/*------------------------------------------------------------------------- * * analyze.c * the Postgres statistics generator * * Portions Copyright (c) 1996-2024, PostgreSQL Global Development Group * Portions Copyright (c) 1994, Regents of the University of California * * * IDENTIFICATION * src/backend/commands/analyze.c * *------------------------------------------------------------------------- */ #include "postgres.h" #include #include "access/detoast.h" #include "access/genam.h" #include "access/multixact.h" #include "access/relation.h" #include "access/table.h" #include "access/tableam.h" #include "access/transam.h" #include "access/tupconvert.h" #include "access/visibilitymap.h" #include "access/xact.h" #include "catalog/index.h" #include "catalog/indexing.h" #include "catalog/pg_inherits.h" #include "commands/dbcommands.h" #include "commands/progress.h" #include "commands/tablecmds.h" #include "commands/vacuum.h" #include "common/pg_prng.h" #include "executor/executor.h" #include "foreign/fdwapi.h" #include "miscadmin.h" #include "nodes/nodeFuncs.h" #include "parser/parse_oper.h" #include "parser/parse_relation.h" #include "pgstat.h" #include "postmaster/autovacuum.h" #include "statistics/extended_stats_internal.h" #include "statistics/statistics.h" #include "storage/bufmgr.h" #include "storage/procarray.h" #include "utils/attoptcache.h" #include "utils/datum.h" #include "utils/guc.h" #include "utils/lsyscache.h" #include "utils/memutils.h" #include "utils/pg_rusage.h" #include "utils/sampling.h" #include "utils/sortsupport.h" #include "utils/spccache.h" #include "utils/syscache.h" #include "utils/timestamp.h" /* Per-index data for ANALYZE */ typedef struct AnlIndexData { IndexInfo *indexInfo; /* BuildIndexInfo result */ double tupleFract; /* fraction of rows for partial index */ VacAttrStats **vacattrstats; /* index attrs to analyze */ int attr_cnt; } AnlIndexData; /* Default statistics target (GUC parameter) */ int default_statistics_target = 100; /* A few variables that don't seem worth passing around as parameters */ static MemoryContext anl_context = NULL; static BufferAccessStrategy vac_strategy; static void do_analyze_rel(Relation onerel, VacuumParams *params, List *va_cols, AcquireSampleRowsFunc acquirefunc, BlockNumber relpages, bool inh, bool in_outer_xact, int elevel); static void compute_index_stats(Relation onerel, double totalrows, AnlIndexData *indexdata, int nindexes, HeapTuple *rows, int numrows, MemoryContext col_context); static VacAttrStats *examine_attribute(Relation onerel, int attnum, Node *index_expr); static int acquire_sample_rows(Relation onerel, int elevel, HeapTuple *rows, int targrows, double *totalrows, double *totaldeadrows); static int compare_rows(const void *a, const void *b, void *arg); static int acquire_inherited_sample_rows(Relation onerel, int elevel, HeapTuple *rows, int targrows, double *totalrows, double *totaldeadrows); static void update_attstats(Oid relid, bool inh, int natts, VacAttrStats **vacattrstats); static Datum std_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull); static Datum ind_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull); /* * analyze_rel() -- analyze one relation * * relid identifies the relation to analyze. If relation is supplied, use * the name therein for reporting any failure to open/lock the rel; do not * use it once we've successfully opened the rel, since it might be stale. */ void analyze_rel(Oid relid, RangeVar *relation, VacuumParams *params, List *va_cols, bool in_outer_xact, BufferAccessStrategy bstrategy) { Relation onerel; int elevel; AcquireSampleRowsFunc acquirefunc = NULL; BlockNumber relpages = 0; /* Select logging level */ if (params->options & VACOPT_VERBOSE) elevel = INFO; else elevel = DEBUG2; /* Set up static variables */ vac_strategy = bstrategy; /* * Check for user-requested abort. */ CHECK_FOR_INTERRUPTS(); /* * Open the relation, getting ShareUpdateExclusiveLock to ensure that two * ANALYZEs don't run on it concurrently. (This also locks out a * concurrent VACUUM, which doesn't matter much at the moment but might * matter if we ever try to accumulate stats on dead tuples.) If the rel * has been dropped since we last saw it, we don't need to process it. * * Make sure to generate only logs for ANALYZE in this case. */ onerel = vacuum_open_relation(relid, relation, params->options & ~(VACOPT_VACUUM), params->log_min_duration >= 0, ShareUpdateExclusiveLock); /* leave if relation could not be opened or locked */ if (!onerel) return; /* * Check if relation needs to be skipped based on privileges. This check * happens also when building the relation list to analyze for a manual * operation, and needs to be done additionally here as ANALYZE could * happen across multiple transactions where privileges could have changed * in-between. Make sure to generate only logs for ANALYZE in this case. */ if (!vacuum_is_permitted_for_relation(RelationGetRelid(onerel), onerel->rd_rel, params->options & ~VACOPT_VACUUM)) { relation_close(onerel, ShareUpdateExclusiveLock); return; } /* * Silently ignore tables that are temp tables of other backends --- * trying to analyze these is rather pointless, since their contents are * probably not up-to-date on disk. (We don't throw a warning here; it * would just lead to chatter during a database-wide ANALYZE.) */ if (RELATION_IS_OTHER_TEMP(onerel)) { relation_close(onerel, ShareUpdateExclusiveLock); return; } /* * We can ANALYZE any table except pg_statistic. See update_attstats */ if (RelationGetRelid(onerel) == StatisticRelationId) { relation_close(onerel, ShareUpdateExclusiveLock); return; } /* * Check that it's of an analyzable relkind, and set up appropriately. */ if (onerel->rd_rel->relkind == RELKIND_RELATION || onerel->rd_rel->relkind == RELKIND_MATVIEW) { /* Regular table, so we'll use the regular row acquisition function */ acquirefunc = acquire_sample_rows; /* Also get regular table's size */ relpages = RelationGetNumberOfBlocks(onerel); } else if (onerel->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(onerel, false); if (fdwroutine->AnalyzeForeignTable != NULL) ok = fdwroutine->AnalyzeForeignTable(onerel, &acquirefunc, &relpages); if (!ok) { ereport(WARNING, (errmsg("skipping \"%s\" --- cannot analyze this foreign table", RelationGetRelationName(onerel)))); relation_close(onerel, ShareUpdateExclusiveLock); return; } } else if (onerel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE) { /* * For partitioned tables, we want to do the recursive ANALYZE below. */ } else { /* No need for a WARNING if we already complained during VACUUM */ if (!(params->options & VACOPT_VACUUM)) ereport(WARNING, (errmsg("skipping \"%s\" --- cannot analyze non-tables or special system tables", RelationGetRelationName(onerel)))); relation_close(onerel, ShareUpdateExclusiveLock); return; } /* * OK, let's do it. First, initialize progress reporting. */ pgstat_progress_start_command(PROGRESS_COMMAND_ANALYZE, RelationGetRelid(onerel)); /* * Do the normal non-recursive ANALYZE. We can skip this for partitioned * tables, which don't contain any rows. */ if (onerel->rd_rel->relkind != RELKIND_PARTITIONED_TABLE) do_analyze_rel(onerel, params, va_cols, acquirefunc, relpages, false, in_outer_xact, elevel); /* * If there are child tables, do recursive ANALYZE. */ if (onerel->rd_rel->relhassubclass) do_analyze_rel(onerel, params, va_cols, acquirefunc, relpages, true, in_outer_xact, elevel); /* * Close source relation now, but keep lock so that no one deletes it * before we commit. (If someone did, they'd fail to clean up the entries * we made in pg_statistic. Also, releasing the lock before