83cd2d8b0f
in all cases when keep_buf = true. This allows ANALYZE's inner loop to use heap_release_fetch, which saves multiple buffer lookups for the same page and avoids overestimation of cost by the vacuum cost mechanism.
2219 lines
63 KiB
C
2219 lines
63 KiB
C
/*-------------------------------------------------------------------------
|
|
*
|
|
* analyze.c
|
|
* the Postgres statistics generator
|
|
*
|
|
* Portions Copyright (c) 1996-2004, PostgreSQL Global Development Group
|
|
* Portions Copyright (c) 1994, Regents of the University of California
|
|
*
|
|
*
|
|
* IDENTIFICATION
|
|
* $PostgreSQL: pgsql/src/backend/commands/analyze.c,v 1.78 2004/10/26 16:05:03 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/index.h"
|
|
#include "catalog/indexing.h"
|
|
#include "catalog/namespace.h"
|
|
#include "catalog/pg_operator.h"
|
|
#include "commands/vacuum.h"
|
|
#include "executor/executor.h"
|
|
#include "miscadmin.h"
|
|
#include "parser/parse_expr.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"
|
|
|
|
|
|
/* Data structure for Algorithm S from Knuth 3.4.2 */
|
|
typedef struct
|
|
{
|
|
BlockNumber N; /* number of blocks, known in advance */
|
|
int n; /* desired sample size */
|
|
BlockNumber t; /* current block number */
|
|
int m; /* blocks selected so far */
|
|
} BlockSamplerData;
|
|
typedef BlockSamplerData *BlockSampler;
|
|
|
|
/* Per-index data for ANALYZE */
|
|
typedef struct AnlIndexData
|
|
{
|
|
IndexInfo *indexInfo; /* BuildIndexInfo result */
|
|
double tupleFract; /* fraction of rows for partial index */
|
|
VacAttrStats **vacattrstats; /* index attrs to analyze */
|
|
int attr_cnt;
|
|
} AnlIndexData;
|
|
|
|
|
|
/* Default statistics target (GUC parameter) */
|
|
int default_statistics_target = 10;
|
|
|
|
static int elevel = -1;
|
|
|
|
static MemoryContext anl_context = NULL;
|
|
|
|
|
|
static void BlockSampler_Init(BlockSampler bs, BlockNumber nblocks,
|
|
int samplesize);
|
|
static bool BlockSampler_HasMore(BlockSampler bs);
|
|
static BlockNumber BlockSampler_Next(BlockSampler bs);
|
|
static void compute_index_stats(Relation onerel, double totalrows,
|
|
AnlIndexData *indexdata, int nindexes,
|
|
HeapTuple *rows, int numrows,
|
|
MemoryContext col_context);
|
|
static VacAttrStats *examine_attribute(Relation onerel, int attnum);
|
|
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 get_next_S(double t, int n, double *stateptr);
|
|
static int compare_rows(const void *a, const void *b);
|
|
static void update_attstats(Oid relid, int natts, VacAttrStats **vacattrstats);
|
|
static Datum std_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull);
|
|
static Datum ind_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull);
|
|
|
|
static bool std_typanalyze(VacAttrStats *stats);
|
|
|
|
|
|
/*
|
|
* analyze_rel() -- analyze one relation
|
|
*/
|
|
void
|
|
analyze_rel(Oid relid, VacuumStmt *vacstmt)
|
|
{
|
|
Relation onerel;
|
|
int attr_cnt,
|
|
tcnt,
|
|
i,
|
|
ind;
|
|
Relation *Irel;
|
|
int nindexes;
|
|
bool hasindex;
|
|
bool analyzableindex;
|
|
VacAttrStats **vacattrstats;
|
|
AnlIndexData *indexdata;
|
|
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 get_rel_oids()
|
|
* 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)
|
|
{
|
|
ListCell *le;
|
|
|
|
vacattrstats = (VacAttrStats **) palloc(list_length(vacstmt->va_cols) *
|
|
sizeof(VacAttrStats *));
|
|
tcnt = 0;
|
|
foreach(le, vacstmt->va_cols)
|
|
{
|
|
char *col = strVal(lfirst(le));
|
|
|
|
i = attnameAttNum(onerel, col, false);
|
|
vacattrstats[tcnt] = examine_attribute(onerel, i);
|
|
if (vacattrstats[tcnt] != NULL)
|
|
tcnt++;
|
|
}
|
|
attr_cnt = tcnt;
|
|
}
|
|
else
|
|
{
|
|
attr_cnt = onerel->rd_att->natts;
|
|
vacattrstats = (VacAttrStats **)
|
|
palloc(attr_cnt * sizeof(VacAttrStats *));
|
|
tcnt = 0;
|
|
for (i = 1; i <= attr_cnt; i++)
|
|
{
|
|
vacattrstats[tcnt] = examine_attribute(onerel, i);
|
|
if (vacattrstats[tcnt] != NULL)
|
|
tcnt++;
|
|
}
|
|
attr_cnt = tcnt;
|
|
}
|
|
|
|
/*
|
|
* Open all indexes of the relation, and see if there are any
|
|
* analyzable columns in the indexes. We do not analyze index columns
|
|
* if there was an explicit column list in the ANALYZE command,
|
|
* however.
|
|
*/
|
|
vac_open_indexes(onerel, AccessShareLock, &nindexes, &Irel);
|
|
hasindex = (nindexes > 0);
|
|
indexdata = NULL;
|
|
analyzableindex = false;
|
|
if (hasindex)
|
|
{
|
|
indexdata = (AnlIndexData *) palloc0(nindexes * sizeof(AnlIndexData));
|
|
for (ind = 0; ind < nindexes; ind++)
|
|
{
|
|
AnlIndexData *thisdata = &indexdata[ind];
|
|
IndexInfo *indexInfo;
|
|
|
|
thisdata->indexInfo = indexInfo = BuildIndexInfo(Irel[ind]);
|
|
thisdata->tupleFract = 1.0; /* fix later if partial */
|
|
if (indexInfo->ii_Expressions != NIL && vacstmt->va_cols == NIL)
|
|
{
|
|
ListCell *indexpr_item = list_head(indexInfo->ii_Expressions);
|
|
|
|
thisdata->vacattrstats = (VacAttrStats **)
|
|
palloc(indexInfo->ii_NumIndexAttrs * sizeof(VacAttrStats *));
|
|
tcnt = 0;
|
|
for (i = 0; i < indexInfo->ii_NumIndexAttrs; i++)
|
|
{
|
|
int keycol = indexInfo->ii_KeyAttrNumbers[i];
|
|
|
|
if (keycol == 0)
|
|
{
|
|
/* Found an index expression */
|
|
Node *indexkey;
|
|
|
|
if (indexpr_item == NULL) /* shouldn't happen */
|
|
elog(ERROR, "too few entries in indexprs list");
|
|
indexkey = (Node *) lfirst(indexpr_item);
|
|
indexpr_item = lnext(indexpr_item);
|
|
|
|
/*
|
|
* Can't analyze if the opclass uses a storage
|
|
* type different from the expression result type.
