analyze.c

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/*-------------------------------------------------------------------------
 *
 * analyze.c
 *    the Postgres statistics generator
 *
 * Portions Copyright (c) 1996-2013, PostgreSQL Global Development Group
 * Portions Copyright (c) 1994, Regents of the University of California
 *
 *
 * IDENTIFICATION
 *    src/backend/commands/analyze.c
 *
 *-------------------------------------------------------------------------
 */
#include "postgres.h"

#include <math.h>

#include "access/multixact.h"
#include "access/transam.h"
#include "access/tupconvert.h"
#include "access/tuptoaster.h"
#include "access/visibilitymap.h"
#include "access/xact.h"
#include "catalog/index.h"
#include "catalog/indexing.h"
#include "catalog/pg_collation.h"
#include "catalog/pg_inherits_fn.h"
#include "catalog/pg_namespace.h"
#include "commands/dbcommands.h"
#include "commands/tablecmds.h"
#include "commands/vacuum.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 "storage/bufmgr.h"
#include "storage/lmgr.h"
#include "storage/proc.h"
#include "storage/procarray.h"
#include "utils/acl.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/sortsupport.h"
#include "utils/syscache.h"
#include "utils/timestamp.h"
#include "utils/tqual.h"


/* Data structure for Algorithm S from Knuth 3.4.2 */
typedef struct
{
    BlockNumber N;              /* number of blocks, known in advance */
    int         n;              /* desired sample size */
    BlockNumber t;              /* current block number */
    int         m;              /* blocks selected so far */
} BlockSamplerData;

typedef BlockSamplerData *BlockSampler;

/* 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, VacuumStmt *vacstmt,
               AcquireSampleRowsFunc acquirefunc, BlockNumber relpages,
               bool inh, int elevel);
static void BlockSampler_Init(BlockSampler bs, BlockNumber nblocks,
                  int samplesize);
static bool BlockSampler_HasMore(BlockSampler bs);
static BlockNumber BlockSampler_Next(BlockSampler bs);
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);
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
 */
void
analyze_rel(Oid relid, VacuumStmt *vacstmt, BufferAccessStrategy bstrategy)
{
    Relation    onerel;
    int         elevel;
    AcquireSampleRowsFunc acquirefunc = NULL;
    BlockNumber relpages = 0;

    /* Select logging level */
    if (vacstmt->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.
     */
    if (!(vacstmt->options & VACOPT_NOWAIT))
        onerel = try_relation_open(relid, ShareUpdateExclusiveLock);
    else if (ConditionalLockRelationOid(relid, ShareUpdateExclusiveLock))
        onerel = try_relation_open(relid, NoLock);
    else
    {
        onerel = NULL;
        if (IsAutoVacuumWorkerProcess() && Log_autovacuum_min_duration >= 0)
            ereport(LOG,
                    (errcode(ERRCODE_LOCK_NOT_AVAILABLE),
                  errmsg("skipping analyze of \"%s\" --- lock not available",
                         vacstmt->relation->relname)));
    }
    if (!onerel)
        return;

    /*
     * Check permissions --- this should match vacuum's check!
     */
    if (!(pg_class_ownercheck(RelationGetRelid(onerel), GetUserId()) ||
          (pg_database_ownercheck(MyDatabaseId, GetUserId()) && !onerel->rd_rel->relisshared)))
    {
        /* No need for a WARNING if we already complained during VACUUM */
        if (!(vacstmt->options & VACOPT_VACUUM))
        {
            if (onerel->rd_rel->relisshared)
                ereport(WARNING,
                 (errmsg("skipping \"%s\" --- only superuser can analyze it",
                         RelationGetRelationName(onerel))));
            else if (onerel->rd_rel->relnamespace == PG_CATALOG_NAMESPACE)
                ereport(WARNING,
                        (errmsg("skipping \"%s\" --- only superuser or database owner can analyze it",
                                RelationGetRelationName(onerel))));
            else
                ereport(WARNING,
                        (errmsg("skipping \"%s\" --- only table or database owner can analyze it",
                                RelationGetRelationName(onerel))));
        }
        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 a plain table, materialized view, or foreign table; we
     * used to do this in get_rel_oids() but seems safer to check after we've
     * locked the relation.
     */
    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
    {
        /* No need for a WARNING if we already complained during VACUUM */
        if (!(vacstmt->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 let other backends know I'm in ANALYZE.
     */
    LWLockAcquire(ProcArrayLock, LW_EXCLUSIVE);
    MyPgXact->vacuumFlags |= PROC_IN_ANALYZE;
    LWLockRelease(ProcArrayLock);

