database/postgres
database/postgres
1
0
Fork 0
mirror of https://github.com/postgres/postgres.git synced 2025-04-22 23:02:54 +03:00
Code
postgres/src/backend/commands/analyze.c
Nathan Bossart e5b0b0ce15 Add is_analyze parameter to vacuum_delay_point(). 
This function is used in both vacuum and analyze code paths, and a
follow-up commit will require distinguishing between the two.  This
commit forces callers to specify whether they are in a vacuum or
analyze path, but it does not use that information for anything
yet.

Author: Nathan Bossart <nathandbossart@gmail.com>
Co-authored-by: Bertrand Drouvot <bertranddrouvot.pg@gmail.com>
Discussion: https://postgr.es/m/ZmaXmWDL829fzAVX%40ip-10-97-1-34.eu-west-3.compute.internal
2025-02-11 16:38:14 -06:00

3076 lines
91 KiB
C
Raw Blame History

/*-------------------------------------------------------------------------
*
* analyze.c
* the Postgres statistics generator
*
* Portions Copyright (c) 1996-2025, PostgreSQL Global Development Group
* Portions Copyright (c) 1994, Regents of the University of California
*
*
* IDENTIFICATION
* src/backend/commands/analyze.c
*
*-------------------------------------------------------------------------
*/
#include "postgres.h"
#include <math.h>
#include "access/detoast.h"
#include "access/genam.h"
#include "access/multixact.h"
#include "access/relation.h"
#include "access/table.h"
#include "access/tableam.h"
#include "access/transam.h"
#include "access/tupconvert.h"
#include "access/visibilitymap.h"
#include "access/xact.h"
#include "catalog/index.h"
#include "catalog/indexing.h"
#include "catalog/pg_inherits.h"
#include "commands/dbcommands.h"
#include "commands/progress.h"
#include "commands/tablecmds.h"
#include "commands/vacuum.h"
#include "common/pg_prng.h"
#include "executor/executor.h"
#include "foreign/fdwapi.h"
#include "miscadmin.h"
#include "nodes/nodeFuncs.h"
#include "parser/parse_oper.h"
#include "parser/parse_relation.h"
#include "pgstat.h"
#include "statistics/extended_stats_internal.h"
#include "statistics/statistics.h"
#include "storage/bufmgr.h"
#include "storage/procarray.h"
#include "utils/attoptcache.h"
#include "utils/datum.h"
#include "utils/guc.h"
#include "utils/lsyscache.h"
#include "utils/memutils.h"
#include "utils/pg_rusage.h"
#include "utils/sampling.h"
#include "utils/sortsupport.h"
#include "utils/syscache.h"
#include "utils/timestamp.h"
/* Per-index data for ANALYZE */
typedef struct AnlIndexData
{
IndexInfo *indexInfo; /* BuildIndexInfo result */
double tupleFract; /* fraction of rows for partial index */
VacAttrStats **vacattrstats; /* index attrs to analyze */
int attr_cnt;
} AnlIndexData;
/* Default statistics target (GUC parameter) */
int default_statistics_target = 100;
/* A few variables that don't seem worth passing around as parameters */
static MemoryContext anl_context = NULL;
static BufferAccessStrategy vac_strategy;
static void do_analyze_rel(Relation onerel,
VacuumParams *params, List *va_cols,
AcquireSampleRowsFunc acquirefunc, BlockNumber relpages,
bool inh, bool in_outer_xact, int elevel);
static void compute_index_stats(Relation onerel, double totalrows,
AnlIndexData *indexdata, int nindexes,
HeapTuple *rows, int numrows,
MemoryContext col_context);
static VacAttrStats *examine_attribute(Relation onerel, int attnum,
Node *index_expr);
static int acquire_sample_rows(Relation onerel, int elevel,
HeapTuple *rows, int targrows,
double *totalrows, double *totaldeadrows);
static int compare_rows(const void *a, const void *b, void *arg);
static int acquire_inherited_sample_rows(Relation onerel, int elevel,
HeapTuple *rows, int targrows,
double *totalrows, double *totaldeadrows);
static void update_attstats(Oid relid, bool inh,
int natts, VacAttrStats **vacattrstats);
static Datum std_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull);
static Datum ind_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull);
/*
* analyze_rel() -- analyze one relation
*
* relid identifies the relation to analyze. If relation is supplied, use
* the name therein for reporting any failure to open/lock the rel; do not
* use it once we've successfully opened the rel, since it might be stale.
*/
void
analyze_rel(Oid relid, RangeVar *relation,
VacuumParams *params, List *va_cols, bool in_outer_xact,
BufferAccessStrategy bstrategy)
{
Relation onerel;
int elevel;
AcquireSampleRowsFunc acquirefunc = NULL;
BlockNumber relpages = 0;
/* Select logging level */
if (params->options & VACOPT_VERBOSE)
elevel = INFO;
else
elevel = DEBUG2;
/* Set up static variables */
vac_strategy = bstrategy;
/*
* Check for user-requested abort.
*/
CHECK_FOR_INTERRUPTS();
/*
* Open the relation, getting ShareUpdateExclusiveLock to ensure that two
* ANALYZEs don't run on it concurrently. (This also locks out a
* concurrent VACUUM, which doesn't matter much at the moment but might
* matter if we ever try to accumulate stats on dead tuples.) If the rel
* has been dropped since we last saw it, we don't need to process it.
*
* Make sure to generate only logs for ANALYZE in this case.
*/
onerel = vacuum_open_relation(relid, relation, params->options & ~(VACOPT_VACUUM),
params->log_min_duration >= 0,
ShareUpdateExclusiveLock);
/* leave if relation could not be opened or locked */
if (!onerel)
return;
/*
* Check if relation needs to be skipped based on privileges. This check
* happens also when building the relation list to analyze for a manual
* operation, and needs to be done additionally here as ANALYZE could
* happen across multiple transactions where privileges could have changed
* in-between. Make sure to generate only logs for ANALYZE in this case.
*/
if (!vacuum_is_permitted_for_relation(RelationGetRelid(onerel),
onerel->rd_rel,
params->options & ~VACOPT_VACUUM))
{
relation_close(onerel, ShareUpdateExclusiveLock);
return;
}
/*
* Silently ignore tables that are temp tables of other backends ---
* trying to analyze these is rather pointless, since their contents are
* probably not up-to-date on disk. (We don't throw a warning here; it
* would just lead to chatter during a database-wide ANALYZE.)
*/
if (RELATION_IS_OTHER_TEMP(onerel))
{
relation_close(onerel, ShareUpdateExclusiveLock);
return;
}
/*
* We can ANALYZE any table except pg_statistic. See update_attstats
*/
if (RelationGetRelid(onerel) == StatisticRelationId)
{
relation_close(onerel, ShareUpdateExclusiveLock);
return;
}
/*
* Check that it's of an analyzable relkind, and set up appropriately.
*/
if (onerel->rd_rel->relkind == RELKIND_RELATION ||
onerel->rd_rel->relkind == RELKIND_MATVIEW)
{
/* Regular table, so we'll use the regular row acquisition function */
acquirefunc = acquire_sample_rows;
/* Also get regular table's size */
relpages = RelationGetNumberOfBlocks(onerel);
}
else if (onerel->rd_rel->relkind == RELKIND_FOREIGN_TABLE)
{
/*
* For a foreign table, call the FDW's hook function to see whether it
* supports analysis.
*/
FdwRoutine *fdwroutine;
bool ok = false;
fdwroutine = GetFdwRoutineForRelation(onerel, false);
if (fdwroutine->AnalyzeForeignTable != NULL)
ok = fdwroutine->AnalyzeForeignTable(onerel,
&acquirefunc,
&relpages);
if (!ok)
{
ereport(WARNING,
(errmsg("skipping \"%s\" --- cannot analyze this foreign table",
RelationGetRelationName(onerel))));
relation_close(onerel, ShareUpdateExclusiveLock);
return;
}
}
else if (onerel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE)
{
/*
* For partitioned tables, we want to do the recursive ANALYZE below.
*/
}
else
{
/* No need for a WARNING if we already complained during VACUUM */
if (!(params->options & VACOPT_VACUUM))
ereport(WARNING,
(errmsg("skipping \"%s\" --- cannot analyze non-tables or special system tables",
RelationGetRelationName(onerel))));
relation_close(onerel, ShareUpdateExclusiveLock);
return;
}
/*
* OK, let's do it. First, initialize progress reporting.
*/
pgstat_progress_start_command(PROGRESS_COMMAND_ANALYZE,
RelationGetRelid(onerel));
/*
* Do the normal non-recursive ANALYZE. We can skip this for partitioned
* tables, which don't contain any rows.
*/
if (onerel->rd_rel->relkind != RELKIND_PARTITIONED_TABLE)
do_analyze_rel(onerel, params, va_cols, acquirefunc,
relpages, false, in_outer_xact, elevel);
/*
* If there are child tables, do recursive ANALYZE.
*/
if (onerel->rd_rel->relhassubclass)
do_analyze_rel(onerel, params, va_cols, acquirefunc, relpages,
true, in_outer_xact, elevel);
/*
* Close source relation now, but keep lock so that no one deletes it
* before we commit. (If someone did, they'd fail to clean up the entries
* we made in pg_statistic. Also, releasing the lock before commit would
* expose us to concurrent-update failures in update_attstats.)
*/
relation_close(onerel, NoLock);
pgstat_progress_end_command();
}
/*
* do_analyze_rel() -- analyze one relation, recursively or not
*
* Note that "acquirefunc" is only relevant for the non-inherited case.
* For the inherited case, acquire_inherited_sample_rows() determines the
* appropriate acquirefunc for each child table.