|
|
* We'd get confused because the type shown in
|
|
* pg_attribute for the index column doesn't match
|
|
* what we are getting from the expression.
|
|
* Perhaps this can be fixed someday, but for now,
|
|
* punt.
|
|
*/
|
|
if (exprType(indexkey) !=
|
|
Irel[ind]->rd_att->attrs[i]->atttypid)
|
|
continue;
|
|
|
|
thisdata->vacattrstats[tcnt] =
|
|
examine_attribute(Irel[ind], i + 1);
|
|
if (thisdata->vacattrstats[tcnt] != NULL)
|
|
{
|
|
tcnt++;
|
|
analyzableindex = true;
|
|
}
|
|
}
|
|
}
|
|
thisdata->attr_cnt = tcnt;
|
|
}
|
|
}
|
|
}
|
|
|
|
/*
|
|
* Quit if no analyzable columns
|
|
*/
|
|
if (attr_cnt <= 0 && !analyzableindex)
|
|
{
|
|
vac_close_indexes(nindexes, Irel, AccessShareLock);
|
|
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;
|
|
}
|
|
for (ind = 0; ind < nindexes; ind++)
|
|
{
|
|
AnlIndexData *thisdata = &indexdata[ind];
|
|
|
|
for (i = 0; i < thisdata->attr_cnt; i++)
|
|
{
|
|
if (targrows < thisdata->vacattrstats[i]->minrows)
|
|
targrows = thisdata->vacattrstats[i]->minrows;
|
|
}
|
|
}
|
|
|
|
/*
|
|
* Acquire the sample rows
|
|
*/
|
|
rows = (HeapTuple *) palloc(targrows * sizeof(HeapTuple));
|
|
numrows = acquire_sample_rows(onerel, rows, targrows, &totalrows);
|
|
|
|
/*
|
|
* Compute the statistics. Temporary results during the calculations
|
|
* for each column are stored in a child context. The calc routines
|
|
* are responsible to make sure that whatever they store into the
|
|
* VacAttrStats structure is allocated in anl_context.
|
|
*/
|
|
if (numrows > 0)
|
|
{
|
|
MemoryContext col_context,
|
|
old_context;
|
|
|
|
col_context = AllocSetContextCreate(anl_context,
|
|
"Analyze Column",
|
|
ALLOCSET_DEFAULT_MINSIZE,
|
|
ALLOCSET_DEFAULT_INITSIZE,
|
|
ALLOCSET_DEFAULT_MAXSIZE);
|
|
old_context = MemoryContextSwitchTo(col_context);
|
|
|
|
for (i = 0; i < attr_cnt; i++)
|
|
{
|
|
VacAttrStats *stats = vacattrstats[i];
|
|
|
|
stats->rows = rows;
|
|
stats->tupDesc = onerel->rd_att;
|
|
(*stats->compute_stats) (stats,
|
|
std_fetch_func,
|
|
numrows,
|
|
totalrows);
|
|
MemoryContextResetAndDeleteChildren(col_context);
|
|
}
|
|
|
|
if (hasindex)
|
|
compute_index_stats(onerel, totalrows,
|
|
indexdata, nindexes,
|
|
rows, numrows,
|
|
col_context);
|
|
|
|
MemoryContextSwitchTo(old_context);
|
|
MemoryContextDelete(col_context);
|
|
|
|
/*
|
|
* Emit the completed stats rows into pg_statistic, replacing any
|
|
* previous statistics for the target columns. (If there are
|
|
* stats in pg_statistic for columns we didn't process, we leave
|
|
* them alone.)
|
|
*/
|
|
update_attstats(relid, attr_cnt, vacattrstats);
|
|
|
|
for (ind = 0; ind < nindexes; ind++)
|
|
{
|
|
AnlIndexData *thisdata = &indexdata[ind];
|
|
|
|
update_attstats(RelationGetRelid(Irel[ind]),
|
|
thisdata->attr_cnt, thisdata->vacattrstats);
|
|
}
|
|
}
|
|
|
|
/*
|
|
* If we are running a standalone ANALYZE, update pages/tuples stats
|
|
* in pg_class. We know the accurate page count from the smgr, 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. The same consideration applies to indexes.
|
|
*/
|
|
if (!vacstmt->vacuum)
|
|
{
|
|
vac_update_relstats(RelationGetRelid(onerel),
|
|
RelationGetNumberOfBlocks(onerel),
|
|
totalrows,
|
|
hasindex);
|
|
for (ind = 0; ind < nindexes; ind++)
|
|
{
|
|
AnlIndexData *thisdata = &indexdata[ind];
|
|
double totalindexrows;
|
|
|
|
totalindexrows = ceil(thisdata->tupleFract * totalrows);
|
|
vac_update_relstats(RelationGetRelid(Irel[ind]),
|
|
RelationGetNumberOfBlocks(Irel[ind]),
|
|
totalindexrows,
|
|
false);
|
|
}
|
|
}
|
|
|
|
/* Done with indexes */
|
|
vac_close_indexes(nindexes, Irel, NoLock);
|
|
|
|
/*
|
|
* 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);
|
|
}
|
|
|
|
/*
|
|
* Compute statistics about indexes of a relation
|
|
*/
|
|
static void
|
|
compute_index_stats(Relation onerel, double totalrows,
|
|
AnlIndexData *indexdata, int nindexes,
|
|
HeapTuple *rows, int numrows,
|
|
MemoryContext col_context)
|
|
{
|
|
MemoryContext ind_context,
|
|
old_context;
|
|
TupleDesc heapDescriptor;
|
|
Datum attdata[INDEX_MAX_KEYS];
|
|
char nulls[INDEX_MAX_KEYS];
|
|
int ind,
|
|
i;
|
|
|
|
heapDescriptor = RelationGetDescr(onerel);
|
|
|
|
ind_context = AllocSetContextCreate(anl_context,
|
|
"Analyze Index",
|
|
ALLOCSET_DEFAULT_MINSIZE,
|
|
ALLOCSET_DEFAULT_INITSIZE,
|
|
ALLOCSET_DEFAULT_MAXSIZE);
|
|
old_context = MemoryContextSwitchTo(ind_context);
|
|
|
|
for (ind = 0; ind < nindexes; ind++)
|
|
{
|
|
AnlIndexData *thisdata = &indexdata[ind];
|
|
IndexInfo *indexInfo = thisdata->indexInfo;
|
|
int attr_cnt = thisdata->attr_cnt;
|
|
TupleTable tupleTable;
|
|
TupleTableSlot *slot;
|
|
EState *estate;
|
|
ExprContext *econtext;
|
|
List *predicate;
|
|
Datum *exprvals;
|
|
bool *exprnulls;
|
|
int numindexrows,
|
|
tcnt,
|
|
rowno;
|
|
double totalindexrows;
|
|
|
|
/* Ignore index if no columns to analyze and not partial */
|
|
if (attr_cnt == 0 && indexInfo->ii_Predicate == NIL)
|
|
continue;
|
|
|
|
/*
|
|
* Need an EState for evaluation of index expressions and
|
|
* partial-index predicates. Create it in the per-index context
|
|
* to be sure it gets cleaned up at the bottom of the loop.
