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postgresql/src/backend/commands/analyze.c

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/*-------------------------------------------------------------------------
*
* analyze.c
* the postgres statistics generator
*
* Portions Copyright (c) 1996-2003, PostgreSQL Global Development Group
* Portions Copyright (c) 1994, Regents of the University of California
*
*
* IDENTIFICATION
* $Header: /cvsroot/pgsql/src/backend/commands/analyze.c,v 1.62 2003/09/11 23:12:31 tgl 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/namespace.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 "parser/parse_relation.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 1024
/*
* 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)
/* Default statistics target (GUC parameter) */
int default_statistics_target = 10;
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;
int attr_cnt,
tcnt,
i;
VacAttrStats **vacattrstats;
int targrows,
numrows;
double totalrows;
HeapTuple *rows;
if (vacstmt->verbose)
elevel = INFO;
else
elevel = DEBUG2;
/*
* 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()) ||
(pg_database_ownercheck(MyDatabaseId, GetUserId()) && !onerel->rd_rel->relisshared)))
{
/* No need for a WARNING if we already complained during VACUUM */
if (!vacstmt->vacuum)
ereport(WARNING,
(errmsg("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)
ereport(WARNING,
(errmsg("skipping \"%s\" --- cannot ANALYZE indexes, views or special system tables",
RelationGetRelationName(onerel))));
relation_close(onerel, AccessShareLock);
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 (isOtherTempNamespace(RelationGetNamespace(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;
}
ereport(elevel,
(errmsg("analyzing \"%s.%s\"",
get_namespace_name(RelationGetNamespace(onerel)),
RelationGetRelationName(onerel))));
/*
* Determine which columns to analyze
*
* Note that system attributes are never analyzed.
*/
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));
i = attnameAttNum(onerel, col, false);
vacattrstats[tcnt] = examine_attribute(onerel, i);
if (vacattrstats[tcnt] != NULL)
tcnt++;
}
attr_cnt = tcnt;
}
else
{
attr_cnt = onerel->rd_att->natts;
/* +1 here is just to avoid palloc(0) with zero-column table */
vacattrstats = (VacAttrStats **) palloc((attr_cnt + 1) *
sizeof(VacAttrStats *));
tcnt = 0;
for (i = 1; i <= attr_cnt; i++)
{
vacattrstats[tcnt] = examine_attribute(onerel, i);
if (vacattrstats[tcnt] != NULL)
tcnt++;
}
attr_cnt = tcnt;
}
/*
* Quit if no analyzable columns
*/
if (attr_cnt <= 0)
{
relation_close(onerel, AccessShareLock);
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;
HeapTuple typtuple;
Oid eqopr = InvalidOid;
Oid eqfunc = InvalidOid;
Oid ltopr = InvalidOid;
VacAttrStats *stats;
/* Don't analyze dropped columns */
if (attr->attisdropped)
return NULL;
/* 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 = equality_oper(attr->atttypid, true);
if (func_operator != NULL)
{
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 *) palloc0(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 failed for type %u", 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;
/* If the attstattarget column is negative, use the default value */
if (stats->attr->attstattarget < 0)
stats->attr->attstattarget = default_statistics_target;
/* Is there a "<" operator with suitable semantics? */
func_operator = ordering_oper(attr->atttypid, true);
if (func_operator != NULL)
{
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 * stats->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 * stats->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;
ereport(elevel,
(errmsg("\"%s\": %u pages, %d rows sampled, %.0f estimated total rows",
RelationGetRelationName(onerel),
onerel->rd_nblocks, numrows, *totalrows)));
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, "ReadBuffer failed");
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);
/*
* Emit some interesting relation info
*/
ereport(elevel,
(errmsg("\"%s\": %u pages, %d rows sampled, %.0f estimated total rows",
RelationGetRelationName(onerel),
onerel->rd_nblocks, numrows, *totalrows)));
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);
bool is_varwidth = (!stats->attr->attbyval &&
stats->attr->attlen < 0);
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 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(DatumGetPointer(value));
/*
* If the value is toasted, we want to detoast it just once to
* avoid repeated detoastings and resultant excess memory
* usage during the comparisons. Also, check to see if the
* value is excessively wide, and if so don't detoast at all
* --- just ignore the value.
*/
if (toast_raw_datum_size(value) > WIDTH_THRESHOLD)
{
toowide_cnt++;
continue;
}
value = PointerGetDatum(PG_DETOAST_DATUM(value));
}
else if (is_varwidth)
{
/* must be cstring */
total_width += strlen(DatumGetCString(value)) + 1;
}
/*
* See if the value matches anything we're already tracking.
*/
match = false;
firstcount1 = track_cnt;
for (j = 0; j < track_cnt; j++)
{
if (DatumGetBool(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_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) 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);
bool is_varwidth = (!stats->attr->attbyval &&
stats->attr->attlen < 0);
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 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(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 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_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) 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.
*
* Note: if two backends concurrently try to analyze the same relation,
* the second one is likely to fail here with a "tuple concurrently
* updated" error. This is slightly annoying, but no real harm is done.
* We could prevent the problem by using a stronger lock on the
* relation for ANALYZE (ie, ShareUpdateExclusiveLock instead
* of AccessShareLock); but that cure seems worse than the disease,
* especially now that ANALYZE doesn't start a new transaction
* for each relation. The lock could be held for a long time...
*/
static void
update_attstats(Oid relid, int natts, VacAttrStats **vacattrstats)
{
Relation sd;
int attno;
sd = heap_openr(StatisticRelationName, RowExclusiveLock);
for (attno = 0; attno < natts; attno++)
{
VacAttrStats *stats = vacattrstats[attno];
HeapTuple stup,
oldtup;
int i,
k,
n;
Datum values[Natts_pg_statistic];
char nulls[Natts_pg_statistic];
char 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] = ' ';
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,
FLOAT4OID,
sizeof(float4), false, 'i');
values[i++] = PointerGetDatum(arry); /* stanumbersN */
}
else
{
nulls[i] = 'n';
values[i++] = (Datum) 0;
}
}
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->attr->atttypid,
stats->attrtype->typlen,
stats->attrtype->typbyval,
stats->attrtype->typalign);
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 indexes too */
CatalogUpdateIndexes(sd, stup);
heap_freetuple(stup);
}
heap_close(sd, RowExclusiveLock);
}
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