*/
static void
do_analyze_rel(Relation onerel, VacuumParams *params,
List *va_cols, AcquireSampleRowsFunc acquirefunc,
BlockNumber relpages, bool inh, bool in_outer_xact,
int elevel)
{
int attr_cnt,
tcnt,
i,
ind;
Relation *Irel;
int nindexes;
bool verbose,
instrument,
hasindex;
VacAttrStats **vacattrstats;
AnlIndexData *indexdata;
int targrows,
numrows,
minrows;
double totalrows,
totaldeadrows;
HeapTuple *rows;
PGRUsage ru0;
TimestampTz starttime = 0;
MemoryContext caller_context;
Oid save_userid;
int save_sec_context;
int save_nestlevel;
WalUsage startwalusage = pgWalUsage;
BufferUsage startbufferusage = pgBufferUsage;
BufferUsage bufferusage;
PgStat_Counter startreadtime = 0;
PgStat_Counter startwritetime = 0;
verbose = (params->options & VACOPT_VERBOSE) != 0;
instrument = (verbose || (AmAutoVacuumWorkerProcess() &&
params->log_min_duration >= 0));
if (inh)
ereport(elevel,
(errmsg("analyzing \"%s.%s\" inheritance tree",
get_namespace_name(RelationGetNamespace(onerel)),
RelationGetRelationName(onerel))));
else
ereport(elevel,
(errmsg("analyzing \"%s.%s\"",
get_namespace_name(RelationGetNamespace(onerel)),
RelationGetRelationName(onerel))));
/*
* Set up a working context so that we can easily free whatever junk gets
* created.
*/
anl_context = AllocSetContextCreate(CurrentMemoryContext,
"Analyze",
ALLOCSET_DEFAULT_SIZES);
caller_context = MemoryContextSwitchTo(anl_context);
/*
* Switch to the table owner's userid, so that any index functions are run
* as that user. Also lock down security-restricted operations and
* arrange to make GUC variable changes local to this command.
*/
GetUserIdAndSecContext(&save_userid, &save_sec_context);
SetUserIdAndSecContext(onerel->rd_rel->relowner,
save_sec_context | SECURITY_RESTRICTED_OPERATION);
save_nestlevel = NewGUCNestLevel();
RestrictSearchPath();
/*
* When verbose or autovacuum logging is used, initialize a resource usage
* snapshot and optionally track I/O timing.
*/
if (instrument)
{
if (track_io_timing)
{
startreadtime = pgStatBlockReadTime;
startwritetime = pgStatBlockWriteTime;
}
pg_rusage_init(&ru0);
}
/* Used for instrumentation and stats report */
starttime = GetCurrentTimestamp();
/*
* Determine which columns to analyze
*
* Note that system attributes are never analyzed, so we just reject them
* at the lookup stage. We also reject duplicate column mentions. (We
* could alternatively ignore duplicates, but analyzing a column twice
* won't work; we'd end up making a conflicting update in pg_statistic.)
*/
if (va_cols != NIL)
{
Bitmapset *unique_cols = NULL;
ListCell *le;
vacattrstats = (VacAttrStats **) palloc(list_length(va_cols) *
sizeof(VacAttrStats *));
tcnt = 0;
foreach(le, va_cols)
{
char *col = strVal(lfirst(le));
i = attnameAttNum(onerel, col, false);
if (i == InvalidAttrNumber)
ereport(ERROR,
(errcode(ERRCODE_UNDEFINED_COLUMN),
errmsg("column \"%s\" of relation \"%s\" does not exist",
col, RelationGetRelationName(onerel))));
if (bms_is_member(i, unique_cols))
ereport(ERROR,
(errcode(ERRCODE_DUPLICATE_COLUMN),
errmsg("column \"%s\" of relation \"%s\" appears more than once",
col, RelationGetRelationName(onerel))));
unique_cols = bms_add_member(unique_cols, i);
vacattrstats[tcnt] = examine_attribute(onerel, i, NULL);
if (vacattrstats[tcnt] != NULL)
tcnt++;
}
attr_cnt = tcnt;
}
else
{
attr_cnt = onerel->rd_att->natts;
vacattrstats = (VacAttrStats **)
palloc(attr_cnt * sizeof(VacAttrStats *));
tcnt = 0;
for (i = 1; i <= attr_cnt; i++)
{
vacattrstats[tcnt] = examine_attribute(onerel, i, NULL);
if (vacattrstats[tcnt] != NULL)
tcnt++;
}
attr_cnt = tcnt;
}
/*
* Open all indexes of the relation, and see if there are any analyzable
* columns in the indexes. We do not analyze index columns if there was
* an explicit column list in the ANALYZE command, however.
*
* If we are doing a recursive scan, we don't want to touch the parent's
* indexes at all. If we're processing a partitioned table, we need to
* know if there are any indexes, but we don't want to process them.
*/
if (onerel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE)
{
List *idxs = RelationGetIndexList(onerel);
Irel = NULL;
nindexes = 0;
hasindex = idxs != NIL;
list_free(idxs);
}
else if (!inh)
{
vac_open_indexes(onerel, AccessShareLock, &nindexes, &Irel);
hasindex = nindexes > 0;
}
else
{
Irel = NULL;
nindexes = 0;
hasindex = false;
}
indexdata = NULL;
if (nindexes > 0)
{
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 && 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_IndexAttrNumbers[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(indexInfo->ii_Expressions,
indexpr_item);
thisdata->vacattrstats[tcnt] =
examine_attribute(Irel[ind], i + 1, indexkey);
if (thisdata->vacattrstats[tcnt] != NULL)
tcnt++;
}
}
thisdata->attr_cnt = tcnt;
}
}
}
/*
* Determine how many rows we need to sample, using the worst case from
* all analyzable columns. We use a lower bound of 100 rows to avoid
* possible overflow in Vitter's algorithm. (Note: that will also be the
* target in the corner case where there are no analyzable columns.)
*/
targrows = 100;
for (i = 0; i < attr_cnt; i++)
{
if (targrows < vacattrstats[i]->minrows)
targrows = vacattrstats[i]->minrows;
}
for (ind = 0; ind < nindexes; ind++)
{
AnlIndexData *thisdata = &indexdata[ind];
for (i = 0; i < thisdata->attr_cnt; i++)
{
if (targrows < thisdata->vacattrstats[i]->minrows)
targrows = thisdata->vacattrstats[i]->minrows;
}
}
/*
* Look at extended statistics objects too, as those may define custom
* statistics target. So we may need to sample more rows and then build
* the statistics with enough detail.
*/
minrows = ComputeExtStatisticsRows(onerel, attr_cnt, vacattrstats);
if (targrows < minrows)
targrows = minrows;
/*
* Acquire the sample rows
*/
rows = (HeapTuple *) palloc(targrows * sizeof(HeapTuple));
pgstat_progress_update_param(PROGRESS_ANALYZE_PHASE,
inh ? PROGRESS_ANALYZE_PHASE_ACQUIRE_SAMPLE_ROWS_INH :
PROGRESS_ANALYZE_PHASE_ACQUIRE_SAMPLE_ROWS);
if (inh)
numrows = acquire_inherited_sample_rows(onerel, elevel,
rows, targrows,
&totalrows, &totaldeadrows);
else
numrows = (*acquirefunc) (onerel, elevel,
rows, targrows,
&totalrows, &totaldeadrows);
/*
* Compute the statistics. Temporary results during the calculations for
* each column are stored in a child context. The calc routines are
* responsible to make sure that whatever they store into the VacAttrStats
* structure is allocated in anl_context.
*/
if (numrows > 0)
{
MemoryContext col_context,
old_context;
pgstat_progress_update_param(PROGRESS_ANALYZE_PHASE,
PROGRESS_ANALYZE_PHASE_COMPUTE_STATS);
col_context = AllocSetContextCreate(anl_context,
"Analyze Column",
ALLOCSET_DEFAULT_SIZES);
old_context = MemoryContextSwitchTo(col_context);
for (i = 0; i < attr_cnt; i++)
{
VacAttrStats *stats = vacattrstats[i];
AttributeOpts *aopt;
stats->rows = rows;
stats->tupDesc = onerel->rd_att;
stats->compute_stats(stats,
std_fetch_func,
numrows,
totalrows);
/*
* If the appropriate flavor of the n_distinct option is
* specified, override with the corresponding value.
*/
aopt = get_attribute_options(onerel->rd_id, stats->tupattnum);
if (aopt != NULL)
{
float8 n_distinct;
n_distinct = inh ? aopt->n_distinct_inherited : aopt->n_distinct;
if (n_distinct != 0.0)
stats->stadistinct = n_distinct;
}
MemoryContextReset(col_context);
}
if (nindexes > 0)
compute_index_stats(onerel, totalrows,
indexdata, nindexes,
rows, numrows,
col_context);
MemoryContextSwitchTo(old_context);
MemoryContextDelete(col_context);
/*
* Emit the completed stats rows into pg_statistic, replacing any
* previous statistics for the target columns. (If there are stats in
* pg_statistic for columns we didn't process, we leave them alone.)
*/
update_attstats(RelationGetRelid(onerel), inh,
attr_cnt, vacattrstats);
for (ind = 0; ind < nindexes; ind++)
{
AnlIndexData *thisdata = &indexdata[ind];
update_attstats(RelationGetRelid(Irel[ind]), false,
thisdata->attr_cnt, thisdata->vacattrstats);
}
/* Build extended statistics (if there are any). */
BuildRelationExtStatistics(onerel, inh, totalrows, numrows, rows,
attr_cnt, vacattrstats);
}
pgstat_progress_update_param(PROGRESS_ANALYZE_PHASE,
PROGRESS_ANALYZE_PHASE_FINALIZE_ANALYZE);
/*
* Update pages/tuples stats in pg_class ... but not if we're doing
* inherited stats.
*
* We assume that VACUUM hasn't set pg_class.reltuples already, even
* during a VACUUM ANALYZE. Although VACUUM often updates pg_class,
* exceptions exist. A "VACUUM (ANALYZE, INDEX_CLEANUP OFF)" command will
* never update pg_class entries for index relations. It's also possible
* that an individual index's pg_class entry won't be updated during
* VACUUM if the index AM returns NULL from its amvacuumcleanup() routine.
*/
if (!inh)
{
BlockNumber relallvisible;
if (RELKIND_HAS_STORAGE(onerel->rd_rel->relkind))
visibilitymap_count(onerel, &relallvisible, NULL);
else
relallvisible = 0;
/*
* Update pg_class for table relation. CCI first, in case acquirefunc
* updated pg_class.