|
|
*/
|
|
estate = CreateExecutorState();
|
|
econtext = GetPerTupleExprContext(estate);
|
|
/* Need a slot to hold the current heap tuple, too */
|
|
tupleTable = ExecCreateTupleTable(1);
|
|
slot = ExecAllocTableSlot(tupleTable);
|
|
ExecSetSlotDescriptor(slot, heapDescriptor, false);
|
|
|
|
/* Arrange for econtext's scan tuple to be the tuple under test */
|
|
econtext->ecxt_scantuple = slot;
|
|
|
|
/* Set up execution state for predicate. */
|
|
predicate = (List *)
|
|
ExecPrepareExpr((Expr *) indexInfo->ii_Predicate,
|
|
estate);
|
|
|
|
/* Compute and save index expression values */
|
|
exprvals = (Datum *) palloc(numrows * attr_cnt * sizeof(Datum));
|
|
exprnulls = (bool *) palloc(numrows * attr_cnt * sizeof(bool));
|
|
numindexrows = 0;
|
|
tcnt = 0;
|
|
for (rowno = 0; rowno < numrows; rowno++)
|
|
{
|
|
HeapTuple heapTuple = rows[rowno];
|
|
|
|
/* Set up for predicate or expression evaluation */
|
|
ExecStoreTuple(heapTuple, slot, InvalidBuffer, false);
|
|
|
|
/* If index is partial, check predicate */
|
|
if (predicate != NIL)
|
|
{
|
|
if (!ExecQual(predicate, econtext, false))
|
|
continue;
|
|
}
|
|
numindexrows++;
|
|
|
|
if (attr_cnt > 0)
|
|
{
|
|
/*
|
|
* Evaluate the index row to compute expression values. We
|
|
* could do this by hand, but FormIndexDatum is
|
|
* convenient.
|
|
*/
|
|
FormIndexDatum(indexInfo,
|
|
heapTuple,
|
|
heapDescriptor,
|
|
estate,
|
|
attdata,
|
|
nulls);
|
|
|
|
/*
|
|
* Save just the columns we care about.
|
|
*/
|
|
for (i = 0; i < attr_cnt; i++)
|
|
{
|
|
VacAttrStats *stats = thisdata->vacattrstats[i];
|
|
int attnum = stats->attr->attnum;
|
|
|
|
exprvals[tcnt] = attdata[attnum - 1];
|
|
exprnulls[tcnt] = (nulls[attnum - 1] == 'n');
|
|
tcnt++;
|
|
}
|
|
}
|
|
}
|
|
|
|
/*
|
|
* Having counted the number of rows that pass the predicate in
|
|
* the sample, we can estimate the total number of rows in the
|
|
* index.
|
|
*/
|
|
thisdata->tupleFract = (double) numindexrows / (double) numrows;
|
|
totalindexrows = ceil(thisdata->tupleFract * totalrows);
|
|
|
|
/*
|
|
* Now we can compute the statistics for the expression columns.
|
|
*/
|
|
if (numindexrows > 0)
|
|
{
|
|
MemoryContextSwitchTo(col_context);
|
|
for (i = 0; i < attr_cnt; i++)
|
|
{
|
|
VacAttrStats *stats = thisdata->vacattrstats[i];
|
|
|
|
stats->exprvals = exprvals + i;
|
|
stats->exprnulls = exprnulls + i;
|
|
stats->rowstride = attr_cnt;
|
|
(*stats->compute_stats) (stats,
|
|
ind_fetch_func,
|
|
numindexrows,
|
|
totalindexrows);
|
|
MemoryContextResetAndDeleteChildren(col_context);
|
|
}
|
|
}
|
|
|
|
/* And clean up */
|
|
MemoryContextSwitchTo(ind_context);
|
|
|
|
ExecDropTupleTable(tupleTable, true);
|
|
FreeExecutorState(estate);
|
|
MemoryContextResetAndDeleteChildren(ind_context);
|
|
}
|
|
|
|
MemoryContextSwitchTo(old_context);
|
|
MemoryContextDelete(ind_context);
|
|
}
|
|
|
|
/*
|
|
* examine_attribute -- pre-analysis of a single column
|
|
*
|
|
* Determine whether the column is analyzable; if so, create and initialize
|
|
* a VacAttrStats struct for it. If not, return NULL.
|
|
*/
|
|
static VacAttrStats *
|
|
examine_attribute(Relation onerel, int attnum)
|
|
{
|
|
Form_pg_attribute attr = onerel->rd_att->attrs[attnum - 1];
|
|
HeapTuple typtuple;
|
|
VacAttrStats *stats;
|
|
bool ok;
|
|
|
|
/* Never analyze dropped columns */
|
|
if (attr->attisdropped)
|
|
return NULL;
|
|
|
|
/* Don't analyze column if user has specified not to */
|
|
if (attr->attstattarget == 0)
|
|
return NULL;
|
|
|
|
/*
|
|
* Create the VacAttrStats struct.
|
|
*/
|
|
stats = (VacAttrStats *) palloc0(sizeof(VacAttrStats));
|
|
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->anl_context = anl_context;
|
|
stats->tupattnum = attnum;
|
|
|
|
/*
|
|
* Call the type-specific typanalyze function. If none is specified,
|
|
* use std_typanalyze().
|
|
*/
|
|
if (OidIsValid(stats->attrtype->typanalyze))
|
|
ok = DatumGetBool(OidFunctionCall1(stats->attrtype->typanalyze,
|
|
PointerGetDatum(stats)));
|
|
else
|
|
ok = std_typanalyze(stats);
|
|
|
|
if (!ok || stats->compute_stats == NULL || stats->minrows <= 0)
|
|
{
|
|
pfree(stats->attrtype);
|
|
pfree(stats->attr);
|
|
pfree(stats);
|
|
return NULL;
|
|
}
|
|
|
|
return stats;
|
|
}
|
|
|
|
/*
|
|
* BlockSampler_Init -- prepare for random sampling of blocknumbers
|
|
*
|
|
* BlockSampler is used for stage one of our new two-stage tuple
|
|
* sampling mechanism as discussed on pgsql-hackers 2004-04-02 (subject
|
|
* "Large DB"). It selects a random sample of samplesize blocks out of
|
|
* the nblocks blocks in the table. If the table has less than
|
|
* samplesize blocks, all blocks are selected.
|
|
*
|
|
* Since we know the total number of blocks in advance, we can use the
|
|
* straightforward Algorithm S from Knuth 3.4.2, rather than Vitter's
|
|
* algorithm.
|
|
*/
|
|
static void
|
|
BlockSampler_Init(BlockSampler bs, BlockNumber nblocks, int samplesize)
|
|
{
|
|
bs->N = nblocks; /* measured table size */
|
|
|
|
/*
|
|
* If we decide to reduce samplesize for tables that have less or not
|
|
* much more than samplesize blocks, here is the place to do it.
