2862 lines
84 KiB
C
2862 lines
84 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) 1996-2019, 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/commands/analyze.c
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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/detoast.h"
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#include "access/genam.h"
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#include "access/multixact.h"
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#include "access/relation.h"
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#include "access/sysattr.h"
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#include "access/table.h"
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#include "access/tableam.h"
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#include "access/transam.h"
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#include "access/tupconvert.h"
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#include "access/visibilitymap.h"
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#include "access/xact.h"
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#include "catalog/catalog.h"
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#include "catalog/index.h"
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#include "catalog/indexing.h"
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#include "catalog/pg_collation.h"
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#include "catalog/pg_inherits.h"
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#include "catalog/pg_namespace.h"
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#include "catalog/pg_statistic_ext.h"
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#include "commands/dbcommands.h"
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#include "commands/tablecmds.h"
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#include "commands/vacuum.h"
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#include "executor/executor.h"
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#include "foreign/fdwapi.h"
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#include "miscadmin.h"
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#include "nodes/nodeFuncs.h"
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#include "parser/parse_oper.h"
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#include "parser/parse_relation.h"
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#include "pgstat.h"
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#include "postmaster/autovacuum.h"
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#include "statistics/extended_stats_internal.h"
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#include "statistics/statistics.h"
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#include "storage/bufmgr.h"
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#include "storage/lmgr.h"
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#include "storage/proc.h"
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#include "storage/procarray.h"
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#include "utils/acl.h"
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#include "utils/attoptcache.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/guc.h"
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#include "utils/lsyscache.h"
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#include "utils/memutils.h"
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#include "utils/pg_rusage.h"
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#include "utils/sampling.h"
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#include "utils/sortsupport.h"
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#include "utils/syscache.h"
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#include "utils/timestamp.h"
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/* Per-index data for ANALYZE */
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typedef struct AnlIndexData
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{
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IndexInfo *indexInfo; /* BuildIndexInfo result */
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double tupleFract; /* fraction of rows for partial index */
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VacAttrStats **vacattrstats; /* index attrs to analyze */
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int attr_cnt;
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} AnlIndexData;
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/* Default statistics target (GUC parameter) */
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int default_statistics_target = 100;
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/* A few variables that don't seem worth passing around as parameters */
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static MemoryContext anl_context = NULL;
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static BufferAccessStrategy vac_strategy;
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static void do_analyze_rel(Relation onerel,
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VacuumParams *params, List *va_cols,
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AcquireSampleRowsFunc acquirefunc, BlockNumber relpages,
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bool inh, bool in_outer_xact, int elevel);
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static void compute_index_stats(Relation onerel, double totalrows,
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AnlIndexData *indexdata, int nindexes,
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HeapTuple *rows, int numrows,
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MemoryContext col_context);
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static VacAttrStats *examine_attribute(Relation onerel, int attnum,
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Node *index_expr);
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static int acquire_sample_rows(Relation onerel, int elevel,
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HeapTuple *rows, int targrows,
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double *totalrows, double *totaldeadrows);
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static int compare_rows(const void *a, const void *b);
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static int acquire_inherited_sample_rows(Relation onerel, int elevel,
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HeapTuple *rows, int targrows,
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double *totalrows, double *totaldeadrows);
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static void update_attstats(Oid relid, bool inh,
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int natts, VacAttrStats **vacattrstats);
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static Datum std_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull);
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static Datum ind_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull);
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/*
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* analyze_rel() -- analyze one relation
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*
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* relid identifies the relation to analyze. If relation is supplied, use
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* the name therein for reporting any failure to open/lock the rel; do not
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* use it once we've successfully opened the rel, since it might be stale.
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*/
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void
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analyze_rel(Oid relid, RangeVar *relation,
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VacuumParams *params, List *va_cols, bool in_outer_xact,
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BufferAccessStrategy bstrategy)
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{
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Relation onerel;
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int elevel;
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AcquireSampleRowsFunc acquirefunc = NULL;
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BlockNumber relpages = 0;
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/* Select logging level */
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if (params->options & VACOPT_VERBOSE)
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elevel = INFO;
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else
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elevel = DEBUG2;
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/* Set up static variables */
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vac_strategy = bstrategy;
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/*
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* Check for user-requested abort.
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*/
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CHECK_FOR_INTERRUPTS();
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/*
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* Open the relation, getting ShareUpdateExclusiveLock to ensure that two
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* ANALYZEs don't run on it concurrently. (This also locks out a
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* concurrent VACUUM, which doesn't matter much at the moment but might
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* matter if we ever try to accumulate stats on dead tuples.) If the rel
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* has been dropped since we last saw it, we don't need to process it.
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*
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* Make sure to generate only logs for ANALYZE in this case.
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*/
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onerel = vacuum_open_relation(relid, relation, params->options & ~(VACOPT_VACUUM),
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params->log_min_duration >= 0,
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ShareUpdateExclusiveLock);
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/* leave if relation could not be opened or locked */
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if (!onerel)
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return;
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/*
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* Check if relation needs to be skipped based on ownership. This check
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* happens also when building the relation list to analyze for a manual
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* operation, and needs to be done additionally here as ANALYZE could
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* happen across multiple transactions where relation ownership could have
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* changed in-between. Make sure to generate only logs for ANALYZE in
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* this case.
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*/
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if (!vacuum_is_relation_owner(RelationGetRelid(onerel),
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onerel->rd_rel,
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params->options & VACOPT_ANALYZE))
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{
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relation_close(onerel, ShareUpdateExclusiveLock);
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return;
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}
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/*
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* Silently ignore tables that are temp tables of other backends ---
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* trying to analyze these is rather pointless, since their contents are
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* probably not up-to-date on disk. (We don't throw a warning here; it
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* would just lead to chatter during a database-wide ANALYZE.)
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*/
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if (RELATION_IS_OTHER_TEMP(onerel))
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{
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relation_close(onerel, ShareUpdateExclusiveLock);
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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 (RelationGetRelid(onerel) == StatisticRelationId)
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{
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relation_close(onerel, ShareUpdateExclusiveLock);
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return;
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}
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/*
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* Check that it's of an analyzable relkind, and set up appropriately.
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*/
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if (onerel->rd_rel->relkind == RELKIND_RELATION ||
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onerel->rd_rel->relkind == RELKIND_MATVIEW)
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{
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/* Regular table, so we'll use the regular row acquisition function */
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acquirefunc = acquire_sample_rows;
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/* Also get regular table's size */
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relpages = RelationGetNumberOfBlocks(onerel);
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}
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else if (onerel->rd_rel->relkind == RELKIND_FOREIGN_TABLE)
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{
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/*
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* For a foreign table, call the FDW's hook function to see whether it
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* supports analysis.
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*/
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FdwRoutine *fdwroutine;
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bool ok = false;
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fdwroutine = GetFdwRoutineForRelation(onerel, false);
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if (fdwroutine->AnalyzeForeignTable != NULL)
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ok = fdwroutine->AnalyzeForeignTable(onerel,
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&acquirefunc,
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&relpages);
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if (!ok)
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{
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ereport(WARNING,
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(errmsg("skipping \"%s\" --- cannot analyze this foreign table",
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RelationGetRelationName(onerel))));
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relation_close(onerel, ShareUpdateExclusiveLock);
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return;
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}
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}
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else if (onerel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE)
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{
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/*
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* For partitioned tables, we want to do the recursive ANALYZE below.
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*/
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}
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else
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{
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/* No need for a WARNING if we already complained during VACUUM */
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if (!(params->options & VACOPT_VACUUM))
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ereport(WARNING,
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(errmsg("skipping \"%s\" --- cannot analyze non-tables or special system tables",
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RelationGetRelationName(onerel))));
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relation_close(onerel, ShareUpdateExclusiveLock);
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return;
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}
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/*
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* OK, let's do it. First let other backends know I'm in ANALYZE.
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*/
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LWLockAcquire(ProcArrayLock, LW_EXCLUSIVE);
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MyPgXact->vacuumFlags |= PROC_IN_ANALYZE;
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LWLockRelease(ProcArrayLock);
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/*
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* Do the normal non-recursive ANALYZE. We can skip this for partitioned
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* tables, which don't contain any rows.
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*/
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if (onerel->rd_rel->relkind != RELKIND_PARTITIONED_TABLE)
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do_analyze_rel(onerel, params, va_cols, acquirefunc,
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relpages, false, in_outer_xact, elevel);
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/*
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* If there are child tables, do recursive ANALYZE.
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*/
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if (onerel->rd_rel->relhassubclass)
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do_analyze_rel(onerel, params, va_cols, acquirefunc, relpages,
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true, in_outer_xact, elevel);
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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 entries
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* we made in pg_statistic. Also, releasing the lock before commit would
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* expose us to concurrent-update failures in update_attstats.)
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*/
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relation_close(onerel, NoLock);
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/*
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* Reset my PGXACT flag. Note: we need this here, and not in vacuum_rel,
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* because the vacuum flag is cleared by the end-of-xact code.
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*/
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LWLockAcquire(ProcArrayLock, LW_EXCLUSIVE);
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MyPgXact->vacuumFlags &= ~PROC_IN_ANALYZE;
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LWLockRelease(ProcArrayLock);
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}
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/*
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* do_analyze_rel() -- analyze one relation, recursively or not
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*
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* Note that "acquirefunc" is only relevant for the non-inherited case.
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* For the inherited case, acquire_inherited_sample_rows() determines the
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* appropriate acquirefunc for each child table.
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*/
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static void
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do_analyze_rel(Relation onerel, VacuumParams *params,
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List *va_cols, AcquireSampleRowsFunc acquirefunc,
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BlockNumber relpages, bool inh, bool in_outer_xact,
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int elevel)
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{
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int attr_cnt,
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tcnt,
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i,
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ind;
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Relation *Irel;
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int nindexes;
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bool hasindex;
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VacAttrStats **vacattrstats;
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AnlIndexData *indexdata;
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int targrows,
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numrows,
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minrows;
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double totalrows,
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totaldeadrows;
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HeapTuple *rows;
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PGRUsage ru0;
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TimestampTz starttime = 0;
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MemoryContext caller_context;
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Oid save_userid;
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int save_sec_context;
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int save_nestlevel;
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if (inh)
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ereport(elevel,
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(errmsg("analyzing \"%s.%s\" inheritance tree",
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get_namespace_name(RelationGetNamespace(onerel)),
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RelationGetRelationName(onerel))));
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else
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ereport(elevel,
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(errmsg("analyzing \"%s.%s\"",
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get_namespace_name(RelationGetNamespace(onerel)),
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RelationGetRelationName(onerel))));
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/*
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* Set up a working context so that we can easily free whatever junk gets
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* created.
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*/
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anl_context = AllocSetContextCreate(CurrentMemoryContext,
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"Analyze",
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ALLOCSET_DEFAULT_SIZES);
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caller_context = MemoryContextSwitchTo(anl_context);
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/*
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* Switch to the table owner's userid, so that any index functions are run
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* as that user. Also lock down security-restricted operations and
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* arrange to make GUC variable changes local to this command.
