This commit was manufactured by cvs2git to create branch 'ecpg_big_bison'.

Sprout from master 2002-06-20 20:29:54 UTC Bruce Momjian <bruce@momjian.us> 'Update copyright to 2002.'
Delete:
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diff --git a/src/backend/commands/analyze.c b/src/backend/commands/analyze.c
deleted file mode 100644
index 6caa968b5d2..00000000000
--- a/src/backend/commands/analyze.c
+++ /dev/null
@@ -1,1794 +0,0 @@
-/*-------------------------------------------------------------------------
- *
- * analyze.c
- *	  the postgres statistics generator
- *
- * Portions Copyright (c) 1996-2002, PostgreSQL Global Development Group
- * Portions Copyright (c) 1994, Regents of the University of California
- *
- *
- * IDENTIFICATION
- *	  $Header: /cvsroot/pgsql/src/backend/commands/analyze.c,v 1.38 2002/06/20 20:29:26 momjian Exp $
- *
- *-------------------------------------------------------------------------
- */
-#include "postgres.h"
-
-#include <math.h>
-
-#include "access/heapam.h"
-#include "access/tuptoaster.h"
-#include "catalog/catalog.h"
-#include "catalog/catname.h"
-#include "catalog/indexing.h"
-#include "catalog/pg_operator.h"
-#include "catalog/pg_statistic.h"
-#include "catalog/pg_type.h"
-#include "commands/vacuum.h"
-#include "miscadmin.h"
-#include "parser/parse_oper.h"
-#include "utils/acl.h"
-#include "utils/builtins.h"
-#include "utils/datum.h"
-#include "utils/fmgroids.h"
-#include "utils/lsyscache.h"
-#include "utils/syscache.h"
-#include "utils/tuplesort.h"
-
-
-/*
- * Analysis algorithms supported
- */
-typedef enum
-{
-	ALG_MINIMAL = 1,			/* Compute only most-common-values */
-	ALG_SCALAR					/* Compute MCV, histogram, sort
-								 * correlation */
-} AlgCode;
-
-/*
- * 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  256
-
-/*
- * We build one of these structs for each attribute (column) that is to be
- * analyzed.  The struct and subsidiary data are in anl_context,
- * so they live until the end of the ANALYZE operation.
- */
-typedef struct
-{
-	/* These fields are set up by examine_attribute */
-	int			attnum;			/* attribute number */
-	AlgCode		algcode;		/* Which algorithm to use for this column */
-	int			minrows;		/* Minimum # of rows wanted for stats */
-	Form_pg_attribute attr;		/* copy of pg_attribute row for column */
-	Form_pg_type attrtype;		/* copy of pg_type row for column */
-	Oid			eqopr;			/* '=' operator for datatype, if any */
-	Oid			eqfunc;			/* and associated function */
-	Oid			ltopr;			/* '<' operator for datatype, if any */
-
-	/*
-	 * These fields are filled in by the actual statistics-gathering
-	 * routine
-	 */
-	bool		stats_valid;
-	float4		stanullfrac;	/* fraction of entries that are NULL */
-	int4		stawidth;		/* average width */
-	float4		stadistinct;	/* # distinct values */
-	int2		stakind[STATISTIC_NUM_SLOTS];
-	Oid			staop[STATISTIC_NUM_SLOTS];
-	int			numnumbers[STATISTIC_NUM_SLOTS];
-	float4	   *stanumbers[STATISTIC_NUM_SLOTS];
-	int			numvalues[STATISTIC_NUM_SLOTS];
-	Datum	   *stavalues[STATISTIC_NUM_SLOTS];
-} VacAttrStats;
-
-
-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;
-
-
-#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)
-
-static int elevel = -1;
-
-static MemoryContext anl_context = NULL;
-
-/* context information for compare_scalars() */
-static FmgrInfo *datumCmpFn;
-static SortFunctionKind datumCmpFnKind;
-static int *datumCmpTupnoLink;
-
-
-static VacAttrStats *examine_attribute(Relation onerel, int attnum);
-static int acquire_sample_rows(Relation onerel, HeapTuple *rows,
-					int targrows, double *totalrows);
-static double random_fract(void);
-static double init_selection_state(int n);
-static double select_next_random_record(double t, int n, double *stateptr);
-static int	compare_rows(const void *a, const void *b);
-static int	compare_scalars(const void *a, const void *b);
-static int	compare_mcvs(const void *a, const void *b);
-static void compute_minimal_stats(VacAttrStats *stats,
-					  TupleDesc tupDesc, double totalrows,
-					  HeapTuple *rows, int numrows);
-static void compute_scalar_stats(VacAttrStats *stats,
-					 TupleDesc tupDesc, double totalrows,
-					 HeapTuple *rows, int numrows);
-static void update_attstats(Oid relid, int natts, VacAttrStats **vacattrstats);
-
-
-/*
- *	analyze_rel() -- analyze one relation
- */
-void
-analyze_rel(Oid relid, VacuumStmt *vacstmt)
-{
-	Relation	onerel;
-	Form_pg_attribute *attr;
-	int			attr_cnt,
-				tcnt,
-				i;
-	VacAttrStats **vacattrstats;
-	int			targrows,
-				numrows;
-	double		totalrows;
-	HeapTuple  *rows;
-
-	if (vacstmt->verbose)
-		elevel = INFO;
-	else
-		elevel = DEBUG1;
-
-	/*
-	 * Use the current context for storing analysis info.  vacuum.c ensures
-	 * that this context will be cleared when I return, thus releasing the
-	 * memory allocated here.
-	 */
-	anl_context = CurrentMemoryContext;
-
-	/*
-	 * Check for user-requested abort.	Note we want this to be inside a
-	 * transaction, so xact.c doesn't issue useless WARNING.
-	 */
-	CHECK_FOR_INTERRUPTS();
-
-	/*
-	 * Race condition -- if the pg_class tuple has gone away since the
-	 * last time we saw it, we don't need to process it.
-	 */
-	if (!SearchSysCacheExists(RELOID,
-							  ObjectIdGetDatum(relid),
-							  0, 0, 0))
-		return;
-
-	/*
-	 * Open the class, getting only a read lock on it, and check
-	 * permissions. Permissions check should match vacuum's check!
-	 */
-	onerel = relation_open(relid, AccessShareLock);
-
-	if (!(pg_class_ownercheck(RelationGetRelid(onerel), GetUserId()) ||
-		  (is_dbadmin(MyDatabaseId) && !onerel->rd_rel->relisshared)))
-	{
-		/* No need for a WARNING if we already complained during VACUUM */
-		if (!vacstmt->vacuum)
-			elog(WARNING, "Skipping \"%s\" --- only table or database owner can ANALYZE it",
-				 RelationGetRelationName(onerel));
-		relation_close(onerel, AccessShareLock);
-		return;
-	}
-
-	/*
-	 * Check that it's a plain table; we used to do this in getrels() but
-	 * seems safer to check after we've locked the relation.
