless NOTICE.
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*/
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CHECK_FOR_INTERRUPTS();
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/*
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* Race condition -- if the pg_class tuple has gone away since the
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* last time we saw it, we don't need to process it.
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*/
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tuple = SearchSysCache(RELOID,
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ObjectIdGetDatum(relid),
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0, 0, 0);
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if (!HeapTupleIsValid(tuple))
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{
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CommitTransactionCommand();
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return;
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}
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/*
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* We can ANALYZE any table except pg_statistic. See update_attstats
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*/
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if (strcmp(NameStr(((Form_pg_class) GETSTRUCT(tuple))->relname),
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StatisticRelationName) == 0)
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{
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ReleaseSysCache(tuple);
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CommitTransactionCommand();
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return;
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}
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ReleaseSysCache(tuple);
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/*
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* Open the class, getting only a read lock on it, and check
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* permissions. Permissions check should match vacuum's check!
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*/
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onerel = heap_open(relid, AccessShareLock);
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if (!(pg_ownercheck(GetUserId(), RelationGetRelationName(onerel),
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RELNAME) ||
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(is_dbadmin(MyDatabaseId) && !onerel->rd_rel->relisshared)))
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{
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/* No need for a notice if we already complained during VACUUM */
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if (!vacstmt->vacuum)
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elog(NOTICE, "Skipping \"%s\" --- only table or database owner can ANALYZE it",
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RelationGetRelationName(onerel));
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heap_close(onerel, NoLock);
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CommitTransactionCommand();
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return;
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}
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elog(MESSAGE_LEVEL, "Analyzing %s", RelationGetRelationName(onerel));
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/*
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* Determine which columns to analyze
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*
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* Note that system attributes are never analyzed.
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*/
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attr = onerel->rd_att->attrs;
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attr_cnt = onerel->rd_att->natts;
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if (vacstmt->va_cols != NIL)
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{
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List *le;
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vacattrstats = (VacAttrStats **) palloc(length(vacstmt->va_cols) *
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sizeof(VacAttrStats *));
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tcnt = 0;
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foreach(le, vacstmt->va_cols)
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{
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char *col = strVal(lfirst(le));
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for (i = 0; i < attr_cnt; i++)
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{
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if (namestrcmp(&(attr[i]->attname), col) == 0)
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break;
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}
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if (i >= attr_cnt)
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elog(ERROR, "ANALYZE: there is no attribute %s in %s",
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col, RelationGetRelationName(onerel));
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vacattrstats[tcnt] = examine_attribute(onerel, i + 1);
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if (vacattrstats[tcnt] != NULL)
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tcnt++;
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}
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attr_cnt = tcnt;
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}
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else
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{
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vacattrstats = (VacAttrStats **) palloc(attr_cnt *
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sizeof(VacAttrStats *));
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tcnt = 0;
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for (i = 0; i < attr_cnt; i++)
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{
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vacattrstats[tcnt] = examine_attribute(onerel, i + 1);
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if (vacattrstats[tcnt] != NULL)
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tcnt++;
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}
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attr_cnt = tcnt;
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}
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/*
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* Quit if no analyzable columns
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*/
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if (attr_cnt <= 0)
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{
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heap_close(onerel, NoLock);
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CommitTransactionCommand();
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return;
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}
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/*
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* Determine how many rows we need to sample, using the worst case
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* from all analyzable columns. We use a lower bound of 100 rows to
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* avoid possible overflow in Vitter's algorithm.
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*/
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targrows = 100;
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for (i = 0; i < attr_cnt; i++)
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{
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if (targrows < vacattrstats[i]->minrows)
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targrows = vacattrstats[i]->minrows;
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}
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/*
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* Acquire the sample rows
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*/
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rows = (HeapTuple *) palloc(targrows * sizeof(HeapTuple));
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numrows = acquire_sample_rows(onerel, rows, targrows, &totalrows);
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/*
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* If we are running a standalone ANALYZE, update pages/tuples stats
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* in pg_class. We have the accurate page count from heap_beginscan,
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* but only an approximate number of tuples; therefore, if we are part
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* of VACUUM ANALYZE do *not* overwrite the accurate count already
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* inserted by VACUUM.
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*/
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if (!vacstmt->vacuum)
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vac_update_relstats(RelationGetRelid(onerel),
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onerel->rd_nblocks,
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totalrows,
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RelationGetForm(onerel)->relhasindex);
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/*
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* Compute the statistics. Temporary results during the calculations
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* for each column are stored in a child context. The calc routines
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* are responsible to make sure that whatever they store into the
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* VacAttrStats structure is allocated in TransactionCommandContext.
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*/
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if (numrows > 0)
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{
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MemoryContext col_context,
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old_context;
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col_context = AllocSetContextCreate(CurrentMemoryContext,
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"Analyze Column",
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ALLOCSET_DEFAULT_MINSIZE,
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ALLOCSET_DEFAULT_INITSIZE,
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ALLOCSET_DEFAULT_MAXSIZE);
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old_context = MemoryContextSwitchTo(col_context);
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for (i = 0; i < attr_cnt; i++)
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{
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switch (vacattrstats[i]->algcode)
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{
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case ALG_MINIMAL:
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compute_minimal_stats(vacattrstats[i],
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onerel->rd_att, totalrows,
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rows, numrows);
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break;
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case ALG_SCALAR:
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compute_scalar_stats(vacattrstats[i],
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onerel->rd_att, totalrows,
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rows, numrows);
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break;
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}
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MemoryContextResetAndDeleteChildren(col_context);
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}
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MemoryContextSwitchTo(old_context);
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MemoryContextDelete(col_context);
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/*
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* Emit the completed stats rows into pg_statistic, replacing any
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* previous statistics for the target columns. (If there are
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* stats in pg_statistic for columns we didn't process, we leave
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* them alone.)
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*/
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update_attstats(relid, attr_cnt, vacattrstats);
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}
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/*
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* Close source relation now, but keep lock so that no one deletes it
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* before we commit. (If someone did, they'd fail to clean up the
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* entries we made in pg_statistic.)
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*/
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heap_close(onerel, NoLock);
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/* Commit and release working memory */
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CommitTransactionCommand();
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}
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/*
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* examine_attribute -- pre-analysis of a single column
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*
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* Determine whether the column is analyzable; if so, create and initialize
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* a VacAttrStats struct for it. If not, return NULL.
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*/
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static VacAttrStats *
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examine_attribute(Relation onerel, int attnum)
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{
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Form_pg_attribute attr = onerel->rd_att->attrs[attnum - 1];
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Operator func_operator;
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Oid oprrest;
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HeapTuple typtuple;
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Oid eqopr = InvalidOid;
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Oid eqfunc = InvalidOid;
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Oid ltopr = InvalidOid;
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VacAttrStats *stats;
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/* Don't analyze column if user has specified not to */
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if (attr->attstattarget <= 0)
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return NULL;
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/* If column has no "=" operator, we can't do much of anything */
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func_operator = compatible_oper("=",
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attr->atttypid,
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attr->atttypid,
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true);
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if (func_operator != NULL)
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{
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oprrest = ((Form_pg_operator) GETSTRUCT(func_operator))->oprrest;
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if (oprrest == F_EQSEL)
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{
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eqopr = oprid(func_operator);
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eqfunc = oprfuncid(func_operator);
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}
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ReleaseSysCache(func_operator);
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}
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if (!OidIsValid(eqfunc))
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return NULL;
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/*
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* If we have "=" then we're at least able to do the minimal
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* algorithm, so start filling in a VacAttrStats struct.