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*/
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GetUserIdAndSecContext(&save_userid, &save_sec_context);
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SetUserIdAndSecContext(onerel->rd_rel->relowner,
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save_sec_context | SECURITY_RESTRICTED_OPERATION);
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save_nestlevel = NewGUCNestLevel();
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/* measure elapsed time iff autovacuum logging requires it */
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if (IsAutoVacuumWorkerProcess() && params->log_min_duration >= 0)
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{
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pg_rusage_init(&ru0);
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if (params->log_min_duration > 0)
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starttime = GetCurrentTimestamp();
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}
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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, so we just reject them
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* at the lookup stage. We also reject duplicate column mentions. (We
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* could alternatively ignore duplicates, but analyzing a column twice
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* won't work; we'd end up making a conflicting update in pg_statistic.)
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*/
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if (va_cols != NIL)
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{
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Bitmapset *unique_cols = NULL;
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ListCell *le;
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vacattrstats = (VacAttrStats **) palloc(list_length(va_cols) *
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sizeof(VacAttrStats *));
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tcnt = 0;
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foreach(le, va_cols)
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{
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char *col = strVal(lfirst(le));
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i = attnameAttNum(onerel, col, false);
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if (i == InvalidAttrNumber)
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ereport(ERROR,
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(errcode(ERRCODE_UNDEFINED_COLUMN),
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errmsg("column \"%s\" of relation \"%s\" does not exist",
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col, RelationGetRelationName(onerel))));
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if (bms_is_member(i, unique_cols))
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ereport(ERROR,
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(errcode(ERRCODE_DUPLICATE_COLUMN),
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errmsg("column \"%s\" of relation \"%s\" appears more than once",
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col, RelationGetRelationName(onerel))));
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unique_cols = bms_add_member(unique_cols, i);
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vacattrstats[tcnt] = examine_attribute(onerel, i, NULL);
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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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attr_cnt = onerel->rd_att->natts;
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vacattrstats = (VacAttrStats **)
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palloc(attr_cnt * sizeof(VacAttrStats *));
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tcnt = 0;
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for (i = 1; i <= attr_cnt; i++)
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{
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vacattrstats[tcnt] = examine_attribute(onerel, i, NULL);
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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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* Open all indexes of the relation, and see if there are any analyzable
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* columns in the indexes. We do not analyze index columns if there was
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* an explicit column list in the ANALYZE command, however. If we are
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* doing a recursive scan, we don't want to touch the parent's indexes at
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* all.
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*/
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if (!inh)
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vac_open_indexes(onerel, AccessShareLock, &nindexes, &Irel);
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else
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{
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Irel = NULL;
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nindexes = 0;
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}
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hasindex = (nindexes > 0);
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indexdata = NULL;
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if (hasindex)
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{
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indexdata = (AnlIndexData *) palloc0(nindexes * sizeof(AnlIndexData));
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for (ind = 0; ind < nindexes; ind++)
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{
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AnlIndexData *thisdata = &indexdata[ind];
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IndexInfo *indexInfo;
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thisdata->indexInfo = indexInfo = BuildIndexInfo(Irel[ind]);
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thisdata->tupleFract = 1.0; /* fix later if partial */
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if (indexInfo->ii_Expressions != NIL && va_cols == NIL)
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{
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ListCell *indexpr_item = list_head(indexInfo->ii_Expressions);
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thisdata->vacattrstats = (VacAttrStats **)
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palloc(indexInfo->ii_NumIndexAttrs * sizeof(VacAttrStats *));
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tcnt = 0;
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for (i = 0; i < indexInfo->ii_NumIndexAttrs; i++)
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{
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int keycol = indexInfo->ii_IndexAttrNumbers[i];
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if (keycol == 0)
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{
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/* Found an index expression */
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Node *indexkey;
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if (indexpr_item == NULL) /* shouldn't happen */
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elog(ERROR, "too few entries in indexprs list");
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indexkey = (Node *) lfirst(indexpr_item);
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indexpr_item = lnext(indexInfo->ii_Expressions,
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indexpr_item);
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thisdata->vacattrstats[tcnt] =
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examine_attribute(Irel[ind], i + 1, indexkey);
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if (thisdata->vacattrstats[tcnt] != NULL)
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tcnt++;
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}
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}
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thisdata->attr_cnt = tcnt;
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}
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}
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}
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|
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/*
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* Determine how many rows we need to sample, using the worst case from
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* all analyzable columns. We use a lower bound of 100 rows to avoid
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* possible overflow in Vitter's algorithm. (Note: that will also be the
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* target in the corner case where there are no analyzable columns.)
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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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for (ind = 0; ind < nindexes; ind++)
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{
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AnlIndexData *thisdata = &indexdata[ind];
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|
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for (i = 0; i < thisdata->attr_cnt; i++)
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{
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if (targrows < thisdata->vacattrstats[i]->minrows)
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targrows = thisdata->vacattrstats[i]->minrows;
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}
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}
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|
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/*
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* Look at extended statistics objects too, as those may define custom
|
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* statistics target. So we may need to sample more rows and then build
|
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* the statistics with enough detail.
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*/
|
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minrows = ComputeExtStatisticsRows(onerel, attr_cnt, vacattrstats);
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|
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if (targrows < minrows)
|
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targrows = minrows;
|
|
|
|
/*
|
|
* Acquire the sample rows
|
|
*/
|
|
rows = (HeapTuple *) palloc(targrows * sizeof(HeapTuple));
|
|
if (inh)
|
|
numrows = acquire_inherited_sample_rows(onerel, elevel,
|
|
rows, targrows,
|
|
&totalrows, &totaldeadrows);
|
|
else
|
|
numrows = (*acquirefunc) (onerel, elevel,
|
|
rows, targrows,
|
|
&totalrows, &totaldeadrows);
|
|
|
|
/*
|
|
* Compute the statistics. Temporary results during the calculations for
|
|
* each column are stored in a child context. The calc routines are
|
|
* responsible to make sure that whatever they store into the VacAttrStats
|
|
* structure is allocated in anl_context.
|
|
*/
|
|
if (numrows > 0)
|
|
{
|
|
MemoryContext col_context,
|
|
old_context;
|
|
|
|
col_context = AllocSetContextCreate(anl_context,
|
|
"Analyze Column",
|
|
ALLOCSET_DEFAULT_SIZES);
|
|
old_context = MemoryContextSwitchTo(col_context);
|
|
|
|
for (i = 0; i < attr_cnt; i++)
|
|
{
|
|
VacAttrStats *stats = vacattrstats[i];
|
|
AttributeOpts *aopt;
|
|
|
|
stats->rows = rows;
|
|
stats->tupDesc = onerel->rd_att;
|
|
stats->compute_stats(stats,
|
|
std_fetch_func,
|
|
numrows,
|
|
totalrows);
|
|
|
|
/*
|
|
* If the appropriate flavor of the n_distinct option is
|
|
* specified, override with the corresponding value.
|
|
*/
|
|
aopt = get_attribute_options(onerel->rd_id, stats->attr->attnum);
|
|
if (aopt != NULL)
|
|
{
|
|
float8 n_distinct;
|
|
|
|
n_distinct = inh ? aopt->n_distinct_inherited : aopt->n_distinct;
|
|
if (n_distinct != 0.0)
|
|
stats->stadistinct = n_distinct;
|
|
}
|
|
|
|
MemoryContextResetAndDeleteChildren(col_context);
|
|
}
|
|
|
|
if (hasindex)
|
|
compute_index_stats(onerel, totalrows,
|
|
indexdata, nindexes,
|
|
rows, numrows,
|
|
col_context);
|
|
|
|
MemoryContextSwitchTo(old_context);
|
|
MemoryContextDelete(col_context);
|
|
|
|
/*
|
|
* Emit the completed stats rows into pg_statistic, replacing any
|
|
* previous statistics for the target columns. (If there are stats in
|
|
* pg_statistic for columns we didn't process, we leave them alone.)
|
|
*/
|
|
update_attstats(RelationGetRelid(onerel), inh,
|
|
attr_cnt, vacattrstats);
|
|
|
|
for (ind = 0; ind < nindexes; ind++)
|
|
{
|
|
AnlIndexData *thisdata = &indexdata[ind];
|
|
|
|
update_attstats(RelationGetRelid(Irel[ind]), false,
|
|
thisdata->attr_cnt, thisdata->vacattrstats);
|
|
}
|
|
|
|
/*
|
|
* Build extended statistics (if there are any).
|
|
*
|
|
* For now we only build extended statistics on individual relations,
|
|
* not for relations representing inheritance trees.
|
|
*/
|
|
if (!inh)
|
|
BuildRelationExtStatistics(onerel, totalrows, numrows, rows,
|
|
attr_cnt, vacattrstats);
|
|
}
|
|
|
|
/*
|
|
* Update pages/tuples stats in pg_class ... but not if we're doing
|
|
* inherited stats.
|
|
*/
|
|
if (!inh)
|
|
{
|
|
BlockNumber relallvisible;
|
|
|
|
visibilitymap_count(onerel, &relallvisible, NULL);
|
|
|
|
vac_update_relstats(onerel,
|
|
relpages,
|
|
totalrows,
|
|
relallvisible,
|
|
hasindex,
|
|
InvalidTransactionId,
|
|
InvalidMultiXactId,
|
|
in_outer_xact);
|
|
}
|
|
|
|
/*
|
|
* Same for indexes. Vacuum always scans all indexes, so if we're part of
|
|
* VACUUM ANALYZE, don't overwrite the accurate count already inserted by
|
|
* VACUUM.
|
|
*/
|
|
if (!inh && !(params->options & VACOPT_VACUUM))
|
|
{
|
|
for (ind = 0; ind < nindexes; ind++)
|
|
{
|
|
AnlIndexData *thisdata = &indexdata[ind];
|
|
double totalindexrows;
|
|
|
|
totalindexrows = ceil(thisdata->tupleFract * totalrows);
|
|
vac_update_relstats(Irel[ind],
|
|
RelationGetNumberOfBlocks(Irel[ind]),
|
|
totalindexrows,
|
|
0,
|
|
false,
|
|
InvalidTransactionId,
|
|
InvalidMultiXactId,
|
|
in_outer_xact);
|
|
}
|
|
}
|
|
|
|
/*
|
|
* Report ANALYZE to the stats collector, too. However, if doing
|
|
* inherited stats we shouldn't report, because the stats collector only
|
|
* tracks per-table stats. Reset the changes_since_analyze counter only
|
|
* if we analyzed all columns; otherwise, there is still work for
|
|
* auto-analyze to do.