-	 */
-	if (onerel->rd_rel->relkind != RELKIND_RELATION)
-	{
-		/* No need for a WARNING if we already complained during VACUUM */
-		if (!vacstmt->vacuum)
-			elog(WARNING, "Skipping \"%s\" --- can not process indexes, views or special system tables",
-				 RelationGetRelationName(onerel));
-		relation_close(onerel, AccessShareLock);
-		return;
-	}
-
-	/*
-	 * We can ANALYZE any table except pg_statistic. See update_attstats
-	 */
-	if (IsSystemNamespace(RelationGetNamespace(onerel)) &&
-		strcmp(RelationGetRelationName(onerel), StatisticRelationName) == 0)
-	{
-		relation_close(onerel, AccessShareLock);
-		return;
-	}
-
-	elog(elevel, "Analyzing %s.%s",
-		 get_namespace_name(RelationGetNamespace(onerel)),
-		 RelationGetRelationName(onerel));
-
-	/*
-	 * Determine which columns to analyze
-	 *
-	 * Note that system attributes are never analyzed.
-	 */
-	attr = onerel->rd_att->attrs;
-	attr_cnt = onerel->rd_att->natts;
-
-	if (vacstmt->va_cols != NIL)
-	{
-		List	   *le;
-
-		vacattrstats = (VacAttrStats **) palloc(length(vacstmt->va_cols) *
-												sizeof(VacAttrStats *));
-		tcnt = 0;
-		foreach(le, vacstmt->va_cols)
-		{
-			char	   *col = strVal(lfirst(le));
-
-			for (i = 0; i < attr_cnt; i++)
-			{
-				if (namestrcmp(&(attr[i]->attname), col) == 0)
-					break;
-			}
-			if (i >= attr_cnt)
-				elog(ERROR, "ANALYZE: there is no attribute %s in %s",
-					 col, RelationGetRelationName(onerel));
-			vacattrstats[tcnt] = examine_attribute(onerel, i + 1);
-			if (vacattrstats[tcnt] != NULL)
-				tcnt++;
-		}
-		attr_cnt = tcnt;
-	}
-	else
-	{
-		vacattrstats = (VacAttrStats **) palloc(attr_cnt *
-												sizeof(VacAttrStats *));
-		tcnt = 0;
-		for (i = 0; i < attr_cnt; i++)
-		{
-			vacattrstats[tcnt] = examine_attribute(onerel, i + 1);
-			if (vacattrstats[tcnt] != NULL)
-				tcnt++;
-		}
-		attr_cnt = tcnt;
-	}
-
-	/*
-	 * Quit if no analyzable columns
-	 */
-	if (attr_cnt <= 0)
-	{
-		relation_close(onerel, NoLock);
-		return;
-	}
-
-	/*
-	 * 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.
-	 */
-	targrows = 100;
-	for (i = 0; i < attr_cnt; i++)
-	{
-		if (targrows < vacattrstats[i]->minrows)
-			targrows = vacattrstats[i]->minrows;
-	}
-
-	/*
-	 * Acquire the sample rows
-	 */
-	rows = (HeapTuple *) palloc(targrows * sizeof(HeapTuple));
-	numrows = acquire_sample_rows(onerel, rows, targrows, &totalrows);
-
-	/*
-	 * If we are running a standalone ANALYZE, update pages/tuples stats
-	 * in pg_class.  We have the accurate page count from heap_beginscan,
-	 * but only an approximate number of tuples; therefore, if we are part
-	 * of VACUUM ANALYZE do *not* overwrite the accurate count already
-	 * inserted by VACUUM.
-	 */
-	if (!vacstmt->vacuum)
-		vac_update_relstats(RelationGetRelid(onerel),
-							onerel->rd_nblocks,
-							totalrows,
-							RelationGetForm(onerel)->relhasindex);
-
-	/*
-	 * 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++)
-		{
-			switch (vacattrstats[i]->algcode)
-			{
-				case ALG_MINIMAL:
-					compute_minimal_stats(vacattrstats[i],
-										  onerel->rd_att, totalrows,
-										  rows, numrows);
-					break;
-				case ALG_SCALAR:
-					compute_scalar_stats(vacattrstats[i],
-										 onerel->rd_att, totalrows,
-										 rows, numrows);
-					break;
-			}
-			MemoryContextResetAndDeleteChildren(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(relid, attr_cnt, vacattrstats);
-	}
-
-	/*
-	 * 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.)
-	 */
-	relation_close(onerel, NoLock);
-}
-
-/*
- * 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.
- */
-static VacAttrStats *
-examine_attribute(Relation onerel, int attnum)
-{
-	Form_pg_attribute attr = onerel->rd_att->attrs[attnum - 1];
-	Operator	func_operator;
-	Oid			oprrest;
-	HeapTuple	typtuple;
-	Oid			eqopr = InvalidOid;
-	Oid			eqfunc = InvalidOid;
-	Oid			ltopr = InvalidOid;
-	VacAttrStats *stats;
-
-	/* Don't analyze column if user has specified not to */
-	if (attr->attstattarget <= 0)
-		return NULL;
-
-	/* If column has no "=" operator, we can't do much of anything */
-	func_operator = compatible_oper(makeList1(makeString("=")),
-									attr->atttypid,
-									attr->atttypid,
-									true);
-	if (func_operator != NULL)
-	{
-		oprrest = ((Form_pg_operator) GETSTRUCT(func_operator))->oprrest;
-		if (oprrest == F_EQSEL)
-		{
-			eqopr = oprid(func_operator);
-			eqfunc = oprfuncid(func_operator);
-		}
-		ReleaseSysCache(func_operator);
-	}
-	if (!OidIsValid(eqfunc))
-		return NULL;
-
-	/*
-	 * If we have "=" then we're at least able to do the minimal
-	 * algorithm, so start filling in a VacAttrStats struct.