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*/
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stats = (VacAttrStats *) palloc(sizeof(VacAttrStats));
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MemSet(stats, 0, sizeof(VacAttrStats));
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stats->attnum = attnum;
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stats->attr = (Form_pg_attribute) palloc(ATTRIBUTE_TUPLE_SIZE);
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memcpy(stats->attr, attr, ATTRIBUTE_TUPLE_SIZE);
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typtuple = SearchSysCache(TYPEOID,
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ObjectIdGetDatum(attr->atttypid),
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0, 0, 0);
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if (!HeapTupleIsValid(typtuple))
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elog(ERROR, "cache lookup of type %u failed", attr->atttypid);
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stats->attrtype = (Form_pg_type) palloc(sizeof(FormData_pg_type));
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memcpy(stats->attrtype, GETSTRUCT(typtuple), sizeof(FormData_pg_type));
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ReleaseSysCache(typtuple);
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stats->eqopr = eqopr;
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stats->eqfunc = eqfunc;
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/* Is there a "<" operator with suitable semantics? */
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func_operator = compatible_oper("<",
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attr->atttypid,
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attr->atttypid,
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true);
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if (func_operator != NULL)
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{
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oprrest = ((Form_pg_operator) GETSTRUCT(func_operator))->oprrest;
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if (oprrest == F_SCALARLTSEL)
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ltopr = oprid(func_operator);
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ReleaseSysCache(func_operator);
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}
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stats->ltopr = ltopr;
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/*
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* Determine the algorithm to use (this will get more complicated
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* later)
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*/
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if (OidIsValid(ltopr))
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{
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/* Seems to be a scalar datatype */
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stats->algcode = ALG_SCALAR;
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/*--------------------
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* The following choice of minrows is based on the paper
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* "Random sampling for histogram construction: how much is enough?"
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* by Surajit Chaudhuri, Rajeev Motwani and Vivek Narasayya, in
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* Proceedings of ACM SIGMOD International Conference on Management
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* of Data, 1998, Pages 436-447. Their Corollary 1 to Theorem 5
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* says that for table size n, histogram size k, maximum relative
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* error in bin size f, and error probability gamma, the minimum
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* random sample size is
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* r = 4 * k * ln(2*n/gamma) / f^2
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* Taking f = 0.5, gamma = 0.01, n = 1 million rows, we obtain
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* r = 305.82 * k
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* Note that because of the log function, the dependence on n is
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* quite weak; even at n = 1 billion, a 300*k sample gives <= 0.59
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* bin size error with probability 0.99. So there's no real need to
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* scale for n, which is a good thing because we don't necessarily
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* know it at this point.
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*--------------------
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*/
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stats->minrows = 300 * attr->attstattarget;
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}
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else
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{
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/* Can't do much but the minimal stuff */
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stats->algcode = ALG_MINIMAL;
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/* Might as well use the same minrows as above */
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stats->minrows = 300 * attr->attstattarget;
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}
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return stats;
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}
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|
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/*
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* acquire_sample_rows -- acquire a random sample of rows from the table
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*
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* Up to targrows rows are collected (if there are fewer than that many
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* rows in the table, all rows are collected). When the table is larger
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* than targrows, a truly random sample is collected: every row has an
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* equal chance of ending up in the final sample.
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*
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* We also estimate the total number of rows in the table, and return that
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* into *totalrows.
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*
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* The returned list of tuples is in order by physical position in the table.
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* (We will rely on this later to derive correlation estimates.)
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*/
|
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static int
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acquire_sample_rows(Relation onerel, HeapTuple *rows, int targrows,
|
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double *totalrows)
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{
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int numrows = 0;
|
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HeapScanDesc scan;
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HeapTuple tuple;
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ItemPointer lasttuple;
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BlockNumber lastblock,
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estblock;
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OffsetNumber lastoffset;
|
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int numest;
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double tuplesperpage;
|
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double t;
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double rstate;
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|
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Assert(targrows > 1);
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|
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/*
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* Do a simple linear scan until we reach the target number of rows.
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*/
|
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scan = heap_beginscan(onerel, false, SnapshotNow, 0, NULL);
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|
while (HeapTupleIsValid(tuple = heap_getnext(scan, 0)))
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{
|
|
rows[numrows++] = heap_copytuple(tuple);
|
|
if (numrows >= targrows)
|
|
break;
|
|
CHECK_FOR_INTERRUPTS();
|
|
}
|
|
heap_endscan(scan);
|
|
|
|
/*
|
|
* If we ran out of tuples then we're done, no matter how few we
|
|
* collected. No sort is needed, since they're already in order.
|
|
*/
|
|
if (!HeapTupleIsValid(tuple))
|
|
{
|
|
*totalrows = (double) numrows;
|
|
return numrows;
|
|
}
|
|
|
|
/*
|
|
* Otherwise, start replacing tuples in the sample until we reach the
|
|
* end of the relation. This algorithm is from Jeff Vitter's paper
|
|
* (see full citation below). It works by repeatedly computing the
|
|
* number of the next tuple we want to fetch, which will replace a
|
|
* randomly chosen element of the reservoir (current set of tuples).
|
|
* At all times the reservoir is a true random sample of the tuples
|
|
* we've passed over so far, so when we fall off the end of the
|
|
* relation we're done.
|
|
*
|
|
* A slight difficulty is that since we don't want to fetch tuples or
|
|
* even pages that we skip over, it's not possible to fetch *exactly*
|
|
* the N'th tuple at each step --- we don't know how many valid tuples
|
|
* are on the skipped pages. We handle this by assuming that the
|
|
* average number of valid tuples/page on the pages already scanned
|
|
* over holds good for the rest of the relation as well; this lets us
|
|
* estimate which page the next tuple should be on and its position in
|
|
* the page. Then we fetch the first valid tuple at or after that
|
|
* position, being careful not to use the same tuple twice. This
|
|
* approach should still give a good random sample, although it's not
|
|
* perfect.
|
|
*/
|
|
lasttuple = &(rows[numrows - 1]->t_self);
|
|
lastblock = ItemPointerGetBlockNumber(lasttuple);
|
|
lastoffset = ItemPointerGetOffsetNumber(lasttuple);
|
|
|
|
/*
|
|
* If possible, estimate tuples/page using only completely-scanned
|
|
* pages.
|
|
*/
|
|
for (numest = numrows; numest > 0; numest--)
|
|
{
|
|
if (ItemPointerGetBlockNumber(&(rows[numest - 1]->t_self)) != lastblock)
|
|
break;
|
|
}
|
|
if (numest == 0)
|
|
{
|
|
numest = numrows; /* don't have a full page? */
|
|
estblock = lastblock + 1;
|
|
}
|
|
else
|
|
estblock = lastblock;
|
|
tuplesperpage = (double) numest / (double) estblock;
|
|
|
|
t = (double) numrows; /* t is the # of records processed so far */
|
|
rstate = init_selection_state(targrows);
|
|
for (;;)
|
|
{
|
|
double targpos;
|
|
BlockNumber targblock;
|
|
Buffer targbuffer;
|
|
Page targpage;
|
|
OffsetNumber targoffset,
|
|
maxoffset;
|
|
|
|
CHECK_FOR_INTERRUPTS();
|
|
|
|
t = select_next_random_record(t, targrows, &rstate);
|
|
/* Try to read the t'th record in the table */
|
|
targpos = t / tuplesperpage;
|
|
targblock = (BlockNumber) targpos;
|
|
targoffset = ((int) ((targpos - targblock) * tuplesperpage)) +
|
|
FirstOffsetNumber;
|
|
/* Make sure we are past the last selected record */
|
|
if (targblock <= lastblock)
|
|
{
|
|
targblock = lastblock;
|
|
if (targoffset <= lastoffset)
|
|
targoffset = lastoffset + 1;
|
|
}
|
|
/* Loop to find first valid record at or after given position */
|
|
pageloop:;
|
|
|
|
/*
|
|
* Have we fallen off the end of the relation? (We rely on
|
|
* heap_beginscan to have updated rd_nblocks.)