|
|
*/
|
|
if (!inh)
|
|
pgstat_report_analyze(onerel, totalrows, totaldeadrows,
|
|
(va_cols == NIL));
|
|
|
|
/* If this isn't part of VACUUM ANALYZE, let index AMs do cleanup */
|
|
if (!(params->options & VACOPT_VACUUM))
|
|
{
|
|
for (ind = 0; ind < nindexes; ind++)
|
|
{
|
|
IndexBulkDeleteResult *stats;
|
|
IndexVacuumInfo ivinfo;
|
|
|
|
ivinfo.index = Irel[ind];
|
|
ivinfo.analyze_only = true;
|
|
ivinfo.estimated_count = true;
|
|
ivinfo.message_level = elevel;
|
|
ivinfo.num_heap_tuples = onerel->rd_rel->reltuples;
|
|
ivinfo.strategy = vac_strategy;
|
|
|
|
stats = index_vacuum_cleanup(&ivinfo, NULL);
|
|
|
|
if (stats)
|
|
pfree(stats);
|
|
}
|
|
}
|
|
|
|
/* Done with indexes */
|
|
vac_close_indexes(nindexes, Irel, NoLock);
|
|
|
|
/* Log the action if appropriate */
|
|
if (IsAutoVacuumWorkerProcess() && params->log_min_duration >= 0)
|
|
{
|
|
if (params->log_min_duration == 0 ||
|
|
TimestampDifferenceExceeds(starttime, GetCurrentTimestamp(),
|
|
params->log_min_duration))
|
|
ereport(LOG,
|
|
(errmsg("automatic analyze of table \"%s.%s.%s\" system usage: %s",
|
|
get_database_name(MyDatabaseId),
|
|
get_namespace_name(RelationGetNamespace(onerel)),
|
|
RelationGetRelationName(onerel),
|
|
pg_rusage_show(&ru0))));
|
|
}
|
|
|
|
/* Roll back any GUC changes executed by index functions */
|
|
AtEOXact_GUC(false, save_nestlevel);
|
|
|
|
/* Restore userid and security context */
|
|
SetUserIdAndSecContext(save_userid, save_sec_context);
|
|
|
|
/* Restore current context and release memory */
|
|
MemoryContextSwitchTo(caller_context);
|
|
MemoryContextDelete(anl_context);
|
|
anl_context = NULL;
|
|
}
|
|
|
|
/*
|
|
* Compute statistics about indexes of a relation
|
|
*/
|
|
static void
|
|
compute_index_stats(Relation onerel, double totalrows,
|
|
AnlIndexData *indexdata, int nindexes,
|
|
HeapTuple *rows, int numrows,
|
|
MemoryContext col_context)
|
|
{
|
|
MemoryContext ind_context,
|
|
old_context;
|
|
Datum values[INDEX_MAX_KEYS];
|
|
bool isnull[INDEX_MAX_KEYS];
|
|
int ind,
|
|
i;
|
|
|
|
ind_context = AllocSetContextCreate(anl_context,
|
|
"Analyze Index",
|
|
ALLOCSET_DEFAULT_SIZES);
|
|
old_context = MemoryContextSwitchTo(ind_context);
|
|
|
|
for (ind = 0; ind < nindexes; ind++)
|
|
{
|
|
AnlIndexData *thisdata = &indexdata[ind];
|
|
IndexInfo *indexInfo = thisdata->indexInfo;
|
|
int attr_cnt = thisdata->attr_cnt;
|
|
TupleTableSlot *slot;
|
|
EState *estate;
|
|
ExprContext *econtext;
|
|
ExprState *predicate;
|
|
Datum *exprvals;
|
|
bool *exprnulls;
|
|
int numindexrows,
|
|
tcnt,
|
|
rowno;
|
|
double totalindexrows;
|
|
|
|
/* Ignore index if no columns to analyze and not partial */
|
|
if (attr_cnt == 0 && indexInfo->ii_Predicate == NIL)
|
|
continue;
|
|
|
|
/*
|
|
* Need an EState for evaluation of index expressions and
|
|
* partial-index predicates. Create it in the per-index context to be
|
|
* sure it gets cleaned up at the bottom of the loop.
|
|
*/
|
|
estate = CreateExecutorState();
|
|
econtext = GetPerTupleExprContext(estate);
|
|
/* Need a slot to hold the current heap tuple, too */
|
|
slot = MakeSingleTupleTableSlot(RelationGetDescr(onerel),
|
|
&TTSOpsHeapTuple);
|
|
|
|
/* Arrange for econtext's scan tuple to be the tuple under test */
|
|
econtext->ecxt_scantuple = slot;
|
|
|
|
/* Set up execution state for predicate. */
|
|
predicate = ExecPrepareQual(indexInfo->ii_Predicate, estate);
|
|
|
|
/* Compute and save index expression values */
|
|
exprvals = (Datum *) palloc(numrows * attr_cnt * sizeof(Datum));
|
|
exprnulls = (bool *) palloc(numrows * attr_cnt * sizeof(bool));
|
|
numindexrows = 0;
|
|
tcnt = 0;
|
|
for (rowno = 0; rowno < numrows; rowno++)
|
|
{
|
|
HeapTuple heapTuple = rows[rowno];
|
|
|
|
vacuum_delay_point();
|
|
|
|
/*
|
|
* Reset the per-tuple context each time, to reclaim any cruft
|
|
* left behind by evaluating the predicate or index expressions.
|
|
*/
|
|
ResetExprContext(econtext);
|
|
|
|
/* Set up for predicate or expression evaluation */
|
|
ExecStoreHeapTuple(heapTuple, slot, false);
|
|
|
|
/* If index is partial, check predicate */
|
|
if (predicate != NULL)
|
|
{
|
|
if (!ExecQual(predicate, econtext))
|
|
continue;
|
|
}
|
|
numindexrows++;
|
|
|
|
if (attr_cnt > 0)
|
|
{
|
|
/*
|
|
* Evaluate the index row to compute expression values. We
|
|
* could do this by hand, but FormIndexDatum is convenient.
|
|
*/
|
|
FormIndexDatum(indexInfo,
|
|
slot,
|
|
estate,
|
|
values,
|
|
isnull);
|
|
|
|
/*
|
|
* Save just the columns we care about. We copy the values
|
|
* into ind_context from the estate's per-tuple context.
|
|
*/
|
|
for (i = 0; i < attr_cnt; i++)
|
|
{
|
|
VacAttrStats *stats = thisdata->vacattrstats[i];
|
|
int attnum = stats->attr->attnum;
|
|
|
|
if (isnull[attnum - 1])
|
|
{
|
|
exprvals[tcnt] = (Datum) 0;
|
|
exprnulls[tcnt] = true;
|
|
}
|
|
else
|
|
{
|
|
exprvals[tcnt] = datumCopy(values[attnum - 1],
|
|
stats->attrtype->typbyval,
|
|
stats->attrtype->typlen);
|
|
exprnulls[tcnt] = false;
|
|
}
|
|
tcnt++;
|
|
}
|
|
}
|
|
}
|
|
|
|
/*
|
|
* Having counted the number of rows that pass the predicate in the
|
|
* sample, we can estimate the total number of rows in the index.
|
|
*/
|
|
thisdata->tupleFract = (double) numindexrows / (double) numrows;
|
|
totalindexrows = ceil(thisdata->tupleFract * totalrows);
|
|
|
|
/*
|
|
* Now we can compute the statistics for the expression columns.
|
|
*/
|
|
if (numindexrows > 0)
|
|
{
|
|
MemoryContextSwitchTo(col_context);
|
|
for (i = 0; i < attr_cnt; i++)
|
|
{
|
|
VacAttrStats *stats = thisdata->vacattrstats[i];
|
|
AttributeOpts *aopt =
|
|
get_attribute_options(stats->attr->attrelid,
|
|
stats->attr->attnum);
|
|
|
|
stats->exprvals = exprvals + i;
|
|
stats->exprnulls = exprnulls + i;
|
|
stats->rowstride = attr_cnt;
|
|
stats->compute_stats(stats,
|
|
ind_fetch_func,
|
|
numindexrows,
|
|
totalindexrows);
|
|
|
|
/*
|
|
* If the n_distinct option is specified, it overrides the
|
|
* above computation. For indices, we always use just
|
|
* n_distinct, not n_distinct_inherited.
|
|
*/
|
|
if (aopt != NULL && aopt->n_distinct != 0.0)
|
|
stats->stadistinct = aopt->n_distinct;
|
|
|
|
MemoryContextResetAndDeleteChildren(col_context);
|
|
}
|
|
}
|
|
|
|
/* And clean up */
|
|
MemoryContextSwitchTo(ind_context);
|
|
|
|
ExecDropSingleTupleTableSlot(slot);
|
|
FreeExecutorState(estate);
|
|
MemoryContextResetAndDeleteChildren(ind_context);
|
|
}
|
|
|
|
MemoryContextSwitchTo(old_context);
|
|
MemoryContextDelete(ind_context);
|
|
}
|
|
|
|
/*
|
|
* examine_attribute -- pre-analysis of a single column
|
|
*
|
|
* Determine whether the column is analyzable; if so, create and initialize
|
|
* a VacAttrStats struct for it. If not, return NULL.
|
|
*
|
|
* If index_expr isn't NULL, then we're trying to analyze an expression index,
|
|
* and index_expr is the expression tree representing the column's data.
|
|
*/
|
|
static VacAttrStats *
|
|
examine_attribute(Relation onerel, int attnum, Node *index_expr)
|
|
{
|
|
Form_pg_attribute attr = TupleDescAttr(onerel->rd_att, attnum - 1);
|
|
HeapTuple typtuple;
|
|
VacAttrStats *stats;
|
|
int i;
|
|
bool ok;
|
|
|
|
/* Never analyze dropped columns */
|
|
if (attr->attisdropped)
|
|
return NULL;
|
|
|
|
/* Don't analyze column if user has specified not to */
|
|
if (attr->attstattarget == 0)
|
|
return NULL;
|
|
|
|
/*
|
|
* Create the VacAttrStats struct. Note that we only have a copy of the
|
|
* fixed fields of the pg_attribute tuple.
|
|
*/
|
|
stats = (VacAttrStats *) palloc0(sizeof(VacAttrStats));
|
|
stats->attr = (Form_pg_attribute) palloc(ATTRIBUTE_FIXED_PART_SIZE);
|
|
memcpy(stats->attr, attr, ATTRIBUTE_FIXED_PART_SIZE);
|
|
|
|
/*
|
|
* When analyzing an expression index, believe the expression tree's type
|
|
* not the column datatype --- the latter might be the opckeytype storage
|
|
* type of the opclass, which is not interesting for our purposes. (Note:
|
|
* if we did anything with non-expression index columns, we'd need to
|
|
* figure out where to get the correct type info from, but for now that's
|
|
* not a problem.) It's not clear whether anyone will care about the
|
|
* typmod, but we store that too just in case.
|
|
*/
|
|
if (index_expr)
|
|
{
|
|
stats->attrtypid = exprType(index_expr);
|
|
stats->attrtypmod = exprTypmod(index_expr);
|
|
|
|
/*
|
|
* If a collation has been specified for the index column, use that in
|
|
* preference to anything else; but if not, fall back to whatever we
|
|
* can get from the expression.
|
|
*/
|
|
if (OidIsValid(onerel->rd_indcollation[attnum - 1]))
|
|
stats->attrcollid = onerel->rd_indcollation[attnum - 1];
|
|
else
|
|
stats->attrcollid = exprCollation(index_expr);
|
|
}
|
|
else
|
|
{
|
|
stats->attrtypid = attr->atttypid;
|
|
stats->attrtypmod = attr->atttypmod;
|
|
stats->attrcollid = attr->attcollation;
|
|
}
|
|
|
|
typtuple = SearchSysCacheCopy1(TYPEOID,
|
|
ObjectIdGetDatum(stats->attrtypid));
|
|
if (!HeapTupleIsValid(typtuple))
|
|
elog(ERROR, "cache lookup failed for type %u", stats->attrtypid);
|
|
stats->attrtype = (Form_pg_type) GETSTRUCT(typtuple);
|
|
stats->anl_context = anl_context;
|
|
stats->tupattnum = attnum;
|
|
|
|
/*
|
|
* The fields describing the stats->stavalues[n] element types default to
|
|
* the type of the data being analyzed, but the type-specific typanalyze
|
|
* function can change them if it wants to store something else.