-	 */
-	stats = (VacAttrStats *) palloc(sizeof(VacAttrStats));
-	MemSet(stats, 0, sizeof(VacAttrStats));
-	stats->attnum = attnum;
-	stats->attr = (Form_pg_attribute) palloc(ATTRIBUTE_TUPLE_SIZE);
-	memcpy(stats->attr, attr, ATTRIBUTE_TUPLE_SIZE);
-	typtuple = SearchSysCache(TYPEOID,
-							  ObjectIdGetDatum(attr->atttypid),
-							  0, 0, 0);
-	if (!HeapTupleIsValid(typtuple))
-		elog(ERROR, "cache lookup of type %u failed", attr->atttypid);
-	stats->attrtype = (Form_pg_type) palloc(sizeof(FormData_pg_type));
-	memcpy(stats->attrtype, GETSTRUCT(typtuple), sizeof(FormData_pg_type));
-	ReleaseSysCache(typtuple);
-	stats->eqopr = eqopr;
-	stats->eqfunc = eqfunc;
-
-	/* Is there a "<" operator with suitable semantics? */
-	func_operator = compatible_oper(makeList1(makeString("<")),
-									attr->atttypid,
-									attr->atttypid,
-									true);
-	if (func_operator != NULL)
-	{
-		oprrest = ((Form_pg_operator) GETSTRUCT(func_operator))->oprrest;
-		if (oprrest == F_SCALARLTSEL)
-			ltopr = oprid(func_operator);
-		ReleaseSysCache(func_operator);
-	}
-	stats->ltopr = ltopr;
-
-	/*
-	 * Determine the algorithm to use (this will get more complicated
-	 * later)
-	 */
-	if (OidIsValid(ltopr))
-	{
-		/* Seems to be a scalar datatype */
-		stats->algcode = ALG_SCALAR;
-		/*--------------------
-		 * 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 = 1 million rows, we obtain
-		 *		r = 305.82 * k
-		 * Note that because of the log function, the dependence on n is
-		 * quite weak; even at n = 1 billion, a 300*k sample gives <= 0.59
-		 * 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->algcode = ALG_MINIMAL;
-		/* Might as well use the same minrows as above */
-		stats->minrows = 300 * attr->attstattarget;
-	}
-
-	return stats;
-}
-
-/*
- * acquire_sample_rows -- acquire a random sample of rows from the table
- *
- * Up to targrows rows are collected (if there are fewer than that many
- * rows in the table, all rows are collected).	When the table is larger
- * than targrows, a truly random sample is collected: every row has an
- * equal chance of ending up in the final sample.
- *
- * We also estimate the total number of rows in the table, and return that
- * into *totalrows.
- *
- * The returned list of tuples is in order by physical position in the table.
- * (We will rely on this later to derive correlation estimates.)
- */
-static int
-acquire_sample_rows(Relation onerel, HeapTuple *rows, int targrows,
-					double *totalrows)
-{
-	int			numrows = 0;
-	HeapScanDesc scan;
-	HeapTuple	tuple;
-	ItemPointer lasttuple;
-	BlockNumber lastblock,
-				estblock;
-	OffsetNumber lastoffset;
-	int			numest;
-	double		tuplesperpage;
-	double		t;
-	double		rstate;
-
-	Assert(targrows > 1);
-
-	/*
-	 * Do a simple linear scan until we reach the target number of rows.
-	 */
-	scan = heap_beginscan(onerel, SnapshotNow, 0, NULL);
-	while ((tuple = heap_getnext(scan, ForwardScanDirection)) != NULL)
-	{
-		rows[numrows++] = heap_copytuple(tuple);
-		if (numrows >= targrows)
-			break;
-		CHECK_FOR_INTERRUPTS();
-	}
-	heap_endscan(scan);
-
-	/*
-	 * If we ran out of tuples then we're done, no matter how few we
-	 * collected.  No sort is needed, since they're already in order.
-	 */
-	if (!HeapTupleIsValid(tuple))
-	{
-		*totalrows = (double) numrows;
-		return numrows;
-	}
-
-	/*
-	 * Otherwise, start replacing tuples in the sample until we reach the
-	 * end of the relation.  This algorithm is from Jeff Vitter's paper
-	 * (see full citation below).  It works by repeatedly computing the
-	 * number of the next tuple we want to fetch, which will replace a
-	 * randomly chosen element of the reservoir (current set of tuples).
-	 * At all times the reservoir is a true random sample of the tuples
-	 * we've passed over so far, so when we fall off the end of the
-	 * relation we're done.
-	 *
-	 * A slight difficulty is that since we don't want to fetch tuples or
-	 * even pages that we skip over, it's not possible to fetch *exactly*
-	 * the N'th tuple at each step --- we don't know how many valid tuples
-	 * are on the skipped pages.  We handle this by assuming that the
-	 * average number of valid tuples/page on the pages already scanned
-	 * over holds good for the rest of the relation as well; this lets us
-	 * estimate which page the next tuple should be on and its position in
-	 * the page.  Then we fetch the first valid tuple at or after that
-	 * position, being careful not to use the same tuple twice.  This
-	 * approach should still give a good random sample, although it's not
-	 * perfect.
-	 */
-	lasttuple = &(rows[numrows - 1]->t_self);
-	lastblock = ItemPointerGetBlockNumber(lasttuple);
-	lastoffset = ItemPointerGetOffsetNumber(lasttuple);
-
-	/*
-	 * If possible, estimate tuples/page using only completely-scanned
-	 * pages.
-	 */
-	for (numest = numrows; numest > 0; numest--)
-	{
-		if (ItemPointerGetBlockNumber(&(rows[numest - 1]->t_self)) != lastblock)
-			break;
-	}
-	if (numest == 0)
-	{
-		numest = numrows;		/* don't have a full page? */
-		estblock = lastblock + 1;
-	}
-	else
-		estblock = lastblock;
-	tuplesperpage = (double) numest / (double) estblock;
-
-	t = (double) numrows;		/* t is the # of records processed so far */
-	rstate = init_selection_state(targrows);
-	for (;;)
-	{
-		double		targpos;
-		BlockNumber targblock;
-		Buffer		targbuffer;
-		Page		targpage;
-		OffsetNumber targoffset,
-					maxoffset;
-
-		CHECK_FOR_INTERRUPTS();
-
-		t = select_next_random_record(t, targrows, &rstate);
-		/* Try to read the t'th record in the table */
-		targpos = t / tuplesperpage;
-		targblock = (BlockNumber) targpos;
-		targoffset = ((int) ((targpos - targblock) * tuplesperpage)) +
-			FirstOffsetNumber;
-		/* Make sure we are past the last selected record */
-		if (targblock <= lastblock)
-		{
-			targblock = lastblock;
-			if (targoffset <= lastoffset)
-				targoffset = lastoffset + 1;
-		}
-		/* Loop to find first valid record at or after given position */
-pageloop:;
-
-		/*
-		 * Have we fallen off the end of the relation?	(We rely on
-		 * heap_beginscan to have updated rd_nblocks.)
-		 */
-		if (targblock >= onerel->rd_nblocks)
-			break;
-
-		/*
-		 * We must maintain a pin on the target page's buffer to ensure
-		 * that the maxoffset value stays good (else concurrent VACUUM
-		 * might delete tuples out from under us).	Hence, pin the page
-		 * until we are done looking at it.  We don't maintain a lock on
-		 * the page, so tuples could get added to it, but we ignore such
-		 * tuples.