|
|
*/
|
|
bs->n = samplesize;
|
|
bs->t = 0; /* blocks scanned so far */
|
|
bs->m = 0; /* blocks selected so far */
|
|
}
|
|
|
|
static bool
|
|
BlockSampler_HasMore(BlockSampler bs)
|
|
{
|
|
return (bs->t < bs->N) && (bs->m < bs->n);
|
|
}
|
|
|
|
static BlockNumber
|
|
BlockSampler_Next(BlockSampler bs)
|
|
{
|
|
BlockNumber K = bs->N - bs->t; /* remaining blocks */
|
|
int k = bs->n - bs->m; /* blocks still to sample */
|
|
double p; /* probability to skip block */
|
|
double V; /* random */
|
|
|
|
Assert(BlockSampler_HasMore(bs)); /* hence K > 0 and k > 0 */
|
|
|
|
if ((BlockNumber) k >= K)
|
|
{
|
|
/* need all the rest */
|
|
bs->m++;
|
|
return bs->t++;
|
|
}
|
|
|
|
/*----------
|
|
* It is not obvious that this code matches Knuth's Algorithm S.
|
|
* Knuth says to skip the current block with probability 1 - k/K.
|
|
* If we are to skip, we should advance t (hence decrease K), and
|
|
* repeat the same probabilistic test for the next block. The naive
|
|
* implementation thus requires a random_fract() call for each block
|
|
* number. But we can reduce this to one random_fract() call per
|
|
* selected block, by noting that each time the while-test succeeds,
|
|
* we can reinterpret V as a uniform random number in the range 0 to p.
|
|
* Therefore, instead of choosing a new V, we just adjust p to be
|
|
* the appropriate fraction of its former value, and our next loop
|
|
* makes the appropriate probabilistic test.
|
|
*
|
|
* We have initially K > k > 0. If the loop reduces K to equal k,
|
|
* the next while-test must fail since p will become exactly zero
|
|
* (we assume there will not be roundoff error in the division).
|
|
* (Note: Knuth suggests a "<=" loop condition, but we use "<" just
|
|
* to be doubly sure about roundoff error.) Therefore K cannot become
|
|
* less than k, which means that we cannot fail to select enough blocks.
|
|
*----------
|
|
*/
|
|
V = random_fract();
|
|
p = 1.0 - (double) k / (double) K;
|
|
while (V < p)
|
|
{
|
|
/* skip */
|
|
bs->t++;
|
|
K--; /* keep K == N - t */
|
|
|
|
/* adjust p to be new cutoff point in reduced range */
|
|
p *= 1.0 - (double) k / (double) K;
|
|
}
|
|
|
|
/* select */
|
|
bs->m++;
|
|
return bs->t++;
|
|
}
|
|
|
|
/*
|
|
* acquire_sample_rows -- acquire a random sample of rows from the table
|
|
*
|
|
* As of May 2004 we use a new two-stage method: Stage one selects up
|
|
* to targrows random blocks (or all blocks, if there aren't so many).
|
|
* Stage two scans these blocks and uses the Vitter algorithm to create
|
|
* a random sample of targrows rows (or less, if there are less in the
|
|
* sample of blocks). The two stages are executed simultaneously: each
|
|
* block is processed as soon as stage one returns its number and while
|
|
* the rows are read stage two controls which ones are to be inserted
|
|
* into the sample.
|
|
*
|
|
* Although every row has an equal chance of ending up in the final
|
|
* sample, this sampling method is not perfect: not every possible
|
|
* sample has an equal chance of being selected. For large relations
|
|
* the number of different blocks represented by the sample tends to be
|
|
* too small. We can live with that for now. Improvements are welcome.
|
|
*
|
|
* We also estimate the total number of rows in the table, and return that
|
|
* into *totalrows. An important property of this sampling method is that
|
|
* because we do look at a statistically unbiased set of blocks, we should
|
|
* get an unbiased estimate of the average number of live rows per block.
|
|
* The previous sampling method put too much credence in the row density near
|
|
* the start of the table.
|
|
*
|
|
* 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; /* # rows collected */
|
|
double liverows = 0; /* # rows seen */
|
|
double deadrows = 0;
|
|
double rowstoskip = -1; /* -1 means not set yet */
|
|
BlockNumber totalblocks;
|
|
BlockSamplerData bs;
|
|
double rstate;
|
|
|
|
Assert(targrows > 1);
|
|
|
|
totalblocks = RelationGetNumberOfBlocks(onerel);
|
|
|
|
/* Prepare for sampling block numbers */
|
|
BlockSampler_Init(&bs, totalblocks, targrows);
|
|
/* Prepare for sampling rows */
|
|
rstate = init_selection_state(targrows);
|
|
|
|
/* Outer loop over blocks to sample */
|
|
while (BlockSampler_HasMore(&bs))
|
|
{
|
|
BlockNumber targblock = BlockSampler_Next(&bs);
|
|
Buffer targbuffer;
|
|
Page targpage;
|
|
OffsetNumber targoffset,
|
|
maxoffset;
|
|
|
|
vacuum_delay_point();
|
|
|
|
/*
|
|
* We must maintain a pin on the target page's buffer to ensure
|
|
* that the maxoffset value stays good (else concurrent VACUUM
|
|
* might delete tuples out from under us). Hence, pin the page
|
|
* until we are done looking at it. We 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);
|
|
|
|
/* Inner loop over all tuples on the selected page */
|
|
for (targoffset = FirstOffsetNumber; targoffset <= maxoffset; targoffset++)
|
|
{
|
|
HeapTupleData targtuple;
|
|
|
|
ItemPointerSet(&targtuple.t_self, targblock, targoffset);
|
|
/* We use heap_release_fetch to avoid useless bufmgr traffic */
|
|
if (heap_release_fetch(onerel, SnapshotNow,
|
|
&targtuple, &targbuffer,
|
|
true, NULL))
|
|
{
|
|
/*
|
|
* The first targrows live rows are simply copied into the
|
|
* reservoir. Then we start replacing tuples in the sample
|
|
* until we reach the end of the relation. This algorithm
|
|
* is from Jeff Vitter's paper (see full citation below).
|
|
* It works by repeatedly computing the number of tuples
|
|
* to skip before selecting a tuple, which replaces a
|
|
* randomly chosen element of the reservoir (current set
|
|
* of tuples). At all times the reservoir is a true
|
|
* random sample of the tuples we've passed over so far,
|
|
* so when we fall off the end of the relation we're done.
|
|
*/
|
|
if (numrows < targrows)
|
|
rows[numrows++] = heap_copytuple(&targtuple);
|
|
else
|
|
{
|
|
/*
|
|
* t in Vitter's paper is the number of records
|
|
* already processed. If we need to compute a new S
|
|
* value, we must use the not-yet-incremented value of
|
|
* liverows as t.
|
|
*/
|
|
if (rowstoskip < 0)
|
|
rowstoskip = get_next_S(liverows, targrows, &rstate);
|
|
|
|
if (rowstoskip <= 0)
|
|
{
|
|
/*
|
|
* 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);
|
|
}
|
|
|
|
rowstoskip -= 1;
|
|
}
|
|
|
|
liverows += 1;
|
|
}
|
|
else
|
|
{
|
|
/*
|
|
* Count dead rows, but not empty slots. This information
|
|
* is currently not used, but it seems likely we'll want
|
|
* it someday.