commit would * expose us to concurrent-update failures in update_attstats.) */ relation_close(onerel, NoLock); pgstat_progress_end_command(); } /* * do_analyze_rel() -- analyze one relation, recursively or not * * Note that "acquirefunc" is only relevant for the non-inherited case. * For the inherited case, acquire_inherited_sample_rows() determines the * appropriate acquirefunc for each child table. */ static void do_analyze_rel(Relation onerel, VacuumParams *params, List *va_cols, AcquireSampleRowsFunc acquirefunc, BlockNumber relpages, bool inh, bool in_outer_xact, int elevel) { int attr_cnt, tcnt, i, ind; Relation *Irel; int nindexes; bool hasindex; VacAttrStats **vacattrstats; AnlIndexData *indexdata; int targrows, numrows, minrows; double totalrows, totaldeadrows; HeapTuple *rows; PGRUsage ru0; TimestampTz starttime = 0; MemoryContext caller_context; Oid save_userid; int save_sec_context; int save_nestlevel; int64 AnalyzePageHit = VacuumPageHit; int64 AnalyzePageMiss = VacuumPageMiss; int64 AnalyzePageDirty = VacuumPageDirty; PgStat_Counter startreadtime = 0; PgStat_Counter startwritetime = 0; if (inh) ereport(elevel, (errmsg("analyzing \"%s.%s\" inheritance tree", get_namespace_name(RelationGetNamespace(onerel)), RelationGetRelationName(onerel)))); else ereport(elevel, (errmsg("analyzing \"%s.%s\"", get_namespace_name(RelationGetNamespace(onerel)), RelationGetRelationName(onerel)))); /* * Set up a working context so that we can easily free whatever junk gets * created. */ anl_context = AllocSetContextCreate(CurrentMemoryContext, "Analyze", ALLOCSET_DEFAULT_SIZES); caller_context = MemoryContextSwitchTo(anl_context); /* * Switch to the table owner's userid, so that any index functions are run * as that user. Also lock down security-restricted operations and * arrange to make GUC variable changes local to this command. */ GetUserIdAndSecContext(&save_userid, &save_sec_context); SetUserIdAndSecContext(onerel->rd_rel->relowner, save_sec_context | SECURITY_RESTRICTED_OPERATION); save_nestlevel = NewGUCNestLevel(); RestrictSearchPath(); /* measure elapsed time iff autovacuum logging requires it */ if (AmAutoVacuumWorkerProcess() && params->log_min_duration >= 0) { if (track_io_timing) { startreadtime = pgStatBlockReadTime; startwritetime = pgStatBlockWriteTime; } pg_rusage_init(&ru0); starttime = GetCurrentTimestamp(); } /* * Determine which columns to analyze * * Note that system attributes are never analyzed, so we just reject them * at the lookup stage. We also reject duplicate column mentions. (We * could alternatively ignore duplicates, but analyzing a column twice * won't work; we'd end up making a conflicting update in pg_statistic.) */ if (va_cols != NIL) { Bitmapset *unique_cols = NULL; ListCell *le; vacattrstats = (VacAttrStats **) palloc(list_length(va_cols) * sizeof(VacAttrStats *)); tcnt = 0; foreach(le, va_cols) { char *col = strVal(lfirst(le)); i = attnameAttNum(onerel, col, false); if (i == InvalidAttrNumber) ereport(ERROR, (errcode(ERRCODE_UNDEFINED_COLUMN), errmsg("column \"%s\" of relation \"%s\" does not exist", col, RelationGetRelationName(onerel)))); if (bms_is_member(i, unique_cols)) ereport(ERROR, (errcode(ERRCODE_DUPLICATE_COLUMN), errmsg("column \"%s\" of relation \"%s\" appears more than once", col, RelationGetRelationName(onerel)))); unique_cols = bms_add_member(unique_cols, i); vacattrstats[tcnt] = examine_attribute(onerel, i, NULL); if (vacattrstats[tcnt] != NULL) tcnt++; } attr_cnt = tcnt; } else { attr_cnt = onerel->rd_att->natts; vacattrstats = (VacAttrStats **) palloc(attr_cnt * sizeof(VacAttrStats *)); tcnt = 0; for (i = 1; i <= attr_cnt; i++) { vacattrstats[tcnt] = examine_attribute(onerel, i, NULL); if (vacattrstats[tcnt] != NULL) tcnt++; } attr_cnt = tcnt; } /* * Open all indexes of the relation, and see if there are