|
|
*/
|
|
if (targblock >= onerel->rd_nblocks)
|
|
break;
|
|
|
|
/*
|
|
* We must maintain a pin on the target page's buffer to ensure
|
|
* that the maxoffset value stays good (else concurrent VACUUM
|
|
* might delete tuples out from under us). Hence, pin the page
|
|
* until we are done looking at it. We don't maintain a lock on
|
|
* the page, so tuples could get added to it, but we ignore such
|
|
* tuples.
|
|
*/
|
|
targbuffer = ReadBuffer(onerel, targblock);
|
|
if (!BufferIsValid(targbuffer))
|
|
elog(ERROR, "acquire_sample_rows: ReadBuffer(%s,%u) failed",
|
|
RelationGetRelationName(onerel), targblock);
|
|
LockBuffer(targbuffer, BUFFER_LOCK_SHARE);
|
|
targpage = BufferGetPage(targbuffer);
|
|
maxoffset = PageGetMaxOffsetNumber(targpage);
|
|
LockBuffer(targbuffer, BUFFER_LOCK_UNLOCK);
|
|
|
|
for (;;)
|
|
{
|
|
HeapTupleData targtuple;
|
|
Buffer tupbuffer;
|
|
|
|
if (targoffset > maxoffset)
|
|
{
|
|
/* Fell off end of this page, try next */
|
|
ReleaseBuffer(targbuffer);
|
|
targblock++;
|
|
targoffset = FirstOffsetNumber;
|
|
goto pageloop;
|
|
}
|
|
ItemPointerSet(&targtuple.t_self, targblock, targoffset);
|
|
heap_fetch(onerel, SnapshotNow, &targtuple, &tupbuffer, NULL);
|
|
if (targtuple.t_data != NULL)
|
|
{
|
|
/*
|
|
* Found a suitable tuple, so save it, replacing one old
|
|
* tuple at random
|
|
*/
|
|
int k = (int) (targrows * random_fract());
|
|
|
|
Assert(k >= 0 && k < targrows);
|
|
heap_freetuple(rows[k]);
|
|
rows[k] = heap_copytuple(&targtuple);
|
|
/* this releases the second pin acquired by heap_fetch: */
|
|
ReleaseBuffer(tupbuffer);
|
|
/* this releases the initial pin: */
|
|
ReleaseBuffer(targbuffer);
|
|
lastblock = targblock;
|
|
lastoffset = targoffset;
|
|
break;
|
|
}
|
|
/* this tuple is dead, so advance to next one on same page */
|
|
targoffset++;
|
|
}
|
|
}
|
|
|
|
/*
|
|
* Now we need to sort the collected tuples by position (itempointer).
|
|
*/
|
|
qsort((void *) rows, numrows, sizeof(HeapTuple), compare_rows);
|
|
|
|
/*
|
|
* Estimate total number of valid rows in relation.
|
|
*/
|
|
*totalrows = floor((double) onerel->rd_nblocks * tuplesperpage + 0.5);
|
|
|
|
return numrows;
|
|
}
|
|
|
|
/* Select a random value R uniformly distributed in 0 < R < 1 */
|
|
static double
|
|
random_fract(void)
|
|
{
|
|
long z;
|
|
|
|
/* random() can produce endpoint values, try again if so */
|
|
do
|
|
{
|
|
z = random();
|
|
} while (!(z > 0 && z < MAX_RANDOM_VALUE));
|
|
return (double) z / (double) MAX_RANDOM_VALUE;
|
|
}
|
|
|
|
/*
|
|
* These two routines embody Algorithm Z from "Random sampling with a
|
|
* reservoir" by Jeffrey S. Vitter, in ACM Trans. Math. Softw. 11, 1
|
|
* (Mar. 1985), Pages 37-57. While Vitter describes his algorithm in terms
|
|
* of the count S of records to skip before processing another record,
|
|
* it is convenient to work primarily with t, the index (counting from 1)
|
|
* of the last record processed and next record to process. The only extra
|
|
* state needed between calls is W, a random state variable.
|
|
*
|
|
* Note: the original algorithm defines t, S, numer, and denom as integers.
|
|
* Here we express them as doubles to avoid overflow if the number of rows
|
|
* in the table exceeds INT_MAX. The algorithm should work as long as the
|
|
* row count does not become so large that it is not represented accurately
|
|
* in a double (on IEEE-math machines this would be around 2^52 rows).
|
|
*
|
|
* init_selection_state computes the initial W value.
|
|
*
|
|
* Given that we've already processed t records (t >= n),
|
|
* select_next_random_record determines the number of the next record to
|
|
* process.
|
|
*/
|
|
static double
|
|
init_selection_state(int n)
|
|
{
|
|
/* Initial value of W (for use when Algorithm Z is first applied) */
|
|
return exp(-log(random_fract()) / n);
|
|
}
|
|
|
|
static double
|
|
select_next_random_record(double t, int n, double *stateptr)
|
|
{
|
|
/* The magic constant here is T from Vitter's paper */
|
|
if (t <= (22.0 * n))
|
|
{
|
|
/* Process records using Algorithm X until t is large enough */
|
|
double V,
|
|
quot;
|
|
|
|
V = random_fract(); /* Generate V */
|
|
t += 1;
|
|
quot = (t - (double) n) / t;
|
|
/* Find min S satisfying (4.1) */
|
|
while (quot > V)
|
|
{
|
|
t += 1;
|
|
quot *= (t - (double) n) / t;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
/* Now apply Algorithm Z */
|
|
double W = *stateptr;
|
|
double term = t - (double) n + 1;
|
|
double S;
|
|
|
|
for (;;)
|
|
{
|
|
double numer,
|
|
numer_lim,
|
|
denom;
|
|
double U,
|
|
X,
|
|
lhs,
|
|
rhs,
|
|
y,
|
|
tmp;
|
|
|
|
/* Generate U and X */
|
|
U = random_fract();
|
|
X = t * (W - 1.0);
|
|
S = floor(X); /* S is tentatively set to floor(X) */
|
|
/* Test if U <= h(S)/cg(X) in the manner of (6.3) */
|
|
tmp = (t + 1) / term;
|
|
lhs = exp(log(((U * tmp * tmp) * (term + S)) / (t + X)) / n);
|
|
rhs = (((t + X) / (term + S)) * term) / t;
|
|
if (lhs <= rhs)
|
|
{
|
|
W = rhs / lhs;
|
|
break;
|
|
}
|
|
/* Test if U <= f(S)/cg(X) */
|
|
y = (((U * (t + 1)) / term) * (t + S + 1)) / (t + X);
|
|
if ((double) n < S)
|
|
{
|
|
denom = t;
|
|
numer_lim = term + S;
|
|
}
|
|
else
|
|
{
|
|
denom = t - (double) n + S;
|
|
numer_lim = t + 1;
|
|
}
|
|
for (numer = t + S; numer >= numer_lim; numer -= 1)
|
|
{
|
|
y *= numer / denom;
|
|
denom -= 1;
|
|
}
|
|
W = exp(-log(random_fract()) / n); /* Generate W in advance */
|
|
if (exp(log(y) / n) <= (t + X) / t)
|
|
break;
|
|
}
|
|
t += S + 1;
|
|
*stateptr = W;
|
|
}
|
|
return t;
|
|
}
|
|
|
|
/*
|
|
* qsort comparator for sorting rows[] array
|
|
*/
|
|
static int
|
|
compare_rows(const void *a, const void *b)
|
|
{
|
|
HeapTuple ha = *(HeapTuple *) a;
|
|
HeapTuple hb = *(HeapTuple *) b;
|
|
BlockNumber ba = ItemPointerGetBlockNumber(&ha->t_self);
|
|
OffsetNumber oa = ItemPointerGetOffsetNumber(&ha->t_self);
|
|
BlockNumber bb = ItemPointerGetBlockNumber(&hb->t_self);
|
|
OffsetNumber ob = ItemPointerGetOffsetNumber(&hb->t_self);
|
|
|
|
if (ba < bb)
|
|
return -1;
|
|
if (ba > bb)
|
|
return 1;
|
|
if (oa < ob)
|
|
return -1;
|
|
if (oa > ob)
|
|
return 1;
|
|
return 0;
|
|
}
|
|
|
|
|
|
/*
|
|
* compute_minimal_stats() -- compute minimal column statistics
|
|
*
|
|
* We use this when we can find only an "=" operator for the datatype.