|
|
*/
|
|
if (targtuple.t_data != NULL)
|
|
deadrows += 1;
|
|
}
|
|
}
|
|
|
|
/* Now release the pin on the page */
|
|
ReleaseBuffer(targbuffer);
|
|
}
|
|
|
|
/*
|
|
* If we didn't find as many tuples as we wanted then we're done. No
|
|
* sort is needed, since they're already in order.
|
|
*
|
|
* Otherwise we need to sort the collected tuples by position
|
|
* (itempointer). It's not worth worrying about corner cases where
|
|
* the tuples are already sorted.
|
|
*/
|
|
if (numrows == targrows)
|
|
qsort((void *) rows, numrows, sizeof(HeapTuple), compare_rows);
|
|
|
|
/*
|
|
* Estimate total number of live rows in relation.
|
|
*/
|
|
if (bs.m > 0)
|
|
*totalrows = floor((liverows * totalblocks) / bs.m + 0.5);
|
|
else
|
|
*totalrows = 0.0;
|
|
|
|
/*
|
|
* Emit some interesting relation info
|
|
*/
|
|
ereport(elevel,
|
|
(errmsg("\"%s\": scanned %d of %u pages, "
|
|
"containing %.0f live rows and %.0f dead rows; "
|
|
"%d rows in sample, %.0f estimated total rows",
|
|
RelationGetRelationName(onerel),
|
|
bs.m, totalblocks,
|
|
liverows, deadrows,
|
|
numrows, *totalrows)));
|
|
|
|
return numrows;
|
|
}
|
|
|
|
/* Select a random value R uniformly distributed in 0 < 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. Vitter describes his algorithm in terms
|
|
* of the count S of records to skip before processing another record.
|
|
* It is computed primarily based on t, the number of records already read.
|
|
* The only extra state needed between calls is W, a random state variable.
|
|
*
|
|
* init_selection_state computes the initial W value.
|
|
*
|
|
* Given that we've already read t records (t >= n), get_next_S
|
|
* determines the number of records to skip before the next record is
|
|
* processed.
|
|
*/
|
|
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
|
|
get_next_S(double t, int n, double *stateptr)
|
|
{
|
|
double S;
|
|
|
|
/* The magic constant here is T from Vitter's paper */
|
|
if (t <= (22.0 * n))
|
|
{
|
|
/* Process records using Algorithm X until t is large enough */
|
|
double V,
|
|
quot;
|
|
|
|
V = random_fract(); /* Generate V */
|
|
S = 0;
|
|
t += 1;
|
|
/* Note: "num" in Vitter's code is always equal to t - n */
|
|
quot = (t - (double) n) / t;
|
|
/* Find min S satisfying (4.1) */
|
|
while (quot > V)
|
|
{
|
|
S += 1;
|
|
t += 1;
|
|
quot *= (t - (double) n) / t;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
/* Now apply Algorithm Z */
|
|
double W = *stateptr;
|
|
double term = t - (double) n + 1;
|
|
|
|
for (;;)
|
|
{
|
|
double numer,
|
|
numer_lim,
|
|
denom;
|
|
double U,
|
|
X,
|
|
lhs,
|
|
rhs,
|
|
y,
|
|
tmp;
|
|
|
|
/* Generate U and X */
|
|
U = 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;
|
|
}
|
|
*stateptr = W;
|
|
}
|
|
return S;
|
|
}
|
|
|
|
/*
|
|
* 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;
|
|
}
|
|
|
|
|
|
/*
|
|
* 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;
|
|
|
|
if (natts <= 0)
|
|
return; /* nothing to do */
|
|
|
|
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->attr->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->attr->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);
|
|
}
|
|
|
|
/*
|
|
* Standard fetch function for use by compute_stats subroutines.
|
|
*
|
|
* This exists to provide some insulation between compute_stats routines
|
|
* and the actual storage of the sample data.
|
|
*/
|
|
static Datum
|
|
std_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull)
|
|
{
|
|
int attnum = stats->tupattnum;
|
|
HeapTuple tuple = stats->rows[rownum];
|
|
TupleDesc tupDesc = stats->tupDesc;
|
|
|
|
return heap_getattr(tuple, attnum, tupDesc, isNull);
|
|
}
|
|
|
|
/*
|
|
* Fetch function for analyzing index expressions.
|
|
*
|
|
* We have not bothered to construct index tuples, instead the data is
|
|
* just in Datum arrays.
|
|
*/
|
|
static Datum
|
|
ind_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull)
|
|
{
|
|
int i;
|
|
|
|
/* exprvals and exprnulls are already offset for proper column */
|
|
i = rownum * stats->rowstride;
|
|
*isNull = stats->exprnulls[i];
|
|
return stats->exprvals[i];
|
|
}
|
|
|
|
|
|
/*==========================================================================
|
|
*
|
|
* Code below this point represents the "standard" type-specific statistics
|
|
* analysis algorithms. This code can be replaced on a per-data-type basis
|
|
* by setting a nonzero value in pg_type.typanalyze.
|
|
*
|
|
*==========================================================================
|
|
*/
|
|
|
|
|
|
/*
|
|
* To avoid consuming too much memory during analysis and/or too much space
|
|
* in the resulting pg_statistic rows, we ignore varlena datums that are wider
|
|
* than WIDTH_THRESHOLD (after detoasting!). This is legitimate for MCV
|
|
* and distinct-value calculations since a wide value is unlikely to be
|
|
* duplicated at all, much less be a most-common value. For the same reason,
|
|
* ignoring wide values will not affect our estimates of histogram bin
|
|
* boundaries very much.