    /*
     * Do the normal non-recursive ANALYZE.
     */
    do_analyze_rel(onerel, vacstmt, acquirefunc, relpages, false, elevel);

    /*
     * If there are child tables, do recursive ANALYZE.
     */
    if (onerel->rd_rel->relhassubclass)
        do_analyze_rel(onerel, vacstmt, acquirefunc, relpages, true, 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);

    /*
     * Reset my PGXACT flag.  Note: we need this here, and not in vacuum_rel,
     * because the vacuum flag is cleared by the end-of-xact code.
     */
    LWLockAcquire(ProcArrayLock, LW_EXCLUSIVE);
    MyPgXact->vacuumFlags &= ~PROC_IN_ANALYZE;
    LWLockRelease(ProcArrayLock);
}

/*
 *  do_analyze_rel() -- analyze one relation, recursively or not
 *
 * Note that "acquirefunc" is only relevant for the non-inherited case.
 * If we supported foreign tables in inheritance trees,
 * acquire_inherited_sample_rows would need to determine the appropriate
 * acquirefunc for each child table.
 */
static void
do_analyze_rel(Relation onerel, VacuumStmt *vacstmt,
               AcquireSampleRowsFunc acquirefunc, BlockNumber relpages,
               bool inh, int elevel)
{
    int         attr_cnt,
                tcnt,
                i,
                ind;
    Relation   *Irel;
    int         nindexes;
    bool        hasindex;
    VacAttrStats **vacattrstats;
    AnlIndexData *indexdata;
    int         targrows,
                numrows;
    double      totalrows,
                totaldeadrows;
    HeapTuple  *rows;
    PGRUsage    ru0;
    TimestampTz starttime = 0;
    MemoryContext caller_context;
    Oid         save_userid;
    int         save_sec_context;
    int         save_nestlevel;

    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_MINSIZE,
                                        ALLOCSET_DEFAULT_INITSIZE,
                                        ALLOCSET_DEFAULT_MAXSIZE);
    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();

    /* measure elapsed time iff autovacuum logging requires it */
    if (IsAutoVacuumWorkerProcess() && Log_autovacuum_min_duration >= 0)
    {
        pg_rusage_init(&ru0);
        if (Log_autovacuum_min_duration > 0)
            starttime = GetCurrentTimestamp();
    }

    /*
     * Determine which columns to analyze
     *
     * Note that system attributes are never analyzed.
     */
    if (vacstmt->va_cols != NIL)
    {
        ListCell   *le;

        vacattrstats = (VacAttrStats **) palloc(list_length(vacstmt->va_cols) *
                                                sizeof(VacAttrStats *));
        tcnt = 0;
        foreach(le, vacstmt->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))));
            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 (!inh)
        vac_open_indexes(onerel, AccessShareLock, &nindexes, &Irel);
    else
    {
        Irel = NULL;
        nindexes = 0;
    }
    hasindex = (nindexes > 0);
    indexdata = NULL;
    if (hasindex)
    {
        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 && vacstmt->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_KeyAttrNumbers[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(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;
        }
    }

    /*
     * 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_MINSIZE,
                                            ALLOCSET_DEFAULT_INITSIZE,
                                            ALLOCSET_DEFAULT_MAXSIZE);
        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);
        }
    }