|
|
*
|
|
* We determine the fraction of non-null rows, the average width, the
|
|
* most common values, and the (estimated) number of distinct values.
|
|
*
|
|
* The most common values are determined by brute force: we keep a list
|
|
* of previously seen values, ordered by number of times seen, as we scan
|
|
* the samples. A newly seen value is inserted just after the last
|
|
* multiply-seen value, causing the bottommost (oldest) singly-seen value
|
|
* to drop off the list. The accuracy of this method, and also its cost,
|
|
* depend mainly on the length of the list we are willing to keep.
|
|
*/
|
|
static void
|
|
compute_minimal_stats(VacAttrStats *stats,
|
|
TupleDesc tupDesc, double totalrows,
|
|
HeapTuple *rows, int numrows)
|
|
{
|
|
int i;
|
|
int null_cnt = 0;
|
|
int nonnull_cnt = 0;
|
|
int toowide_cnt = 0;
|
|
double total_width = 0;
|
|
bool is_varlena = (!stats->attr->attbyval &&
|
|
stats->attr->attlen == -1);
|
|
FmgrInfo f_cmpeq;
|
|
typedef struct
|
|
{
|
|
Datum value;
|
|
int count;
|
|
} TrackItem;
|
|
TrackItem *track;
|
|
int track_cnt,
|
|
track_max;
|
|
int num_mcv = stats->attr->attstattarget;
|
|
|
|
/*
|
|
* We track up to 2*n values for an n-element MCV list; but at least
|
|
* 10
|
|
*/
|
|
track_max = 2 * num_mcv;
|
|
if (track_max < 10)
|
|
track_max = 10;
|
|
track = (TrackItem *) palloc(track_max * sizeof(TrackItem));
|
|
track_cnt = 0;
|
|
|
|
fmgr_info(stats->eqfunc, &f_cmpeq);
|
|
|
|
for (i = 0; i < numrows; i++)
|
|
{
|
|
HeapTuple tuple = rows[i];
|
|
Datum value;
|
|
bool isnull;
|
|
bool match;
|
|
int firstcount1,
|
|
j;
|
|
|
|
CHECK_FOR_INTERRUPTS();
|
|
|
|
value = heap_getattr(tuple, stats->attnum, tupDesc, &isnull);
|
|
|
|
/* Check for null/nonnull */
|
|
if (isnull)
|
|
{
|
|
null_cnt++;
|
|
continue;
|
|
}
|
|
nonnull_cnt++;
|
|
|
|
/*
|
|
* If it's a varlena field, add up widths for average width
|
|
* calculation. Note that if the value is toasted, we use the
|
|
* toasted width. We don't bother with this calculation if it's a
|
|
* fixed-width type.
|
|
*/
|
|
if (is_varlena)
|
|
{
|
|
total_width += VARSIZE(DatumGetPointer(value));
|
|
|
|
/*
|
|
* If the value is toasted, we want to detoast it just once to
|
|
* avoid repeated detoastings and resultant excess memory
|
|
* usage during the comparisons. Also, check to see if the
|
|
* value is excessively wide, and if so don't detoast at all
|
|
* --- just ignore the value.
|
|
*/
|
|
if (toast_raw_datum_size(value) > WIDTH_THRESHOLD)
|
|
{
|
|
toowide_cnt++;
|
|
continue;
|
|
}
|
|
value = PointerGetDatum(PG_DETOAST_DATUM(value));
|
|
}
|
|
|
|
/*
|
|
* See if the value matches anything we're already tracking.
|
|
*/
|
|
match = false;
|
|
firstcount1 = track_cnt;
|
|
for (j = 0; j < track_cnt; j++)
|
|
{
|
|
if (DatumGetBool(FunctionCall2(&f_cmpeq, value, track[j].value)))
|
|
{
|
|
match = true;
|
|
break;
|
|
}
|
|
if (j < firstcount1 && track[j].count == 1)
|
|
firstcount1 = j;
|
|
}
|
|
|
|
if (match)
|
|
{
|
|
/* Found a match */
|
|
track[j].count++;
|
|
/* This value may now need to "bubble up" in the track list */
|
|
while (j > 0 && track[j].count > track[j - 1].count)
|
|
{
|
|
swapDatum(track[j].value, track[j - 1].value);
|
|
swapInt(track[j].count, track[j - 1].count);
|
|
j--;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
/* No match. Insert at head of count-1 list */
|
|
if (track_cnt < track_max)
|
|
track_cnt++;
|
|
for (j = track_cnt - 1; j > firstcount1; j--)
|
|
{
|
|
track[j].value = track[j - 1].value;
|
|
track[j].count = track[j - 1].count;
|
|
}
|
|
if (firstcount1 < track_cnt)
|
|
{
|
|
track[firstcount1].value = value;
|
|
track[firstcount1].count = 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
/* We can only compute valid stats if we found some non-null values. */
|
|
if (nonnull_cnt > 0)
|
|
{
|
|
int nmultiple,
|
|
summultiple;
|
|
|
|
stats->stats_valid = true;
|
|
/* Do the simple null-frac and width stats */
|
|
stats->stanullfrac = (double) null_cnt / (double) numrows;
|
|
if (is_varlena)
|
|
stats->stawidth = total_width / (double) nonnull_cnt;
|
|
else
|
|
stats->stawidth = stats->attrtype->typlen;
|
|
|
|
/* Count the number of values we found multiple times */
|
|
summultiple = 0;
|
|
for (nmultiple = 0; nmultiple < track_cnt; nmultiple++)
|
|
{
|
|
if (track[nmultiple].count == 1)
|
|
break;
|
|
summultiple += track[nmultiple].count;
|
|
}
|
|
|
|
if (nmultiple == 0)
|
|
{
|
|
/* If we found no repeated values, assume it's a unique column */
|
|
stats->stadistinct = -1.0;
|
|
}
|
|
else if (track_cnt < track_max && toowide_cnt == 0 &&
|
|
nmultiple == track_cnt)
|
|
{
|
|
/*
|
|
* Our track list includes every value in the sample, and
|
|
* every value appeared more than once. Assume the column has
|
|
* just these values.