*/
CommandCounterIncrement();
vac_update_relstats(onerel,
relpages,
totalrows,
relallvisible,
hasindex,
InvalidTransactionId,
InvalidMultiXactId,
NULL, NULL,
in_outer_xact);
/* Same for indexes */
for (ind = 0; ind < nindexes; ind++)
{
AnlIndexData *thisdata = &indexdata[ind];
double totalindexrows;
totalindexrows = ceil(thisdata->tupleFract * totalrows);
vac_update_relstats(Irel[ind],
RelationGetNumberOfBlocks(Irel[ind]),
totalindexrows,
0,
false,
InvalidTransactionId,
InvalidMultiXactId,
NULL, NULL,
in_outer_xact);
}
}
else if (onerel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE)
{
/*
* Partitioned tables don't have storage, so we don't set any fields
* in their pg_class entries except for reltuples and relhasindex.
*/
CommandCounterIncrement();
vac_update_relstats(onerel, -1, totalrows,
0, hasindex, InvalidTransactionId,
InvalidMultiXactId,
NULL, NULL,
in_outer_xact);
}
/*
* Now report ANALYZE to the cumulative stats system. For regular tables,
* we do it only if not doing inherited stats. For partitioned tables, we
* only do it for inherited stats. (We're never called for not-inherited
* stats on partitioned tables anyway.)
*
* Reset the changes_since_analyze counter only if we analyzed all
* columns; otherwise, there is still work for auto-analyze to do.
*/
if (!inh)
pgstat_report_analyze(onerel, totalrows, totaldeadrows,
(va_cols == NIL), starttime);
else if (onerel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE)
pgstat_report_analyze(onerel, 0, 0, (va_cols == NIL), starttime);
/*
* If this isn't part of VACUUM ANALYZE, let index AMs do cleanup.
*
* Note that most index AMs perform a no-op as a matter of policy for
* amvacuumcleanup() when called in ANALYZE-only mode. The only exception
* among core index AMs is GIN/ginvacuumcleanup().
*/
if (!(params->options & VACOPT_VACUUM))
{
for (ind = 0; ind < nindexes; ind++)
{
IndexBulkDeleteResult *stats;
IndexVacuumInfo ivinfo;
ivinfo.index = Irel[ind];
ivinfo.heaprel = onerel;
ivinfo.analyze_only = true;
ivinfo.estimated_count = true;
ivinfo.message_level = elevel;
ivinfo.num_heap_tuples = onerel->rd_rel->reltuples;
ivinfo.strategy = vac_strategy;
stats = index_vacuum_cleanup(&ivinfo, NULL);
if (stats)
pfree(stats);
}
}
/* Done with indexes */
vac_close_indexes(nindexes, Irel, NoLock);
/* Log the action if appropriate */
if (instrument)
{
TimestampTz endtime = GetCurrentTimestamp();
if (verbose || params->log_min_duration == 0 ||
TimestampDifferenceExceeds(starttime, endtime,
params->log_min_duration))
{
long delay_in_ms;
WalUsage walusage;
double read_rate = 0;
double write_rate = 0;
char *msgfmt;
StringInfoData buf;
int64 total_blks_hit;
int64 total_blks_read;
int64 total_blks_dirtied;
memset(&bufferusage, 0, sizeof(BufferUsage));
BufferUsageAccumDiff(&bufferusage, &pgBufferUsage, &startbufferusage);
memset(&walusage, 0, sizeof(WalUsage));
WalUsageAccumDiff(&walusage, &pgWalUsage, &startwalusage);
total_blks_hit = bufferusage.shared_blks_hit +
bufferusage.local_blks_hit;
total_blks_read = bufferusage.shared_blks_read +
bufferusage.local_blks_read;
total_blks_dirtied = bufferusage.shared_blks_dirtied +
bufferusage.local_blks_dirtied;
/*
* We do not expect an analyze to take > 25 days and it simplifies
* things a bit to use TimestampDifferenceMilliseconds.
*/
delay_in_ms = TimestampDifferenceMilliseconds(starttime, endtime);
/*
* Note that we are reporting these read/write rates in the same
* manner as VACUUM does, which means that while the 'average read
* rate' here actually corresponds to page misses and resulting
* reads which are also picked up by track_io_timing, if enabled,
* the 'average write rate' is actually talking about the rate of
* pages being dirtied, not being written out, so it's typical to
* have a non-zero 'avg write rate' while I/O timings only reports
* reads.
*
* It's not clear that an ANALYZE will ever result in
* FlushBuffer() being called, but we track and support reporting
* on I/O write time in case that changes as it's practically free
* to do so anyway.
*/
if (delay_in_ms > 0)
{
read_rate = (double) BLCKSZ * total_blks_read /
(1024 * 1024) / (delay_in_ms / 1000.0);
write_rate = (double) BLCKSZ * total_blks_dirtied /
(1024 * 1024) / (delay_in_ms / 1000.0);
}
/*
* We split this up so we don't emit empty I/O timing values when
* track_io_timing isn't enabled.
*/
initStringInfo(&buf);
if (AmAutoVacuumWorkerProcess())
msgfmt = _("automatic analyze of table \"%s.%s.%s\"\n");
else
msgfmt = _("finished analyzing table \"%s.%s.%s\"\n");
appendStringInfo(&buf, msgfmt,
get_database_name(MyDatabaseId),
get_namespace_name(RelationGetNamespace(onerel)),
RelationGetRelationName(onerel));
if (track_io_timing)
{
double read_ms = (double) (pgStatBlockReadTime - startreadtime) / 1000;
double write_ms = (double) (pgStatBlockWriteTime - startwritetime) / 1000;
appendStringInfo(&buf, _("I/O timings: read: %.3f ms, write: %.3f ms\n"),
read_ms, write_ms);
}
appendStringInfo(&buf, _("avg read rate: %.3f MB/s, avg write rate: %.3f MB/s\n"),
read_rate, write_rate);
appendStringInfo(&buf, _("buffer usage: %lld hits, %lld reads, %lld dirtied\n"),
(long long) total_blks_hit,
(long long) total_blks_read,
(long long) total_blks_dirtied);
appendStringInfo(&buf,
_("WAL usage: %lld records, %lld full page images, %llu bytes\n"),
(long long) walusage.wal_records,
(long long) walusage.wal_fpi,
(unsigned long long) walusage.wal_bytes);
appendStringInfo(&buf, _("system usage: %s"), pg_rusage_show(&ru0));
ereport(verbose ? INFO : LOG,
(errmsg_internal("%s", buf.data)));
pfree(buf.data);
}
}
/* Roll back any GUC changes executed by index functions */
AtEOXact_GUC(false, save_nestlevel);
/* Restore userid and security context */
SetUserIdAndSecContext(save_userid, save_sec_context);
/* Restore current context and release memory */
MemoryContextSwitchTo(caller_context);
MemoryContextDelete(anl_context);
anl_context = NULL;
}
/*
* Compute statistics about indexes of a relation
*/
static void
compute_index_stats(Relation onerel, double totalrows,
AnlIndexData *indexdata, int nindexes,
HeapTuple *rows, int numrows,
MemoryContext col_context)
{
MemoryContext ind_context,
old_context;
Datum values[INDEX_MAX_KEYS];
bool isnull[INDEX_MAX_KEYS];
int ind,
i;
ind_context = AllocSetContextCreate(anl_context,
"Analyze Index",
ALLOCSET_DEFAULT_SIZES);
old_context = MemoryContextSwitchTo(ind_context);
for (ind = 0; ind < nindexes; ind++)
{
AnlIndexData *thisdata = &indexdata[ind];
IndexInfo *indexInfo = thisdata->indexInfo;
int attr_cnt = thisdata->attr_cnt;
TupleTableSlot *slot;
EState *estate;
ExprContext *econtext;
ExprState *predicate;
Datum *exprvals;
bool *exprnulls;
int numindexrows,
tcnt,
rowno;
double totalindexrows;
/* Ignore index if no columns to analyze and not partial */
if (attr_cnt == 0 && indexInfo->ii_Predicate == NIL)
continue;
/*
* Need an EState for evaluation of index expressions and
* partial-index predicates. Create it in the per-index context to be
* sure it gets cleaned up at the bottom of the loop.
*/
estate = CreateExecutorState();
econtext = GetPerTupleExprContext(estate);
/* Need a slot to hold the current heap tuple, too */
slot = MakeSingleTupleTableSlot(RelationGetDescr(onerel),
&TTSOpsHeapTuple);
/* Arrange for econtext's scan tuple to be the tuple under test */
econtext->ecxt_scantuple = slot;
/* Set up execution state for predicate. */
predicate = ExecPrepareQual(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];
vacuum_delay_point(true);
/*
* Reset the per-tuple context each time, to reclaim any cruft
* left behind by evaluating the predicate or index expressions.
*/
ResetExprContext(econtext);
/* Set up for predicate or expression evaluation */
ExecStoreHeapTuple(heapTuple, slot, false);
/* If index is partial, check predicate */
if (predicate != NULL)
{
if (!ExecQual(predicate, econtext))
continue;
}
numindexrows++;
if (attr_cnt > 0)
{
/*
* Evaluate the index row to compute expression values. We
* could do this by hand, but FormIndexDatum is convenient.
*/
FormIndexDatum(indexInfo,
slot,
estate,
values,
isnull);
/*
* Save just the columns we care about. We copy the values
* into ind_context from the estate's per-tuple context.
*/
for (i = 0; i < attr_cnt; i++)
{
VacAttrStats *stats = thisdata->vacattrstats[i];
int attnum = stats->tupattnum;
if (isnull[attnum - 1])
{
exprvals[tcnt] = (Datum) 0;
exprnulls[tcnt] = true;
}
else
{
exprvals[tcnt] = datumCopy(values[attnum - 1],
stats->attrtype->typbyval,
stats->attrtype->typlen);
exprnulls[tcnt] = false;
}
tcnt++;
}
}
}
/*
* Having counted the number of rows that pass the predicate in the
* sample, we can estimate the total number of rows in the index.
*/
thisdata->tupleFract = (double) numindexrows / (double) numrows;
totalindexrows = ceil(thisdata->tupleFract * totalrows);
/*
* Now we can compute the statistics for the expression columns.