    /*
     * Update pages/tuples stats in pg_class ... but not if we're doing
     * inherited stats.
     */
    if (!inh)
        vac_update_relstats(onerel,
                            relpages,
                            totalrows,
                            visibilitymap_count(onerel),
                            hasindex,
                            InvalidTransactionId,
                            InvalidMultiXactId);

    /*
     * 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 && !(vacstmt->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);
        }
    }

    /*
     * 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.
     */
    if (!inh)
        pgstat_report_analyze(onerel, totalrows, totaldeadrows);

    /* If this isn't part of VACUUM ANALYZE, let index AMs do cleanup */
    if (!(vacstmt->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() && Log_autovacuum_min_duration >= 0)
    {
        if (Log_autovacuum_min_duration == 0 ||
            TimestampDifferenceExceeds(starttime, GetCurrentTimestamp(),
                                       Log_autovacuum_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_MINSIZE,
                                        ALLOCSET_DEFAULT_INITSIZE,
                                        ALLOCSET_DEFAULT_MAXSIZE);
    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;
        List       *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));

        /* Arrange for econtext's scan tuple to be the tuple under test */
        econtext->ecxt_scantuple = slot;

        /* Set up execution state for predicate. */
        predicate = (List *)
            ExecPrepareExpr((Expr *) 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];

            /*
             * 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 */
            ExecStoreTuple(heapTuple, slot, InvalidBuffer, false);

            /* If index is partial, check predicate */
            if (predicate != NIL)
            {
                if (!ExecQual(predicate, econtext, false))
                    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 = onerel->rd_att->attrs[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);
    }
    else
    {
        stats->attrtypid = attr->atttypid;
        stats->attrtypmod = attr->atttypmod;
    }

    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;
}

/*
 * BlockSampler_Init -- prepare for random sampling of blocknumbers
 *
 * BlockSampler is used for stage one of our new two-stage tuple
 * sampling mechanism as discussed on pgsql-hackers 2004-04-02 (subject
 * "Large DB").  It selects a random sample of samplesize blocks out of
 * the nblocks blocks in the table.  If the table has less than
 * samplesize blocks, all blocks are selected.
 *
 * Since we know the total number of blocks in advance, we can use the
 * straightforward Algorithm S from Knuth 3.4.2, rather than Vitter's
 * algorithm.
 */
static void
BlockSampler_Init(BlockSampler bs, BlockNumber nblocks, int samplesize)
{
    bs->N = nblocks;            /* measured table size */

    /*
     * If we decide to reduce samplesize for tables that have less or not much
     * more than samplesize blocks, here is the place to do it.
     */
    bs->n = samplesize;
    bs->t = 0;                  /* blocks scanned so far */
    bs->m = 0;                  /* blocks selected so far */
}

static bool
BlockSampler_HasMore(BlockSampler bs)
{
    return (bs->t < bs->N) && (bs->m < bs->n);
}

static BlockNumber
BlockSampler_Next(BlockSampler bs)
{
    BlockNumber K = bs->N - bs->t;      /* remaining blocks */
    int         k = bs->n - bs->m;      /* blocks still to sample */
    double      p;              /* probability to skip block */
    double      V;              /* random */

    Assert(BlockSampler_HasMore(bs));   /* hence K > 0 and k > 0 */

    if ((BlockNumber) k >= K)
    {
        /* need all the rest */
        bs->m++;
        return bs->t++;
    }