|
|
*/
|
|
for (i = 0; i < STATISTIC_NUM_SLOTS; i++)
|
|
{
|
|
stats->statypid[i] = stats->attrtypid;
|
|
stats->statyplen[i] = stats->attrtype->typlen;
|
|
stats->statypbyval[i] = stats->attrtype->typbyval;
|
|
stats->statypalign[i] = stats->attrtype->typalign;
|
|
}
|
|
|
|
/*
|
|
* Call the type-specific typanalyze function. If none is specified, use
|
|
* std_typanalyze().
|
|
*/
|
|
if (OidIsValid(stats->attrtype->typanalyze))
|
|
ok = DatumGetBool(OidFunctionCall1(stats->attrtype->typanalyze,
|
|
PointerGetDatum(stats)));
|
|
else
|
|
ok = std_typanalyze(stats);
|
|
|
|
if (!ok || stats->compute_stats == NULL || stats->minrows <= 0)
|
|
{
|
|
heap_freetuple(typtuple);
|
|
pfree(stats->attr);
|
|
pfree(stats);
|
|
return NULL;
|
|
}
|
|
|
|
return stats;
|
|
}
|
|
|
|
/*
|
|
* acquire_sample_rows -- acquire a random sample of rows from the table
|
|
*
|
|
* Selected rows are returned in the caller-allocated array rows[], which
|
|
* must have at least targrows entries.
|
|
* The actual number of rows selected is returned as the function result.
|
|
* We also estimate the total numbers of live and dead rows in the table,
|
|
* and return them into *totalrows and *totaldeadrows, respectively.
|
|
*
|
|
* The returned list of tuples is in order by physical position in the table.
|
|
* (We will rely on this later to derive correlation estimates.)
|
|
*
|
|
* As of May 2004 we use a new two-stage method: Stage one selects up
|
|
* to targrows random blocks (or all blocks, if there aren't so many).
|
|
* Stage two scans these blocks and uses the Vitter algorithm to create
|
|
* a random sample of targrows rows (or less, if there are less in the
|
|
* sample of blocks). The two stages are executed simultaneously: each
|
|
* block is processed as soon as stage one returns its number and while
|
|
* the rows are read stage two controls which ones are to be inserted
|
|
* into the sample.
|
|
*
|
|
* Although every row has an equal chance of ending up in the final
|
|
* sample, this sampling method is not perfect: not every possible
|
|
* sample has an equal chance of being selected. For large relations
|
|
* the number of different blocks represented by the sample tends to be
|
|
* too small. We can live with that for now. Improvements are welcome.
|
|
*
|
|
* An important property of this sampling method is that because we do
|
|
* look at a statistically unbiased set of blocks, we should get
|
|
* unbiased estimates of the average numbers of live and dead rows per
|
|
* block. The previous sampling method put too much credence in the row
|
|
* density near the start of the table.
|
|
*/
|
|
static int
|
|
acquire_sample_rows(Relation onerel, int elevel,
|
|
HeapTuple *rows, int targrows,
|
|
double *totalrows, double *totaldeadrows)
|
|
{
|
|
int numrows = 0; /* # rows now in reservoir */
|
|
double samplerows = 0; /* total # rows collected */
|
|
double liverows = 0; /* # live rows seen */
|
|
double deadrows = 0; /* # dead rows seen */
|
|
double rowstoskip = -1; /* -1 means not set yet */
|
|
BlockNumber totalblocks;
|
|
TransactionId OldestXmin;
|
|
BlockSamplerData bs;
|
|
ReservoirStateData rstate;
|
|
TupleTableSlot *slot;
|
|
TableScanDesc scan;
|
|
|
|
Assert(targrows > 0);
|
|
|
|
totalblocks = RelationGetNumberOfBlocks(onerel);
|
|
|
|
/* Need a cutoff xmin for HeapTupleSatisfiesVacuum */
|
|
OldestXmin = GetOldestXmin(onerel, PROCARRAY_FLAGS_VACUUM);
|
|
|
|
/* Prepare for sampling block numbers */
|
|
BlockSampler_Init(&bs, totalblocks, targrows, random());
|
|
/* Prepare for sampling rows */
|
|
reservoir_init_selection_state(&rstate, targrows);
|
|
|
|
scan = table_beginscan_analyze(onerel);
|
|
slot = table_slot_create(onerel, NULL);
|
|
|
|
/* Outer loop over blocks to sample */
|
|
while (BlockSampler_HasMore(&bs))
|
|
{
|
|
BlockNumber targblock = BlockSampler_Next(&bs);
|
|
|
|
vacuum_delay_point();
|
|
|
|
if (!table_scan_analyze_next_block(scan, targblock, vac_strategy))
|
|
continue;
|
|
|
|
while (table_scan_analyze_next_tuple(scan, OldestXmin, &liverows, &deadrows, slot))
|
|
{
|
|
/*
|
|
* The first targrows sample rows are simply copied into the
|
|
* reservoir. Then we 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 in utils/misc/sampling.c). It
|
|
* works by repeatedly computing the number of tuples to skip
|
|
* before selecting a tuple, which replaces 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.
|
|
*/
|
|
if (numrows < targrows)
|
|
rows[numrows++] = ExecCopySlotHeapTuple(slot);
|
|
else
|
|
{
|
|
/*
|
|
* t in Vitter's paper is the number of records already
|
|
* processed. If we need to compute a new S value, we must
|
|
* use the not-yet-incremented value of samplerows as t.
|
|
*/
|
|
if (rowstoskip < 0)
|
|
rowstoskip = reservoir_get_next_S(&rstate, samplerows, targrows);
|
|
|
|
if (rowstoskip <= 0)
|
|
{
|
|
/*
|
|
* Found a suitable tuple, so save it, replacing one old
|
|
* tuple at random
|
|
*/
|
|
int k = (int) (targrows * sampler_random_fract(rstate.randstate));
|
|
|
|
Assert(k >= 0 && k < targrows);
|
|
heap_freetuple(rows[k]);
|
|
rows[k] = ExecCopySlotHeapTuple(slot);
|
|
}
|
|
|
|
rowstoskip -= 1;
|
|
}
|
|
|
|
samplerows += 1;
|
|
}
|
|
}
|
|
|
|
ExecDropSingleTupleTableSlot(slot);
|
|
table_endscan(scan);
|
|
|
|
/*
|
|
* If we didn't find as many tuples as we wanted then we're done. No sort
|
|
* is needed, since they're already in order.
|
|
*
|
|
* Otherwise we need to sort the collected tuples by position
|
|
* (itempointer). It's not worth worrying about corner cases where the
|
|
* tuples are already sorted.
|
|
*/
|
|
if (numrows == targrows)
|
|
qsort((void *) rows, numrows, sizeof(HeapTuple), compare_rows);
|
|
|
|
/*
|
|
* Estimate total numbers of live and dead rows in relation, extrapolating
|
|
* on the assumption that the average tuple density in pages we didn't
|
|
* scan is the same as in the pages we did scan. Since what we scanned is
|
|
* a random sample of the pages in the relation, this should be a good
|
|
* assumption.
|
|
*/
|
|
if (bs.m > 0)
|
|
{
|
|
*totalrows = floor((liverows / bs.m) * totalblocks + 0.5);
|
|
*totaldeadrows = floor((deadrows / bs.m) * totalblocks + 0.5);
|
|
}
|
|
else
|
|
{
|
|
*totalrows = 0.0;
|
|
*totaldeadrows = 0.0;
|
|
}
|
|
|
|
/*
|
|
* Emit some interesting relation info
|
|
*/
|
|
ereport(elevel,
|
|
(errmsg("\"%s\": scanned %d of %u pages, "
|
|
"containing %.0f live rows and %.0f dead rows; "
|
|
"%d rows in sample, %.0f estimated total rows",
|
|
RelationGetRelationName(onerel),
|
|
bs.m, totalblocks,
|
|
liverows, deadrows,
|
|
numrows, *totalrows)));
|
|
|
|
return numrows;
|
|
}
|
|
|
|
/*
|
|
* qsort comparator for sorting rows[] array
|
|
*/
|
|
static int
|
|
compare_rows(const void *a, const void *b)
|
|
{
|
|
HeapTuple ha = *(const HeapTuple *) a;
|
|
HeapTuple hb = *(const 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;
|
|
}
|
|
|
|
|
|
/*
|
|
* acquire_inherited_sample_rows -- acquire sample rows from inheritance tree
|
|
*
|
|
* This has the same API as acquire_sample_rows, except that rows are
|
|
* collected from all inheritance children as well as the specified table.
|
|
* We fail and return zero if there are no inheritance children, or if all
|
|
* children are foreign tables that don't support ANALYZE.
|
|
*/
|
|
static int
|
|
acquire_inherited_sample_rows(Relation onerel, int elevel,
|
|
HeapTuple *rows, int targrows,
|
|
double *totalrows, double *totaldeadrows)
|
|
{
|
|
List *tableOIDs;
|
|
Relation *rels;
|
|
AcquireSampleRowsFunc *acquirefuncs;
|
|
double *relblocks;
|
|
double totalblocks;
|
|
int numrows,
|
|
nrels,
|
|
i;
|
|
ListCell *lc;
|
|
bool has_child;
|
|
|
|
/*
|
|
* Find all members of inheritance set. We only need AccessShareLock on
|
|
* the children.
|
|
*/
|
|
tableOIDs =
|
|
find_all_inheritors(RelationGetRelid(onerel), AccessShareLock, NULL);
|
|
|
|
/*
|
|
* Check that there's at least one descendant, else fail. This could
|
|
* happen despite analyze_rel's relhassubclass check, if table once had a
|
|
* child but no longer does. In that case, we can clear the
|
|
* relhassubclass field so as not to make the same mistake again later.
|
|
* (This is safe because we hold ShareUpdateExclusiveLock.)
|
|
*/
|
|
if (list_length(tableOIDs) < 2)
|
|
{
|
|
/* CCI because we already updated the pg_class row in this command */
|
|
CommandCounterIncrement();
|
|
SetRelationHasSubclass(RelationGetRelid(onerel), false);
|
|
ereport(elevel,
|
|
(errmsg("skipping analyze of \"%s.%s\" inheritance tree --- this inheritance tree contains no child tables",
|
|
get_namespace_name(RelationGetNamespace(onerel)),
|
|
RelationGetRelationName(onerel))));
|
|
return 0;
|
|
}
|
|
|
|
/*
|
|
* Identify acquirefuncs to use, and count blocks in all the relations.
|
|
* The result could overflow BlockNumber, so we use double arithmetic.