|
|
*/
|
|
stats->stadistinct = track_cnt;
|
|
}
|
|
else
|
|
{
|
|
/*----------
|
|
* Estimate the number of distinct values using the estimator
|
|
* proposed by Chaudhuri et al (see citation above). This is
|
|
* sqrt(n/r) * max(f1,1) + f2 + f3 + ...
|
|
* where fk is the number of distinct values that occurred
|
|
* exactly k times in our sample of r rows (from a total of n).
|
|
* We assume (not very reliably!) that all the multiply-occurring
|
|
* values are reflected in the final track[] list, and the other
|
|
* nonnull values all appeared but once. (XXX this usually
|
|
* results in a drastic overestimate of ndistinct. Can we do
|
|
* any better?)
|
|
*----------
|
|
*/
|
|
int f1 = nonnull_cnt - summultiple;
|
|
double term1;
|
|
|
|
if (f1 < 1)
|
|
f1 = 1;
|
|
term1 = sqrt(totalrows / (double) numrows) * f1;
|
|
stats->stadistinct = floor(term1 + nmultiple + 0.5);
|
|
}
|
|
|
|
/*
|
|
* If we estimated the number of distinct values at more than 10%
|
|
* of the total row count (a very arbitrary limit), then assume
|
|
* that stadistinct should scale with the row count rather than be
|
|
* a fixed value.
|
|
*/
|
|
if (stats->stadistinct > 0.1 * totalrows)
|
|
stats->stadistinct = -(stats->stadistinct / totalrows);
|
|
|
|
/*
|
|
* Decide how many values are worth storing as most-common values.
|
|
* If we are able to generate a complete MCV list (all the values
|
|
* in the sample will fit, and we think these are all the ones in
|
|
* the table), then do so. Otherwise, store only those values
|
|
* that are significantly more common than the (estimated)
|
|
* average. We set the threshold rather arbitrarily at 25% more
|
|
* than average, with at least 2 instances in the sample.
|
|
*/
|
|
if (track_cnt < track_max && toowide_cnt == 0 &&
|
|
stats->stadistinct > 0 &&
|
|
track_cnt <= num_mcv)
|
|
{
|
|
/* Track list includes all values seen, and all will fit */
|
|
num_mcv = track_cnt;
|
|
}
|
|
else
|
|
{
|
|
double ndistinct = stats->stadistinct;
|
|
double avgcount,
|
|
mincount;
|
|
|
|
if (ndistinct < 0)
|
|
ndistinct = -ndistinct * totalrows;
|
|
/* estimate # of occurrences in sample of a typical value */
|
|
avgcount = (double) numrows / ndistinct;
|
|
/* set minimum threshold count to store a value */
|
|
mincount = avgcount * 1.25;
|
|
if (mincount < 2)
|
|
mincount = 2;
|
|
if (num_mcv > track_cnt)
|
|
num_mcv = track_cnt;
|
|
for (i = 0; i < num_mcv; i++)
|
|
{
|
|
if (track[i].count < mincount)
|
|
{
|
|
num_mcv = i;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
/* Generate MCV slot entry */
|
|
if (num_mcv > 0)
|
|
{
|
|
MemoryContext old_context;
|
|
Datum *mcv_values;
|
|
float4 *mcv_freqs;
|
|
|
|
/* Must copy the target values into TransactionCommandContext */
|
|
old_context = MemoryContextSwitchTo(TransactionCommandContext);
|
|
mcv_values = (Datum *) palloc(num_mcv * sizeof(Datum));
|
|
mcv_freqs = (float4 *) palloc(num_mcv * sizeof(float4));
|
|
for (i = 0; i < num_mcv; i++)
|
|
{
|
|
mcv_values[i] = datumCopy(track[i].value,
|
|
stats->attr->attbyval,
|
|
stats->attr->attlen);
|
|
mcv_freqs[i] = (double) track[i].count / (double) numrows;
|
|
}
|
|
MemoryContextSwitchTo(old_context);
|
|
|
|
stats->stakind[0] = STATISTIC_KIND_MCV;
|
|
stats->staop[0] = stats->eqopr;
|
|
stats->stanumbers[0] = mcv_freqs;
|
|
stats->numnumbers[0] = num_mcv;
|
|
stats->stavalues[0] = mcv_values;
|
|
stats->numvalues[0] = num_mcv;
|
|
}
|
|
}
|
|
|
|
/* We don't need to bother cleaning up any of our temporary palloc's */
|
|
}
|
|
|
|
|
|
/*
|
|
* compute_scalar_stats() -- compute column statistics
|
|
*
|
|
* We use this when we can find "=" and "<" operators for the datatype.
|
|
*
|
|
* We determine the fraction of non-null rows, the average width, the
|
|
* most common values, the (estimated) number of distinct values, the
|
|
* distribution histogram, and the correlation of physical to logical order.
|
|
*
|
|
* The desired stats can be determined fairly easily after sorting the
|
|
* data values into order.
|
|
*/
|
|
static void
|
|
compute_scalar_stats(VacAttrStats *stats,
|
|
TupleDesc tupDesc, double totalrows,
|
|
HeapTuple *rows, int numrows)
|
|
{
|
|
int i;
|
|
int null_cnt = 0;
|
|
int nonnull_cnt = 0;
|
|
int toowide_cnt = 0;
|
|
double total_width = 0;
|
|
bool is_varlena = (!stats->attr->attbyval &&
|
|
stats->attr->attlen == -1);
|
|
double corr_xysum;
|
|
RegProcedure cmpFn;
|
|
SortFunctionKind cmpFnKind;
|
|
FmgrInfo f_cmpfn;
|
|
ScalarItem *values;
|
|
int values_cnt = 0;
|
|
int *tupnoLink;
|
|
ScalarMCVItem *track;
|
|
int track_cnt = 0;
|
|
int num_mcv = stats->attr->attstattarget;
|
|
int num_bins = stats->attr->attstattarget;
|
|
|
|
values = (ScalarItem *) palloc(numrows * sizeof(ScalarItem));
|
|
tupnoLink = (int *) palloc(numrows * sizeof(int));
|
|
track = (ScalarMCVItem *) palloc(num_mcv * sizeof(ScalarMCVItem));
|
|
|
|
SelectSortFunction(stats->ltopr, &cmpFn, &cmpFnKind);
|
|
fmgr_info(cmpFn, &f_cmpfn);
|
|
|
|
/* Initial scan to find sortable values */
|
|
for (i = 0; i < numrows; i++)
|
|
{
|
|
HeapTuple tuple = rows[i];
|
|
Datum value;
|
|
bool isnull;
|
|
|
|
CHECK_FOR_INTERRUPTS();
|
|
|
|
value = heap_getattr(tuple, stats->attnum, tupDesc, &isnull);
|
|
|
|
/* Check for null/nonnull */
|
|
if (isnull)
|
|
{
|
|
null_cnt++;
|
|
continue;
|
|
}
|
|
nonnull_cnt++;
|
|
|
|
/*
|
|
* If it's a varlena field, add up widths for average width
|
|
* calculation. Note that if the value is toasted, we use the
|
|
* toasted width. We don't bother with this calculation if it's a
|
|
* fixed-width type.
|
|
*/
|
|
if (is_varlena)
|
|
{
|
|
total_width += VARSIZE(DatumGetPointer(value));
|
|
|
|
/*
|
|
* If the value is toasted, we want to detoast it just once to
|
|
* avoid repeated detoastings and resultant excess memory
|
|
* usage during the comparisons. Also, check to see if the
|
|
* value is excessively wide, and if so don't detoast at all
|
|
* --- just ignore the value.