    /*----------
     * It is not obvious that this code matches Knuth's Algorithm S.
     * Knuth says to skip the current block with probability 1 - k/K.
     * If we are to skip, we should advance t (hence decrease K), and
     * repeat the same probabilistic test for the next block.  The naive
     * implementation thus requires an anl_random_fract() call for each block
     * number.  But we can reduce this to one anl_random_fract() call per
     * selected block, by noting that each time the while-test succeeds,
     * we can reinterpret V as a uniform random number in the range 0 to p.
     * Therefore, instead of choosing a new V, we just adjust p to be
     * the appropriate fraction of its former value, and our next loop
     * makes the appropriate probabilistic test.
     *
     * We have initially K > k > 0.  If the loop reduces K to equal k,
     * the next while-test must fail since p will become exactly zero
     * (we assume there will not be roundoff error in the division).
     * (Note: Knuth suggests a "<=" loop condition, but we use "<" just
     * to be doubly sure about roundoff error.)  Therefore K cannot become
     * less than k, which means that we cannot fail to select enough blocks.
     *----------
     */
    V = anl_random_fract();
    p = 1.0 - (double) k / (double) K;
    while (V < p)
    {
        /* skip */
        bs->t++;
        K--;                    /* keep K == N - t */

        /* adjust p to be new cutoff point in reduced range */
        p *= 1.0 - (double) k / (double) K;
    }

    /* select */
    bs->m++;
    return bs->t++;
}

/*
 * 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;
    double      rstate;

    Assert(targrows > 0);

    totalblocks = RelationGetNumberOfBlocks(onerel);

    /* Need a cutoff xmin for HeapTupleSatisfiesVacuum */
    OldestXmin = GetOldestXmin(onerel->rd_rel->relisshared, true);

    /* Prepare for sampling block numbers */
    BlockSampler_Init(&bs, totalblocks, targrows);
    /* Prepare for sampling rows */
    rstate = anl_init_selection_state(targrows);

    /* Outer loop over blocks to sample */
    while (BlockSampler_HasMore(&bs))
    {
        BlockNumber targblock = BlockSampler_Next(&bs);
        Buffer      targbuffer;
        Page        targpage;
        OffsetNumber targoffset,
                    maxoffset;

        vacuum_delay_point();

        /*
         * We must maintain a pin on the target page's buffer to ensure that
         * the maxoffset value stays good (else concurrent VACUUM might delete
         * tuples out from under us).  Hence, pin the page until we are done
         * looking at it.  We also choose to hold sharelock on the buffer
         * throughout --- we could release and re-acquire sharelock for each
         * tuple, but since we aren't doing much work per tuple, the extra
         * lock traffic is probably better avoided.
         */
        targbuffer = ReadBufferExtended(onerel, MAIN_FORKNUM, targblock,
                                        RBM_NORMAL, vac_strategy);
        LockBuffer(targbuffer, BUFFER_LOCK_SHARE);
        targpage = BufferGetPage(targbuffer);
        maxoffset = PageGetMaxOffsetNumber(targpage);

        /* Inner loop over all tuples on the selected page */
        for (targoffset = FirstOffsetNumber; targoffset <= maxoffset; targoffset++)
        {
            ItemId      itemid;
            HeapTupleData targtuple;
            bool        sample_it = false;

            itemid = PageGetItemId(targpage, targoffset);

            /*
             * We ignore unused and redirect line pointers.  DEAD line
             * pointers should be counted as dead, because we need vacuum to
             * run to get rid of them.  Note that this rule agrees with the
             * way that heap_page_prune() counts things.
             */
            if (!ItemIdIsNormal(itemid))
            {
                if (ItemIdIsDead(itemid))
                    deadrows += 1;
                continue;
            }

            ItemPointerSet(&targtuple.t_self, targblock, targoffset);

            targtuple.t_data = (HeapTupleHeader) PageGetItem(targpage, itemid);
            targtuple.t_len = ItemIdGetLength(itemid);

            switch (HeapTupleSatisfiesVacuum(targtuple.t_data,
                                             OldestXmin,
                                             targbuffer))
            {
                case HEAPTUPLE_LIVE:
                    sample_it = true;
                    liverows += 1;
                    break;

                case HEAPTUPLE_DEAD:
                case HEAPTUPLE_RECENTLY_DEAD:
                    /* Count dead and recently-dead rows */
                    deadrows += 1;
                    break;

                case HEAPTUPLE_INSERT_IN_PROGRESS:

                    /*
                     * Insert-in-progress rows are not counted.  We assume
                     * that when the inserting transaction commits or aborts,
                     * it will send a stats message to increment the proper
                     * count.  This works right only if that transaction ends
                     * after we finish analyzing the table; if things happen
                     * in the other order, its stats update will be
                     * overwritten by ours.  However, the error will be large
                     * only if the other transaction runs long enough to
                     * insert many tuples, so assuming it will finish after us
                     * is the safer option.
                     *
                     * A special case is that the inserting transaction might
                     * be our own.  In this case we should count and sample
                     * the row, to accommodate users who load a table and
                     * analyze it in one transaction.  (pgstat_report_analyze
                     * has to adjust the numbers we send to the stats
                     * collector to make this come out right.)
                     */
                    if (TransactionIdIsCurrentTransactionId(HeapTupleHeaderGetXmin(targtuple.t_data)))
                    {
                        sample_it = true;
                        liverows += 1;
                    }
                    break;

                case HEAPTUPLE_DELETE_IN_PROGRESS:

                    /*
                     * We count delete-in-progress rows as still live, using
                     * the same reasoning given above; but we don't bother to
                     * include them in the sample.
                     *
                     * If the delete was done by our own transaction, however,
                     * we must count the row as dead to make
                     * pgstat_report_analyze's stats adjustments come out
                     * right.  (Note: this works out properly when the row was
                     * both inserted and deleted in our xact.)
                     */
                    if (TransactionIdIsCurrentTransactionId(HeapTupleHeaderGetUpdateXid(targtuple.t_data)))
                        deadrows += 1;
                    else
                        liverows += 1;
                    break;

                default:
                    elog(ERROR, "unexpected HeapTupleSatisfiesVacuum result");
                    break;
            }

            if (sample_it)
            {
                /*
                 * 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 below). 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++] = heap_copytuple(&targtuple);
                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 = anl_get_next_S(samplerows, targrows,
                                                    &rstate);

                    if (rowstoskip <= 0)
                    {
                        /*
                         * Found a suitable tuple, so save it, replacing one
                         * old tuple at random
                         */
                        int         k = (int) (targrows * anl_random_fract());

                        Assert(k >= 0 && k < targrows);
                        heap_freetuple(rows[k]);
                        rows[k] = heap_copytuple(&targtuple);
                    }

                    rowstoskip -= 1;
                }

                samplerows += 1;
            }
        }

        /* Now release the lock and pin on the page */
        UnlockReleaseBuffer(targbuffer);
    }

    /*
     * 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 rows in relation.  For live rows, use
     * vac_estimate_reltuples; for dead rows, we have no source of old
     * information, so we have to assume the density is the same in unseen
     * pages as in the pages we scanned.
     */
    *totalrows = vac_estimate_reltuples(onerel, true,
                                        totalblocks,
                                        bs.m,
                                        liverows);
    if (bs.m > 0)
        *totaldeadrows = floor((deadrows / bs.m) * totalblocks + 0.5);
    else
        *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;
}

/* Select a random value R uniformly distributed in (0 - 1) */
double
anl_random_fract(void)
{
    return ((double) random() + 1) / ((double) MAX_RANDOM_VALUE + 2);
}

/*
 * These two routines embody Algorithm Z from "Random sampling with a
 * reservoir" by Jeffrey S. Vitter, in ACM Trans. Math. Softw. 11, 1
 * (Mar. 1985), Pages 37-57.  Vitter describes his algorithm in terms
 * of the count S of records to skip before processing another record.
 * It is computed primarily based on t, the number of records already read.
 * The only extra state needed between calls is W, a random state variable.
 *
 * anl_init_selection_state computes the initial W value.
 *
 * Given that we've already read t records (t >= n), anl_get_next_S
 * determines the number of records to skip before the next record is
 * processed.
 */
double
anl_init_selection_state(int n)
{
    /* Initial value of W (for use when Algorithm Z is first applied) */
    return exp(-log(anl_random_fract()) / n);
}

double
anl_get_next_S(double t, int n, double *stateptr)
{
    double      S;

