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pgindent run on all C files. Java run to follow. initdb/regression

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tests pass.
This commit is contained in:
Bruce Momjian
2001-10-25 05:50:21 +00:00
parent 59da2105d8
commit b81844b173
818 changed files with 21684 additions and 20491 deletions
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471
src/backend/commands/analyze.c
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@ -8,7 +8,7 @@
*
*
* IDENTIFICATION
* $Header: /cvsroot/pgsql/src/backend/commands/analyze.c,v 1.22 2001/07/05 19:33:35 tgl Exp $
* $Header: /cvsroot/pgsql/src/backend/commands/analyze.c,v 1.23 2001/10/25 05:49:23 momjian Exp $
*
*-------------------------------------------------------------------------
*/
@ -37,9 +37,11 @@
/*
* Analysis algorithms supported
*/
typedef enum {
typedef enum
{
ALG_MINIMAL = 1, /* Compute only most-common-values */
ALG_SCALAR /* Compute MCV, histogram, sort correlation */
ALG_SCALAR /* Compute MCV, histogram, sort
* correlation */
} AlgCode;
/*
@ -70,7 +72,10 @@ typedef struct
Oid eqfunc; /* and associated function */
Oid ltopr; /* '<' operator for datatype, if any */
/* These fields are filled in by the actual statistics-gathering routine */
/*
* 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 */
@ -101,7 +106,7 @@ typedef struct
#define swapDatum(a,b) do {Datum _tmp; _tmp=a; a=b; b=_tmp;} while(0)
static int MESSAGE_LEVEL;
static int MESSAGE_LEVEL;
/* context information for compare_scalars() */
static FmgrInfo *datumCmpFn;
@ -111,19 +116,19 @@ static int *datumCmpTupnoLink;
static VacAttrStats *examine_attribute(Relation onerel, int attnum);
static int acquire_sample_rows(Relation onerel, HeapTuple *rows,
int targrows, double *totalrows);
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 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);
TupleDesc tupDesc, double totalrows,
HeapTuple *rows, int numrows);
static void compute_scalar_stats(VacAttrStats *stats,
TupleDesc tupDesc, double totalrows,
HeapTuple *rows, int numrows);
TupleDesc tupDesc, double totalrows,
HeapTuple *rows, int numrows);
static void update_attstats(Oid relid, int natts, VacAttrStats **vacattrstats);
@ -154,8 +159,8 @@ analyze_rel(Oid relid, VacuumStmt *vacstmt)
* Begin a transaction for analyzing this relation.
*
* Note: All memory allocated during ANALYZE will live in
* TransactionCommandContext or a subcontext thereof, so it will
* all be released by transaction commit at the end of this routine.
* TransactionCommandContext or a subcontext thereof, so it will all
* be released by transaction commit at the end of this routine.
*/
StartTransactionCommand();
@ -191,14 +196,14 @@ analyze_rel(Oid relid, VacuumStmt *vacstmt)
ReleaseSysCache(tuple);
/*
* Open the class, getting only a read lock on it, and check permissions.
* Permissions check should match vacuum's check!
* Open the class, getting only a read lock on it, and check
* permissions. Permissions check should match vacuum's check!