*/
if (numindexrows > 0)
{
MemoryContextSwitchTo(col_context);
for (i = 0; i < attr_cnt; i++)
{
VacAttrStats *stats = thisdata->vacattrstats[i];
stats->exprvals = exprvals + i;
stats->exprnulls = exprnulls + i;
stats->rowstride = attr_cnt;
stats->compute_stats(stats,
ind_fetch_func,
numindexrows,
totalindexrows);
MemoryContextReset(col_context);
}
}
/* And clean up */
MemoryContextSwitchTo(ind_context);
ExecDropSingleTupleTableSlot(slot);
FreeExecutorState(estate);
MemoryContextReset(ind_context);
}
MemoryContextSwitchTo(old_context);
MemoryContextDelete(ind_context);
}
/*
* examine_attribute -- pre-analysis of a single column
*
* Determine whether the column is analyzable; if so, create and initialize
* a VacAttrStats struct for it. If not, return NULL.
*
* If index_expr isn't NULL, then we're trying to analyze an expression index,
* and index_expr is the expression tree representing the column's data.
*/
static VacAttrStats *
examine_attribute(Relation onerel, int attnum, Node *index_expr)
{
Form_pg_attribute attr = TupleDescAttr(onerel->rd_att, attnum - 1);
int attstattarget;
HeapTuple atttuple;
Datum dat;
bool isnull;
HeapTuple typtuple;
VacAttrStats *stats;
int i;
bool ok;
/* Never analyze dropped columns */
if (attr->attisdropped)
return NULL;
/* Don't analyze virtual generated columns */
if (attr->attgenerated == ATTRIBUTE_GENERATED_VIRTUAL)
return NULL;
/*
* Get attstattarget value. Set to -1 if null. (Analyze functions expect
* -1 to mean use default_statistics_target; see for example
* std_typanalyze.)
*/
atttuple = SearchSysCache2(ATTNUM, ObjectIdGetDatum(RelationGetRelid(onerel)), Int16GetDatum(attnum));
if (!HeapTupleIsValid(atttuple))
elog(ERROR, "cache lookup failed for attribute %d of relation %u",
attnum, RelationGetRelid(onerel));
dat = SysCacheGetAttr(ATTNUM, atttuple, Anum_pg_attribute_attstattarget, &isnull);
attstattarget = isnull ? -1 : DatumGetInt16(dat);
ReleaseSysCache(atttuple);
/* Don't analyze column if user has specified not to */
if (attstattarget == 0)
return NULL;
/*
* Create the VacAttrStats struct.
*/
stats = (VacAttrStats *) palloc0(sizeof(VacAttrStats));
stats->attstattarget = attstattarget;
/*
* When analyzing an expression index, believe the expression tree's type
* not the column datatype --- the latter might be the opckeytype storage
* type of the opclass, which is not interesting for our purposes. (Note:
* if we did anything with non-expression index columns, we'd need to
* figure out where to get the correct type info from, but for now that's
* not a problem.) It's not clear whether anyone will care about the
* typmod, but we store that too just in case.
*/
if (index_expr)
{
stats->attrtypid = exprType(index_expr);
stats->attrtypmod = exprTypmod(index_expr);
/*
* If a collation has been specified for the index column, use that in
* preference to anything else; but if not, fall back to whatever we
* can get from the expression.
*/
if (OidIsValid(onerel->rd_indcollation[attnum - 1]))
stats->attrcollid = onerel->rd_indcollation[attnum - 1];
else
stats->attrcollid = exprCollation(index_expr);
}
else
{
stats->attrtypid = attr->atttypid;
stats->attrtypmod = attr->atttypmod;
stats->attrcollid = attr->attcollation;
}
typtuple = SearchSysCacheCopy1(TYPEOID,
ObjectIdGetDatum(stats->attrtypid));
if (!HeapTupleIsValid(typtuple))
elog(ERROR, "cache lookup failed for type %u", stats->attrtypid);
stats->attrtype = (Form_pg_type) GETSTRUCT(typtuple);
stats->anl_context = anl_context;
stats->tupattnum = attnum;
/*
* The fields describing the stats->stavalues[n] element types default to
* the type of the data being analyzed, but the type-specific typanalyze
* function can change them if it wants to store something else.
*/
for (i = 0; i < STATISTIC_NUM_SLOTS; i++)
{
stats->statypid[i] = stats->attrtypid;
stats->statyplen[i] = stats->attrtype->typlen;
stats->statypbyval[i] = stats->attrtype->typbyval;
stats->statypalign[i] = stats->attrtype->typalign;
}
/*
* Call the type-specific typanalyze function. If none is specified, use
* std_typanalyze().
*/
if (OidIsValid(stats->attrtype->typanalyze))
ok = DatumGetBool(OidFunctionCall1(stats->attrtype->typanalyze,
PointerGetDatum(stats)));
else
ok = std_typanalyze(stats);
if (!ok || stats->compute_stats == NULL || stats->minrows <= 0)
{
heap_freetuple(typtuple);
pfree(stats);
return NULL;
}
return stats;
}
/*
* Read stream callback returning the next BlockNumber as chosen by the
* BlockSampling algorithm.
*/
static BlockNumber
block_sampling_read_stream_next(ReadStream *stream,
void *callback_private_data,
void *per_buffer_data)
{
BlockSamplerData *bs = callback_private_data;
return BlockSampler_HasMore(bs) ? BlockSampler_Next(bs) : InvalidBlockNumber;
}
/*
* acquire_sample_rows -- acquire a random sample of rows from the table
*
* Selected rows are returned in the caller-allocated array rows[], which
* must have at least targrows entries.
* The actual number of rows selected is returned as the function result.
* We also estimate the total numbers of live and dead rows in the table,
* and return them into *totalrows and *totaldeadrows, respectively.
*
* The returned list of tuples is in order by physical position in the table.
* (We will rely on this later to derive correlation estimates.)
*
* As of May 2004 we use a new two-stage method: Stage one selects up
* to targrows random blocks (or all blocks, if there aren't so many).
* Stage two scans these blocks and uses the Vitter algorithm to create
* a random sample of targrows rows (or less, if there are less in the
* sample of blocks). The two stages are executed simultaneously: each
* block is processed as soon as stage one returns its number and while
* the rows are read stage two controls which ones are to be inserted
* into the sample.
*
* Although every row has an equal chance of ending up in the final
* sample, this sampling method is not perfect: not every possible
* sample has an equal chance of being selected. For large relations
* the number of different blocks represented by the sample tends to be
* too small. We can live with that for now. Improvements are welcome.
*
* An important property of this sampling method is that because we do
* look at a statistically unbiased set of blocks, we should get
* unbiased estimates of the average numbers of live and dead rows per
* block. The previous sampling method put too much credence in the row
* density near the start of the table.
*/
static int
acquire_sample_rows(Relation onerel, int elevel,
HeapTuple *rows, int targrows,
double *totalrows, double *totaldeadrows)
{
int numrows = 0; /* # rows now in reservoir */
double samplerows = 0; /* total # rows collected */
double liverows = 0; /* # live rows seen */
double deadrows = 0; /* # dead rows seen */
double rowstoskip = -1; /* -1 means not set yet */
uint32 randseed; /* Seed for block sampler(s) */
BlockNumber totalblocks;
TransactionId OldestXmin;
BlockSamplerData bs;
ReservoirStateData rstate;
TupleTableSlot *slot;
TableScanDesc scan;
BlockNumber nblocks;
BlockNumber blksdone = 0;
ReadStream *stream;
Assert(targrows > 0);
totalblocks = RelationGetNumberOfBlocks(onerel);
/* Need a cutoff xmin for HeapTupleSatisfiesVacuum */
OldestXmin = GetOldestNonRemovableTransactionId(onerel);
/* Prepare for sampling block numbers */
randseed = pg_prng_uint32(&pg_global_prng_state);
nblocks = BlockSampler_Init(&bs, totalblocks, targrows, randseed);
/* Report sampling block numbers */
pgstat_progress_update_param(PROGRESS_ANALYZE_BLOCKS_TOTAL,
nblocks);
/* Prepare for sampling rows */
reservoir_init_selection_state(&rstate, targrows);
scan = table_beginscan_analyze(onerel);
slot = table_slot_create(onerel, NULL);
stream = read_stream_begin_relation(READ_STREAM_MAINTENANCE,
vac_strategy,
scan->rs_rd,
MAIN_FORKNUM,
block_sampling_read_stream_next,
&bs,
0);
/* Outer loop over blocks to sample */
while (table_scan_analyze_next_block(scan, stream))
{
vacuum_delay_point(true);
while (table_scan_analyze_next_tuple(scan, OldestXmin, &liverows, &deadrows, slot))
{
/*
* The first targrows sample rows are simply copied into the
* reservoir. Then we start replacing tuples in the sample until
* we reach the end of the relation. This algorithm is from Jeff
* Vitter's paper (see full citation in utils/misc/sampling.c). 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++] = ExecCopySlotHeapTuple(slot);
else
{
/*
* t in Vitter's paper is the number of records already
* processed. If we need to compute a new S value, we must
* use the not-yet-incremented value of samplerows as t.
*/
if (rowstoskip < 0)
rowstoskip = reservoir_get_next_S(&rstate, samplerows, targrows);
if (rowstoskip <= 0)
{
/*
* Found a suitable tuple, so save it, replacing one old
* tuple at random
*/
int k = (int) (targrows * sampler_random_fract(&rstate.randstate));
Assert(k >= 0 && k < targrows);
heap_freetuple(rows[k]);
rows[k] = ExecCopySlotHeapTuple(slot);
}
rowstoskip -= 1;
}
samplerows += 1;
}
pgstat_progress_update_param(PROGRESS_ANALYZE_BLOCKS_DONE,
++blksdone);
}
read_stream_end(stream);
ExecDropSingleTupleTableSlot(slot);
table_endscan(scan);
/*
* 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_interruptible(rows, numrows, sizeof(HeapTuple),
compare_rows, NULL);
/*
* Estimate total numbers of live and dead rows in relation, extrapolating
* on the assumption that the average tuple density in pages we didn't
* scan is the same as in the pages we did scan. Since what we scanned is
* a random sample of the pages in the relation, this should be a good
* assumption.