any analyzable * columns in the indexes. We do not analyze index columns if there was * an explicit column list in the ANALYZE command, however. * * If we are doing a recursive scan, we don't want to touch the parent's * indexes at all. If we're processing a partitioned table, we need to * know if there are any indexes, but we don't want to process them. */ if (onerel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE) { List *idxs = RelationGetIndexList(onerel); Irel = NULL; nindexes = 0; hasindex = idxs != NIL; list_free(idxs); } else if (!inh) { vac_open_indexes(onerel, AccessShareLock, &nindexes, &Irel); hasindex = nindexes > 0; } else { Irel = NULL; nindexes = 0; hasindex = false; } indexdata = NULL; if (nindexes > 0) { indexdata = (AnlIndexData *) palloc0(nindexes * sizeof(AnlIndexData)); for (ind = 0; ind < nindexes; ind++) { AnlIndexData *thisdata = &indexdata[ind]; IndexInfo *indexInfo; thisdata->indexInfo = indexInfo = BuildIndexInfo(Irel[ind]); thisdata->tupleFract = 1.0; /* fix later if partial */ if (indexInfo->ii_Expressions != NIL && va_cols == NIL) { ListCell *indexpr_item = list_head(indexInfo->ii_Expressions); thisdata->vacattrstats = (VacAttrStats **) palloc(indexInfo->ii_NumIndexAttrs * sizeof(VacAttrStats *)); tcnt = 0; for (i = 0; i < indexInfo->ii_NumIndexAttrs; i++) { int keycol = indexInfo->ii_IndexAttrNumbers[i]; if (keycol == 0) { /* Found an index expression */ Node *indexkey; if (indexpr_item == NULL) /* shouldn't happen */ elog(ERROR, "too few entries in indexprs list"); indexkey = (Node *) lfirst(indexpr_item); indexpr_item = lnext(indexInfo->ii_Expressions, indexpr_item); thisdata->vacattrstats[tcnt] = examine_attribute(Irel[ind], i + 1, indexkey); if (thisdata->vacattrstats[tcnt] != NULL) tcnt++; } } thisdata->attr_cnt = tcnt; } } } /* * Determine how many rows we need to sample, using the worst case from * all analyzable columns. We use a lower bound of 100 rows to avoid * possible overflow in Vitter's algorithm. (Note: that will also be the * target in the corner case where there are no analyzable columns.) */ targrows = 100; for (i = 0; i < attr_cnt; i++) { if (targrows < vacattrstats[i]->minrows) targrows = vacattrstats[i]->minrows; } for (ind = 0; ind < nindexes; ind++) { AnlIndexData *thisdata = &indexdata[ind]; for (i = 0; i < thisdata->attr_cnt; i++) { if (targrows < thisdata->vacattrstats[i]->minrows) targrows = thisdata->vacattrstats[i]->minrows; } } /* * Look at extended statistics objects too, as those may define custom * statistics target. So we may need to sample more rows and then build * the statistics with enough detail. */ minrows = ComputeExtStatisticsRows(onerel, attr_cnt, vacattrstats); if (targrows < minrows) targrows = minrows; /* * Acquire the sample rows */ rows = (HeapTuple *) palloc(targrows * sizeof(HeapTuple)); pgstat_progress_update_param(PROGRESS_ANALYZE_PHASE, inh ? PROGRESS_ANALYZE_PHASE_ACQUIRE_SAMPLE_ROWS_INH : PROGRESS_ANALYZE_PHASE_ACQUIRE_SAMPLE_ROWS); 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; pgstat_progress_update_param(PROGRESS_ANALYZE_PHASE, PROGRESS_ANALYZE_PHASE_COMPUTE_STATS); 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->tupattnum); 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; } MemoryContextReset(col_context); } if (nindexes > 0) 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). */ BuildRelationExtStatistics(onerel, inh, totalrows, numrows, rows, attr_cnt, vacattrstats); } pgstat_progress_update_param(PROGRESS_ANALYZE_PHASE, PROGRESS_ANALYZE_PHASE_FINALIZE_ANALYZE); /* * Update pages/tuples stats in pg_class ... but not if we're doing * inherited stats. * * We assume that VACUUM hasn't set pg_class.reltuples already, even * during a VACUUM ANALYZE. Although VACUUM often updates pg_class, * exceptions exist. A "VACUUM (ANALYZE, INDEX_CLEANUP