*/
onerel = heap_open(relid, AccessShareLock);
if (! (pg_ownercheck(GetUserId(), RelationGetRelationName(onerel),
RELNAME) ||
(is_dbadmin(MyDatabaseId) && !onerel->rd_rel->relisshared)))
if (!(pg_ownercheck(GetUserId(), RelationGetRelationName(onerel),
RELNAME) ||
(is_dbadmin(MyDatabaseId) && !onerel->rd_rel->relisshared)))
{
/* No need for a notice if we already complained during VACUUM */
if (!vacstmt->vacuum)
@ -238,7 +243,7 @@ analyze_rel(Oid relid, VacuumStmt *vacstmt)
if (i >= attr_cnt)
elog(ERROR, "ANALYZE: there is no attribute %s in %s",
col, RelationGetRelationName(onerel));
vacattrstats[tcnt] = examine_attribute(onerel, i+1);
vacattrstats[tcnt] = examine_attribute(onerel, i + 1);
if (vacattrstats[tcnt] != NULL)
tcnt++;
}
@ -251,7 +256,7 @@ analyze_rel(Oid relid, VacuumStmt *vacstmt)
tcnt = 0;
for (i = 0; i < attr_cnt; i++)
{
vacattrstats[tcnt] = examine_attribute(onerel, i+1);
vacattrstats[tcnt] = examine_attribute(onerel, i + 1);
if (vacattrstats[tcnt] != NULL)
tcnt++;
}
@ -270,8 +275,8 @@ analyze_rel(Oid relid, VacuumStmt *vacstmt)
/*
* 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.
* 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++)
@ -289,8 +294,8 @@ analyze_rel(Oid relid, VacuumStmt *vacstmt)
/*
* 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
* 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)
@ -300,7 +305,7 @@ analyze_rel(Oid relid, VacuumStmt *vacstmt)
RelationGetForm(onerel)->relhasindex);
/*
* Compute the statistics. Temporary results during the calculations
* 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 TransactionCommandContext.
@ -338,8 +343,9 @@ analyze_rel(Oid relid, VacuumStmt *vacstmt)
/*
* 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.)
* 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);
}
@ -364,7 +370,7 @@ analyze_rel(Oid relid, VacuumStmt *vacstmt)
static VacAttrStats *
examine_attribute(Relation onerel, int attnum)
{
Form_pg_attribute attr = onerel->rd_att->attrs[attnum-1];
Form_pg_attribute attr = onerel->rd_att->attrs[attnum - 1];
Operator func_operator;
Oid oprrest;
HeapTuple typtuple;
@ -396,8 +402,8 @@ examine_attribute(Relation onerel, int attnum)
return NULL;
/*
* If we have "=" then we're at least able to do the minimal algorithm,
* so start filling in a VacAttrStats struct.
* 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));
@ -424,15 +430,14 @@ examine_attribute(Relation onerel, int attnum)
{
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)
* Determine the algorithm to use (this will get more complicated
* later)
*/
if (OidIsValid(ltopr))
{
@ -474,7 +479,7 @@ examine_attribute(Relation onerel, int attnum)
* 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
* 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.
*
@ -491,8 +496,8 @@ acquire_sample_rows(Relation onerel, HeapTuple *rows, int targrows,
int numrows = 0;
HeapScanDesc scan;
HeapTuple tuple;
ItemPointer lasttuple;
BlockNumber lastblock,
ItemPointer lasttuple;
BlockNumber lastblock,
estblock;
OffsetNumber lastoffset;
int numest;
@ -501,6 +506,7 @@ acquire_sample_rows(Relation onerel, HeapTuple *rows, int targrows,
double rstate;
Assert(targrows > 1);
/*
* Do a simple linear scan until we reach the target number of rows.
*/
@ -512,8 +518,9 @@ acquire_sample_rows(Relation onerel, HeapTuple *rows, int targrows,
break;
}
heap_endscan(scan);
/*
* If we ran out of tuples then we're done, no matter how few we
* 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))
@ -521,35 +528,40 @@ acquire_sample_rows(Relation onerel, HeapTuple *rows, int targrows,
*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.
* (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.
* 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);
lasttuple = &(rows[numrows - 1]->t_self);
lastblock = ItemPointerGetBlockNumber(lasttuple);
lastoffset = ItemPointerGetOffsetNumber(lasttuple);
/*
* If possible, estimate tuples/page using only completely-scanned pages.
* If possible, estimate tuples/page using only completely-scanned
* pages.