    /* The magic constant here is T from Vitter's paper */
    if (t <= (22.0 * n))
    {
        /* Process records using Algorithm X until t is large enough */
        double      V,
                    quot;

        V = anl_random_fract(); /* Generate V */
        S = 0;
        t += 1;
        /* Note: "num" in Vitter's code is always equal to t - n */
        quot = (t - (double) n) / t;
        /* Find min S satisfying (4.1) */
        while (quot > V)
        {
            S += 1;
            t += 1;
            quot *= (t - (double) n) / t;
        }
    }
    else
    {
        /* Now apply Algorithm Z */
        double      W = *stateptr;
        double      term = t - (double) n + 1;

        for (;;)
        {
            double      numer,
                        numer_lim,
                        denom;
            double      U,
                        X,
                        lhs,
                        rhs,
                        y,
                        tmp;

            /* Generate U and X */
            U = anl_random_fract();
            X = t * (W - 1.0);
            S = floor(X);       /* S is tentatively set to floor(X) */
            /* Test if U <= h(S)/cg(X) in the manner of (6.3) */
            tmp = (t + 1) / term;
            lhs = exp(log(((U * tmp * tmp) * (term + S)) / (t + X)) / n);
            rhs = (((t + X) / (term + S)) * term) / t;
            if (lhs <= rhs)
            {
                W = rhs / lhs;
                break;
            }
            /* Test if U <= f(S)/cg(X) */
            y = (((U * (t + 1)) / term) * (t + S + 1)) / (t + X);
            if ((double) n < S)
            {
                denom = t;
                numer_lim = term + S;
            }
            else
            {
                denom = t - (double) n + S;
                numer_lim = t + 1;
            }
            for (numer = t + S; numer >= numer_lim; numer -= 1)
            {
                y *= numer / denom;
                denom -= 1;
            }
            W = exp(-log(anl_random_fract()) / n);      /* Generate W in advance */
            if (exp(log(y) / n) <= (t + X) / t)
                break;
        }
        *stateptr = W;
    }
    return S;
}

/*
 * 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.
 */
static int
acquire_inherited_sample_rows(Relation onerel, int elevel,
                              HeapTuple *rows, int targrows,
                              double *totalrows, double *totaldeadrows)
{
    List       *tableOIDs;
    Relation   *rels;
    double     *relblocks;
    double      totalblocks;
    int         numrows,
                nrels,
                i;
    ListCell   *lc;

    /*
     * 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);
        return 0;
    }

    /*
     * Count the blocks in all the relations.  The result could overflow
     * BlockNumber, so we use double arithmetic.
     */
    rels = (Relation *) palloc(list_length(tableOIDs) * sizeof(Relation));
    relblocks = (double *) palloc(list_length(tableOIDs) * sizeof(double));
    totalblocks = 0;
    nrels = 0;
    foreach(lc, tableOIDs)
    {
        Oid         childOID = lfirst_oid(lc);
        Relation    childrel;

        /* We already got the needed lock */
        childrel = heap_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);
            heap_close(childrel, AccessShareLock);
            continue;
        }

        rels[nrels] = childrel;
        relblocks[nrels] = (double) RelationGetNumberOfBlocks(childrel);
        totalblocks += relblocks[nrels];
        nrels++;
    }

    /*
     * 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];
        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 = acquire_sample_rows(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),
                                 gettext_noop("could not convert row type"));
                    if (map != NULL)
                    {
                        int         j;

                        for (j = 0; j < childrows; j++)
                        {
                            HeapTuple   newtup;

                            newtup = do_convert_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.
         */
        heap_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 = heap_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_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), FLOAT4PASSBYVAL, '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);
            simple_heap_update(sd, &stup->t_self, stup);
        }
        else
        {
            /* No, insert new tuple */
            stup = heap_form_tuple(RelationGetDescr(sd), values, nulls);
            simple_heap_insert(sd, stup);
        }