|
|
*/
|
|
#define WIDTH_THRESHOLD 1024
|
|
|
|
#define swapInt(a,b) do {int _tmp; _tmp=a; a=b; b=_tmp;} while(0)
|
|
#define swapDatum(a,b) do {Datum _tmp; _tmp=a; a=b; b=_tmp;} while(0)
|
|
|
|
/*
|
|
* Extra information used by the default analysis routines
|
|
*/
|
|
typedef struct
|
|
{
|
|
Oid eqopr; /* '=' operator for datatype, if any */
|
|
Oid eqfunc; /* and associated function */
|
|
Oid ltopr; /* '<' operator for datatype, if any */
|
|
} StdAnalyzeData;
|
|
|
|
typedef struct
|
|
{
|
|
Datum value; /* a data value */
|
|
int tupno; /* position index for tuple it came from */
|
|
} ScalarItem;
|
|
|
|
typedef struct
|
|
{
|
|
int count; /* # of duplicates */
|
|
int first; /* values[] index of first occurrence */
|
|
} ScalarMCVItem;
|
|
|
|
|
|
/* context information for compare_scalars() */
|
|
static FmgrInfo *datumCmpFn;
|
|
static SortFunctionKind datumCmpFnKind;
|
|
static int *datumCmpTupnoLink;
|
|
|
|
|
|
static void compute_minimal_stats(VacAttrStatsP stats,
|
|
AnalyzeAttrFetchFunc fetchfunc,
|
|
int samplerows,
|
|
double totalrows);
|
|
static void compute_scalar_stats(VacAttrStatsP stats,
|
|
AnalyzeAttrFetchFunc fetchfunc,
|
|
int samplerows,
|
|
double totalrows);
|
|
static int compare_scalars(const void *a, const void *b);
|
|
static int compare_mcvs(const void *a, const void *b);
|
|
|
|
|
|
/*
|
|
* std_typanalyze -- the default type-specific typanalyze function
|
|
*/
|
|
static bool
|
|
std_typanalyze(VacAttrStats *stats)
|
|
{
|
|
Form_pg_attribute attr = stats->attr;
|
|
Operator func_operator;
|
|
Oid eqopr = InvalidOid;
|
|
Oid eqfunc = InvalidOid;
|
|
Oid ltopr = InvalidOid;
|
|
StdAnalyzeData *mystats;
|
|
|
|
/* If the attstattarget column is negative, use the default value */
|
|
/* NB: it is okay to scribble on stats->attr since it's a copy */
|
|
if (attr->attstattarget < 0)
|
|
attr->attstattarget = default_statistics_target;
|
|
|
|
/* 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 false;
|
|
|
|
/* 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);
|
|
}
|
|
|
|
/* Save the operator info for compute_stats routines */
|
|
mystats = (StdAnalyzeData *) palloc(sizeof(StdAnalyzeData));
|
|
mystats->eqopr = eqopr;
|
|
mystats->eqfunc = eqfunc;
|
|
mystats->ltopr = ltopr;
|
|
stats->extra_data = mystats;
|
|
|
|
/*
|
|
* Determine which standard statistics algorithm to use
|
|
*/
|
|
if (OidIsValid(ltopr))
|
|
{
|
|
/* Seems to be a scalar datatype */
|
|
stats->compute_stats = compute_scalar_stats;
|
|
/*--------------------
|
|
* The following choice of minrows is based on the paper
|
|
* "Random sampling for histogram construction: how much is enough?"
|
|
* by Surajit Chaudhuri, Rajeev Motwani and Vivek Narasayya, in
|
|
* Proceedings of ACM SIGMOD International Conference on Management
|
|
* of Data, 1998, Pages 436-447. Their Corollary 1 to Theorem 5
|
|
* says that for table size n, histogram size k, maximum relative
|
|
* error in bin size f, and error probability gamma, the minimum
|
|
* random sample size is
|
|
* r = 4 * k * ln(2*n/gamma) / f^2
|
|
* Taking f = 0.5, gamma = 0.01, n = 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->compute_stats = compute_minimal_stats;
|
|
/* Might as well use the same minrows as above */
|
|
stats->minrows = 300 * attr->attstattarget;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
/*
|
|
* compute_minimal_stats() -- compute minimal column statistics
|
|
*
|
|
* We use this when we can find only an "=" operator for the datatype.
|
|
*
|
|
* We determine the fraction of non-null rows, the average width, the
|
|
* most common values, and the (estimated) number of distinct values.
|
|
*
|
|
* The most common values are determined by brute force: we keep a list
|
|
* of previously seen values, ordered by number of times seen, as we scan
|
|
* the samples. A newly seen value is inserted just after the last
|
|
* multiply-seen value, causing the bottommost (oldest) singly-seen value
|
|
* to drop off the list. The accuracy of this method, and also its cost,
|
|
* depend mainly on the length of the list we are willing to keep.
|
|
*/
|
|
static void
|
|
compute_minimal_stats(VacAttrStatsP stats,
|
|
AnalyzeAttrFetchFunc fetchfunc,
|
|
int samplerows,
|
|
double totalrows)
|
|
{
|
|
int i;
|
|
int null_cnt = 0;
|
|
int nonnull_cnt = 0;
|
|
int toowide_cnt = 0;
|
|
double total_width = 0;
|
|
bool is_varlena = (!stats->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;
|
|
StdAnalyzeData *mystats = (StdAnalyzeData *) stats->extra_data;
|
|
|
|
/*
|
|
* We track up to 2*n values for an n-element MCV list; but at least
|
|
* 10
|
|
*/
|
|
track_max = 2 * num_mcv;
|
|
if (track_max < 10)
|
|
track_max = 10;
|
|
track = (TrackItem *) palloc(track_max * sizeof(TrackItem));
|
|
track_cnt = 0;
|
|
|
|
fmgr_info(mystats->eqfunc, &f_cmpeq);
|
|
|
|
for (i = 0; i < samplerows; i++)
|
|
{
|
|
Datum value;
|
|
bool isnull;
|
|
bool match;
|
|
int firstcount1,
|
|
j;
|
|
|
|
vacuum_delay_point();
|
|
|
|
value = fetchfunc(stats, i, &isnull);
|
|
|
|
/* Check for null/nonnull */
|
|
if (isnull)
|
|
{
|
|
null_cnt++;
|
|
continue;
|
|
}
|
|
nonnull_cnt++;
|
|
|
|
/*
|
|
* If it's a variable-width field, add up widths for average width
|
|
* calculation. Note that if the value is toasted, we use the
|
|
* toasted width. We don't bother with this calculation if it's a
|
|
* fixed-width type.
|
|
*/
|
|
if (is_varlena)
|
|
{
|
|
total_width += VARSIZE(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) samplerows;
|
|
if (is_varwidth)
|
|
stats->stawidth = total_width / (double) nonnull_cnt;
|
|
else
|
|
stats->stawidth = stats->attrtype->typlen;
|
|
|
|
/* Count the number of values we found multiple times */
|
|
summultiple = 0;
|
|
for (nmultiple = 0; nmultiple < track_cnt; nmultiple++)
|
|
{
|
|
if (track[nmultiple].count == 1)
|
|
break;
|
|
summultiple += track[nmultiple].count;
|
|
}
|
|
|
|
if (nmultiple == 0)
|
|
{
|
|
/* If we found no repeated values, assume it's a unique column */
|
|
stats->stadistinct = -1.0;
|
|
}
|
|
else if (track_cnt < track_max && toowide_cnt == 0 &&
|
|
nmultiple == track_cnt)
|
|
{
|
|
/*
|
|
* Our track list includes every value in the sample, and
|
|
* every value appeared more than once. Assume the column has
|
|
* just these values.
|
|
*/
|
|
stats->stadistinct = track_cnt;
|
|
}
|
|
else
|
|
{
|
|
/*----------
|
|
* Estimate the number of distinct values using the estimator
|
|
* proposed by Haas and Stokes in IBM Research Report RJ 10025:
|
|
* n*d / (n - f1 + f1*n/N)
|
|
* where f1 is the number of distinct values that occurred
|
|
* exactly once in our sample of n rows (from a total of N),
|
|
* and d is the total number of distinct values in the sample.
|
|
* This is their Duj1 estimator; the other estimators they
|
|
* recommend are considerably more complex, and are numerically
|
|
* very unstable when n is much smaller than N.