*/
if (bs.m > 0)
{
*totalrows = floor((liverows / bs.m) * totalblocks + 0.5);
*totaldeadrows = floor((deadrows / bs.m) * totalblocks + 0.5);
}
else
{
*totalrows = 0.0;
*totaldeadrows = 0.0;
}
/*
* Emit some interesting relation info
*/
ereport(elevel,
(errmsg("\"%s\": scanned %d of %u pages, "
"containing %.0f live rows and %.0f dead rows; "
"%d rows in sample, %.0f estimated total rows",
RelationGetRelationName(onerel),
bs.m, totalblocks,
liverows, deadrows,
numrows, *totalrows)));
return numrows;
}
/*
* Comparator for sorting rows[] array
*/
static int
compare_rows(const void *a, const void *b, void *arg)
{
HeapTuple ha = *(const HeapTuple *) a;
HeapTuple hb = *(const HeapTuple *) b;
BlockNumber ba = ItemPointerGetBlockNumber(&ha->t_self);
OffsetNumber oa = ItemPointerGetOffsetNumber(&ha->t_self);
BlockNumber bb = ItemPointerGetBlockNumber(&hb->t_self);
OffsetNumber ob = ItemPointerGetOffsetNumber(&hb->t_self);
if (ba < bb)
return -1;
if (ba > bb)
return 1;
if (oa < ob)
return -1;
if (oa > ob)
return 1;
return 0;
}
/*
* acquire_inherited_sample_rows -- acquire sample rows from inheritance tree
*
* This has the same API as acquire_sample_rows, except that rows are
* collected from all inheritance children as well as the specified table.
* We fail and return zero if there are no inheritance children, or if all
* children are foreign tables that don't support ANALYZE.
*/
static int
acquire_inherited_sample_rows(Relation onerel, int elevel,
HeapTuple *rows, int targrows,
double *totalrows, double *totaldeadrows)
{
List *tableOIDs;
Relation *rels;
AcquireSampleRowsFunc *acquirefuncs;
double *relblocks;
double totalblocks;
int numrows,
nrels,
i;
ListCell *lc;
bool has_child;
/* Initialize output parameters to zero now, in case we exit early */
*totalrows = 0;
*totaldeadrows = 0;
/*
* Find all members of inheritance set. We only need AccessShareLock on
* the children.
*/
tableOIDs =
find_all_inheritors(RelationGetRelid(onerel), AccessShareLock, NULL);
/*
* Check that there's at least one descendant, else fail. This could
* happen despite analyze_rel's relhassubclass check, if table once had a
* child but no longer does. In that case, we can clear the
* relhassubclass field so as not to make the same mistake again later.
* (This is safe because we hold ShareUpdateExclusiveLock.)
*/
if (list_length(tableOIDs) < 2)
{
/* CCI because we already updated the pg_class row in this command */
CommandCounterIncrement();
SetRelationHasSubclass(RelationGetRelid(onerel), false);
ereport(elevel,
(errmsg("skipping analyze of \"%s.%s\" inheritance tree --- this inheritance tree contains no child tables",
get_namespace_name(RelationGetNamespace(onerel)),
RelationGetRelationName(onerel))));
return 0;
}
/*
* Identify acquirefuncs to use, and count blocks in all the relations.
* The result could overflow BlockNumber, so we use double arithmetic.
*/
rels = (Relation *) palloc(list_length(tableOIDs) * sizeof(Relation));
acquirefuncs = (AcquireSampleRowsFunc *)
palloc(list_length(tableOIDs) * sizeof(AcquireSampleRowsFunc));
relblocks = (double *) palloc(list_length(tableOIDs) * sizeof(double));
totalblocks = 0;
nrels = 0;
has_child = false;
foreach(lc, tableOIDs)
{
Oid childOID = lfirst_oid(lc);
Relation childrel;
AcquireSampleRowsFunc acquirefunc = NULL;
BlockNumber relpages = 0;
/* We already got the needed lock */
childrel = table_open(childOID, NoLock);
/* Ignore if temp table of another backend */
if (RELATION_IS_OTHER_TEMP(childrel))
{
/* ... but release the lock on it */
Assert(childrel != onerel);
table_close(childrel, AccessShareLock);
continue;
}
/* Check table type (MATVIEW can't happen, but might as well allow) */
if (childrel->rd_rel->relkind == RELKIND_RELATION ||
childrel->rd_rel->relkind == RELKIND_MATVIEW)
{
/* Regular table, so use the regular row acquisition function */
acquirefunc = acquire_sample_rows;
relpages = RelationGetNumberOfBlocks(childrel);
}
else if (childrel->rd_rel->relkind == RELKIND_FOREIGN_TABLE)
{
/*
* For a foreign table, call the FDW's hook function to see
* whether it supports analysis.
*/
FdwRoutine *fdwroutine;
bool ok = false;
fdwroutine = GetFdwRoutineForRelation(childrel, false);
if (fdwroutine->AnalyzeForeignTable != NULL)
ok = fdwroutine->AnalyzeForeignTable(childrel,
&acquirefunc,
&relpages);
if (!ok)
{
/* ignore, but release the lock on it */
Assert(childrel != onerel);
table_close(childrel, AccessShareLock);
continue;
}
}
else
{
/*
* ignore, but release the lock on it. don't try to unlock the
* passed-in relation
*/
Assert(childrel->rd_rel->relkind == RELKIND_PARTITIONED_TABLE);
if (childrel != onerel)
table_close(childrel, AccessShareLock);
else
table_close(childrel, NoLock);
continue;
}
/* OK, we'll process this child */
has_child = true;
rels[nrels] = childrel;
acquirefuncs[nrels] = acquirefunc;
relblocks[nrels] = (double) relpages;
totalblocks += (double) relpages;
nrels++;
}
/*
* If we don't have at least one child table to consider, fail. If the
* relation is a partitioned table, it's not counted as a child table.
*/
if (!has_child)
{
ereport(elevel,
(errmsg("skipping analyze of \"%s.%s\" inheritance tree --- this inheritance tree contains no analyzable child tables",
get_namespace_name(RelationGetNamespace(onerel)),
RelationGetRelationName(onerel))));
return 0;
}
/*
* Now sample rows from each relation, proportionally to its fraction of
* the total block count. (This might be less than desirable if the child
* rels have radically different free-space percentages, but it's not
* clear that it's worth working harder.)
*/
pgstat_progress_update_param(PROGRESS_ANALYZE_CHILD_TABLES_TOTAL,
nrels);
numrows = 0;
for (i = 0; i < nrels; i++)
{
Relation childrel = rels[i];
AcquireSampleRowsFunc acquirefunc = acquirefuncs[i];
double childblocks = relblocks[i];
/*
* Report progress. The sampling function will normally report blocks
* done/total, but we need to reset them to 0 here, so that they don't
* show an old value until that.
*/
{
const int progress_index[] = {
PROGRESS_ANALYZE_CURRENT_CHILD_TABLE_RELID,
PROGRESS_ANALYZE_BLOCKS_DONE,
PROGRESS_ANALYZE_BLOCKS_TOTAL
};
const int64 progress_vals[] = {
RelationGetRelid(childrel),
0,
0,
};
pgstat_progress_update_multi_param(3, progress_index, progress_vals);
}
if (childblocks > 0)
{
int childtargrows;
childtargrows = (int) rint(targrows * childblocks / totalblocks);
/* Make sure we don't overrun due to roundoff error */
childtargrows = Min(childtargrows, targrows - numrows);
if (childtargrows > 0)
{
int childrows;
double trows,
tdrows;
/* Fetch a random sample of the child's rows */
childrows = (*acquirefunc) (childrel, elevel,
rows + numrows, childtargrows,
&trows, &tdrows);
/* We may need to convert from child's rowtype to parent's */
if (childrows > 0 &&
!equalRowTypes(RelationGetDescr(childrel),
RelationGetDescr(onerel)))
{
TupleConversionMap *map;
map = convert_tuples_by_name(RelationGetDescr(childrel),
RelationGetDescr(onerel));
if (map != NULL)
{
int j;
for (j = 0; j < childrows; j++)
{
HeapTuple newtup;
newtup = execute_attr_map_tuple(rows[numrows + j], map);
heap_freetuple(rows[numrows + j]);
rows[numrows + j] = newtup;
}
free_conversion_map(map);
}
}
/* And add to counts */
numrows += childrows;
*totalrows += trows;
*totaldeadrows += tdrows;
}
}
/*
* Note: we cannot release the child-table locks, since we may have
* pointers to their TOAST tables in the sampled rows.
*/
table_close(childrel, NoLock);
pgstat_progress_update_param(PROGRESS_ANALYZE_CHILD_TABLES_DONE,
i + 1);
}
return numrows;
}
/*
* update_attstats() -- update attribute statistics for one relation
*
* Statistics are stored in several places: the pg_class row for the
* relation has stats about the whole relation, and there is a
* pg_statistic row for each (non-system) attribute that has ever
* been analyzed. The pg_class values are updated by VACUUM, not here.
*
* pg_statistic rows are just added or updated normally. This means
* that pg_statistic will probably contain some deleted rows at the
* completion of a vacuum cycle, unless it happens to get vacuumed last.
*
* To keep things simple, we punt for pg_statistic, and don't try
* to compute or store rows for pg_statistic itself in pg_statistic.
* This could possibly be made to work, but it's not worth the trouble.
* Note analyze_rel() has seen to it that we won't come here when
* vacuuming pg_statistic itself.
*
* Note: there would be a race condition here if two backends could
* ANALYZE the same table concurrently. Presently, we lock that out
* by taking a self-exclusive lock on the relation in analyze_rel().