|
|
*/
|
|
rels = (Relation *) palloc(list_length(tableOIDs) * sizeof(Relation));
|
|
acquirefuncs = (AcquireSampleRowsFunc *)
|
|
palloc(list_length(tableOIDs) * sizeof(AcquireSampleRowsFunc));
|
|
relblocks = (double *) palloc(list_length(tableOIDs) * sizeof(double));
|
|
totalblocks = 0;
|
|
nrels = 0;
|
|
has_child = false;
|
|
foreach(lc, tableOIDs)
|
|
{
|
|
Oid childOID = lfirst_oid(lc);
|
|
Relation childrel;
|
|
AcquireSampleRowsFunc acquirefunc = NULL;
|
|
BlockNumber relpages = 0;
|
|
|
|
/* We already got the needed lock */
|
|
childrel = table_open(childOID, NoLock);
|
|
|
|
/* Ignore if temp table of another backend */
|
|
if (RELATION_IS_OTHER_TEMP(childrel))
|
|
{
|
|
/* ... but release the lock on it */
|
|
Assert(childrel != onerel);
|
|
table_close(childrel, AccessShareLock);
|
|
continue;
|
|
}
|
|
|
|
/* Check table type (MATVIEW can't happen, but might as well allow) */
|
|
if (childrel->rd_rel->relkind == RELKIND_RELATION ||
|
|
childrel->rd_rel->relkind == RELKIND_MATVIEW)
|
|
{
|
|
/* Regular table, so use the regular row acquisition function */
|
|
acquirefunc = acquire_sample_rows;
|
|
relpages = RelationGetNumberOfBlocks(childrel);
|
|
}
|
|
else if (childrel->rd_rel->relkind == RELKIND_FOREIGN_TABLE)
|
|
{
|
|
/*
|
|
* For a foreign table, call the FDW's hook function to see
|
|
* whether it supports analysis.
|
|
*/
|
|
FdwRoutine *fdwroutine;
|
|
bool ok = false;
|
|
|
|
fdwroutine = GetFdwRoutineForRelation(childrel, false);
|
|
|
|
if (fdwroutine->AnalyzeForeignTable != NULL)
|
|
ok = fdwroutine->AnalyzeForeignTable(childrel,
|
|
&acquirefunc,
|
|
&relpages);
|
|
|
|
if (!ok)
|
|
{
|
|
/* ignore, but release the lock on it */
|
|
Assert(childrel != onerel);
|
|
table_close(childrel, AccessShareLock);
|
|
continue;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
/*
|
|
* ignore, but release the lock on it. don't try to unlock the
|
|
* passed-in relation
|
|
*/
|
|
Assert(childrel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE);
|
|
if (childrel != onerel)
|
|
table_close(childrel, AccessShareLock);
|
|
else
|
|
table_close(childrel, NoLock);
|
|
continue;
|
|
}
|
|
|
|
/* OK, we'll process this child */
|
|
has_child = true;
|
|
rels[nrels] = childrel;
|
|
acquirefuncs[nrels] = acquirefunc;
|
|
relblocks[nrels] = (double) relpages;
|
|
totalblocks += (double) relpages;
|
|
nrels++;
|
|
}
|
|
|
|
/*
|
|
* If we don't have at least one child table to consider, fail. If the
|
|
* relation is a partitioned table, it's not counted as a child table.
|
|
*/
|
|
if (!has_child)
|
|
{
|
|
ereport(elevel,
|
|
(errmsg("skipping analyze of \"%s.%s\" inheritance tree --- this inheritance tree contains no analyzable child tables",
|
|
get_namespace_name(RelationGetNamespace(onerel)),
|
|
RelationGetRelationName(onerel))));
|
|
return 0;
|
|
}
|
|
|
|
/*
|
|
* Now sample rows from each relation, proportionally to its fraction of
|
|
* the total block count. (This might be less than desirable if the child
|
|
* rels have radically different free-space percentages, but it's not
|
|
* clear that it's worth working harder.)
|
|
*/
|
|
numrows = 0;
|
|
*totalrows = 0;
|
|
*totaldeadrows = 0;
|
|
for (i = 0; i < nrels; i++)
|
|
{
|
|
Relation childrel = rels[i];
|
|
AcquireSampleRowsFunc acquirefunc = acquirefuncs[i];
|
|
double childblocks = relblocks[i];
|
|
|
|
if (childblocks > 0)
|
|
{
|
|
int childtargrows;
|
|
|
|
childtargrows = (int) rint(targrows * childblocks / totalblocks);
|
|
/* Make sure we don't overrun due to roundoff error */
|
|
childtargrows = Min(childtargrows, targrows - numrows);
|
|
if (childtargrows > 0)
|
|
{
|
|
int childrows;
|
|
double trows,
|
|
tdrows;
|
|
|
|
/* Fetch a random sample of the child's rows */
|
|
childrows = (*acquirefunc) (childrel, elevel,
|
|
rows + numrows, childtargrows,
|
|
&trows, &tdrows);
|
|
|
|
/* We may need to convert from child's rowtype to parent's */
|
|
if (childrows > 0 &&
|
|
!equalTupleDescs(RelationGetDescr(childrel),
|
|
RelationGetDescr(onerel)))
|
|
{
|
|
TupleConversionMap *map;
|
|
|
|
map = convert_tuples_by_name(RelationGetDescr(childrel),
|
|
RelationGetDescr(onerel));
|
|
if (map != NULL)
|
|
{
|
|
int j;
|
|
|
|
for (j = 0; j < childrows; j++)
|
|
{
|
|
HeapTuple newtup;
|
|
|
|
newtup = execute_attr_map_tuple(rows[numrows + j], map);
|
|
heap_freetuple(rows[numrows + j]);
|
|
rows[numrows + j] = newtup;
|
|
}
|
|
free_conversion_map(map);
|
|
}
|
|
}
|
|
|
|
/* And add to counts */
|
|
numrows += childrows;
|
|
*totalrows += trows;
|
|
*totaldeadrows += tdrows;
|
|
}
|
|
}
|
|
|
|
/*
|
|
* Note: we cannot release the child-table locks, since we may have
|
|
* pointers to their TOAST tables in the sampled rows.
|
|
*/
|
|
table_close(childrel, NoLock);
|
|
}
|
|
|
|
return numrows;
|
|
}
|
|
|
|
|
|
/*
|
|
* 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.
|
|
*
|
|
* Note: there would be a race condition here if two backends could
|
|
* ANALYZE the same table concurrently. Presently, we lock that out
|
|
* by taking a self-exclusive lock on the relation in analyze_rel().
|
|
*/
|
|
static void
|
|
update_attstats(Oid relid, bool inh, int natts, VacAttrStats **vacattrstats)
|
|
{
|
|
Relation sd;
|
|
int attno;
|
|
|
|
if (natts <= 0)
|
|
return; /* nothing to do */
|
|
|
|
sd = table_open(StatisticRelationId, RowExclusiveLock);
|
|
|
|
for (attno = 0; attno < natts; attno++)
|
|
{
|
|
VacAttrStats *stats = vacattrstats[attno];
|
|
HeapTuple stup,
|
|
oldtup;
|
|
int i,
|
|
k,
|
|
n;
|
|
Datum values[Natts_pg_statistic];
|
|
bool nulls[Natts_pg_statistic];
|
|
bool replaces[Natts_pg_statistic];
|
|
|
|
/* Ignore attr if we weren't able to collect stats */
|
|
if (!stats->stats_valid)
|
|
continue;
|
|
|
|
/*
|
|
* Construct a new pg_statistic tuple
|
|
*/
|
|
for (i = 0; i < Natts_pg_statistic; ++i)
|
|
{
|
|
nulls[i] = false;
|
|
replaces[i] = true;
|
|
}
|
|
|
|
values[Anum_pg_statistic_starelid - 1] = ObjectIdGetDatum(relid);
|
|
values[Anum_pg_statistic_staattnum - 1] = Int16GetDatum(stats->attr->attnum);
|
|
values[Anum_pg_statistic_stainherit - 1] = BoolGetDatum(inh);
|
|
values[Anum_pg_statistic_stanullfrac - 1] = Float4GetDatum(stats->stanullfrac);
|
|
values[Anum_pg_statistic_stawidth - 1] = Int32GetDatum(stats->stawidth);
|
|
values[Anum_pg_statistic_stadistinct - 1] = Float4GetDatum(stats->stadistinct);
|
|
i = Anum_pg_statistic_stakind1 - 1;
|
|
for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
|
|
{
|
|
values[i++] = Int16GetDatum(stats->stakind[k]); /* stakindN */
|
|
}
|
|
i = Anum_pg_statistic_staop1 - 1;
|
|
for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
|
|
{
|
|
values[i++] = ObjectIdGetDatum(stats->staop[k]); /* staopN */
|
|
}
|
|
i = Anum_pg_statistic_stacoll1 - 1;
|
|
for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
|
|
{
|
|
values[i++] = ObjectIdGetDatum(stats->stacoll[k]); /* stacollN */
|
|
}
|
|
i = Anum_pg_statistic_stanumbers1 - 1;
|
|
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,
|
|
FLOAT4OID,
|
|
sizeof(float4), true, 'i');
|
|
values[i++] = PointerGetDatum(arry); /* stanumbersN */
|
|
}
|
|
else
|
|
{
|
|
nulls[i] = true;
|
|
values[i++] = (Datum) 0;
|
|
}
|
|
}
|
|
i = Anum_pg_statistic_stavalues1 - 1;
|
|
for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
|
|
{
|
|
if (stats->numvalues[k] > 0)
|
|
{
|
|
ArrayType *arry;
|
|
|
|
arry = construct_array(stats->stavalues[k],
|
|
stats->numvalues[k],
|
|
stats->statypid[k],
|
|
stats->statyplen[k],
|
|
stats->statypbyval[k],
|
|
stats->statypalign[k]);
|
|
values[i++] = PointerGetDatum(arry); /* stavaluesN */
|
|
}
|
|
else
|
|
{
|
|
nulls[i] = true;
|
|
values[i++] = (Datum) 0;
|
|
}
|
|
}
|
|
|
|
/* Is there already a pg_statistic tuple for this attribute? */
|
|
oldtup = SearchSysCache3(STATRELATTINH,
|
|
ObjectIdGetDatum(relid),
|
|
Int16GetDatum(stats->attr->attnum),
|
|
BoolGetDatum(inh));
|
|
|
|
if (HeapTupleIsValid(oldtup))
|
|
{
|
|
/* Yes, replace it */
|
|
stup = heap_modify_tuple(oldtup,
|
|
RelationGetDescr(sd),
|
|
values,
|
|
nulls,
|
|
replaces);
|
|
ReleaseSysCache(oldtup);
|
|
CatalogTupleUpdate(sd, &stup->t_self, stup);
|
|
}
|
|
else
|
|
{
|
|
/* No, insert new tuple */
|
|
stup = heap_form_tuple(RelationGetDescr(sd), values, nulls);
|
|
CatalogTupleInsert(sd, stup);
|
|
}
|
|
|
|
heap_freetuple(stup);
|
|
}
|
|
|
|
table_close(sd, RowExclusiveLock);
|
|
}
|
|
|
|
/*
|
|
* Standard fetch function for use by compute_stats subroutines.
|
|
*
|
|
* This exists to provide some insulation between compute_stats routines
|
|
* and the actual storage of the sample data.
|
|
*/
|
|
static Datum
|
|
std_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull)
|
|
{
|
|
int attnum = stats->tupattnum;
|
|
HeapTuple tuple = stats->rows[rownum];
|
|
TupleDesc tupDesc = stats->tupDesc;
|
|
|
|
return heap_getattr(tuple, attnum, tupDesc, isNull);
|
|
}
|
|
|
|
/*
|
|
* Fetch function for analyzing index expressions.
|
|
*
|
|
* We have not bothered to construct index tuples, instead the data is
|
|
* just in Datum arrays.