*/
for (numest = numrows; numest > 0; numest--)
{
if (ItemPointerGetBlockNumber(&(rows[numest-1]->t_self)) != lastblock)
if (ItemPointerGetBlockNumber(&(rows[numest - 1]->t_self)) != lastblock)
break;
}
if (numest == 0)
@ -558,27 +570,25 @@ acquire_sample_rows(Relation onerel, HeapTuple *rows, int targrows,
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;
double targpos;
BlockNumber targblock;
Buffer targbuffer;
Page targpage;
OffsetNumber targoffset,
maxoffset;
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)) +
targoffset = ((int) ((targpos - targblock) * tuplesperpage)) +
FirstOffsetNumber;
/* Make sure we are past the last selected record */
if (targblock <= lastblock)
@ -588,19 +598,22 @@ acquire_sample_rows(Relation onerel, HeapTuple *rows, int targrows,
targoffset = lastoffset + 1;
}
/* Loop to find first valid record at or after given position */
pageloop:;
pageloop:;
/*
* Have we fallen off the end of the relation? (We rely on
* 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.
* 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))
@ -632,7 +645,7 @@ acquire_sample_rows(Relation onerel, HeapTuple *rows, int targrows,
* Found a suitable tuple, so save it, replacing one old
* tuple at random
*/
int k = (int) (targrows * random_fract());
int k = (int) (targrows * random_fract());
Assert(k >= 0 && k < targrows);
heap_freetuple(rows[k]);
@ -667,13 +680,13 @@ acquire_sample_rows(Relation onerel, HeapTuple *rows, int targrows,
static double
random_fract(void)
{
long z;
long z;
/* random() can produce endpoint values, try again if so */
do
{
z = random();
} while (! (z > 0 && z < MAX_RANDOM_VALUE));
} while (!(z > 0 && z < MAX_RANDOM_VALUE));
return (double) z / (double) MAX_RANDOM_VALUE;
}
@ -702,7 +715,7 @@ 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);
return exp(-log(random_fract()) / n);
}
static double
@ -712,8 +725,8 @@ select_next_random_record(double t, int n, double *stateptr)
if (t <= (22.0 * n))
{
/* Process records using Algorithm X until t is large enough */
double V,
quot;
double V,
quot;
V = random_fract(); /* Generate V */
t += 1;
@ -728,21 +741,21 @@ select_next_random_record(double t, int n, double *stateptr)
else
{
/* Now apply Algorithm Z */
double W = *stateptr;
double term = t - (double) n + 1;
double S;
double W = *stateptr;
double term = t - (double) n + 1;
double S;
for (;;)
{
double numer,
numer_lim,
denom;
double U,
X,
lhs,
rhs,
y,
tmp;
double numer,
numer_lim,
denom;
double U,
X,
lhs,
rhs,
y,
tmp;
/* Generate U and X */
U = random_fract();
@ -750,15 +763,15 @@ select_next_random_record(double t, int n, double *stateptr)
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;
lhs = exp(log(((U * tmp * tmp) * (term + S)) / (t + X)) / n);
rhs = (((t + X) / (term + S)) * term) / t;
if (lhs <= rhs)
{
W = rhs/lhs;
W = rhs / lhs;
break;
}
/* Test if U <= f(S)/cg(X) */
y = (((U * (t + 1))/term) * (t + S + 1))/(t + X);
y = (((U * (t + 1)) / term) * (t + S + 1)) / (t + X);
if ((double) n < S)
{
denom = t;
@ -774,8 +787,8 @@ select_next_random_record(double t, int n, double *stateptr)
y *= numer / denom;
denom -= 1;
}
W = exp(- log(random_fract())/n); /* Generate W in advance */
if (exp(log(y)/n) <= (t + X)/t)
W = exp(-log(random_fract()) / n); /* Generate W in advance */
if (exp(log(y) / n) <= (t + X) / t)
break;
}
t += S + 1;
@ -790,11 +803,11 @@ select_next_random_record(double t, int n, double *stateptr)
static int
compare_rows(const void *a, const void *b)
{
HeapTuple ha = * (HeapTuple *) a;
HeapTuple hb = * (HeapTuple *) b;
BlockNumber ba = ItemPointerGetBlockNumber(&ha->t_self);
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);
BlockNumber bb = ItemPointerGetBlockNumber(&hb->t_self);
OffsetNumber ob = ItemPointerGetOffsetNumber(&hb->t_self);
if (ba < bb)
@ -839,15 +852,18 @@ compute_minimal_stats(VacAttrStats *stats,
FmgrInfo f_cmpeq;
typedef struct
{
Datum value;
int count;
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 */
/*
* 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;
@ -877,19 +893,20 @@ compute_minimal_stats(VacAttrStats *stats,
/*
* 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.