|
|
*
|
|
* We assume (not very reliably!) that all the multiply-occurring
|
|
* values are reflected in the final track[] list, and the other
|
|
* nonnull values all appeared but once. (XXX this usually
|
|
* results in a drastic overestimate of ndistinct. Can we do
|
|
* any better?)
|
|
*----------
|
|
*/
|
|
int f1 = nonnull_cnt - summultiple;
|
|
int d = f1 + nmultiple;
|
|
double numer,
|
|
denom,
|
|
stadistinct;
|
|
|
|
numer = (double) samplerows *(double) d;
|
|
|
|
denom = (double) (samplerows - f1) +
|
|
(double) f1 *(double) samplerows / totalrows;
|
|
|
|
stadistinct = numer / denom;
|
|
/* Clamp to sane range in case of roundoff error */
|
|
if (stadistinct < (double) d)
|
|
stadistinct = (double) d;
|
|
if (stadistinct > totalrows)
|
|
stadistinct = totalrows;
|
|
stats->stadistinct = floor(stadistinct + 0.5);
|
|
}
|
|
|
|
/*
|
|
* If we estimated the number of distinct values at more than 10%
|
|
* of the total row count (a very arbitrary limit), then assume
|
|
* that stadistinct should scale with the row count rather than be
|
|
* a fixed value.
|
|
*/
|
|
if (stats->stadistinct > 0.1 * totalrows)
|
|
stats->stadistinct = -(stats->stadistinct / totalrows);
|
|
|
|
/*
|
|
* Decide how many values are worth storing as most-common values.
|
|
* If we are able to generate a complete MCV list (all the values
|
|
* in the sample will fit, and we think these are all the ones in
|
|
* the table), then do so. Otherwise, store only those values
|
|
* that are significantly more common than the (estimated)
|
|
* average. We set the threshold rather arbitrarily at 25% more
|
|
* than average, with at least 2 instances in the sample.
|
|
*/
|
|
if (track_cnt < track_max && toowide_cnt == 0 &&
|
|
stats->stadistinct > 0 &&
|
|
track_cnt <= num_mcv)
|
|
{
|
|
/* Track list includes all values seen, and all will fit */
|
|
num_mcv = track_cnt;
|
|
}
|
|
else
|
|
{
|
|
double ndistinct = stats->stadistinct;
|
|
double avgcount,
|
|
mincount;
|
|
|
|
if (ndistinct < 0)
|
|
ndistinct = -ndistinct * totalrows;
|
|
/* estimate # of occurrences in sample of a typical value */
|
|
avgcount = (double) samplerows / ndistinct;
|
|
/* set minimum threshold count to store a value */
|
|
mincount = avgcount * 1.25;
|
|
if (mincount < 2)
|
|
mincount = 2;
|
|
if (num_mcv > track_cnt)
|
|
num_mcv = track_cnt;
|
|
for (i = 0; i < num_mcv; i++)
|
|
{
|
|
if (track[i].count < mincount)
|
|
{
|
|
num_mcv = i;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
/* Generate MCV slot entry */
|
|
if (num_mcv > 0)
|
|
{
|
|
MemoryContext old_context;
|
|
Datum *mcv_values;
|
|
float4 *mcv_freqs;
|
|
|
|
/* Must copy the target values into anl_context */
|
|
old_context = MemoryContextSwitchTo(stats->anl_context);
|
|
mcv_values = (Datum *) palloc(num_mcv * sizeof(Datum));
|
|
mcv_freqs = (float4 *) palloc(num_mcv * sizeof(float4));
|
|
for (i = 0; i < num_mcv; i++)
|
|
{
|
|
mcv_values[i] = datumCopy(track[i].value,
|
|
stats->attr->attbyval,
|
|
stats->attr->attlen);
|
|
mcv_freqs[i] = (double) track[i].count / (double) samplerows;
|
|
}
|
|
MemoryContextSwitchTo(old_context);
|
|
|
|
stats->stakind[0] = STATISTIC_KIND_MCV;
|
|
stats->staop[0] = mystats->eqopr;
|
|
stats->stanumbers[0] = mcv_freqs;
|
|
stats->numnumbers[0] = num_mcv;
|
|
stats->stavalues[0] = mcv_values;
|
|
stats->numvalues[0] = num_mcv;
|
|
}
|
|
}
|
|
|
|
/* We don't need to bother cleaning up any of our temporary palloc's */
|
|
}
|
|
|
|
|
|
/*
|
|
* compute_scalar_stats() -- compute column statistics
|
|
*
|
|
* We use this when we can find "=" and "<" operators for the datatype.
|
|
*
|
|
* We determine the fraction of non-null rows, the average width, the
|
|
* most common values, the (estimated) number of distinct values, the
|
|
* distribution histogram, and the correlation of physical to logical order.
|
|
*
|
|
* The desired stats can be determined fairly easily after sorting the
|
|
* data values into order.
|
|
*/
|
|
static void
|
|
compute_scalar_stats(VacAttrStatsP stats,
|
|
AnalyzeAttrFetchFunc fetchfunc,
|
|
int samplerows,
|
|
double totalrows)
|
|
{
|
|
int i;
|
|
int null_cnt = 0;
|
|
int nonnull_cnt = 0;
|
|
int toowide_cnt = 0;
|
|
double total_width = 0;
|
|
bool is_varlena = (!stats->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;
|
|
StdAnalyzeData *mystats = (StdAnalyzeData *) stats->extra_data;
|
|
|
|
values = (ScalarItem *) palloc(samplerows * sizeof(ScalarItem));
|
|
tupnoLink = (int *) palloc(samplerows * sizeof(int));
|
|
track = (ScalarMCVItem *) palloc(num_mcv * sizeof(ScalarMCVItem));
|
|
|
|
SelectSortFunction(mystats->ltopr, &cmpFn, &cmpFnKind);
|
|
fmgr_info(cmpFn, &f_cmpfn);
|
|
|
|
/* Initial scan to find sortable values */
|
|
for (i = 0; i < samplerows; i++)
|
|
{
|
|
Datum value;
|
|
bool isnull;
|
|
|
|
vacuum_delay_point();
|
|
|
|
value = fetchfunc(stats, i, &isnull);
|
|
|
|
/* Check for null/nonnull */
|
|
if (isnull)
|
|
{
|
|
null_cnt++;
|
|
continue;
|
|
}
|
|
nonnull_cnt++;
|
|
|
|
/*
|
|
* If it's a variable-width field, add up widths for average width
|
|
* calculation. Note that if the value is toasted, we use the
|
|
* toasted width. We don't bother with this calculation if it's a
|
|
* fixed-width type.
|
|
*/
|
|
if (is_varlena)
|
|
{
|
|
total_width += VARSIZE(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) samplerows;
|
|
if (is_varwidth)
|
|
stats->stawidth = total_width / (double) nonnull_cnt;
|
|
else
|
|
stats->stawidth = stats->attrtype->typlen;
|
|
|
|
if (nmultiple == 0)
|
|
{
|
|
/* If we found no repeated values, assume it's a unique column */
|
|
stats->stadistinct = -1.0;
|
|
}
|
|
else if (toowide_cnt == 0 && nmultiple == ndistinct)
|
|
{
|
|
/*
|
|
* Every value in the sample appeared more than once. Assume
|
|
* the column has just these values.