|
|
*/
|
|
static Datum
|
|
ind_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull)
|
|
{
|
|
int i;
|
|
|
|
/* exprvals and exprnulls are already offset for proper column */
|
|
i = rownum * stats->rowstride;
|
|
*isNull = stats->exprnulls[i];
|
|
return stats->exprvals[i];
|
|
}
|
|
|
|
|
|
/*==========================================================================
|
|
*
|
|
* Code below this point represents the "standard" type-specific statistics
|
|
* analysis algorithms. This code can be replaced on a per-data-type basis
|
|
* by setting a nonzero value in pg_type.typanalyze.
|
|
*
|
|
*==========================================================================
|
|
*/
|
|
|
|
|
|
/*
|
|
* To avoid consuming too much memory during analysis and/or too much space
|
|
* in the resulting pg_statistic rows, we ignore varlena datums that are wider
|
|
* than WIDTH_THRESHOLD (after detoasting!). This is legitimate for MCV
|
|
* and distinct-value calculations since a wide value is unlikely to be
|
|
* duplicated at all, much less be a most-common value. For the same reason,
|
|
* ignoring wide values will not affect our estimates of histogram bin
|
|
* boundaries very much.
|
|
*/
|
|
#define WIDTH_THRESHOLD 1024
|
|
|
|
#define swapInt(a,b) do {int _tmp; _tmp=a; a=b; b=_tmp;} while(0)
|
|
#define swapDatum(a,b) do {Datum _tmp; _tmp=a; a=b; b=_tmp;} while(0)
|
|
|
|
/*
|
|
* Extra information used by the default analysis routines
|
|
*/
|
|
typedef struct
|
|
{
|
|
int count; /* # of duplicates */
|
|
int first; /* values[] index of first occurrence */
|
|
} ScalarMCVItem;
|
|
|
|
typedef struct
|
|
{
|
|
SortSupport ssup;
|
|
int *tupnoLink;
|
|
} CompareScalarsContext;
|
|
|
|
|
|
static void compute_trivial_stats(VacAttrStatsP stats,
|
|
AnalyzeAttrFetchFunc fetchfunc,
|
|
int samplerows,
|
|
double totalrows);
|
|
static void compute_distinct_stats(VacAttrStatsP stats,
|
|
AnalyzeAttrFetchFunc fetchfunc,
|
|
int samplerows,
|
|
double totalrows);
|
|
static void compute_scalar_stats(VacAttrStatsP stats,
|
|
AnalyzeAttrFetchFunc fetchfunc,
|
|
int samplerows,
|
|
double totalrows);
|
|
static int compare_scalars(const void *a, const void *b, void *arg);
|
|
static int compare_mcvs(const void *a, const void *b);
|
|
static int analyze_mcv_list(int *mcv_counts,
|
|
int num_mcv,
|
|
double stadistinct,
|
|
double stanullfrac,
|
|
int samplerows,
|
|
double totalrows);
|
|
|
|
|
|
/*
|
|
* std_typanalyze -- the default type-specific typanalyze function
|
|
*/
|
|
bool
|
|
std_typanalyze(VacAttrStats *stats)
|
|
{
|
|
Form_pg_attribute attr = stats->attr;
|
|
Oid ltopr;
|
|
Oid eqopr;
|
|
StdAnalyzeData *mystats;
|
|
|
|
/* If the attstattarget column is negative, use the default value */
|
|
/* NB: it is okay to scribble on stats->attr since it's a copy */
|
|
if (attr->attstattarget < 0)
|
|
attr->attstattarget = default_statistics_target;
|
|
|
|
/* Look for default "<" and "=" operators for column's type */
|
|
get_sort_group_operators(stats->attrtypid,
|
|
false, false, false,
|
|
<opr, &eqopr, NULL,
|
|
NULL);
|
|
|
|
/* Save the operator info for compute_stats routines */
|
|
mystats = (StdAnalyzeData *) palloc(sizeof(StdAnalyzeData));
|
|
mystats->eqopr = eqopr;
|
|
mystats->eqfunc = OidIsValid(eqopr) ? get_opcode(eqopr) : InvalidOid;
|
|
mystats->ltopr = ltopr;
|
|
stats->extra_data = mystats;
|
|
|
|
/*
|
|
* Determine which standard statistics algorithm to use
|
|
*/
|
|
if (OidIsValid(eqopr) && OidIsValid(ltopr))
|
|
{
|
|
/* Seems to be a scalar datatype */
|
|
stats->compute_stats = compute_scalar_stats;
|
|
/*--------------------
|
|
* The following choice of minrows is based on the paper
|
|
* "Random sampling for histogram construction: how much is enough?"
|
|
* by Surajit Chaudhuri, Rajeev Motwani and Vivek Narasayya, in
|
|
* Proceedings of ACM SIGMOD International Conference on Management
|
|
* of Data, 1998, Pages 436-447. Their Corollary 1 to Theorem 5
|
|
* says that for table size n, histogram size k, maximum relative
|
|
* error in bin size f, and error probability gamma, the minimum
|
|
* random sample size is
|
|
* r = 4 * k * ln(2*n/gamma) / f^2
|
|
* Taking f = 0.5, gamma = 0.01, n = 10^6 rows, we obtain
|
|
* r = 305.82 * k
|
|
* Note that because of the log function, the dependence on n is
|
|
* quite weak; even at n = 10^12, a 300*k sample gives <= 0.66
|
|
* bin size error with probability 0.99. So there's no real need to
|
|
* scale for n, which is a good thing because we don't necessarily
|
|
* know it at this point.
|
|
*--------------------
|
|
*/
|
|
stats->minrows = 300 * attr->attstattarget;
|
|
}
|
|
else if (OidIsValid(eqopr))
|
|
{
|
|
/* We can still recognize distinct values */
|
|
stats->compute_stats = compute_distinct_stats;
|
|
/* Might as well use the same minrows as above */
|
|
stats->minrows = 300 * attr->attstattarget;
|
|
}
|
|
else
|
|
{
|
|
/* Can't do much but the trivial stuff */
|
|
stats->compute_stats = compute_trivial_stats;
|
|
/* Might as well use the same minrows as above */
|
|
stats->minrows = 300 * attr->attstattarget;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
|
|
/*
|
|
* compute_trivial_stats() -- compute very basic column statistics
|
|
*
|
|
* We use this when we cannot find a hash "=" operator for the datatype.
|
|
*
|
|
* We determine the fraction of non-null rows and the average datum width.
|
|
*/
|
|
static void
|
|
compute_trivial_stats(VacAttrStatsP stats,
|
|
AnalyzeAttrFetchFunc fetchfunc,
|
|
int samplerows,
|
|
double totalrows)
|
|
{
|
|
int i;
|
|
int null_cnt = 0;
|
|
int nonnull_cnt = 0;
|
|
double total_width = 0;
|
|
bool is_varlena = (!stats->attrtype->typbyval &&
|
|
stats->attrtype->typlen == -1);
|
|
bool is_varwidth = (!stats->attrtype->typbyval &&
|
|
stats->attrtype->typlen < 0);
|
|
|
|
for (i = 0; i < samplerows; i++)
|
|
{
|
|
Datum value;
|
|
bool isnull;
|
|
|
|
vacuum_delay_point();
|
|
|
|
value = fetchfunc(stats, i, &isnull);
|
|
|
|
/* Check for null/nonnull */
|
|
if (isnull)
|
|
{
|
|
null_cnt++;
|
|
continue;
|
|
}
|
|
nonnull_cnt++;
|
|
|
|
/*
|
|
* If it's a variable-width 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_ANY(DatumGetPointer(value));
|
|
}
|
|
else if (is_varwidth)
|
|
{
|
|
/* must be cstring */
|
|
total_width += strlen(DatumGetCString(value)) + 1;
|
|
}
|
|
}
|
|
|
|
/* We can only compute average width if we found some non-null values. */
|
|
if (nonnull_cnt > 0)
|
|
{
|
|
stats->stats_valid = true;
|
|
/* Do the simple null-frac and width stats */
|
|
stats->stanullfrac = (double) null_cnt / (double) samplerows;
|
|
if (is_varwidth)
|
|
stats->stawidth = total_width / (double) nonnull_cnt;
|
|
else
|
|
stats->stawidth = stats->attrtype->typlen;
|
|
stats->stadistinct = 0.0; /* "unknown" */
|
|
}
|
|
else if (null_cnt > 0)
|
|
{
|
|
/* We found only nulls; assume the column is entirely null */
|
|
stats->stats_valid = true;
|
|
stats->stanullfrac = 1.0;
|
|
if (is_varwidth)
|
|
stats->stawidth = 0; /* "unknown" */
|
|
else
|
|
stats->stawidth = stats->attrtype->typlen;
|
|
stats->stadistinct = 0.0; /* "unknown" */
|
|
}
|
|
}
|
|
|
|
|
|
/*
|
|
* compute_distinct_stats() -- compute column statistics including ndistinct
|
|
*
|
|
* 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_distinct_stats(VacAttrStatsP stats,
|
|
AnalyzeAttrFetchFunc fetchfunc,
|
|
int samplerows,
|
|
double totalrows)
|
|
{
|
|
int i;
|
|
int null_cnt = 0;
|
|
int nonnull_cnt = 0;
|
|
int toowide_cnt = 0;
|
|
double total_width = 0;
|
|
bool is_varlena = (!stats->attrtype->typbyval &&
|
|
stats->attrtype->typlen == -1);
|
|
bool is_varwidth = (!stats->attrtype->typbyval &&
|
|
stats->attrtype->typlen < 0);
|
|
FmgrInfo f_cmpeq;
|
|
typedef struct
|
|
{
|
|
Datum value;
|
|
int count;
|
|
} TrackItem;
|
|
TrackItem *track;
|
|
int track_cnt,
|
|
track_max;
|
|
int num_mcv = stats->attr->attstattarget;
|
|
StdAnalyzeData *mystats = (StdAnalyzeData *) stats->extra_data;
|
|
|
|
/*
|
|
* 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(mystats->eqfunc, &f_cmpeq);
|
|
|
|
for (i = 0; i < samplerows; i++)
|
|
{
|
|
Datum value;
|
|
bool isnull;
|
|
bool match;
|
|
int firstcount1,
|
|
j;
|
|
|
|
vacuum_delay_point();
|
|
|
|
value = fetchfunc(stats, i, &isnull);
|
|
|
|
/* Check for null/nonnull */
|
|
if (isnull)
|
|
{
|
|
null_cnt++;
|
|
continue;
|
|
}
|
|
nonnull_cnt++;
|
|
|
|
/*
|
|
* If it's a variable-width 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_ANY(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));
|
|
}
|
|
else if (is_varwidth)
|
|
{
|
|
/* must be cstring */
|
|
total_width += strlen(DatumGetCString(value)) + 1;
|
|
}
|
|
|
|
/*
|
|
* See if the value matches anything we're already tracking.
|
|
*/
|
|
match = false;
|
|
firstcount1 = track_cnt;
|
|
for (j = 0; j < track_cnt; j++)
|
|
{
|
|
if (DatumGetBool(FunctionCall2Coll(&f_cmpeq,
|
|
stats->attrcollid,
|
|
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 real 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) samplerows;
|
|
if (is_varwidth)
|
|
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 non-null values, assume it's a unique
|
|
* column; but be sure to discount for any nulls we found.
|
|
*/
|
|
stats->stadistinct = -1.0 * (1.0 - stats->stanullfrac);
|
|
}
|
|
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. (This case is meant to address columns with
|
|
* small, fixed sets of possible values, such as boolean or enum
|
|
* columns. If there are any values that appear just once in the
|
|
* sample, including too-wide values, we should assume that that's
|
|
* not what we're dealing with.)