OFF)" command will * never update pg_class entries for index relations. It's also possible * that an individual index's pg_class entry won't be updated during * VACUUM if the index AM returns NULL from its amvacuumcleanup() routine. */ if (!inh) { BlockNumber relallvisible; if (RELKIND_HAS_STORAGE(onerel->rd_rel->relkind)) visibilitymap_count(onerel, &relallvisible, NULL); else relallvisible = 0; /* Update pg_class for table relation */ vac_update_relstats(onerel, relpages, totalrows, relallvisible, hasindex, InvalidTransactionId, InvalidMultiXactId, NULL, NULL, in_outer_xact); /* Same for indexes */ 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, NULL, NULL, in_outer_xact); } } else if (onerel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE) { /* * Partitioned tables don't have storage, so we don't set any fields * in their pg_class entries except for reltuples and relhasindex. */ vac_update_relstats(onerel, -1, totalrows, 0, hasindex, InvalidTransactionId, InvalidMultiXactId, NULL, NULL, in_outer_xact); } /* * Now report ANALYZE to the cumulative stats system. For regular tables, * we do it only if not doing inherited stats. For partitioned tables, we * only do it for inherited stats. (We're never called for not-inherited * stats on partitioned tables anyway.) * * 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)); else if (onerel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE) pgstat_report_analyze(onerel, 0, 0, (va_cols == NIL)); /* * If this isn't part of VACUUM ANALYZE, let index AMs do cleanup. * * Note that most index AMs perform a no-op as a matter of policy for * amvacuumcleanup() when called in ANALYZE-only mode. The only exception * among core index AMs is GIN/ginvacuumcleanup(). */ if (!(params->options & VACOPT_VACUUM)) { for (ind = 0; ind < nindexes; ind++) { IndexBulkDeleteResult *stats; IndexVacuumInfo ivinfo; ivinfo.index = Irel[ind]; ivinfo.heaprel = onerel; 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 (AmAutoVacuumWorkerProcess() && params->log_min_duration >= 0) { TimestampTz endtime = GetCurrentTimestamp(); if (params->log_min_duration == 0 || TimestampDifferenceExceeds(starttime, endtime, params->log_min_duration)) { long delay_in_ms; double read_rate = 0; double write_rate = 0; StringInfoData buf; /* * Calculate the difference in the Page Hit/Miss/Dirty that * happened as part of the analyze by subtracting out the * pre-analyze values which we saved above. */ AnalyzePageHit = VacuumPageHit - AnalyzePageHit; AnalyzePageMiss = VacuumPageMiss - AnalyzePageMiss; AnalyzePageDirty = VacuumPageDirty - AnalyzePageDirty; /* * We do not expect an analyze to take > 25 days and it simplifies * things a bit to use TimestampDifferenceMilliseconds. */ delay_in_ms = TimestampDifferenceMilliseconds(starttime, endtime); /* * Note that we are reporting these read/write rates in the same * manner as VACUUM does, which means that while the 'average read * rate' here actually corresponds to page misses and resulting * reads which are also picked up by track_io_timing, if enabled, * the 'average write rate' is actually talking about the rate of * pages being dirtied, not being written out, so it's typical to * have a non-zero 'avg write rate' while I/O timings only reports * reads. * * It's not clear that an ANALYZE will ever result in * FlushBuffer() being called, but we track and support reporting * on I/O write time in case that changes as it's practically free * to do so anyway. */ if (delay_in_ms > 0) { read_rate = (double) BLCKSZ * AnalyzePageMiss / (1024 * 1024) / (delay_in_ms / 1000.0); write_rate = (double) BLCKSZ * AnalyzePageDirty / (1024 * 1024) / (delay_in_ms / 1000.0); } /* * We split this up so we don't emit empty I/O timing values when * track_io_timing isn't enabled. */ initStringInfo(&buf); appendStringInfo(&buf, _("automatic analyze of table \"%s.