|
|
*/
|
|
if (toast_raw_datum_size(value) > WIDTH_THRESHOLD)
|
|
{
|
|
toowide_cnt++;
|
|
continue;
|
|
}
|
|
value = PointerGetDatum(PG_DETOAST_DATUM(value));
|
|
}
|
|
|
|
/* Add it to the list to be sorted */
|
|
values[values_cnt].value = value;
|
|
values[values_cnt].tupno = values_cnt;
|
|
tupnoLink[values_cnt] = values_cnt;
|
|
values_cnt++;
|
|
}
|
|
|
|
/* We can only compute valid stats if we found some sortable values. */
|
|
if (values_cnt > 0)
|
|
{
|
|
int ndistinct, /* # distinct values in sample */
|
|
nmultiple, /* # that appear multiple times */
|
|
num_hist,
|
|
dups_cnt;
|
|
int slot_idx = 0;
|
|
|
|
/* Sort the collected values */
|
|
datumCmpFn = &f_cmpfn;
|
|
datumCmpFnKind = cmpFnKind;
|
|
datumCmpTupnoLink = tupnoLink;
|
|
qsort((void *) values, values_cnt,
|
|
sizeof(ScalarItem), compare_scalars);
|
|
|
|
/*
|
|
* Now scan the values in order, find the most common ones, and
|
|
* also accumulate ordering-correlation statistics.
|
|
*
|
|
* To determine which are most common, we first have to count the
|
|
* number of duplicates of each value. The duplicates are
|
|
* adjacent in the sorted list, so a brute-force approach is to
|
|
* compare successive datum values until we find two that are not
|
|
* equal. However, that requires N-1 invocations of the datum
|
|
* comparison routine, which are completely redundant with work
|
|
* that was done during the sort. (The sort algorithm must at
|
|
* some point have compared each pair of items that are adjacent
|
|
* in the sorted order; otherwise it could not know that it's
|
|
* ordered the pair correctly.) We exploit this by having
|
|
* compare_scalars remember the highest tupno index that each
|
|
* ScalarItem has been found equal to. At the end of the sort, a
|
|
* ScalarItem's tupnoLink will still point to itself if and only
|
|
* if it is the last item of its group of duplicates (since the
|
|
* group will be ordered by tupno).
|
|
*/
|
|
corr_xysum = 0;
|
|
ndistinct = 0;
|
|
nmultiple = 0;
|
|
dups_cnt = 0;
|
|
for (i = 0; i < values_cnt; i++)
|
|
{
|
|
int tupno = values[i].tupno;
|
|
|
|
corr_xysum += ((double) i) * ((double) tupno);
|
|
dups_cnt++;
|
|
if (tupnoLink[tupno] == tupno)
|
|
{
|
|
/* Reached end of duplicates of this value */
|
|
ndistinct++;
|
|
if (dups_cnt > 1)
|
|
{
|
|
nmultiple++;
|
|
if (track_cnt < num_mcv ||
|
|
dups_cnt > track[track_cnt - 1].count)
|
|
{
|
|
/*
|
|
* Found a new item for the mcv list; find its
|
|
* position, bubbling down old items if needed.
|
|
* Loop invariant is that j points at an empty/
|
|
* replaceable slot.
|
|
*/
|
|
int j;
|
|
|
|
if (track_cnt < num_mcv)
|
|
track_cnt++;
|
|
for (j = track_cnt - 1; j > 0; j--)
|
|
{
|
|
if (dups_cnt <= track[j - 1].count)
|
|
break;
|
|
track[j].count = track[j - 1].count;
|
|
track[j].first = track[j - 1].first;
|
|
}
|
|
track[j].count = dups_cnt;
|
|
track[j].first = i + 1 - dups_cnt;
|
|
}
|
|
}
|
|
dups_cnt = 0;
|
|
}
|
|
}
|
|
|
|
stats->stats_valid = true;
|
|
/* Do the simple null-frac and width stats */
|
|
stats->stanullfrac = (double) null_cnt / (double) numrows;
|
|
if (is_varlena)
|
|
stats->stawidth = total_width / (double) nonnull_cnt;
|
|
else
|
|
stats->stawidth = stats->attrtype->typlen;
|
|
|
|
if (nmultiple == 0)
|
|
{
|
|
/* If we found no repeated values, assume it's a unique column */
|
|
stats->stadistinct = -1.0;
|
|
}
|
|
else if (toowide_cnt == 0 && nmultiple == ndistinct)
|
|
{
|
|
/*
|
|
* Every value in the sample appeared more than once. Assume
|
|
* the column has just these values.
|
|
*/
|
|
stats->stadistinct = ndistinct;
|
|
}
|
|
else
|
|
{
|
|
/*----------
|
|
* Estimate the number of distinct values using the estimator
|
|
* proposed by Chaudhuri et al (see citation above). This is
|
|
* sqrt(n/r) * max(f1,1) + f2 + f3 + ...
|
|
* where fk is the number of distinct values that occurred
|
|
* exactly k times in our sample of r rows (from a total of n).
|
|
* Overwidth values are assumed to have been distinct.
|
|
*----------
|
|
*/
|
|
int f1 = ndistinct - nmultiple + toowide_cnt;
|
|
double term1;
|
|
|
|
if (f1 < 1)
|
|
f1 = 1;
|
|
term1 = sqrt(totalrows / (double) numrows) * f1;
|
|
stats->stadistinct = floor(term1 + nmultiple + 0.5);
|
|
}
|
|
|
|
/*
|
|
* If we estimated the number of distinct values at more than 10%
|
|
* of the total row count (a very arbitrary limit), then assume
|
|
* that stadistinct should scale with the row count rather than be
|
|
* a fixed value.
|
|
*/
|
|
if (stats->stadistinct > 0.1 * totalrows)
|
|
stats->stadistinct = -(stats->stadistinct / totalrows);
|
|
|
|
/*
|
|
* Decide how many values are worth storing as most-common values.
|
|
* If we are able to generate a complete MCV list (all the values
|
|
* in the sample will fit, and we think these are all the ones in
|
|
* the table), then do so. Otherwise, store only those values
|
|
* that are significantly more common than the (estimated)
|
|
* average. We set the threshold rather arbitrarily at 25% more
|
|
* than average, with at least 2 instances in the sample. Also,
|
|
* we won't suppress values that have a frequency of at least 1/K
|
|
* where K is the intended number of histogram bins; such values
|
|
* might otherwise cause us to emit duplicate histogram bin
|
|
* boundaries.