*/
static void
update_attstats(Oid relid, bool inh, int natts, VacAttrStats **vacattrstats)
{
Relation sd;
int attno;
CatalogIndexState indstate = NULL;
if (natts <= 0)
return; /* nothing to do */
sd = table_open(StatisticRelationId, RowExclusiveLock);
for (attno = 0; attno < natts; attno++)
{
VacAttrStats *stats = vacattrstats[attno];
HeapTuple stup,
oldtup;
int i,
k,
n;
Datum values[Natts_pg_statistic];
bool nulls[Natts_pg_statistic];
bool replaces[Natts_pg_statistic];
/* Ignore attr if we weren't able to collect stats */
if (!stats->stats_valid)
continue;
/*
* Construct a new pg_statistic tuple
*/
for (i = 0; i < Natts_pg_statistic; ++i)
{
nulls[i] = false;
replaces[i] = true;
}
values[Anum_pg_statistic_starelid - 1] = ObjectIdGetDatum(relid);
values[Anum_pg_statistic_staattnum - 1] = Int16GetDatum(stats->tupattnum);
values[Anum_pg_statistic_stainherit - 1] = BoolGetDatum(inh);
values[Anum_pg_statistic_stanullfrac - 1] = Float4GetDatum(stats->stanullfrac);
values[Anum_pg_statistic_stawidth - 1] = Int32GetDatum(stats->stawidth);
values[Anum_pg_statistic_stadistinct - 1] = Float4GetDatum(stats->stadistinct);
i = Anum_pg_statistic_stakind1 - 1;
for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
{
values[i++] = Int16GetDatum(stats->stakind[k]); /* stakindN */
}
i = Anum_pg_statistic_staop1 - 1;
for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
{
values[i++] = ObjectIdGetDatum(stats->staop[k]); /* staopN */
}
i = Anum_pg_statistic_stacoll1 - 1;
for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
{
values[i++] = ObjectIdGetDatum(stats->stacoll[k]); /* stacollN */
}
i = Anum_pg_statistic_stanumbers1 - 1;
for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
{
int nnum = stats->numnumbers[k];
if (nnum > 0)
{
Datum *numdatums = (Datum *) palloc(nnum * sizeof(Datum));
ArrayType *arry;
for (n = 0; n < nnum; n++)
numdatums[n] = Float4GetDatum(stats->stanumbers[k][n]);
arry = construct_array_builtin(numdatums, nnum, FLOAT4OID);
values[i++] = PointerGetDatum(arry); /* stanumbersN */
}
else
{
nulls[i] = true;
values[i++] = (Datum) 0;
}
}
i = Anum_pg_statistic_stavalues1 - 1;
for (k = 0; k < STATISTIC_NUM_SLOTS; k++)
{
if (stats->numvalues[k] > 0)
{
ArrayType *arry;
arry = construct_array(stats->stavalues[k],
stats->numvalues[k],
stats->statypid[k],
stats->statyplen[k],
stats->statypbyval[k],
stats->statypalign[k]);
values[i++] = PointerGetDatum(arry); /* stavaluesN */
}
else
{
nulls[i] = true;
values[i++] = (Datum) 0;
}
}
/* Is there already a pg_statistic tuple for this attribute? */
oldtup = SearchSysCache3(STATRELATTINH,
ObjectIdGetDatum(relid),
Int16GetDatum(stats->tupattnum),
BoolGetDatum(inh));
/* Open index information when we know we need it */
if (indstate == NULL)
indstate = CatalogOpenIndexes(sd);
if (HeapTupleIsValid(oldtup))
{
/* Yes, replace it */
stup = heap_modify_tuple(oldtup,
RelationGetDescr(sd),
values,
nulls,
replaces);
ReleaseSysCache(oldtup);
CatalogTupleUpdateWithInfo(sd, &stup->t_self, stup, indstate);
}
else
{
/* No, insert new tuple */
stup = heap_form_tuple(RelationGetDescr(sd), values, nulls);
CatalogTupleInsertWithInfo(sd, stup, indstate);
}
heap_freetuple(stup);
}
if (indstate != NULL)
CatalogCloseIndexes(indstate);
table_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
{
int count; /* # of duplicates */
int first; /* values[] index of first occurrence */
} ScalarMCVItem;
typedef struct
{
SortSupport ssup;
int *tupnoLink;
} CompareScalarsContext;
static void compute_trivial_stats(VacAttrStatsP stats,
AnalyzeAttrFetchFunc fetchfunc,
int samplerows,
double totalrows);
static void compute_distinct_stats(VacAttrStatsP stats,
AnalyzeAttrFetchFunc fetchfunc,
int samplerows,
double totalrows);
static void compute_scalar_stats(VacAttrStatsP stats,
AnalyzeAttrFetchFunc fetchfunc,
int samplerows,
double totalrows);
static int compare_scalars(const void *a, const void *b, void *arg);
static int compare_mcvs(const void *a, const void *b, void *arg);
static int analyze_mcv_list(int *mcv_counts,
int num_mcv,
double stadistinct,
double stanullfrac,
int samplerows,
double totalrows);
/*
* std_typanalyze -- the default type-specific typanalyze function
*/
bool
std_typanalyze(VacAttrStats *stats)
{
Oid ltopr;
Oid eqopr;
StdAnalyzeData *mystats;
/* If the attstattarget column is negative, use the default value */
if (stats->attstattarget < 0)
stats->attstattarget = default_statistics_target;
/* Look for default "<" and "=" operators for column's type */
get_sort_group_operators(stats->attrtypid,
false, false, false,
&ltopr, &eqopr, NULL,
NULL);
/* Save the operator info for compute_stats routines */
mystats = (StdAnalyzeData *) palloc(sizeof(StdAnalyzeData));
mystats->eqopr = eqopr;
mystats->eqfunc = OidIsValid(eqopr) ? get_opcode(eqopr) : InvalidOid;
mystats->ltopr = ltopr;
stats->extra_data = mystats;
/*
* Determine which standard statistics algorithm to use
*/
if (OidIsValid(eqopr) && OidIsValid(ltopr))
{
/* Seems to be a scalar datatype */
stats->compute_stats = compute_scalar_stats;
/*--------------------
* The following choice of minrows is based on the paper
* "Random sampling for histogram construction: how much is enough?"
* by Surajit Chaudhuri, Rajeev Motwani and Vivek Narasayya, in
* Proceedings of ACM SIGMOD International Conference on Management
* of Data, 1998, Pages 436-447. Their Corollary 1 to Theorem 5
* says that for table size n, histogram size k, maximum relative
* error in bin size f, and error probability gamma, the minimum
* random sample size is
* r = 4 * k * ln(2*n/gamma) / f^2
* Taking f = 0.5, gamma = 0.01, n = 10^6 rows, we obtain
* r = 305.82 * k
* Note that because of the log function, the dependence on n is
* quite weak; even at n = 10^12, a 300*k sample gives <= 0.66
* bin size error with probability 0.99. So there's no real need to
* scale for n, which is a good thing because we don't necessarily
* know it at this point.
*--------------------
*/
stats->minrows = 300 * stats->attstattarget;
}
else if (OidIsValid(eqopr))
{
/* We can still recognize distinct values */
stats->compute_stats = compute_distinct_stats;
/* Might as well use the same minrows as above */
stats->minrows = 300 * stats->attstattarget;
}
else
{
/* Can't do much but the trivial stuff */
stats->compute_stats = compute_trivial_stats;
/* Might as well use the same minrows as above */
stats->minrows = 300 * stats->attstattarget;
}
return true;
}
/*
* compute_trivial_stats() -- compute very basic column statistics
*
* We use this when we cannot find a hash "=" operator for the datatype.
*
* We determine the fraction of non-null rows and the average datum width.
*/
static void
compute_trivial_stats(VacAttrStatsP stats,
AnalyzeAttrFetchFunc fetchfunc,
int samplerows,
double totalrows)
{
int i;
int null_cnt = 0;
int nonnull_cnt = 0;
double total_width = 0;
bool is_varlena = (!stats->attrtype->typbyval &&
stats->attrtype->typlen == -1);
bool is_varwidth = (!stats->attrtype->typbyval &&
stats->attrtype->typlen < 0);
for (i = 0; i < samplerows; i++)
{
Datum value;
bool isnull;
vacuum_delay_point(true);
value = fetchfunc(stats, i, &isnull);
/* Check for null/nonnull */
if (isnull)
{
null_cnt++;
continue;
}
nonnull_cnt++;
/*
* If it's a variable-width field, add up widths for average width
* calculation. Note that if the value is toasted, we use the toasted
* width. We don't bother with this calculation if it's a fixed-width
* type.
*/
if (is_varlena)
{
total_width += VARSIZE_ANY(DatumGetPointer(value));
}
else if (is_varwidth)
{
/* must be cstring */
total_width += strlen(DatumGetCString(value)) + 1;
}
}
/* We can only compute average width if we found some non-null values. */
if (nonnull_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;
stats->stadistinct = 0.0; /* "unknown" */
}
else if (null_cnt > 0)
{
/* We found only nulls; assume the column is entirely null */
stats->stats_valid = true;
stats->stanullfrac = 1.0;
if (is_varwidth)
stats->stawidth = 0; /* "unknown" */
else
stats->stawidth = stats->attrtype->typlen;
stats->stadistinct = 0.0; /* "unknown" */
}
}
/*
* compute_distinct_stats() -- compute column statistics including ndistinct
*
* 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_distinct_stats(VacAttrStatsP stats,
AnalyzeAttrFetchFunc fetchfunc,
int samplerows,
double totalrows)
{
int i;
int null_cnt = 0;
int nonnull_cnt = 0;
int toowide_cnt = 0;
double total_width = 0;
bool is_varlena = (!stats->attrtype->typbyval &&
stats->attrtype->typlen == -1);
bool is_varwidth = (!stats->attrtype->typbyval &&
stats->attrtype->typlen < 0);
FmgrInfo f_cmpeq;
typedef struct
{
Datum value;
int count;
} TrackItem;
TrackItem *track;
int track_cnt,
track_max;
int num_mcv = stats->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(true);
value = fetchfunc(stats, i, &isnull);
/* Check for null/nonnull */
if (isnull)
{
null_cnt++;
continue;
}
nonnull_cnt++;
/*
* If it's a variable-width field, add up widths for average width
* calculation. Note that if the value is toasted, we use the toasted
* width. We don't bother with this calculation if it's a fixed-width
* type.