        /* update indexes too */
        CatalogUpdateIndexes(sd, stup);

        heap_freetuple(stup);
    }

    heap_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
{
    Oid         eqopr;          /* '=' operator for datatype, if any */
    Oid         eqfunc;         /* and associated function */
    Oid         ltopr;          /* '<' operator for datatype, if any */
} StdAnalyzeData;

typedef struct
{
    Datum       value;          /* a data value */
    int         tupno;          /* position index for tuple it came from */
} ScalarItem;

typedef struct
{
    int         count;          /* # of duplicates */
    int         first;          /* values[] index of first occurrence */
} ScalarMCVItem;

typedef struct
{
    SortSupport ssup;
    int        *tupnoLink;
} CompareScalarsContext;


static void compute_minimal_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);


/*
 * 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,
                             &ltopr, &eqopr, NULL,
                             NULL);

    /* If column has no "=" operator, we can't do much of anything */
    if (!OidIsValid(eqopr))
        return false;

    /* Save the operator info for compute_stats routines */
    mystats = (StdAnalyzeData *) palloc(sizeof(StdAnalyzeData));
    mystats->eqopr = eqopr;
    mystats->eqfunc = get_opcode(eqopr);
    mystats->ltopr = ltopr;
    stats->extra_data = mystats;

    /*
     * Determine which standard statistics algorithm to use
     */
    if (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
    {
        /* Can't do much but the minimal stuff */
        stats->compute_stats = compute_minimal_stats;
        /* Might as well use the same minrows as above */
        stats->minrows = 300 * attr->attstattarget;
    }

    return true;
}

/*
 *  compute_minimal_stats() -- compute minimal column statistics
 *
 *  We use this when we can find only an "=" operator for the datatype.
 *
 *  We determine the fraction of non-null rows, the average width, the
 *  most common values, and the (estimated) number of distinct values.
 *
 *  The most common values are determined by brute force: we keep a list
 *  of previously seen values, ordered by number of times seen, as we scan
 *  the samples.  A newly seen value is inserted just after the last
 *  multiply-seen value, causing the bottommost (oldest) singly-seen value
 *  to drop off the list.  The accuracy of this method, and also its cost,
 *  depend mainly on the length of the list we are willing to keep.
 */
static void
compute_minimal_stats(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++)
        {
            /* We always use the default collation for statistics */
            if (DatumGetBool(FunctionCall2Coll(&f_cmpeq,
                                               DEFAULT_COLLATION_OID,
                                               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 values, assume it's a unique column */
            stats->stadistinct = -1.0;
        }
        else if (track_cnt < track_max && toowide_cnt == 0 &&
                 nmultiple == track_cnt)
        {
            /*
             * Our track list includes every value in the sample, and every
             * value appeared more than once.  Assume the column has just
             * these values.
             */
            stats->stadistinct = track_cnt;
        }
        else
        {
            /*----------
             * Estimate the number of distinct values using the estimator
             * proposed by 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.
             *
             * 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      numer,
                        denom,
                        stadistinct;

            numer = (double) samplerows *(double) d;

            denom = (double) (samplerows - f1) +
                (double) f1 *(double) samplerows / totalrows;

            stadistinct = numer / denom;
            /* Clamp to sane range in case of roundoff error */
            if (stadistinct < (double) d)
                stadistinct = (double) d;
            if (stadistinct > totalrows)
                stadistinct = totalrows;
            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 (estimated) average. We set the
         * threshold rather arbitrarily at 25% more than average, with at
         * least 2 instances in the sample.
         */
        if (track_cnt < track_max && toowide_cnt == 0 &&
            stats->stadistinct > 0 &&
            track_cnt <= num_mcv)
        {
            /* Track list includes all values seen, and all will fit */
            num_mcv = track_cnt;
        }
        else
        {
            double      ndistinct = stats->stadistinct;
            double      avgcount,
                        mincount;

            if (ndistinct < 0)
                ndistinct = -ndistinct * totalrows;
            /* estimate # of occurrences in sample of a typical value */
            avgcount = (double) samplerows / ndistinct;
            /* set minimum threshold count to store a value */
            mincount = avgcount * 1.25;
            if (mincount < 2)
                mincount = 2;
            if (num_mcv > track_cnt)
                num_mcv = track_cnt;
            for (i = 0; i < num_mcv; i++)
            {
                if (track[i].count < mincount)
                {
                    num_mcv = i;
                    break;
                }
            }
        }