-		 */
-		targbuffer = ReadBuffer(onerel, targblock);
-		if (!BufferIsValid(targbuffer))
-			elog(ERROR, "acquire_sample_rows: ReadBuffer(%s,%u) failed",
-				 RelationGetRelationName(onerel), targblock);
-		LockBuffer(targbuffer, BUFFER_LOCK_SHARE);
-		targpage = BufferGetPage(targbuffer);
-		maxoffset = PageGetMaxOffsetNumber(targpage);
-		LockBuffer(targbuffer, BUFFER_LOCK_UNLOCK);
-
-		for (;;)
-		{
-			HeapTupleData targtuple;
-			Buffer		tupbuffer;
-
-			if (targoffset > maxoffset)
-			{
-				/* Fell off end of this page, try next */
-				ReleaseBuffer(targbuffer);
-				targblock++;
-				targoffset = FirstOffsetNumber;
-				goto pageloop;
-			}
-			ItemPointerSet(&targtuple.t_self, targblock, targoffset);
-			if (heap_fetch(onerel, SnapshotNow, &targtuple, &tupbuffer,
-						   false, NULL))
-			{
-				/*
-				 * Found a suitable tuple, so save it, replacing one old
-				 * tuple at random
-				 */
-				int			k = (int) (targrows * random_fract());
-
-				Assert(k >= 0 && k < targrows);
-				heap_freetuple(rows[k]);
-				rows[k] = heap_copytuple(&targtuple);
-				/* this releases the second pin acquired by heap_fetch: */
-				ReleaseBuffer(tupbuffer);
-				/* this releases the initial pin: */
-				ReleaseBuffer(targbuffer);
-				lastblock = targblock;
-				lastoffset = targoffset;
-				break;
-			}
-			/* this tuple is dead, so advance to next one on same page */
-			targoffset++;
-		}
-	}
-
-	/*
-	 * Now we need to sort the collected tuples by position (itempointer).
-	 */
-	qsort((void *) rows, numrows, sizeof(HeapTuple), compare_rows);
-
-	/*
-	 * Estimate total number of valid rows in relation.
-	 */
-	*totalrows = floor((double) onerel->rd_nblocks * tuplesperpage + 0.5);
-
-	return numrows;
-}
-
-/* Select a random value R uniformly distributed in 0 < R < 1 */
-static double
-random_fract(void)
-{
-	long		z;
-
-	/* random() can produce endpoint values, try again if so */
-	do
-	{
-		z = random();
-	} while (!(z > 0 && z < MAX_RANDOM_VALUE));
-	return (double) z / (double) MAX_RANDOM_VALUE;
-}
-
-/*
- * These two routines embody Algorithm Z from "Random sampling with a
- * reservoir" by Jeffrey S. Vitter, in ACM Trans. Math. Softw. 11, 1
- * (Mar. 1985), Pages 37-57.  While Vitter describes his algorithm in terms
- * of the count S of records to skip before processing another record,
- * it is convenient to work primarily with t, the index (counting from 1)
- * of the last record processed and next record to process.  The only extra
- * state needed between calls is W, a random state variable.
- *
- * Note: the original algorithm defines t, S, numer, and denom as integers.
- * Here we express them as doubles to avoid overflow if the number of rows
- * in the table exceeds INT_MAX.  The algorithm should work as long as the
- * row count does not become so large that it is not represented accurately
- * in a double (on IEEE-math machines this would be around 2^52 rows).
- *
- * init_selection_state computes the initial W value.
- *
- * Given that we've already processed t records (t >= n),
- * select_next_random_record determines the number of the next record to
- * process.
- */
-static double
-init_selection_state(int n)
-{
-	/* Initial value of W (for use when Algorithm Z is first applied) */
-	return exp(-log(random_fract()) / n);
-}
-
-static double
-select_next_random_record(double t, int n, double *stateptr)
-{
-	/* The magic constant here is T from Vitter's paper */
-	if (t <= (22.0 * n))
-	{
-		/* Process records using Algorithm X until t is large enough */
-		double		V,
-					quot;
-
-		V = random_fract();		/* Generate V */
-		t += 1;
-		quot = (t - (double) n) / t;
-		/* Find min S satisfying (4.1) */
-		while (quot > V)
-		{
-			t += 1;
-			quot *= (t - (double) n) / t;
-		}
-	}
-	else
-	{
-		/* Now apply Algorithm Z */
-		double		W = *stateptr;
-		double		term = t - (double) n + 1;
-		double		S;
-
-		for (;;)
-		{
-			double		numer,
-						numer_lim,
-						denom;
-			double		U,
-						X,
-						lhs,
-						rhs,
-						y,
-						tmp;
-
-			/* Generate U and X */
-			U = random_fract();
-			X = t * (W - 1.0);
-			S = floor(X);		/* S is tentatively set to floor(X) */
-			/* Test if U <= h(S)/cg(X) in the manner of (6.3) */
-			tmp = (t + 1) / term;
-			lhs = exp(log(((U * tmp * tmp) * (term + S)) / (t + X)) / n);
-			rhs = (((t + X) / (term + S)) * term) / t;
-			if (lhs <= rhs)
-			{
-				W = rhs / lhs;
-				break;
-			}
-			/* Test if U <= f(S)/cg(X) */
-			y = (((U * (t + 1)) / term) * (t + S + 1)) / (t + X);
-			if ((double) n < S)
-			{
-				denom = t;
-				numer_lim = term + S;
-			}
-			else
-			{
-				denom = t - (double) n + S;
-				numer_lim = t + 1;
-			}
-			for (numer = t + S; numer >= numer_lim; numer -= 1)
-			{
-				y *= numer / denom;
-				denom -= 1;
-			}
-			W = exp(-log(random_fract()) / n);	/* Generate W in advance */
-			if (exp(log(y) / n) <= (t + X) / t)
-				break;
-		}
-		t += S + 1;
-		*stateptr = W;
-	}
-	return t;
-}
-
-/*
- * qsort comparator for sorting rows[] array
- */
-static int
-compare_rows(const void *a, const void *b)
-{
-	HeapTuple	ha = *(HeapTuple *) a;
-	HeapTuple	hb = *(HeapTuple *) b;
-	BlockNumber ba = ItemPointerGetBlockNumber(&ha->t_self);
-	OffsetNumber oa = ItemPointerGetOffsetNumber(&ha->t_self);
-	BlockNumber bb = ItemPointerGetBlockNumber(&hb->t_self);
-	OffsetNumber ob = ItemPointerGetOffsetNumber(&hb->t_self);
-
-	if (ba < bb)
-		return -1;
-	if (ba > bb)
-		return 1;
-	if (oa < ob)
-		return -1;
-	if (oa > ob)
-		return 1;
-	return 0;
-}
-
-
-/*
- *	compute_minimal_stats() -- compute minimal column statistics
- *
- *	We use this when we can find only an "=" operator for the datatype.
- *
- *	We determine the fraction of non-null rows, the average width, the
- *	most common values, and the (estimated) number of distinct values.