*/
if (is_varlena)
{
total_width += VARSIZE_ANY(DatumGetPointer(value));
/*
* If the value is toasted, we want to detoast it just once to
* avoid repeated detoastings and resultant excess memory usage
* during the comparisons. Also, check to see if the value is
* excessively wide, and if so don't detoast at all --- just
* ignore the value.
*/
if (toast_raw_datum_size(value) > WIDTH_THRESHOLD)
{
toowide_cnt++;
continue;
}
value = PointerGetDatum(PG_DETOAST_DATUM(value));
}
else if (is_varwidth)
{
/* must be cstring */
total_width += strlen(DatumGetCString(value)) + 1;
}
/*
* See if the value matches anything we're already tracking.
*/
match = false;
firstcount1 = track_cnt;
for (j = 0; j < track_cnt; j++)
{
if (DatumGetBool(FunctionCall2Coll(&f_cmpeq,
stats->attrcollid,
value, track[j].value)))
{
match = true;
break;
}
if (j < firstcount1 && track[j].count == 1)
firstcount1 = j;
}
if (match)
{
/* Found a match */
track[j].count++;
/* This value may now need to "bubble up" in the track list */
while (j > 0 && track[j].count > track[j - 1].count)
{
swapDatum(track[j].value, track[j - 1].value);
swapInt(track[j].count, track[j - 1].count);
j--;
}
}
else
{
/* No match. Insert at head of count-1 list */
if (track_cnt < track_max)
track_cnt++;
for (j = track_cnt - 1; j > firstcount1; j--)
{
track[j].value = track[j - 1].value;
track[j].count = track[j - 1].count;
}
if (firstcount1 < track_cnt)
{
track[firstcount1].value = value;
track[firstcount1].count = 1;
}
}
}
/* We can only compute real stats if we found some non-null values. */
if (nonnull_cnt > 0)
{
int nmultiple,
summultiple;
stats->stats_valid = true;
/* Do the simple null-frac and width stats */
stats->stanullfrac = (double) null_cnt / (double) samplerows;
if (is_varwidth)
stats->stawidth = total_width / (double) nonnull_cnt;
else
stats->stawidth = stats->attrtype->typlen;
/* Count the number of values we found multiple times */
summultiple = 0;
for (nmultiple = 0; nmultiple < track_cnt; nmultiple++)
{
if (track[nmultiple].count == 1)
break;
summultiple += track[nmultiple].count;
}
if (nmultiple == 0)
{
/*
* If we found no repeated non-null values, assume it's a unique
* column; but be sure to discount for any nulls we found.
*/
stats->stadistinct = -1.0 * (1.0 - stats->stanullfrac);
}
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. (This case is meant to address columns with
* small, fixed sets of possible values, such as boolean or enum
* columns. If there are any values that appear just once in the
* sample, including too-wide values, we should assume that that's
* not what we're dealing with.)
*/
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.
*
* In this calculation, we consider only non-nulls. We used to
* include rows with null values in the n and N counts, but that
* leads to inaccurate answers in columns with many nulls, and
* it's intuitively bogus anyway considering the desired result is
* the number of distinct non-null values.
*
* 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 n = samplerows - null_cnt;
double N = totalrows * (1.0 - stats->stanullfrac);
double stadistinct;
/* N == 0 shouldn't happen, but just in case ... */
if (N > 0)
stadistinct = (n * d) / ((n - f1) + f1 * n / N);
else
stadistinct = 0;
/* Clamp to sane range in case of roundoff error */
if (stadistinct < d)
stadistinct = d;
if (stadistinct > N)
stadistinct = N;
/* And round to integer */
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 values not in the list.
*
* Note: the first of these cases is meant to address columns with
* small, fixed sets of possible values, such as boolean or enum
* columns. If we can *completely* represent the column population by
* an MCV list that will fit into the stats target, then we should do
* so and thus provide the planner with complete information. But if
* the MCV list is not complete, it's generally worth being more
* selective, and not just filling it all the way up to the stats
* target.
*/
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
{
int *mcv_counts;
/* Incomplete list; decide how many values are worth keeping */
if (num_mcv > track_cnt)
num_mcv = track_cnt;
if (num_mcv > 0)
{
mcv_counts = (int *) palloc(num_mcv * sizeof(int));
for (i = 0; i < num_mcv; i++)
mcv_counts[i] = track[i].count;
num_mcv = analyze_mcv_list(mcv_counts, num_mcv,
stats->stadistinct,
stats->stanullfrac,
samplerows, totalrows);
}
}
/* Generate MCV slot entry */
if (num_mcv > 0)
{
MemoryContext old_context;
Datum *mcv_values;
float4 *mcv_freqs;
/* Must copy the target values into anl_context */
old_context = MemoryContextSwitchTo(stats->anl_context);
mcv_values = (Datum *) palloc(num_mcv * sizeof(Datum));
mcv_freqs = (float4 *) palloc(num_mcv * sizeof(float4));
for (i = 0; i < num_mcv; i++)
{
mcv_values[i] = datumCopy(track[i].value,
stats->attrtype->typbyval,
stats->attrtype->typlen);
mcv_freqs[i] = (double) track[i].count / (double) samplerows;
}
MemoryContextSwitchTo(old_context);
stats->stakind[0] = STATISTIC_KIND_MCV;
stats->staop[0] = mystats->eqopr;
stats->stacoll[0] = stats->attrcollid;
stats->stanumbers[0] = mcv_freqs;
stats->numnumbers[0] = num_mcv;
stats->stavalues[0] = mcv_values;
stats->numvalues[0] = num_mcv;
/*
* Accept the defaults for stats->statypid and others. They have
* been set before we were called (see vacuum.h)
*/
}
}
else if (null_cnt > 0)
{
/* We found only nulls; assume the column is entirely null */
stats->stats_valid = true;
stats->stanullfrac = 1.0;
if (is_varwidth)
stats->stawidth = 0; /* "unknown" */
else
stats->stawidth = stats->attrtype->typlen;
stats->stadistinct = 0.0; /* "unknown" */
}
/* We don't need to bother cleaning up any of our temporary palloc's */
}
/*
* compute_scalar_stats() -- compute column statistics
*
* We use this when we can find "=" and "<" operators for the datatype.
*
* We determine the fraction of non-null rows, the average width, the
* most common values, the (estimated) number of distinct values, the
* distribution histogram, and the correlation of physical to logical order.
*
* The desired stats can be determined fairly easily after sorting the
* data values into order.
*/
static void
compute_scalar_stats(VacAttrStatsP stats,
AnalyzeAttrFetchFunc fetchfunc,
int samplerows,
double totalrows)
{
int i;
int null_cnt = 0;
int nonnull_cnt = 0;
int toowide_cnt = 0;
double total_width = 0;
bool is_varlena = (!stats->attrtype->typbyval &&
stats->attrtype->typlen == -1);
bool is_varwidth = (!stats->attrtype->typbyval &&
stats->attrtype->typlen < 0);
double corr_xysum;
SortSupportData ssup;
ScalarItem *values;
int values_cnt = 0;
int *tupnoLink;
ScalarMCVItem *track;
int track_cnt = 0;
int num_mcv = stats->attstattarget;
int num_bins = stats->attstattarget;
StdAnalyzeData *mystats = (StdAnalyzeData *) stats->extra_data;
values = (ScalarItem *) palloc(samplerows * sizeof(ScalarItem));
tupnoLink = (int *) palloc(samplerows * sizeof(int));
track = (ScalarMCVItem *) palloc(num_mcv * sizeof(ScalarMCVItem));
memset(&ssup, 0, sizeof(ssup));
ssup.ssup_cxt = CurrentMemoryContext;
ssup.ssup_collation = stats->attrcollid;
ssup.ssup_nulls_first = false;
/*
* For now, don't perform abbreviated key conversion, because full values
* are required for MCV slot generation. Supporting that optimization
* would necessitate teaching compare_scalars() to call a tie-breaker.
*/
ssup.abbreviate = false;
PrepareSortSupportFromOrderingOp(mystats->ltopr, &ssup);
/* Initial scan to find sortable values */
for (i = 0; i < samplerows; i++)
{
Datum value;
bool isnull;
vacuum_delay_point(true);
value = fetchfunc(stats, i, &isnull);
/* Check for null/nonnull */
if (isnull)
{
null_cnt++;
continue;
}
nonnull_cnt++;
/*
* If it's a variable-width field, add up widths for average width
* calculation. Note that if the value is toasted, we use the toasted
* width. We don't bother with this calculation if it's a fixed-width
* type.
*/
if (is_varlena)
{
total_width += VARSIZE_ANY(DatumGetPointer(value));
/*
* If the value is toasted, we want to detoast it just once to
* avoid repeated detoastings and resultant excess memory usage
* during the comparisons. Also, check to see if the value is
* excessively wide, and if so don't detoast at all --- just
* ignore the value.
*/
if (toast_raw_datum_size(value) > WIDTH_THRESHOLD)
{
toowide_cnt++;
continue;
}
value = PointerGetDatum(PG_DETOAST_DATUM(value));
}
else if (is_varwidth)
{
/* must be cstring */
total_width += strlen(DatumGetCString(value)) + 1;
}
/* Add it to the list to be sorted */
values[values_cnt].value = value;
values[values_cnt].tupno = values_cnt;
tupnoLink[values_cnt] = values_cnt;
values_cnt++;
}
/* We can only compute real stats if we found some sortable values. */
if (values_cnt > 0)
{
int ndistinct, /* # distinct values in sample */
nmultiple, /* # that appear multiple times */
num_hist,
dups_cnt;
int slot_idx = 0;
CompareScalarsContext cxt;
/* Sort the collected values */
cxt.ssup = &ssup;
cxt.tupnoLink = tupnoLink;
qsort_interruptible(values, values_cnt, sizeof(ScalarItem),
compare_scalars, &cxt);
/*
* Now scan the values in order, find the most common ones, and also
* accumulate ordering-correlation statistics.