|
|
*/
|
|
if (track_cnt == ndistinct && toowide_cnt == 0 &&
|
|
stats->stadistinct > 0 &&
|
|
track_cnt <= num_mcv)
|
|
{
|
|
/* Track list includes all values seen, and all will fit */
|
|
num_mcv = track_cnt;
|
|
}
|
|
else
|
|
{
|
|
double ndistinct = stats->stadistinct;
|
|
double avgcount,
|
|
mincount,
|
|
maxmincount;
|
|
|
|
if (ndistinct < 0)
|
|
ndistinct = -ndistinct * totalrows;
|
|
/* estimate # of occurrences in sample of a typical value */
|
|
avgcount = (double) numrows / ndistinct;
|
|
/* set minimum threshold count to store a value */
|
|
mincount = avgcount * 1.25;
|
|
if (mincount < 2)
|
|
mincount = 2;
|
|
/* don't let threshold exceed 1/K, however */
|
|
maxmincount = (double) numrows / (double) num_bins;
|
|
if (mincount > maxmincount)
|
|
mincount = maxmincount;
|
|
if (num_mcv > track_cnt)
|
|
num_mcv = track_cnt;
|
|
for (i = 0; i < num_mcv; i++)
|
|
{
|
|
if (track[i].count < mincount)
|
|
{
|
|
num_mcv = i;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
/* Generate MCV slot entry */
|
|
if (num_mcv > 0)
|
|
{
|
|
MemoryContext old_context;
|
|
Datum *mcv_values;
|
|
float4 *mcv_freqs;
|
|
|
|
/* Must copy the target values into TransactionCommandContext */
|
|
old_context = MemoryContextSwitchTo(TransactionCommandContext);
|
|
mcv_values = (Datum *) palloc(num_mcv * sizeof(Datum));
|
|
mcv_freqs = (float4 *) palloc(num_mcv * sizeof(float4));
|
|
for (i = 0; i < num_mcv; i++)
|
|
{
|
|
mcv_values[i] = datumCopy(values[track[i].first].value,
|
|
stats->attr->attbyval,
|
|
stats->attr->attlen);
|
|
mcv_freqs[i] = (double) track[i].count / (double) numrows;
|
|
}
|
|
MemoryContextSwitchTo(old_context);
|
|
|
|
stats->stakind[slot_idx] = STATISTIC_KIND_MCV;
|
|
stats->staop[slot_idx] = stats->eqopr;
|
|
stats->stanumbers[slot_idx] = mcv_freqs;
|
|
stats->numnumbers[slot_idx] = num_mcv;
|
|
stats->stavalues[slot_idx] = mcv_values;
|
|
stats->numvalues[slot_idx] = num_mcv;
|
|
slot_idx++;
|
|
}
|
|
|
|
/*
|
|
* Generate a histogram slot entry if there are at least two
|
|
* distinct values not accounted for in the MCV list. (This
|
|
* ensures the histogram won't collapse to empty or a singleton.)
|
|
*/
|
|
num_hist = ndistinct - num_mcv;
|
|
if (num_hist > num_bins)
|
|
num_hist = num_bins + 1;
|
|
if (num_hist >= 2)
|
|
{
|
|
MemoryContext old_context;
|
|
Datum *hist_values;
|
|
int nvals;
|
|
|
|
/* Sort the MCV items into position order to speed next loop */
|
|
qsort((void *) track, num_mcv,
|
|
sizeof(ScalarMCVItem), compare_mcvs);
|
|
|
|
/*
|
|
* Collapse out the MCV items from the values[] array.
|
|
*
|
|
* Note we destroy the values[] array here... but we don't need
|
|
* it for anything more. We do, however, still need
|
|
* values_cnt. nvals will be the number of remaining entries
|
|
* in values[].
|
|
*/
|
|
if (num_mcv > 0)
|
|
{
|
|
int src,
|
|
dest;
|
|
int j;
|
|
|
|
src = dest = 0;
|
|
j = 0; /* index of next interesting MCV item */
|
|
while (src < values_cnt)
|
|
{
|
|
int ncopy;
|
|
|
|
if (j < num_mcv)
|
|
{
|
|
int first = track[j].first;
|
|
|
|
if (src >= first)
|
|
{
|
|
/* advance past this MCV item */
|
|
src = first + track[j].count;
|
|
j++;
|
|
continue;
|
|
}
|
|
ncopy = first - src;
|
|
}
|
|
else
|
|
ncopy = values_cnt - src;
|
|
memmove(&values[dest], &values[src],
|
|
ncopy * sizeof(ScalarItem));
|
|
src += ncopy;
|
|
dest += ncopy;
|
|
}
|
|
nvals = dest;
|
|
}
|
|
else
|
|
nvals = values_cnt;
|
|
Assert(nvals >= num_hist);
|
|
|
|
/* Must copy the target values into TransactionCommandContext */
|
|
old_context = MemoryContextSwitchTo(TransactionCommandContext);
|
|
hist_values = (Datum *) palloc(num_hist * sizeof(Datum));
|
|
for (i = 0; i < num_hist; i++)
|
|
{
|
|
int pos;
|
|
|
|
pos = (i * (nvals - 1)) / (num_hist - 1);
|
|
hist_values[i] = datumCopy(values[pos].value,
|
|
stats->attr->attbyval,
|
|
stats->attr->attlen);
|
|
}
|
|
MemoryContextSwitchTo(old_context);
|
|
|
|
stats->stakind[slot_idx] = STATISTIC_KIND_HISTOGRAM;
|
|
stats->staop[slot_idx] = stats->ltopr;
|
|
stats->stavalues[slot_idx] = hist_values;
|
|
stats->numvalues[slot_idx] = num_hist;
|
|
slot_idx++;
|
|
}
|
|
|
|
/* Generate a correlation entry if there are multiple values */
|
|
if (values_cnt > 1)
|
|
{
|
|
MemoryContext old_context;
|
|
float4 *corrs;
|
|
double corr_xsum,
|
|
corr_x2sum;
|
|
|
|
/* Must copy the target values into TransactionCommandContext */
|
|
old_context = MemoryContextSwitchTo(TransactionCommandContext);
|
|
corrs = (float4 *) palloc(sizeof(float4));
|
|
MemoryContextSwitchTo(old_context);
|
|
|
|
/*----------
|
|
* Since we know the x and y value sets are both
|
|
* 0, 1, ..., values_cnt-1
|
|
* we have sum(x) = sum(y) =
|
|
* (values_cnt-1)*values_cnt / 2
|
|
* and sum(x^2) = sum(y^2) =
|
|
* (values_cnt-1)*values_cnt*(2*values_cnt-1) / 6.
|
|
*----------
|
|
*/
|
|
corr_xsum = ((double) (values_cnt - 1)) *
|
|
((double) values_cnt) / 2.0;
|
|
corr_x2sum = ((double) (values_cnt - 1)) *
|
|
((double) values_cnt) * (double) (2 * values_cnt - 1) / 6.0;
|
|
|
|
/* And the correlation coefficient reduces to */
|
|
corrs[0] = (values_cnt * corr_xysum - corr_xsum * corr_xsum) /
|
|
(values_cnt * corr_x2sum - corr_xsum * corr_xsum);
|
|
|
|
stats->stakind[slot_idx] = STATISTIC_KIND_CORRELATION;
|
|
stats->staop[slot_idx] = stats->ltopr;
|
|
stats->stanumbers[slot_idx] = corrs;
|
|
stats->numnumbers[slot_idx] = 1;
|
|
slot_idx++;
|
|
}
|
|
}
|
|
|
|
/* We don't need to bother cleaning up any of our temporary palloc's */
|
|
}
|
|
|
|
/*
|
|
* qsort comparator for sorting ScalarItems
|
|
*
|
|
* Aside from sorting the items, we update the datumCmpTupnoLink[] array
|
|
* whenever two ScalarItems are found to contain equal datums. The array
|
|
* is indexed by tupno; for each ScalarItem, it contains the highest
|
|
* tupno that that item's datum has been found to be equal to. This allows
|
|
* us to avoid additional comparisons in compute_scalar_stats().
|
|
*/
|
|
static int
|
|
compare_scalars(const void *a, const void *b)
|
|
{
|
|
Datum da = ((ScalarItem *) a)->value;
|
|
int ta = ((ScalarItem *) a)->tupno;
|
|
Datum db = ((ScalarItem *) b)->value;
|
|
int tb = ((ScalarItem *) b)->tupno;
|
|
int32 compare;
|
|
|
|
compare = ApplySortFunction(datumCmpFn, datumCmpFnKind,
|
|
da, false, db, false);
|
|
if (compare != 0)
|
|
return compare;
|
|
|
|
/*
|
|
* The two datums are equal, so update datumCmpTupnoLink[].