|
|
*/
|
|
stats->stadistinct = track_cnt;
|
|
}
|
|
else
|
|
{
|
|
/*----------
|
|
* Estimate the number of distinct values using the estimator
|
|
* proposed by Haas and Stokes in IBM Research Report RJ 10025:
|
|
* n*d / (n - f1 + f1*n/N)
|
|
* where f1 is the number of distinct values that occurred
|
|
* exactly once in our sample of n rows (from a total of N),
|
|
* and d is the total number of distinct values in the sample.
|
|
* This is their Duj1 estimator; the other estimators they
|
|
* recommend are considerably more complex, and are numerically
|
|
* very unstable when n is much smaller than N.
|
|
*
|
|
* In this calculation, we consider only non-nulls. We used to
|
|
* include rows with null values in the n and N counts, but that
|
|
* leads to inaccurate answers in columns with many nulls, and
|
|
* it's intuitively bogus anyway considering the desired result is
|
|
* the number of distinct non-null values.
|
|
*
|
|
* 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;
|
|
int d = f1 + nmultiple;
|
|
double n = samplerows - null_cnt;
|
|
double N = totalrows * (1.0 - stats->stanullfrac);
|
|
double stadistinct;
|
|
|
|
/* N == 0 shouldn't happen, but just in case ... */
|
|
if (N > 0)
|
|
stadistinct = (n * d) / ((n - f1) + f1 * n / N);
|
|
else
|
|
stadistinct = 0;
|
|
|
|
/* Clamp to sane range in case of roundoff error */
|
|
if (stadistinct < d)
|
|
stadistinct = d;
|
|
if (stadistinct > N)
|
|
stadistinct = N;
|
|
/* And round to integer */
|
|
stats->stadistinct = floor(stadistinct + 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 values not in the list.
|
|
*
|
|
* Note: the first of these cases is meant to address columns with
|
|
* small, fixed sets of possible values, such as boolean or enum
|
|
* columns. If we can *completely* represent the column population by
|
|
* an MCV list that will fit into the stats target, then we should do
|
|
* so and thus provide the planner with complete information. But if
|
|
* the MCV list is not complete, it's generally worth being more
|
|
* selective, and not just filling it all the way up to the stats
|
|
* target.
|
|
*/
|
|
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
|
|
{
|
|
int *mcv_counts;
|
|
|
|
/* Incomplete list; decide how many values are worth keeping */
|
|
if (num_mcv > track_cnt)
|
|
num_mcv = track_cnt;
|
|
|
|
if (num_mcv > 0)
|
|
{
|
|
mcv_counts = (int *) palloc(num_mcv * sizeof(int));
|
|
for (i = 0; i < num_mcv; i++)
|
|
mcv_counts[i] = track[i].count;
|
|
|
|
num_mcv = analyze_mcv_list(mcv_counts, num_mcv,
|
|
stats->stadistinct,
|
|
stats->stanullfrac,
|
|
samplerows, totalrows);
|
|
}
|
|
}
|
|
|
|
/* Generate MCV slot entry */
|
|
if (num_mcv > 0)
|
|
{
|
|
MemoryContext old_context;
|
|
Datum *mcv_values;
|
|
float4 *mcv_freqs;
|
|
|
|
/* Must copy the target values into anl_context */
|
|
old_context = MemoryContextSwitchTo(stats->anl_context);
|
|
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->attrtype->typbyval,
|
|
stats->attrtype->typlen);
|
|
mcv_freqs[i] = (double) track[i].count / (double) samplerows;
|
|
}
|
|
MemoryContextSwitchTo(old_context);
|
|
|
|
stats->stakind[0] = STATISTIC_KIND_MCV;
|
|
stats->staop[0] = mystats->eqopr;
|
|
stats->stacoll[0] = stats->attrcollid;
|
|
stats->stanumbers[0] = mcv_freqs;
|
|
stats->numnumbers[0] = num_mcv;
|
|
stats->stavalues[0] = mcv_values;
|
|
stats->numvalues[0] = num_mcv;
|
|
|
|
/*
|
|
* Accept the defaults for stats->statypid and others. They have
|
|
* been set before we were called (see vacuum.h)
|
|
*/
|
|
}
|
|
}
|
|
else if (null_cnt > 0)
|
|
{
|
|
/* We found only nulls; assume the column is entirely null */
|
|
stats->stats_valid = true;
|
|
stats->stanullfrac = 1.0;
|
|
if (is_varwidth)
|
|
stats->stawidth = 0; /* "unknown" */
|
|
else
|
|
stats->stawidth = stats->attrtype->typlen;
|
|
stats->stadistinct = 0.0; /* "unknown" */
|
|
}
|
|
|
|
/* 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(VacAttrStatsP stats,
|
|
AnalyzeAttrFetchFunc fetchfunc,
|
|
int samplerows,
|
|
double totalrows)
|
|
{
|
|
int i;
|
|
int null_cnt = 0;
|
|
int nonnull_cnt = 0;
|
|
int toowide_cnt = 0;
|
|
double total_width = 0;
|
|
bool is_varlena = (!stats->attrtype->typbyval &&
|
|
stats->attrtype->typlen == -1);
|
|
bool is_varwidth = (!stats->attrtype->typbyval &&
|
|
stats->attrtype->typlen < 0);
|
|
double corr_xysum;
|
|
SortSupportData ssup;
|
|
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;
|
|
StdAnalyzeData *mystats = (StdAnalyzeData *) stats->extra_data;
|
|
|
|
values = (ScalarItem *) palloc(samplerows * sizeof(ScalarItem));
|
|
tupnoLink = (int *) palloc(samplerows * sizeof(int));
|
|
track = (ScalarMCVItem *) palloc(num_mcv * sizeof(ScalarMCVItem));
|
|
|
|
memset(&ssup, 0, sizeof(ssup));
|
|
ssup.ssup_cxt = CurrentMemoryContext;
|
|
ssup.ssup_collation = stats->attrcollid;
|
|
ssup.ssup_nulls_first = false;
|
|
|
|
/*
|
|
* For now, don't perform abbreviated key conversion, because full values
|
|
* are required for MCV slot generation. Supporting that optimization
|
|
* would necessitate teaching compare_scalars() to call a tie-breaker.
|
|
*/
|
|
ssup.abbreviate = false;
|
|
|
|
PrepareSortSupportFromOrderingOp(mystats->ltopr, &ssup);
|
|
|
|
/* Initial scan to find sortable values */
|
|
for (i = 0; i < samplerows; i++)
|
|
{
|
|
Datum value;
|
|
bool isnull;
|
|
|
|
vacuum_delay_point();
|
|
|
|
value = fetchfunc(stats, i, &isnull);
|
|
|
|
/* Check for null/nonnull */
|
|
if (isnull)
|
|
{
|
|
null_cnt++;
|
|
continue;
|
|
}
|
|
nonnull_cnt++;
|
|
|
|
/*
|
|
* If it's a variable-width 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_ANY(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));
|
|
}
|
|
else if (is_varwidth)
|
|
{
|
|
/* must be cstring */
|
|
total_width += strlen(DatumGetCString(value)) + 1;
|
|
}
|
|
|
|
/* 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 real 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;
|
|
CompareScalarsContext cxt;
|
|
|
|
/* Sort the collected values */
|
|
cxt.ssup = &ssup;
|
|
cxt.tupnoLink = tupnoLink;
|
|
qsort_arg((void *) values, values_cnt, sizeof(ScalarItem),
|
|
compare_scalars, (void *) &cxt);
|
|
|
|
/*
|
|
* 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) samplerows;
|
|
if (is_varwidth)
|
|
stats->stawidth = total_width / (double) nonnull_cnt;
|
|
else
|
|
stats->stawidth = stats->attrtype->typlen;
|
|
|
|
if (nmultiple == 0)
|
|
{
|
|
/*
|
|
* If we found no repeated non-null values, assume it's a unique
|
|
* column; but be sure to discount for any nulls we found.
|
|
*/
|
|
stats->stadistinct = -1.0 * (1.0 - stats->stanullfrac);
|
|
}
|
|
else if (toowide_cnt == 0 && nmultiple == ndistinct)
|
|
{
|
|
/*
|
|
* Every value in the sample appeared more than once. Assume the
|
|
* column has just these values. (This case is meant to address
|
|
* columns with small, fixed sets of possible values, such as
|
|
* boolean or enum columns. If there are any values that appear
|
|
* just once in the sample, including too-wide values, we should
|
|
* assume that that's not what we're dealing with.)
|
|
*/
|
|
stats->stadistinct = ndistinct;
|
|
}
|
|
else
|
|
{
|
|
/*----------
|
|
* Estimate the number of distinct values using the estimator
|
|
* proposed by Haas and Stokes in IBM Research Report RJ 10025:
|
|
* n*d / (n - f1 + f1*n/N)
|
|
* where f1 is the number of distinct values that occurred
|
|
* exactly once in our sample of n rows (from a total of N),
|
|
* and d is the total number of distinct values in the sample.
|
|
* This is their Duj1 estimator; the other estimators they
|
|
* recommend are considerably more complex, and are numerically
|
|
* very unstable when n is much smaller than N.
|
|
*
|
|
* In this calculation, we consider only non-nulls. We used to
|
|
* include rows with null values in the n and N counts, but that
|
|
* leads to inaccurate answers in columns with many nulls, and
|
|
* it's intuitively bogus anyway considering the desired result is
|
|
* the number of distinct non-null values.
|
|
*
|
|
* Overwidth values are assumed to have been distinct.
|
|
*----------
|
|
*/
|
|
int f1 = ndistinct - nmultiple + toowide_cnt;
|
|
int d = f1 + nmultiple;
|
|
double n = samplerows - null_cnt;
|
|
double N = totalrows * (1.0 - stats->stanullfrac);
|
|
double stadistinct;
|
|
|
|
/* N == 0 shouldn't happen, but just in case ... */
|
|
if (N > 0)
|
|
stadistinct = (n * d) / ((n - f1) + f1 * n / N);
|
|
else
|
|
stadistinct = 0;
|
|
|
|
/* Clamp to sane range in case of roundoff error */
|
|
if (stadistinct < d)
|
|
stadistinct = d;
|
|
if (stadistinct > N)
|
|
stadistinct = N;
|
|
/* And round to integer */
|
|
stats->stadistinct = floor(stadistinct + 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 values not in the list.
|
|
*
|
|
* Note: the first of these cases is meant to address columns with
|
|
* small, fixed sets of possible values, such as boolean or enum
|
|
* columns. If we can *completely* represent the column population by
|
|
* an MCV list that will fit into the stats target, then we should do
|
|
* so and thus provide the planner with complete information. But if
|
|
* the MCV list is not complete, it's generally worth being more
|
|
* selective, and not just filling it all the way up to the stats
|
|
* target.