        /* Generate MCV slot entry */
        if (num_mcv > 0)
        {
            MemoryContext old_context;
            Datum      *mcv_values;
            float4     *mcv_freqs;

            /* Must copy the target values into 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->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;
    /* We always use the default collation for statistics */
    ssup.ssup_collation = DEFAULT_COLLATION_OID;
    ssup.ssup_nulls_first = 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 values, assume it's a unique column */
            stats->stadistinct = -1.0;
        }
        else if (toowide_cnt == 0 && nmultiple == ndistinct)
        {
            /*
             * Every value in the sample appeared more than once.  Assume the
             * column has just these values.
             */
            stats->stadistinct = ndistinct;
        }
        else
        {
            /*----------
             * Estimate the number of distinct values using the estimator
             * proposed by 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.
             *
             * Overwidth values are assumed to have been distinct.
             *----------
             */
            int         f1 = ndistinct - nmultiple + toowide_cnt;
            int         d = f1 + nmultiple;
            double      numer,
                        denom,
                        stadistinct;

            numer = (double) samplerows *(double) d;

            denom = (double) (samplerows - f1) +
                (double) f1 *(double) samplerows / totalrows;

            stadistinct = numer / denom;
            /* Clamp to sane range in case of roundoff error */
            if (stadistinct < (double) d)
                stadistinct = (double) d;
            if (stadistinct > totalrows)
                stadistinct = totalrows;
            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 (estimated) average. We set the
         * threshold rather arbitrarily at 25% more than average, with at
         * least 2 instances in the sample.  Also, we won't suppress values
         * that have a frequency of at least 1/K where K is the intended
         * number of histogram bins; such values might otherwise cause us to
         * emit duplicate histogram bin boundaries.  (We might end up with
         * duplicate histogram entries anyway, if the distribution is skewed;
         * but we prefer to treat such values as MCVs if at all possible.)
         */
        if (track_cnt == ndistinct && toowide_cnt == 0 &&
            stats->stadistinct > 0 &&
            track_cnt <= num_mcv)
        {
            /* Track list includes all values seen, and all will fit */
            num_mcv = track_cnt;
        }
        else
        {
            double      ndistinct = stats->stadistinct;
            double      avgcount,
                        mincount,
                        maxmincount;

            if (ndistinct < 0)
                ndistinct = -ndistinct * totalrows;
            /* estimate # of occurrences in sample of a typical value */
            avgcount = (double) samplerows / ndistinct;
            /* set minimum threshold count to store a value */
            mincount = avgcount * 1.25;
            if (mincount < 2)
                mincount = 2;
            /* don't let threshold exceed 1/K, however */
            maxmincount = (double) samplerows / (double) num_bins;
            if (mincount > maxmincount)
                mincount = maxmincount;
            if (num_mcv > track_cnt)
                num_mcv = track_cnt;
            for (i = 0; i < num_mcv; i++)
            {
                if (track[i].count < mincount)
                {
                    num_mcv = i;
                    break;
                }
            }
        }

        /* Generate MCV slot entry */
        if (num_mcv > 0)
        {
            MemoryContext old_context;
            Datum      *mcv_values;
            float4     *mcv_freqs;

            /* Must copy the target values into 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->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->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->stanumbers[slot_idx] = corrs;
            stats->numnumbers[slot_idx] = 1;
            slot_idx++;
        }
    }
    else if (nonnull_cnt == 0 && 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;
}