|
|
*/
|
|
if (datumCmpTupnoLink[ta] < tb)
|
|
datumCmpTupnoLink[ta] = tb;
|
|
if (datumCmpTupnoLink[tb] < ta)
|
|
datumCmpTupnoLink[tb] = ta;
|
|
|
|
/*
|
|
* For equal datums, sort by tupno
|
|
*/
|
|
return ta - tb;
|
|
}
|
|
|
|
/*
|
|
* qsort comparator for sorting ScalarMCVItems by position
|
|
*/
|
|
static int
|
|
compare_mcvs(const void *a, const void *b)
|
|
{
|
|
int da = ((ScalarMCVItem *) a)->first;
|
|
int db = ((ScalarMCVItem *) b)->first;
|
|
|
|
return da - db;
|
|
}
|
|
|
|
|
|
/*
|
|
* update_attstats() -- update attribute statistics for one relation
|
|
*
|
|
* Statistics are stored in several places: the pg_class row for the
|
|
* relation has stats about the whole relation, and there is a
|
|
* pg_statistic row for each (non-system) attribute that has ever
|
|
* been analyzed. The pg_class values are updated by VACUUM, not here.
|
|
*
|
|
* pg_statistic rows are just added or updated normally. This means
|
|
* that pg_statistic will probably contain some deleted rows at the
|
|
* completion of a vacuum cycle, unless it happens to get vacuumed last.
|
|
*
|
|
* To keep things simple, we punt for pg_statistic, and don't try
|
|
* to compute or store rows for pg_statistic itself in pg_statistic.
|
|
* This could possibly be made to work, but it's not worth the trouble.
|
|
* Note analyze_rel() has seen to it that we won't come here when
|
|
* vacuuming pg_statistic itself.
|
|
*/
|
|
static void
|
|
update_attstats(Oid relid, int natts, VacAttrStats **vacattrstats)
|
|
{
|
|
Relation sd;
|
|
int attno;
|
|
|
|
/*
|
|
* We use an ExclusiveLock on pg_statistic to ensure that only one
|
|
* backend is writing it at a time --- without that, we might have to
|
|
* deal with concurrent updates here, and it's not worth the trouble.
|
|
*/
|
|
sd = heap_openr(StatisticRelationName, ExclusiveLock);
|
|
|
|
for (attno = 0; attno < natts; attno++)
|
|
{
|
|
VacAttrStats *stats = vacattrstats[attno];
|
|
FmgrInfo out_function;
|
|
HeapTuple stup,
|
|
oldtup;
|
|
int i,
|
|
k,
|
|
n;
|
|
Datum values[Natts_pg_statistic];
|
|
char nulls[Natts_pg_statistic];
|
|
char replaces[Natts_pg_statistic];
|
|
Relation irelations[Num_pg_statistic_indices];
|
|
|
|
/* Ignore attr if we weren't able to collect stats */
|
|
if (!stats->stats_valid)
|
|
continue;
|
|
|
|
fmgr_info(stats->attrtype->typoutput, &out_function);
|
|
|
|
/*
|
|
* Construct a new pg_statistic tuple
|
|
*/
|
|
for (i = 0; i < Natts_pg_statistic; ++i)
|
|
{
|
|
nulls[i] = ' ';
|
|
replaces[i] = 'r';
|
|
}
|
|
|
|
i = 0;
|
|
values[i++] = ObjectIdGetDatum(relid); /* starelid */
|
|
values[i++] = Int16GetDatum(stats->attnum); /* staattnum */
|
|
values[i++] = Float4GetDatum(stats->stanullfrac); /* stanullfrac */
|
|
values[i++] = Int32GetDatum(stats->stawidth); /* stawidth */
|
|
values[i++] = Float4GetDatum(stats->stadistinct); /* stadistinct */
|
|
for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
|
|
{
|
|
values[i++] = Int16GetDatum(stats->stakind[k]); /* stakindN */
|
|
}
|
|
for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
|
|
{
|
|
values[i++] = ObjectIdGetDatum(stats->staop[k]); /* staopN */
|
|
}
|
|
for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
|
|
{
|
|
int nnum = stats->numnumbers[k];
|
|
|
|
if (nnum > 0)
|
|
{
|
|
Datum *numdatums = (Datum *) palloc(nnum * sizeof(Datum));
|
|
ArrayType *arry;
|
|
|
|
for (n = 0; n < nnum; n++)
|
|
numdatums[n] = Float4GetDatum(stats->stanumbers[k][n]);
|
|
/* XXX knows more than it should about type float4: */
|
|
arry = construct_array(numdatums, nnum,
|
|
false, sizeof(float4), 'i');
|
|
values[i++] = PointerGetDatum(arry); /* stanumbersN */
|
|
}
|
|
else
|
|
{
|
|
nulls[i] = 'n';
|
|
values[i++] = (Datum) 0;
|
|
}
|
|
}
|
|
for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
|
|
{
|
|
int ntxt = stats->numvalues[k];
|
|
|
|
if (ntxt > 0)
|
|
{
|
|
Datum *txtdatums = (Datum *) palloc(ntxt * sizeof(Datum));
|
|
ArrayType *arry;
|
|
|
|
for (n = 0; n < ntxt; n++)
|
|
{
|
|
/*
|
|
* Convert data values to a text string to be inserted
|
|
* into the text array.
|
|
*/
|
|
Datum stringdatum;
|
|
|
|
stringdatum =
|
|
FunctionCall3(&out_function,
|
|
stats->stavalues[k][n],
|
|
ObjectIdGetDatum(stats->attrtype->typelem),
|
|
Int32GetDatum(stats->attr->atttypmod));
|
|
txtdatums[n] = DirectFunctionCall1(textin, stringdatum);
|
|
pfree(DatumGetPointer(stringdatum));
|
|
}
|
|
/* XXX knows more than it should about type text: */
|
|
arry = construct_array(txtdatums, ntxt,
|
|
false, -1, 'i');
|
|
values[i++] = PointerGetDatum(arry); /* stavaluesN */
|
|
}
|
|
else
|
|
{
|
|
nulls[i] = 'n';
|
|
values[i++] = (Datum) 0;
|
|
}
|
|
}
|
|
|
|
/* Is there already a pg_statistic tuple for this attribute? */
|
|
oldtup = SearchSysCache(STATRELATT,
|
|
ObjectIdGetDatum(relid),
|
|
Int16GetDatum(stats->attnum),
|
|
0, 0);
|
|
|
|
if (HeapTupleIsValid(oldtup))
|
|
{
|
|
/* Yes, replace it */
|
|
stup = heap_modifytuple(oldtup,
|
|
sd,
|
|
values,
|
|
nulls,
|
|
replaces);
|
|
ReleaseSysCache(oldtup);
|
|
simple_heap_update(sd, &stup->t_self, stup);
|
|
}
|
|
else
|
|
{
|
|
/* No, insert new tuple */
|
|
stup = heap_formtuple(sd->rd_att, values, nulls);
|
|
heap_insert(sd, stup);
|
|
}
|
|
|
|
/* update indices too */
|
|
CatalogOpenIndices(Num_pg_statistic_indices, Name_pg_statistic_indices,
|
|
irelations);
|
|
CatalogIndexInsert(irelations, Num_pg_statistic_indices, sd, stup);
|
|
CatalogCloseIndices(Num_pg_statistic_indices, irelations);
|
|
|
|
heap_freetuple(stup);
|
|
}
|
|
|
|
/* close rel, but hold lock till upcoming commit */
|
|
heap_close(sd, NoLock);
|
|
}
|