%s.%s\"\n"), get_database_name(MyDatabaseId), get_namespace_name(RelationGetNamespace(onerel)), RelationGetRelationName(onerel)); if (track_io_timing) { double read_ms = (double) (pgStatBlockReadTime - startreadtime) / 1000; double write_ms = (double) (pgStatBlockWriteTime - startwritetime) / 1000; appendStringInfo(&buf, _("I/O timings: read: %.3f ms, write: %.3f ms\n"), read_ms, write_ms); } appendStringInfo(&buf, _("avg read rate: %.3f MB/s, avg write rate: %.3f MB/s\n"), read_rate, write_rate); appendStringInfo(&buf, _("buffer usage: %lld hits, %lld misses, %lld dirtied\n"), (long long) AnalyzePageHit, (long long) AnalyzePageMiss, (long long) AnalyzePageDirty); appendStringInfo(&buf, _("system usage: %s"), pg_rusage_show(&ru0)); ereport(LOG, (errmsg_internal("%s", buf.data))); pfree(buf.data); } } /* 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->tupattnum; 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]; stats->exprvals = exprvals + i; stats->exprnulls = exprnulls + i; stats->rowstride = attr_cnt; stats->compute_stats(stats, ind_fetch_func, numindexrows, totalindexrows); MemoryContextReset(col_context); } } /* And clean up */ MemoryContextSwitchTo(ind_context); ExecDropSingleTupleTableSlot(slot); FreeExecutorState(estate); MemoryContextReset(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); int attstattarget; HeapTuple atttuple; Datum dat; bool isnull; HeapTuple typtuple; VacAttrStats *stats; int i; bool ok; /* Never analyze dropped columns */ if (attr->attisdropped) return NULL; /* * Get attstattarget value. Set to -1 if null. (Analyze functions expect * -1 to mean use default_statistics_target; see for example * std_typanalyze.) */ atttuple = SearchSysCache2(ATTNUM, ObjectIdGetDatum(RelationGetRelid(onerel)), Int16GetDatum(attnum)); if (!HeapTupleIsValid(atttuple)) elog(ERROR, "cache lookup failed for attribute %d of relation %u", attnum, RelationGetRelid(onerel)); dat = SysCacheGetAttr(ATTNUM, atttuple, Anum_pg_attribute_attstattarget, &isnull); attstattarget = isnull ? -1 : DatumGetInt16(dat); ReleaseSysCache(atttuple); /* Don't analyze column if user has specified not to */ if (attstattarget == 0) return NULL; /* * Create the VacAttrStats struct. */ stats = (VacAttrStats *) palloc0(sizeof(VacAttrStats)); stats->attstattarget = attstattarget; /* * 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); return NULL; } return stats; } /* * Read stream callback returning the next BlockNumber as chosen by the * BlockSampling algorithm. */ static BlockNumber block_sampling_read_stream_next(ReadStream *stream, void *callback_private_data, void *per_buffer_data) { BlockSamplerData *bs = callback_private_data; return BlockSampler_HasMore(bs) ? BlockSampler_Next(bs) : InvalidBlockNumber; } /* * 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 */ uint32 randseed; /* Seed for block sampler(s) */ BlockNumber totalblocks; TransactionId OldestXmin; BlockSamplerData bs; ReservoirStateData rstate; TupleTableSlot *slot; TableScanDesc scan; BlockNumber nblocks; BlockNumber blksdone = 0; ReadStream *stream; Assert(targrows > 0); totalblocks = RelationGetNumberOfBlocks(onerel); /* Need a cutoff xmin for HeapTupleSatisfiesVacuum */ OldestXmin = GetOldestNonRemovableTransactionId(onerel); /* Prepare for sampling block numbers */ randseed = pg_prng_uint32(&pg_global_prng_state); nblocks = BlockSampler_Init(&bs, totalblocks, targrows, randseed); /* Report sampling block numbers */ pgstat_progress_update_param(PROGRESS_ANALYZE_BLOCKS_TOTAL, nblocks); /* Prepare for sampling rows */ reservoir_init_selection_state(&rstate, targrows); scan = table_beginscan_analyze(onerel); slot = table_slot_create(onerel, NULL); stream = read_stream_begin_relation(READ_STREAM_MAINTENANCE, vac_strategy, scan->rs_rd, MAIN_FORKNUM, block_sampling_read_stream_next, &bs, 