|
|
*/
|
|
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
|
|
{
|
|
int *mcv_counts;
|
|
|
|
/* Incomplete list; decide how many values are worth keeping */
|
|
if (num_mcv > track_cnt)
|
|
num_mcv = track_cnt;
|
|
|
|
if (num_mcv > 0)
|
|
{
|
|
mcv_counts = (int *) palloc(num_mcv * sizeof(int));
|
|
for (i = 0; i < num_mcv; i++)
|
|
mcv_counts[i] = track[i].count;
|
|
|
|
num_mcv = analyze_mcv_list(mcv_counts, num_mcv,
|
|
stats->stadistinct,
|
|
stats->stanullfrac,
|
|
samplerows, totalrows);
|
|
}
|
|
}
|
|
|
|
/* Generate MCV slot entry */
|
|
if (num_mcv > 0)
|
|
{
|
|
MemoryContext old_context;
|
|
Datum *mcv_values;
|
|
float4 *mcv_freqs;
|
|
|
|
/* Must copy the target values into anl_context */
|
|
old_context = MemoryContextSwitchTo(stats->anl_context);
|
|
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->attrtype->typbyval,
|
|
stats->attrtype->typlen);
|
|
mcv_freqs[i] = (double) track[i].count / (double) samplerows;
|
|
}
|
|
MemoryContextSwitchTo(old_context);
|
|
|
|
stats->stakind[slot_idx] = STATISTIC_KIND_MCV;
|
|
stats->staop[slot_idx] = mystats->eqopr;
|
|
stats->stacoll[slot_idx] = stats->attrcollid;
|
|
stats->stanumbers[slot_idx] = mcv_freqs;
|
|
stats->numnumbers[slot_idx] = num_mcv;
|
|
stats->stavalues[slot_idx] = mcv_values;
|
|
stats->numvalues[slot_idx] = num_mcv;
|
|
|
|
/*
|
|
* Accept the defaults for stats->statypid and others. They have
|
|
* been set before we were called (see vacuum.h)
|
|
*/
|
|
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;
|
|
int pos,
|
|
posfrac,
|
|
delta,
|
|
deltafrac;
|
|
|
|
/* 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 anl_context */
|
|
old_context = MemoryContextSwitchTo(stats->anl_context);
|
|
hist_values = (Datum *) palloc(num_hist * sizeof(Datum));
|
|
|
|
/*
|
|
* The object of this loop is to copy the first and last values[]
|
|
* entries along with evenly-spaced values in between. So the
|
|
* i'th value is values[(i * (nvals - 1)) / (num_hist - 1)]. But
|
|
* computing that subscript directly risks integer overflow when
|
|
* the stats target is more than a couple thousand. Instead we
|
|
* add (nvals - 1) / (num_hist - 1) to pos at each step, tracking
|
|
* the integral and fractional parts of the sum separately.
|
|
*/
|
|
delta = (nvals - 1) / (num_hist - 1);
|
|
deltafrac = (nvals - 1) % (num_hist - 1);
|
|
pos = posfrac = 0;
|
|
|
|
for (i = 0; i < num_hist; i++)
|
|
{
|
|
hist_values[i] = datumCopy(values[pos].value,
|
|
stats->attrtype->typbyval,
|
|
stats->attrtype->typlen);
|
|
pos += delta;
|
|
posfrac += deltafrac;
|
|
if (posfrac >= (num_hist - 1))
|
|
{
|
|
/* fractional part exceeds 1, carry to integer part */
|
|
pos++;
|
|
posfrac -= (num_hist - 1);
|
|
}
|
|
}
|
|
|
|
MemoryContextSwitchTo(old_context);
|
|
|
|
stats->stakind[slot_idx] = STATISTIC_KIND_HISTOGRAM;
|
|
stats->staop[slot_idx] = mystats->ltopr;
|
|
stats->stacoll[slot_idx] = stats->attrcollid;
|
|
stats->stavalues[slot_idx] = hist_values;
|
|
stats->numvalues[slot_idx] = num_hist;
|
|
|
|
/*
|
|
* Accept the defaults for stats->statypid and others. They have
|
|
* been set before we were called (see vacuum.h)
|
|
*/
|
|
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 anl_context */
|
|
old_context = MemoryContextSwitchTo(stats->anl_context);
|
|
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] = mystats->ltopr;
|
|
stats->stacoll[slot_idx] = stats->attrcollid;
|
|
stats->stanumbers[slot_idx] = corrs;
|
|
stats->numnumbers[slot_idx] = 1;
|
|
slot_idx++;
|
|
}
|
|
}
|
|
else if (nonnull_cnt > 0)
|
|
{
|
|
/* We found some non-null values, but they were all too wide */
|
|
Assert(nonnull_cnt == toowide_cnt);
|
|
stats->stats_valid = true;
|
|
/* Do the simple null-frac and width stats */
|
|
stats->stanullfrac = (double) null_cnt / (double) samplerows;
|
|
if (is_varwidth)
|
|
stats->stawidth = total_width / (double) nonnull_cnt;
|
|
else
|
|
stats->stawidth = stats->attrtype->typlen;
|
|
/* Assume all too-wide values are distinct, so it's a unique column */
|
|
stats->stadistinct = -1.0 * (1.0 - stats->stanullfrac);
|
|
}
|
|
else if (null_cnt > 0)
|
|
{
|
|
/* We found only nulls; assume the column is entirely null */
|
|
stats->stats_valid = true;
|
|
stats->stanullfrac = 1.0;
|
|
if (is_varwidth)
|
|
stats->stawidth = 0; /* "unknown" */
|
|
else
|
|
stats->stawidth = stats->attrtype->typlen;
|
|
stats->stadistinct = 0.0; /* "unknown" */
|
|
}
|
|
|
|
/* We don't need to bother cleaning up any of our temporary palloc's */
|
|
}
|
|
|
|
/*
|
|
* qsort_arg comparator for sorting ScalarItems
|
|
*
|
|
* Aside from sorting the items, we update the tupnoLink[] 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, void *arg)
|
|
{
|
|
Datum da = ((const ScalarItem *) a)->value;
|
|
int ta = ((const ScalarItem *) a)->tupno;
|
|
Datum db = ((const ScalarItem *) b)->value;
|
|
int tb = ((const ScalarItem *) b)->tupno;
|
|
CompareScalarsContext *cxt = (CompareScalarsContext *) arg;
|
|
int compare;
|
|
|
|
compare = ApplySortComparator(da, false, db, false, cxt->ssup);
|
|
if (compare != 0)
|
|
return compare;
|
|
|
|
/*
|
|
* The two datums are equal, so update cxt->tupnoLink[].
|
|
*/
|
|
if (cxt->tupnoLink[ta] < tb)
|
|
cxt->tupnoLink[ta] = tb;
|
|
if (cxt->tupnoLink[tb] < ta)
|
|
cxt->tupnoLink[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 = ((const ScalarMCVItem *) a)->first;
|
|
int db = ((const ScalarMCVItem *) b)->first;
|
|
|
|
return da - db;
|
|
}
|
|
|
|
/*
|
|
* Analyze the list of common values in the sample and decide how many are
|
|
* worth storing in the table's MCV list.
|
|
*
|
|
* mcv_counts is assumed to be a list of the counts of the most common values
|
|
* seen in the sample, starting with the most common. The return value is the
|
|
* number that are significantly more common than the values not in the list,
|
|
* and which are therefore deemed worth storing in the table's MCV list.
|
|
*/
|
|
static int
|
|
analyze_mcv_list(int *mcv_counts,
|
|
int num_mcv,
|
|
double stadistinct,
|
|
double stanullfrac,
|
|
int samplerows,
|
|
double totalrows)
|
|
{
|
|
double ndistinct_table;
|
|
double sumcount;
|
|
int i;
|
|
|
|
/*
|
|
* If the entire table was sampled, keep the whole list. This also
|
|
* protects us against division by zero in the code below.
|
|
*/
|
|
if (samplerows == totalrows || totalrows <= 1.0)
|
|
return num_mcv;
|
|
|
|
/* Re-extract the estimated number of distinct nonnull values in table */
|
|
ndistinct_table = stadistinct;
|
|
if (ndistinct_table < 0)
|
|
ndistinct_table = -ndistinct_table * totalrows;
|
|
|
|
/*
|
|
* Exclude the least common values from the MCV list, if they are not
|
|
* significantly more common than the estimated selectivity they would
|
|
* have if they weren't in the list. All non-MCV values are assumed to be
|
|
* equally common, after taking into account the frequencies of all the
|
|
* values in the MCV list and the number of nulls (c.f. eqsel()).
|
|
*
|
|
* Here sumcount tracks the total count of all but the last (least common)
|
|
* value in the MCV list, allowing us to determine the effect of excluding
|
|
* that value from the list.
|
|
*
|
|
* Note that we deliberately do this by removing values from the full
|
|
* list, rather than starting with an empty list and adding values,
|
|
* because the latter approach can fail to add any values if all the most
|
|
* common values have around the same frequency and make up the majority
|
|
* of the table, so that the overall average frequency of all values is
|
|
* roughly the same as that of the common values. This would lead to any
|
|
* uncommon values being significantly overestimated.
|
|
*/
|
|
sumcount = 0.0;
|
|
for (i = 0; i < num_mcv - 1; i++)
|
|
sumcount += mcv_counts[i];
|
|
|
|
while (num_mcv > 0)
|
|
{
|
|
double selec,
|
|
otherdistinct,
|
|
N,
|
|
n,
|
|
K,
|
|
variance,
|
|
stddev;
|
|
|
|
/*
|
|
* Estimated selectivity the least common value would have if it
|
|
* wasn't in the MCV list (c.f. eqsel()).
|
|
*/
|
|
selec = 1.0 - sumcount / samplerows - stanullfrac;
|
|
if (selec < 0.0)
|
|
selec = 0.0;
|
|
if (selec > 1.0)
|
|
selec = 1.0;
|
|
otherdistinct = ndistinct_table - (num_mcv - 1);
|
|
if (otherdistinct > 1)
|
|
selec /= otherdistinct;
|
|
|
|
/*
|
|
* If the value is kept in the MCV list, its population frequency is
|
|
* assumed to equal its sample frequency. We use the lower end of a
|
|
* textbook continuity-corrected Wald-type confidence interval to
|
|
* determine if that is significantly more common than the non-MCV
|
|
* frequency --- specifically we assume the population frequency is
|
|
* highly likely to be within around 2 standard errors of the sample
|
|
* frequency, which equates to an interval of 2 standard deviations
|
|
* either side of the sample count, plus an additional 0.5 for the
|
|
* continuity correction. Since we are sampling without replacement,
|
|
* this is a hypergeometric distribution.
|
|
*
|
|
* XXX: Empirically, this approach seems to work quite well, but it
|
|
* may be worth considering more advanced techniques for estimating
|
|
* the confidence interval of the hypergeometric distribution.
|
|
*/
|
|
N = totalrows;
|
|
n = samplerows;
|
|
K = N * mcv_counts[num_mcv - 1] / n;
|
|
variance = n * K * (N - K) * (N - n) / (N * N * (N - 1));
|
|
stddev = sqrt(variance);
|
|
|
|
if (mcv_counts[num_mcv - 1] > selec * samplerows + 2 * stddev + 0.5)
|
|
{
|
|
/*
|
|
* The value is significantly more common than the non-MCV
|
|
* selectivity would suggest. Keep it, and all the other more
|
|
* common values in the list.
|
|
*/
|
|
break;
|
|
}
|
|
else
|
|
{
|
|
/* Discard this value and consider the next least common value */
|
|
num_mcv--;
|
|
if (num_mcv == 0)
|
|
break;
|
|
sumcount -= mcv_counts[num_mcv - 1];
|
|
}
|
|
}
|
|
return num_mcv;
|
|
}
|