- *
- *	The most common values are determined by brute force: we keep a list
- *	of previously seen values, ordered by number of times seen, as we scan
- *	the samples.  A newly seen value is inserted just after the last
- *	multiply-seen value, causing the bottommost (oldest) singly-seen value
- *	to drop off the list.  The accuracy of this method, and also its cost,
- *	depend mainly on the length of the list we are willing to keep.
- */
-static void
-compute_minimal_stats(VacAttrStats *stats,
-					  TupleDesc tupDesc, double totalrows,
-					  HeapTuple *rows, int numrows)
-{
-	int			i;
-	int			null_cnt = 0;
-	int			nonnull_cnt = 0;
-	int			toowide_cnt = 0;
-	double		total_width = 0;
-	bool		is_varlena = (!stats->attr->attbyval &&
-							  stats->attr->attlen == -1);
-	FmgrInfo	f_cmpeq;
-	typedef struct
-	{
-		Datum		value;
-		int			count;
-	} TrackItem;
-	TrackItem  *track;
-	int			track_cnt,
-				track_max;
-	int			num_mcv = stats->attr->attstattarget;
-
-	/*
-	 * We track up to 2*n values for an n-element MCV list; but at least
-	 * 10
-	 */
-	track_max = 2 * num_mcv;
-	if (track_max < 10)
-		track_max = 10;
-	track = (TrackItem *) palloc(track_max * sizeof(TrackItem));
-	track_cnt = 0;
-
-	fmgr_info(stats->eqfunc, &f_cmpeq);
-
-	for (i = 0; i < numrows; i++)
-	{
-		HeapTuple	tuple = rows[i];
-		Datum		value;
-		bool		isnull;
-		bool		match;
-		int			firstcount1,
-					j;
-
-		CHECK_FOR_INTERRUPTS();
-
-		value = heap_getattr(tuple, stats->attnum, tupDesc, &isnull);
-
-		/* Check for null/nonnull */
-		if (isnull)
-		{
-			null_cnt++;
-			continue;
-		}
-		nonnull_cnt++;
-
-		/*
-		 * If it's a varlena field, add up widths for average width
-		 * calculation.  Note that if the value is toasted, we use the
-		 * toasted width.  We don't bother with this calculation if it's a
-		 * fixed-width type.
-		 */
-		if (is_varlena)
-		{
-			total_width += VARSIZE(DatumGetPointer(value));
-
-			/*
-			 * If the value is toasted, we want to detoast it just once to
-			 * avoid repeated detoastings and resultant excess memory
-			 * usage during the comparisons.  Also, check to see if the
-			 * value is excessively wide, and if so don't detoast at all
-			 * --- just ignore the value.
-			 */
-			if (toast_raw_datum_size(value) > WIDTH_THRESHOLD)
-			{
-				toowide_cnt++;
-				continue;
-			}
-			value = PointerGetDatum(PG_DETOAST_DATUM(value));
-		}
-
-		/*
-		 * See if the value matches anything we're already tracking.
-		 */
-		match = false;
-		firstcount1 = track_cnt;
-		for (j = 0; j < track_cnt; j++)
-		{
-			if (DatumGetBool(FunctionCall2(&f_cmpeq, value, track[j].value)))
-			{
-				match = true;
-				break;
-			}
-			if (j < firstcount1 && track[j].count == 1)
-				firstcount1 = j;
-		}
-
-		if (match)
-		{
-			/* Found a match */
-			track[j].count++;
-			/* This value may now need to "bubble up" in the track list */
-			while (j > 0 && track[j].count > track[j - 1].count)
-			{
-				swapDatum(track[j].value, track[j - 1].value);
-				swapInt(track[j].count, track[j - 1].count);
-				j--;
-			}
-		}
-		else
-		{
-			/* No match.  Insert at head of count-1 list */
-			if (track_cnt < track_max)
-				track_cnt++;
-			for (j = track_cnt - 1; j > firstcount1; j--)
-			{
-				track[j].value = track[j - 1].value;
-				track[j].count = track[j - 1].count;
-			}
-			if (firstcount1 < track_cnt)
-			{
-				track[firstcount1].value = value;
-				track[firstcount1].count = 1;
-			}
-		}
-	}
-
-	/* We can only compute valid stats if we found some non-null values. */
-	if (nonnull_cnt > 0)
-	{
-		int			nmultiple,
-					summultiple;
-
-		stats->stats_valid = true;
-		/* Do the simple null-frac and width stats */
-		stats->stanullfrac = (double) null_cnt / (double) numrows;
-		if (is_varlena)
-			stats->stawidth = total_width / (double) nonnull_cnt;
-		else
-			stats->stawidth = stats->attrtype->typlen;
-
-		/* Count the number of values we found multiple times */
-		summultiple = 0;
-		for (nmultiple = 0; nmultiple < track_cnt; nmultiple++)
-		{
-			if (track[nmultiple].count == 1)
-				break;
-			summultiple += track[nmultiple].count;
-		}
-
-		if (nmultiple == 0)
-		{
-			/* If we found no repeated values, assume it's a unique column */
-			stats->stadistinct = -1.0;
-		}
-		else if (track_cnt < track_max && toowide_cnt == 0 &&
-				 nmultiple == track_cnt)
-		{
-			/*
-			 * Our track list includes every value in the sample, and
-			 * every value appeared more than once.  Assume the column has
-			 * just these values.
-			 */
-			stats->stadistinct = track_cnt;
-		}
-		else
-		{
-			/*----------
-			 * Estimate the number of distinct values using the estimator
-			 * proposed by 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) numrows * (double) d;
-			denom = (double) (numrows - f1) +
-				(double) f1 * (double) numrows / 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) numrows / ndistinct;
-			/* set minimum threshold count to store a value */
-			mincount = avgcount * 1.25;
-			if (mincount < 2)
-				mincount = 2;
-			if (num_mcv > track_cnt)
-				num_mcv = track_cnt;
-			for (i = 0; i < num_mcv; i++)
-			{
-				if (track[i].count < mincount)
-				{
-					num_mcv = i;
-					break;
-				}
-			}
-		}
-
-		/* Generate MCV slot entry */
-		if (num_mcv > 0)
-		{
-			MemoryContext old_context;
-			Datum	   *mcv_values;
-			float4	   *mcv_freqs;
-
-			/* Must copy the target values into anl_context */
-			old_context = MemoryContextSwitchTo(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->attr->attbyval,
-										  stats->attr->attlen);
-				mcv_freqs[i] = (double) track[i].count / (double) numrows;
-			}
-			MemoryContextSwitchTo(old_context);
-
-			stats->stakind[0] = STATISTIC_KIND_MCV;
-			stats->staop[0] = stats->eqopr;
-			stats->stanumbers[0] = mcv_freqs;
-			stats->numnumbers[0] = num_mcv;
-			stats->stavalues[0] = mcv_values;
-			stats->numvalues[0] = num_mcv;
-		}
-	}
-
-	/* We don't need to bother cleaning up any of our temporary palloc's */
-}
-
-
-/*
- *	compute_scalar_stats() -- compute column statistics
- *
- *	We use this when we can find "=" and "<" operators for the datatype.