|
|
*/
|
|
stats->stadistinct = ndistinct;
|
|
}
|
|
else
|
|
{
|
|
/*----------
|
|
* Estimate the number of distinct values using the estimator
|
|
* proposed by Haas and Stokes in IBM Research Report RJ 10025:
|
|
* n*d / (n - f1 + f1*n/N)
|
|
* where f1 is the number of distinct values that occurred
|
|
* exactly once in our sample of n rows (from a total of N),
|
|
* and d is the total number of distinct values in the sample.
|
|
* This is their Duj1 estimator; the other estimators they
|
|
* recommend are considerably more complex, and are numerically
|
|
* very unstable when n is much smaller than N.
|
|
*
|
|
* Overwidth values are assumed to have been distinct.
|
|
*----------
|
|
*/
|
|
int f1 = ndistinct - nmultiple + toowide_cnt;
|
|
int d = f1 + nmultiple;
|
|
double numer,
|
|
denom,
|
|
stadistinct;
|
|
|
|
numer = (double) samplerows *(double) d;
|
|
|
|
denom = (double) (samplerows - f1) +
|
|
(double) f1 *(double) samplerows / totalrows;
|
|
|
|
stadistinct = numer / denom;
|
|
/* Clamp to sane range in case of roundoff error */
|
|
if (stadistinct < (double) d)
|
|
stadistinct = (double) d;
|
|
if (stadistinct > totalrows)
|
|
stadistinct = totalrows;
|
|
stats->stadistinct = floor(stadistinct + 0.5);
|
|
}
|
|
|
|
/*
|
|
* If we estimated the number of distinct values at more than 10%
|
|
* of the total row count (a very arbitrary limit), then assume
|
|
* that stadistinct should scale with the row count rather than be
|
|
* a fixed value.
|
|
*/
|
|
if (stats->stadistinct > 0.1 * totalrows)
|
|
stats->stadistinct = -(stats->stadistinct / totalrows);
|
|
|
|
/*
|
|
* Decide how many values are worth storing as most-common values.
|
|
* If we are able to generate a complete MCV list (all the values
|
|
* in the sample will fit, and we think these are all the ones in
|
|
* the table), then do so. Otherwise, store only those values
|
|
* that are significantly more common than the (estimated)
|
|
* average. We set the threshold rather arbitrarily at 25% more
|
|
* than average, with at least 2 instances in the sample. Also,
|
|
* we won't suppress values that have a frequency of at least 1/K
|
|
* where K is the intended number of histogram bins; such values
|
|
* might otherwise cause us to emit duplicate histogram bin
|
|
* boundaries.
|
|
*/
|
|
if (track_cnt == ndistinct && toowide_cnt == 0 &&
|
|
stats->stadistinct > 0 &&
|
|
track_cnt <= num_mcv)
|
|
{
|
|
/* Track list includes all values seen, and all will fit */
|
|
num_mcv = track_cnt;
|
|
}
|
|
else
|
|
{
|
|
double ndistinct = stats->stadistinct;
|
|
double avgcount,
|
|
mincount,
|
|
maxmincount;
|
|
|
|
if (ndistinct < 0)
|
|
ndistinct = -ndistinct * totalrows;
|
|
/* estimate # of occurrences in sample of a typical value */
|
|
avgcount = (double) samplerows / ndistinct;
|
|
/* set minimum threshold count to store a value */
|
|
mincount = avgcount * 1.25;
|
|
if (mincount < 2)
|
|
mincount = 2;
|
|
/* don't let threshold exceed 1/K, however */
|
|
maxmincount = (double) samplerows / (double) num_bins;
|
|
if (mincount > maxmincount)
|
|
mincount = maxmincount;
|
|
if (num_mcv > track_cnt)
|
|
num_mcv = track_cnt;
|
|
for (i = 0; i < num_mcv; i++)
|
|
{
|
|
if (track[i].count < mincount)
|
|
{
|
|
num_mcv = i;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
/* Generate MCV slot entry */
|
|
if (num_mcv > 0)
|
|
{
|
|
MemoryContext old_context;
|
|
Datum *mcv_values;
|
|
float4 *mcv_freqs;
|
|
|
|
/* Must copy the target values into anl_context */
|
|
old_context = MemoryContextSwitchTo(stats->anl_context);
|
|
mcv_values = (Datum *) palloc(num_mcv * sizeof(Datum));
|
|
mcv_freqs = (float4 *) palloc(num_mcv * sizeof(float4));
|
|
for (i = 0; i < num_mcv; i++)
|
|
{
|
|
mcv_values[i] = datumCopy(values[track[i].first].value,
|
|
stats->attr->attbyval,
|
|
stats->attr->attlen);
|
|
mcv_freqs[i] = (double) track[i].count / (double) samplerows;
|
|
}
|
|
MemoryContextSwitchTo(old_context);
|
|
|
|
stats->stakind[slot_idx] = STATISTIC_KIND_MCV;
|
|
stats->staop[slot_idx] = mystats->eqopr;
|
|
stats->stanumbers[slot_idx] = mcv_freqs;
|
|
stats->numnumbers[slot_idx] = num_mcv;
|
|
stats->stavalues[slot_idx] = mcv_values;
|
|
stats->numvalues[slot_idx] = num_mcv;
|
|
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(stats->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] = mystats->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(stats->anl_context);
|
|
corrs = (float4 *) palloc(sizeof(float4));
|
|
MemoryContextSwitchTo(old_context);
|
|
|
|
/*----------
|
|
* Since we know the x and y value sets are both
|
|
* 0, 1, ..., values_cnt-1
|
|
* we have sum(x) = sum(y) =
|
|
* (values_cnt-1)*values_cnt / 2
|
|
* and sum(x^2) = sum(y^2) =
|
|
* (values_cnt-1)*values_cnt*(2*values_cnt-1) / 6.
|
|
*----------
|
|
*/
|
|
corr_xsum = ((double) (values_cnt - 1)) *
|
|
((double) values_cnt) / 2.0;
|
|
corr_x2sum = ((double) (values_cnt - 1)) *
|
|
((double) values_cnt) * (double) (2 * values_cnt - 1) / 6.0;
|
|
|
|
/* And the correlation coefficient reduces to */
|
|
corrs[0] = (values_cnt * corr_xysum - corr_xsum * corr_xsum) /
|
|
(values_cnt * corr_x2sum - corr_xsum * corr_xsum);
|
|
|
|
stats->stakind[slot_idx] = STATISTIC_KIND_CORRELATION;
|
|
stats->staop[slot_idx] = mystats->ltopr;
|
|
stats->stanumbers[slot_idx] = corrs;
|
|
stats->numnumbers[slot_idx] = 1;
|
|
slot_idx++;
|
|
}
|
|
}
|
|
|
|
/* 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;
|
|
}
|