* 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.
* 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)
{
@ -920,10 +937,10 @@ compute_minimal_stats(VacAttrStats *stats,
/* 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)
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);
swapDatum(track[j].value, track[j - 1].value);
swapInt(track[j].count, track[j - 1].count);
j--;
}
}
@ -932,10 +949,10 @@ compute_minimal_stats(VacAttrStats *stats,
/* No match. Insert at head of count-1 list */
if (track_cnt < track_max)
track_cnt++;
for (j = track_cnt-1; j > firstcount1; j--)
for (j = track_cnt - 1; j > firstcount1; j--)
{
track[j].value = track[j-1].value;
track[j].count = track[j-1].count;
track[j].value = track[j - 1].value;
track[j].count = track[j - 1].count;
}
if (firstcount1 < track_cnt)
{
@ -948,8 +965,8 @@ compute_minimal_stats(VacAttrStats *stats,
/* We can only compute valid stats if we found some non-null values. */
if (nonnull_cnt > 0)
{
int nmultiple,
summultiple;
int nmultiple,
summultiple;
stats->stats_valid = true;
/* Do the simple null-frac and width stats */
@ -977,9 +994,9 @@ compute_minimal_stats(VacAttrStats *stats,
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.
* 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;
}
@ -994,12 +1011,12 @@ compute_minimal_stats(VacAttrStats *stats,
* 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
* results in a drastic overestimate of ndistinct. Can we do
* any better?)
*----------
*/
int f1 = nonnull_cnt - summultiple;
double term1;
int f1 = nonnull_cnt - summultiple;
double term1;
if (f1 < 1)
f1 = 1;
@ -1014,16 +1031,16 @@ compute_minimal_stats(VacAttrStats *stats,
* a fixed value.
*/
if (stats->stadistinct > 0.1 * totalrows)
stats->stadistinct = - (stats->stadistinct / 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.
* 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 &&
@ -1034,12 +1051,12 @@ compute_minimal_stats(VacAttrStats *stats,
}
else
{
double ndistinct = stats->stadistinct;
double avgcount,
mincount;
double ndistinct = stats->stadistinct;
double avgcount,
mincount;
if (ndistinct < 0)
ndistinct = - ndistinct * totalrows;
ndistinct = -ndistinct * totalrows;
/* estimate # of occurrences in sample of a typical value */
avgcount = (double) numrows / ndistinct;
/* set minimum threshold count to store a value */
@ -1062,8 +1079,8 @@ compute_minimal_stats(VacAttrStats *stats,
if (num_mcv > 0)
{
MemoryContext old_context;
Datum *mcv_values;
float4 *mcv_freqs;
Datum *mcv_values;
float4 *mcv_freqs;
/* Must copy the target values into TransactionCommandContext */
old_context = MemoryContextSwitchTo(TransactionCommandContext);
@ -1153,19 +1170,20 @@ compute_scalar_stats(VacAttrStats *stats,
/*
* 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.
* 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.