- *
- *	We determine the fraction of non-null rows, the average width, the
- *	most common values, the (estimated) number of distinct values, the
- *	distribution histogram, and the correlation of physical to logical order.
- *
- *	The desired stats can be determined fairly easily after sorting the
- *	data values into order.
- */
-static void
-compute_scalar_stats(VacAttrStats *stats,
-					 TupleDesc tupDesc, double totalrows,
-					 HeapTuple *rows, int numrows)
-{
-	int			i;
-	int			null_cnt = 0;
-	int			nonnull_cnt = 0;
-	int			toowide_cnt = 0;
-	double		total_width = 0;
-	bool		is_varlena = (!stats->attr->attbyval &&
-							  stats->attr->attlen == -1);
-	double		corr_xysum;
-	RegProcedure cmpFn;
-	SortFunctionKind cmpFnKind;
-	FmgrInfo	f_cmpfn;
-	ScalarItem *values;
-	int			values_cnt = 0;
-	int		   *tupnoLink;
-	ScalarMCVItem *track;
-	int			track_cnt = 0;
-	int			num_mcv = stats->attr->attstattarget;
-	int			num_bins = stats->attr->attstattarget;
-
-	values = (ScalarItem *) palloc(numrows * sizeof(ScalarItem));
-	tupnoLink = (int *) palloc(numrows * sizeof(int));
-	track = (ScalarMCVItem *) palloc(num_mcv * sizeof(ScalarMCVItem));
-
-	SelectSortFunction(stats->ltopr, &cmpFn, &cmpFnKind);
-	fmgr_info(cmpFn, &f_cmpfn);
-
-	/* Initial scan to find sortable values */
-	for (i = 0; i < numrows; i++)
-	{
-		HeapTuple	tuple = rows[i];
-		Datum		value;
-		bool		isnull;
-
-		CHECK_FOR_INTERRUPTS();
-
-		value = heap_getattr(tuple, stats->attnum, tupDesc, &isnull);
-
-		/* Check for null/nonnull */
-		if (isnull)
-		{
-			null_cnt++;
-			continue;
-		}
-		nonnull_cnt++;
-
-		/*
-		 * If it's a varlena field, add up widths for average width
-		 * calculation.  Note that if the value is toasted, we use the
-		 * toasted width.  We don't bother with this calculation if it's a
-		 * fixed-width type.
-		 */
-		if (is_varlena)
-		{
-			total_width += VARSIZE(DatumGetPointer(value));
-
-			/*
-			 * If the value is toasted, we want to detoast it just once to
-			 * avoid repeated detoastings and resultant excess memory
-			 * usage during the comparisons.  Also, check to see if the
-			 * value is excessively wide, and if so don't detoast at all
-			 * --- just ignore the value.
-			 */
-			if (toast_raw_datum_size(value) > WIDTH_THRESHOLD)
-			{
-				toowide_cnt++;
-				continue;
-			}
-			value = PointerGetDatum(PG_DETOAST_DATUM(value));
-		}
-
-		/* Add it to the list to be sorted */
-		values[values_cnt].value = value;
-		values[values_cnt].tupno = values_cnt;
-		tupnoLink[values_cnt] = values_cnt;
-		values_cnt++;
-	}
-
-	/* We can only compute valid stats if we found some sortable values. */
-	if (values_cnt > 0)
-	{
-		int			ndistinct,	/* # distinct values in sample */
-					nmultiple,	/* # that appear multiple times */
-					num_hist,
-					dups_cnt;
-		int			slot_idx = 0;
-
-		/* Sort the collected values */
-		datumCmpFn = &f_cmpfn;
-		datumCmpFnKind = cmpFnKind;
-		datumCmpTupnoLink = tupnoLink;
-		qsort((void *) values, values_cnt,
-			  sizeof(ScalarItem), compare_scalars);
-
-		/*
-		 * Now scan the values in order, find the most common ones, and
-		 * also accumulate ordering-correlation statistics.
-		 *
-		 * To determine which are most common, we first have to count the
-		 * number of duplicates of each value.	The duplicates are
-		 * adjacent in the sorted list, so a brute-force approach is to
-		 * compare successive datum values until we find two that are not
-		 * equal. However, that requires N-1 invocations of the datum
-		 * comparison routine, which are completely redundant with work
-		 * that was done during the sort.  (The sort algorithm must at
-		 * some point have compared each pair of items that are adjacent
-		 * in the sorted order; otherwise it could not know that it's
-		 * ordered the pair correctly.) We exploit this by having
-		 * compare_scalars remember the highest tupno index that each
-		 * ScalarItem has been found equal to.	At the end of the sort, a
-		 * ScalarItem's tupnoLink will still point to itself if and only
-		 * if it is the last item of its group of duplicates (since the
-		 * group will be ordered by tupno).
-		 */
-		corr_xysum = 0;
-		ndistinct = 0;
-		nmultiple = 0;
-		dups_cnt = 0;
-		for (i = 0; i < values_cnt; i++)
-		{
-			int			tupno = values[i].tupno;
-
-			corr_xysum += ((double) i) * ((double) tupno);
-			dups_cnt++;
-			if (tupnoLink[tupno] == tupno)
-			{
-				/* Reached end of duplicates of this value */
-				ndistinct++;
-				if (dups_cnt > 1)
-				{
-					nmultiple++;
-					if (track_cnt < num_mcv ||
-						dups_cnt > track[track_cnt - 1].count)
-					{
-						/*
-						 * Found a new item for the mcv list; find its
-						 * position, bubbling down old items if needed.
-						 * Loop invariant is that j points at an empty/
-						 * replaceable slot.
-						 */
-						int			j;
-
-						if (track_cnt < num_mcv)
-							track_cnt++;
-						for (j = track_cnt - 1; j > 0; j--)
-						{
-							if (dups_cnt <= track[j - 1].count)
-								break;
-							track[j].count = track[j - 1].count;
-							track[j].first = track[j - 1].first;
-						}
-						track[j].count = dups_cnt;
-						track[j].first = i + 1 - dups_cnt;
-					}
-				}
-				dups_cnt = 0;
-			}
-		}
-
-		stats->stats_valid = true;
-		/* Do the simple null-frac and width stats */
-		stats->stanullfrac = (double) null_cnt / (double) numrows;
-		if (is_varlena)
-			stats->stawidth = total_width / (double) nonnull_cnt;
-		else
-			stats->stawidth = stats->attrtype->typlen;
-
-		if (nmultiple == 0)
-		{
-			/* If we found no repeated values, assume it's a unique column */
-			stats->stadistinct = -1.0;
-		}
-		else if (toowide_cnt == 0 && nmultiple == ndistinct)
-		{
-			/*
-			 * Every value in the sample appeared more than once.  Assume
-			 * the column has just these values.