0); /* Outer loop over blocks to sample */ while (table_scan_analyze_next_block(scan, stream)) { vacuum_delay_point(); 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; } pgstat_progress_update_param(PROGRESS_ANALYZE_BLOCKS_DONE, ++blksdone); } read_stream_end(stream); 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_interruptible(rows, numrows, sizeof(HeapTuple), compare_rows, NULL); /* * 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; } /* * Comparator for sorting rows[] array */ static int compare_rows(const void *a, const void *b, void *arg) { 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; /* Initialize output parameters to zero now, in case we exit early */ *totalrows = 0; *totaldeadrows = 0; /* * 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.) */ pgstat_progress_update_param(PROGRESS_ANALYZE_CHILD_TABLES_TOTAL, nrels); numrows = 0; for (i = 0; i < nrels; i++) { Relation childrel = rels[i]; AcquireSampleRowsFunc acquirefunc = acquirefuncs[i]; double childblocks = relblocks[i]; /* * Report progress. The sampling function will normally report blocks * done/total, but we need to reset them to 0 here, so that they don't * show an old value until that. */ { const int progress_index[] = { PROGRESS_ANALYZE_CURRENT_CHILD_TABLE_RELID, PROGRESS_ANALYZE_BLOCKS_DONE, PROGRESS_ANALYZE_BLOCKS_TOTAL }; const int64 progress_vals[] = { RelationGetRelid(childrel), 0, 0, }; pgstat_progress_update_multi_param(3, progress_index, progress_vals); } 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 && !equalRowTypes(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); pgstat_progress_update_param(PROGRESS_ANALYZE_CHILD_TABLES_DONE, i + 1); } 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; CatalogIndexState indstate = NULL; 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->tupattnum); 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]); arry = construct_array_builtin(numdatums, nnum, FLOAT4OID); 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->tupattnum), BoolGetDatum(inh)); /* Open index information when we know we need it */ if (indstate == NULL) indstate = CatalogOpenIndexes(sd); if (HeapTupleIsValid(oldtup)) { /* Yes, replace it */ stup = heap_modify_tuple(oldtup, RelationGetDescr(sd), values, nulls, replaces); ReleaseSysCache(oldtup); CatalogTupleUpdateWithInfo(sd, &stup->t_self, stup, indstate); } else { /* No, insert new tuple */ stup = heap_form_tuple(RelationGetDescr(sd), values, nulls); CatalogTupleInsertWithInfo(sd, stup, indstate); } heap_freetuple(stup); } if (indstate != NULL) CatalogCloseIndexes(indstate); 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, void *arg); 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) { Oid ltopr; Oid eqopr; StdAnalyzeData *mystats; /* If the attstattarget column is negative, use the default value */ if (stats->attstattarget < 0) stats->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 * stats->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 * stats->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 * stats->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->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->attstattarget; int num_bins = stats->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_interruptible(values, values_cnt, sizeof(ScalarItem), compare_scalars, &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_interruptible(track, num_mcv, sizeof(ScalarMCVItem), compare_mcvs, NULL); /* * 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 */ } /* * 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; } /* * Comparator for sorting ScalarMCVItems by position */ static int compare_mcvs(const void *a, const void *b, void *arg) { 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; }