* 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)
{
@ -1185,11 +1203,11 @@ compute_scalar_stats(VacAttrStats *stats,
/* 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;
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;
@ -1199,23 +1217,24 @@ compute_scalar_stats(VacAttrStats *stats,
sizeof(ScalarItem), compare_scalars);
/*
* Now scan the values in order, find the most common ones,
* and also accumulate ordering-correlation statistics.
* 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).
* 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;
@ -1225,7 +1244,8 @@ compute_scalar_stats(VacAttrStats *stats,
{
int tupno = values[i].tupno;
corr_xysum += (double) i * (double) tupno;
corr_xysum += (double) i *(double) tupno;
dups_cnt++;
if (tupnoLink[tupno] == tupno)
{
@ -1235,7 +1255,7 @@ compute_scalar_stats(VacAttrStats *stats,
{
nmultiple++;
if (track_cnt < num_mcv ||
dups_cnt > track[track_cnt-1].count)
dups_cnt > track[track_cnt - 1].count)
{
/*
* Found a new item for the mcv list; find its
@ -1243,16 +1263,16 @@ compute_scalar_stats(VacAttrStats *stats,
* Loop invariant is that j points at an empty/
* replaceable slot.
*/
int j;
int j;
if (track_cnt < num_mcv)
track_cnt++;
for (j = track_cnt-1; j > 0; j--)
for (j = track_cnt - 1; j > 0; j--)
{
if (dups_cnt <= track[j-1].count)
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 = track[j - 1].count;
track[j].first = track[j - 1].first;
}
track[j].count = dups_cnt;
track[j].first = i + 1 - dups_cnt;
@ -1278,8 +1298,8 @@ compute_scalar_stats(VacAttrStats *stats,
else if (toowide_cnt == 0 && nmultiple == ndistinct)
{
/*
* Every value in the sample appeared more than once. Assume the
* column has just these values.
* Every value in the sample appeared more than once. Assume
* the column has just these values.
*/
stats->stadistinct = ndistinct;
}
@ -1294,8 +1314,8 @@ compute_scalar_stats(VacAttrStats *stats,
* Overwidth values are assumed to have been distinct.
*----------
*/
int f1 = ndistinct - nmultiple + toowide_cnt;
double term1;
int f1 = ndistinct - nmultiple + toowide_cnt;
double term1;
if (f1 < 1)
f1 = 1;
@ -1310,19 +1330,20 @@ compute_scalar_stats(VacAttrStats *stats,
* a fixed value.
*/
if (stats->stadistinct > 0.1 * totalrows)
stats->stadistinct = - (stats->stadistinct / 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.
* 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 &&
@ -1333,13 +1354,13 @@ compute_scalar_stats(VacAttrStats *stats,
}
else
{
double ndistinct = stats->stadistinct;
double avgcount,
mincount,
maxmincount;
double ndistinct = stats->stadistinct;
double avgcount,
mincount,
maxmincount;
if (ndistinct < 0)
ndistinct = - ndistinct * totalrows;
ndistinct = -ndistinct * totalrows;
/* estimate # of occurrences in sample of a typical value */
avgcount = (double) numrows / ndistinct;
/* set minimum threshold count to store a value */
@ -1366,8 +1387,8 @@ compute_scalar_stats(VacAttrStats *stats,
if (num_mcv > 0)
{
MemoryContext old_context;
Datum *mcv_values;
float4 *mcv_freqs;
Datum *mcv_values;
float4 *mcv_freqs;
/* Must copy the target values into TransactionCommandContext */
old_context = MemoryContextSwitchTo(TransactionCommandContext);
@ -1402,8 +1423,8 @@ compute_scalar_stats(VacAttrStats *stats,
if (num_hist >= 2)
{
MemoryContext old_context;
Datum *hist_values;
int nvals;
Datum *hist_values;
int nvals;
/* Sort the MCV items into position order to speed next loop */
qsort((void *) track, num_mcv,
@ -1413,24 +1434,25 @@ compute_scalar_stats(VacAttrStats *stats,
* 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[].