-			 */
-			stats->stadistinct = ndistinct;
-		}
-		else
-		{
-			/*----------
-			 * Estimate the number of distinct values using the estimator
-			 * proposed by 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) numrows * (double) d;
-			denom = (double) (numrows - f1) +
-				(double) f1 * (double) numrows / 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.
-		 */
-		if (track_cnt == ndistinct && toowide_cnt == 0 &&
-			stats->stadistinct > 0 &&
-			track_cnt <= num_mcv)
-		{
-			/* Track list includes all values seen, and all will fit */
-			num_mcv = track_cnt;
-		}
-		else
-		{
-			double		ndistinct = stats->stadistinct;
-			double		avgcount,
-						mincount,
-						maxmincount;
-
-			if (ndistinct < 0)
-				ndistinct = -ndistinct * totalrows;
-			/* estimate # of occurrences in sample of a typical value */
-			avgcount = (double) numrows / ndistinct;
-			/* set minimum threshold count to store a value */
-			mincount = avgcount * 1.25;
-			if (mincount < 2)
-				mincount = 2;
-			/* don't let threshold exceed 1/K, however */
-			maxmincount = (double) numrows / (double) num_bins;
-			if (mincount > maxmincount)
-				mincount = maxmincount;
-			if (num_mcv > track_cnt)
-				num_mcv = track_cnt;
-			for (i = 0; i < num_mcv; i++)
-			{
-				if (track[i].count < mincount)
-				{
-					num_mcv = i;
-					break;
-				}
-			}
-		}
-
-		/* Generate MCV slot entry */
-		if (num_mcv > 0)
-		{
-			MemoryContext old_context;
-			Datum	   *mcv_values;
-			float4	   *mcv_freqs;
-
-			/* Must copy the target values into anl_context */
-			old_context = MemoryContextSwitchTo(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->attr->attbyval,
-										  stats->attr->attlen);
-				mcv_freqs[i] = (double) track[i].count / (double) numrows;
-			}
-			MemoryContextSwitchTo(old_context);
-
-			stats->stakind[slot_idx] = STATISTIC_KIND_MCV;
-			stats->staop[slot_idx] = stats->eqopr;
-			stats->stanumbers[slot_idx] = mcv_freqs;
-			stats->numnumbers[slot_idx] = num_mcv;
-			stats->stavalues[slot_idx] = mcv_values;
-			stats->numvalues[slot_idx] = num_mcv;
-			slot_idx++;
-		}
-
-		/*
-		 * Generate a histogram slot entry if there are at least two
-		 * distinct values not accounted for in the MCV list.  (This
-		 * ensures the histogram won't collapse to empty or a singleton.)
-		 */
-		num_hist = ndistinct - num_mcv;
-		if (num_hist > num_bins)
-			num_hist = num_bins + 1;
-		if (num_hist >= 2)
-		{
-			MemoryContext old_context;
-			Datum	   *hist_values;
-			int			nvals;
-
-			/* Sort the MCV items into position order to speed next loop */
-			qsort((void *) track, num_mcv,
-				  sizeof(ScalarMCVItem), compare_mcvs);
-
-			/*
-			 * Collapse out the MCV items from the values[] array.
-			 *
-			 * Note we destroy the values[] array here... but we don't need
-			 * it for anything more.  We do, however, still need
-			 * values_cnt. nvals will be the number of remaining entries
-			 * in values[].
-			 */
-			if (num_mcv > 0)
-			{
-				int			src,
-							dest;
-				int			j;
-
-				src = dest = 0;
-				j = 0;			/* index of next interesting MCV item */
-				while (src < values_cnt)
-				{
-					int			ncopy;
-
-					if (j < num_mcv)
-					{
-						int			first = track[j].first;
-
-						if (src >= first)
-						{
-							/* advance past this MCV item */
-							src = first + track[j].count;
-							j++;
-							continue;
-						}
-						ncopy = first - src;
-					}
-					else
-						ncopy = values_cnt - src;
-					memmove(&values[dest], &values[src],
-							ncopy * sizeof(ScalarItem));
-					src += ncopy;
-					dest += ncopy;
-				}
-				nvals = dest;
-			}
-			else
-				nvals = values_cnt;
-			Assert(nvals >= num_hist);
-
-			/* Must copy the target values into anl_context */
-			old_context = MemoryContextSwitchTo(anl_context);
-			hist_values = (Datum *) palloc(num_hist * sizeof(Datum));
-			for (i = 0; i < num_hist; i++)
-			{
-				int			pos;
-
-				pos = (i * (nvals - 1)) / (num_hist - 1);
-				hist_values[i] = datumCopy(values[pos].value,
-										   stats->attr->attbyval,
-										   stats->attr->attlen);
-			}
-			MemoryContextSwitchTo(old_context);
-
-			stats->stakind[slot_idx] = STATISTIC_KIND_HISTOGRAM;
-			stats->staop[slot_idx] = stats->ltopr;
-			stats->stavalues[slot_idx] = hist_values;
-			stats->numvalues[slot_idx] = num_hist;
-			slot_idx++;
-		}
-
-		/* Generate a correlation entry if there are multiple values */
-		if (values_cnt > 1)
-		{
-			MemoryContext old_context;
-			float4	   *corrs;
-			double		corr_xsum,
-						corr_x2sum;
-
-			/* Must copy the target values into anl_context */
-			old_context = MemoryContextSwitchTo(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] = stats->ltopr;
-			stats->stanumbers[slot_idx] = corrs;
-			stats->numnumbers[slot_idx] = 1;
-			slot_idx++;
-		}
-	}
-
-	/* We don't need to bother cleaning up any of our temporary palloc's */
-}
-
-/*
- * qsort comparator for sorting ScalarItems
- *
- * Aside from sorting the items, we update the datumCmpTupnoLink[] array
- * whenever two ScalarItems are found to contain equal datums.	The array
- * is indexed by tupno; for each ScalarItem, it contains the highest
- * tupno that that item's datum has been found to be equal to.  This allows
- * us to avoid additional comparisons in compute_scalar_stats().
- */
-static int
-compare_scalars(const void *a, const void *b)
-{
-	Datum		da = ((ScalarItem *) a)->value;
-	int			ta = ((ScalarItem *) a)->tupno;
-	Datum		db = ((ScalarItem *) b)->value;
-	int			tb = ((ScalarItem *) b)->tupno;
-	int32		compare;
-
-	compare = ApplySortFunction(datumCmpFn, datumCmpFnKind,
-								da, false, db, false);
-	if (compare != 0)
-		return compare;
-
-	/*
-	 * The two datums are equal, so update datumCmpTupnoLink[].