* 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;
int src,
dest;
int j;
src = dest = 0;
j = 0; /* index of next interesting MCV item */
while (src < values_cnt)
{
int ncopy;
int ncopy;
if (j < num_mcv)
{
int first = track[j].first;
int first = track[j].first;
if (src >= first)
{
@ -1442,9 +1464,7 @@ compute_scalar_stats(VacAttrStats *stats,
ncopy = first - src;
}
else
{
ncopy = values_cnt - src;
}
memmove(&values[dest], &values[src],
ncopy * sizeof(ScalarItem));
src += ncopy;
@ -1461,7 +1481,7 @@ compute_scalar_stats(VacAttrStats *stats,
hist_values = (Datum *) palloc(num_hist * sizeof(Datum));
for (i = 0; i < num_hist; i++)
{
int pos;
int pos;
pos = (i * (nvals - 1)) / (num_hist - 1);
hist_values[i] = datumCopy(values[pos].value,
@ -1481,9 +1501,9 @@ compute_scalar_stats(VacAttrStats *stats,
if (values_cnt > 1)
{
MemoryContext old_context;
float4 *corrs;
double corr_xsum,
corr_x2sum;
float4 *corrs;
double corr_xsum,
corr_x2sum;
/* Must copy the target values into TransactionCommandContext */
old_context = MemoryContextSwitchTo(TransactionCommandContext);
@ -1499,9 +1519,10 @@ compute_scalar_stats(VacAttrStats *stats,
* (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;
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);
@ -1521,7 +1542,7 @@ compute_scalar_stats(VacAttrStats *stats,
* 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
* 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().
@ -1573,7 +1594,7 @@ compare_mcvs(const void *a, const void *b)
* 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.
* 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
@ -1604,7 +1625,9 @@ update_attstats(Oid relid, int natts, VacAttrStats **vacattrstats)
FmgrInfo out_function;
HeapTuple stup,
oldtup;
int i, k, n;
int i,
k,
n;
Datum values[Natts_pg_statistic];
char nulls[Natts_pg_statistic];
char replaces[Natts_pg_statistic];
@ -1626,22 +1649,22 @@ update_attstats(Oid relid, int natts, VacAttrStats **vacattrstats)
}
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 */
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 */
values[i++] = Int16GetDatum(stats->stakind[k]); /* stakindN */
}
for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
{
values[i++] = ObjectIdGetDatum(stats->staop[k]); /* staopN */
values[i++] = ObjectIdGetDatum(stats->staop[k]); /* staopN */
}
for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
{
int nnum = stats->numnumbers[k];
int nnum = stats->numnumbers[k];
if (nnum > 0)
{
@ -1653,7 +1676,7 @@ update_attstats(Oid relid, int natts, VacAttrStats **vacattrstats)
/* XXX knows more than it should about type float4: */
arry = construct_array(numdatums, nnum,
false, sizeof(float4), 'i');
values[i++] = PointerGetDatum(arry); /* stanumbersN */
values[i++] = PointerGetDatum(arry); /* stanumbersN */
}
else
{
@ -1663,7 +1686,7 @@ update_attstats(Oid relid, int natts, VacAttrStats **vacattrstats)
}
for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
{
int ntxt = stats->numvalues[k];
int ntxt = stats->numvalues[k];
if (ntxt > 0)
{
@ -1676,20 +1699,20 @@ update_attstats(Oid relid, int natts, VacAttrStats **vacattrstats)
* Convert data values to a text string to be inserted
* into the text array.
*/
Datum stringdatum;
Datum stringdatum;
stringdatum =
FunctionCall3(&out_function,
stats->stavalues[k][n],
ObjectIdGetDatum(stats->attrtype->typelem),
Int32GetDatum(stats->attr->atttypmod));
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 */
values[i++] = PointerGetDatum(arry); /* stavaluesN */
}
else
{