-	 */
-	if (datumCmpTupnoLink[ta] < tb)
-		datumCmpTupnoLink[ta] = tb;
-	if (datumCmpTupnoLink[tb] < ta)
-		datumCmpTupnoLink[tb] = ta;
-
-	/*
-	 * For equal datums, sort by tupno
-	 */
-	return ta - tb;
-}
-
-/*
- * qsort comparator for sorting ScalarMCVItems by position
- */
-static int
-compare_mcvs(const void *a, const void *b)
-{
-	int			da = ((ScalarMCVItem *) a)->first;
-	int			db = ((ScalarMCVItem *) b)->first;
-
-	return da - db;
-}
-
-
-/*
- *	update_attstats() -- update attribute statistics for one relation
- *
- *		Statistics are stored in several places: the pg_class row for the
- *		relation has stats about the whole relation, and there is a
- *		pg_statistic row for each (non-system) attribute that has ever
- *		been analyzed.	The pg_class values are updated by VACUUM, not here.
- *
- *		pg_statistic rows are just added or updated normally.  This means
- *		that pg_statistic will probably contain some deleted rows at the
- *		completion of a vacuum cycle, unless it happens to get vacuumed last.
- *
- *		To keep things simple, we punt for pg_statistic, and don't try
- *		to compute or store rows for pg_statistic itself in pg_statistic.
- *		This could possibly be made to work, but it's not worth the trouble.
- *		Note analyze_rel() has seen to it that we won't come here when
- *		vacuuming pg_statistic itself.
- */
-static void
-update_attstats(Oid relid, int natts, VacAttrStats **vacattrstats)
-{
-	Relation	sd;
-	int			attno;
-
-	/*
-	 * We use an ExclusiveLock on pg_statistic to ensure that only one
-	 * backend is writing it at a time --- without that, we might have to
-	 * deal with concurrent updates here, and it's not worth the trouble.
-	 */
-	sd = heap_openr(StatisticRelationName, ExclusiveLock);
-
-	for (attno = 0; attno < natts; attno++)
-	{
-		VacAttrStats *stats = vacattrstats[attno];
-		FmgrInfo	out_function;
-		HeapTuple	stup,
-					oldtup;
-		int			i,
-					k,
-					n;
-		Datum		values[Natts_pg_statistic];
-		char		nulls[Natts_pg_statistic];
-		char		replaces[Natts_pg_statistic];
-		Relation	irelations[Num_pg_statistic_indices];
-
-		/* Ignore attr if we weren't able to collect stats */
-		if (!stats->stats_valid)
-			continue;
-
-		fmgr_info(stats->attrtype->typoutput, &out_function);
-
-		/*
-		 * Construct a new pg_statistic tuple
-		 */
-		for (i = 0; i < Natts_pg_statistic; ++i)
-		{
-			nulls[i] = ' ';
-			replaces[i] = 'r';
-		}
-
-		i = 0;
-		values[i++] = ObjectIdGetDatum(relid);	/* starelid */
-		values[i++] = Int16GetDatum(stats->attnum);		/* staattnum */
-		values[i++] = Float4GetDatum(stats->stanullfrac);		/* stanullfrac */
-		values[i++] = Int32GetDatum(stats->stawidth);	/* stawidth */
-		values[i++] = Float4GetDatum(stats->stadistinct);		/* stadistinct */
-		for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
-		{
-			values[i++] = Int16GetDatum(stats->stakind[k]);		/* stakindN */
-		}
-		for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
-		{
-			values[i++] = ObjectIdGetDatum(stats->staop[k]);	/* staopN */
-		}
-		for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
-		{
-			int			nnum = stats->numnumbers[k];
-
-			if (nnum > 0)
-			{
-				Datum	   *numdatums = (Datum *) palloc(nnum * sizeof(Datum));
-				ArrayType  *arry;
-
-				for (n = 0; n < nnum; n++)
-					numdatums[n] = Float4GetDatum(stats->stanumbers[k][n]);
-				/* XXX knows more than it should about type float4: */
-				arry = construct_array(numdatums, nnum,
-									   false, sizeof(float4), 'i');
-				values[i++] = PointerGetDatum(arry);	/* stanumbersN */
-			}
-			else
-			{
-				nulls[i] = 'n';
-				values[i++] = (Datum) 0;
-			}
-		}
-		for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
-		{
-			int			ntxt = stats->numvalues[k];
-
-			if (ntxt > 0)
-			{
-				Datum	   *txtdatums = (Datum *) palloc(ntxt * sizeof(Datum));
-				ArrayType  *arry;
-
-				for (n = 0; n < ntxt; n++)
-				{
-					/*
-					 * Convert data values to a text string to be inserted
-					 * into the text array.
-					 */
-					Datum		stringdatum;
-
-					stringdatum =
-						FunctionCall3(&out_function,
-									  stats->stavalues[k][n],
-							  ObjectIdGetDatum(stats->attrtype->typelem),
-								  Int32GetDatum(stats->attr->atttypmod));
-					txtdatums[n] = DirectFunctionCall1(textin, stringdatum);
-					pfree(DatumGetPointer(stringdatum));
-				}
-				/* XXX knows more than it should about type text: */
-				arry = construct_array(txtdatums, ntxt,
-									   false, -1, 'i');
-				values[i++] = PointerGetDatum(arry);	/* stavaluesN */
-			}
-			else
-			{
-				nulls[i] = 'n';
-				values[i++] = (Datum) 0;
-			}
-		}
-
-		/* Is there already a pg_statistic tuple for this attribute? */
-		oldtup = SearchSysCache(STATRELATT,
-								ObjectIdGetDatum(relid),
-								Int16GetDatum(stats->attnum),
-								0, 0);
-
-		if (HeapTupleIsValid(oldtup))
-		{
-			/* Yes, replace it */
-			stup = heap_modifytuple(oldtup,
-									sd,
-									values,
-									nulls,
-									replaces);
-			ReleaseSysCache(oldtup);
-			simple_heap_update(sd, &stup->t_self, stup);
-		}
-		else
-		{
-			/* No, insert new tuple */
-			stup = heap_formtuple(sd->rd_att, values, nulls);
-			simple_heap_insert(sd, stup);
-		}
-
-		/* update indices too */
-		CatalogOpenIndices(Num_pg_statistic_indices, Name_pg_statistic_indices,
-						   irelations);
-		CatalogIndexInsert(irelations, Num_pg_statistic_indices, sd, stup);
-		CatalogCloseIndices(Num_pg_statistic_indices, irelations);
-
-		heap_freetuple(stup);
-	}
-
-	/* close rel, but hold lock till upcoming commit */
-	heap_close(sd, NoLock);
-}