*
* To determine which are most common, we first have to count the
* number of duplicates of each value. The duplicates are adjacent in
* the sorted list, so a brute-force approach is to compare successive
* datum values until we find two that are not equal. However, that
* requires N-1 invocations of the datum comparison routine, which are
* completely redundant with work that was done during the sort. (The
* sort algorithm must at some point have compared each pair of items
* that are adjacent in the sorted order; otherwise it could not know
* that it's ordered the pair correctly.) We exploit this by having
* compare_scalars remember the highest tupno index that each
* ScalarItem has been found equal to. At the end of the sort, a
* ScalarItem's tupnoLink will still point to itself if and only if it
* is the last item of its group of duplicates (since the group will
* be ordered by tupno).
*/
corr_xysum = 0;
ndistinct = 0;
nmultiple = 0;
dups_cnt = 0;
for (i = 0; i < values_cnt; i++)
{
int tupno = values[i].tupno;
corr_xysum += ((double) i) * ((double) tupno);
dups_cnt++;
if (tupnoLink[tupno] == tupno)
{
/* Reached end of duplicates of this value */
ndistinct++;
if (dups_cnt > 1)
{
nmultiple++;
if (track_cnt < num_mcv ||
dups_cnt > track[track_cnt - 1].count)
{
/*
* Found a new item for the mcv list; find its
* position, bubbling down old items if needed. Loop
* invariant is that j points at an empty/ replaceable
* slot.
*/
int j;
if (track_cnt < num_mcv)
track_cnt++;
for (j = track_cnt - 1; j > 0; j--)
{
if (dups_cnt <= track[j - 1].count)
break;
track[j].count = track[j - 1].count;
track[j].first = track[j - 1].first;
}
track[j].count = dups_cnt;
track[j].first = i + 1 - dups_cnt;
}
}
dups_cnt = 0;
}
}
stats->stats_valid = true;
/* Do the simple null-frac and width stats */
stats->stanullfrac = (double) null_cnt / (double) samplerows;
if (is_varwidth)
stats->stawidth = total_width / (double) nonnull_cnt;
else
stats->stawidth = stats->attrtype->typlen;
if (nmultiple == 0)
{
/*
* If we found no repeated non-null values, assume it's a unique
* column; but be sure to discount for any nulls we found.
*/
stats->stadistinct = -1.0 * (1.0 - stats->stanullfrac);
}
else if (toowide_cnt == 0 && nmultiple == ndistinct)
{
/*
* Every value in the sample appeared more than once. Assume the
* column has just these values. (This case is meant to address
* columns with small, fixed sets of possible values, such as
* boolean or enum columns. If there are any values that appear
* just once in the sample, including too-wide values, we should
* assume that that's not what we're dealing with.)
*/
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.
*
* In this calculation, we consider only non-nulls. We used to
* include rows with null values in the n and N counts, but that
* leads to inaccurate answers in columns with many nulls, and
* it's intuitively bogus anyway considering the desired result is
* the number of distinct non-null values.
*
* Overwidth values are assumed to have been distinct.
*----------
*/
int f1 = ndistinct - nmultiple + toowide_cnt;
int d = f1 + nmultiple;
double n = samplerows - null_cnt;
double N = totalrows * (1.0 - stats->stanullfrac);
double stadistinct;
/* N == 0 shouldn't happen, but just in case ... */
if (N > 0)
stadistinct = (n * d) / ((n - f1) + f1 * n / N);
else
stadistinct = 0;
/* Clamp to sane range in case of roundoff error */
if (stadistinct < d)
stadistinct = d;
if (stadistinct > N)
stadistinct = N;
/* And round to integer */
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 values not in the list.
*
* Note: the first of these cases is meant to address columns with
* small, fixed sets of possible values, such as boolean or enum
* columns. If we can *completely* represent the column population by
* an MCV list that will fit into the stats target, then we should do
* so and thus provide the planner with complete information. But if
* the MCV list is not complete, it's generally worth being more
* selective, and not just filling it all the way up to the stats
* target.
*/
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
{
int *mcv_counts;
/* Incomplete list; decide how many values are worth keeping */
if (num_mcv > track_cnt)
num_mcv = track_cnt;
if (num_mcv > 0)
{
mcv_counts = (int *) palloc(num_mcv * sizeof(int));
for (i = 0; i < num_mcv; i++)
mcv_counts[i] = track[i].count;
num_mcv = analyze_mcv_list(mcv_counts, num_mcv,
stats->stadistinct,
stats->stanullfrac,
samplerows, totalrows);
}
}
/* Generate MCV slot entry */
if (num_mcv > 0)
{
MemoryContext old_context;
Datum *mcv_values;
float4 *mcv_freqs;
/* Must copy the target values into anl_context */
old_context = MemoryContextSwitchTo(stats->anl_context);
mcv_values = (Datum *) palloc(num_mcv * sizeof(Datum));
mcv_freqs = (float4 *) palloc(num_mcv * sizeof(float4));
for (i = 0; i < num_mcv; i++)
{
mcv_values[i] = datumCopy(values[track[i].first].value,
stats->attrtype->typbyval,
stats->attrtype->typlen);
mcv_freqs[i] = (double) track[i].count / (double) samplerows;
}
MemoryContextSwitchTo(old_context);
stats->stakind[slot_idx] = STATISTIC_KIND_MCV;
stats->staop[slot_idx] = mystats->eqopr;
stats->stacoll[slot_idx] = stats->attrcollid;
stats->stanumbers[slot_idx] = mcv_freqs;
stats->numnumbers[slot_idx] = num_mcv;
stats->stavalues[slot_idx] = mcv_values;
stats->numvalues[slot_idx] = num_mcv;
/*
* Accept the defaults for stats->statypid and others. They have
* been set before we were called (see vacuum.h)
*/
slot_idx++;
}
/*
* Generate a histogram slot entry if there are at least two distinct
* values not accounted for in the MCV list. (This ensures the
* histogram won't collapse to empty or a singleton.)
*/
num_hist = ndistinct - num_mcv;
if (num_hist > num_bins)
num_hist = num_bins + 1;
if (num_hist >= 2)
{
MemoryContext old_context;
Datum *hist_values;
int nvals;
int pos,
posfrac,
delta,
deltafrac;
/* Sort the MCV items into position order to speed next loop */
qsort_interruptible(track, num_mcv, sizeof(ScalarMCVItem),
compare_mcvs, NULL);
/*
* Collapse out the MCV items from the values[] array.
*
* Note we destroy the values[] array here... but we don't need it
* for anything more. We do, however, still need values_cnt.
* nvals will be the number of remaining entries in values[].
*/
if (num_mcv > 0)
{
int src,
dest;
int j;
src = dest = 0;
j = 0; /* index of next interesting MCV item */
while (src < values_cnt)
{
int ncopy;
if (j < num_mcv)
{
int first = track[j].first;
if (src >= first)
{
/* advance past this MCV item */
src = first + track[j].count;
j++;
continue;
}
ncopy = first - src;
}
else
ncopy = values_cnt - src;
memmove(&values[dest], &values[src],
ncopy * sizeof(ScalarItem));
src += ncopy;
dest += ncopy;
}
nvals = dest;
}
else
nvals = values_cnt;
Assert(nvals >= num_hist);
/* Must copy the target values into anl_context */
old_context = MemoryContextSwitchTo(stats->anl_context);
hist_values = (Datum *) palloc(num_hist * sizeof(Datum));
/*
* The object of this loop is to copy the first and last values[]
* entries along with evenly-spaced values in between. So the
* i'th value is values[(i * (nvals - 1)) / (num_hist - 1)]. But
* computing that subscript directly risks integer overflow when
* the stats target is more than a couple thousand. Instead we
* add (nvals - 1) / (num_hist - 1) to pos at each step, tracking
* the integral and fractional parts of the sum separately.
*/
delta = (nvals - 1) / (num_hist - 1);
deltafrac = (nvals - 1) % (num_hist - 1);
pos = posfrac = 0;
for (i = 0; i < num_hist; i++)
{
hist_values[i] = datumCopy(values[pos].value,
stats->attrtype->typbyval,
stats->attrtype->typlen);
pos += delta;
posfrac += deltafrac;
if (posfrac >= (num_hist - 1))
{
/* fractional part exceeds 1, carry to integer part */
pos++;
posfrac -= (num_hist - 1);
}
}
MemoryContextSwitchTo(old_context);
stats->stakind[slot_idx] = STATISTIC_KIND_HISTOGRAM;
stats->staop[slot_idx] = mystats->ltopr;
stats->stacoll[slot_idx] = stats->attrcollid;
stats->stavalues[slot_idx] = hist_values;
stats->numvalues[slot_idx] = num_hist;
/*
* Accept the defaults for stats->statypid and others. They have
* been set before we were called (see vacuum.h)
*/
slot_idx++;
}
/* Generate a correlation entry if there are multiple values */
if (values_cnt > 1)
{
MemoryContext old_context;
float4 *corrs;
double corr_xsum,
corr_x2sum;
/* Must copy the target values into anl_context */
old_context = MemoryContextSwitchTo(stats->anl_context);
corrs = (float4 *) palloc(sizeof(float4));
MemoryContextSwitchTo(old_context);
/*----------
* Since we know the x and y value sets are both
* 0, 1, ..., values_cnt-1
* we have sum(x) = sum(y) =
* (values_cnt-1)*values_cnt / 2
* and sum(x^2) = sum(y^2) =
* (values_cnt-1)*values_cnt*(2*values_cnt-1) / 6.
*----------
*/
corr_xsum = ((double) (values_cnt - 1)) *
((double) values_cnt) / 2.0;
corr_x2sum = ((double) (values_cnt - 1)) *
((double) values_cnt) * (double) (2 * values_cnt - 1) / 6.0;
/* And the correlation coefficient reduces to */
corrs[0] = (values_cnt * corr_xysum - corr_xsum * corr_xsum) /
(values_cnt * corr_x2sum - corr_xsum * corr_xsum);
stats->stakind[slot_idx] = STATISTIC_KIND_CORRELATION;
stats->staop[slot_idx] = mystats->ltopr;
stats->stacoll[slot_idx] = stats->attrcollid;
stats->stanumbers[slot_idx] = corrs;
stats->numnumbers[slot_idx] = 1;
slot_idx++;
}
}
else if (nonnull_cnt > 0)
{
/* We found some non-null values, but they were all too wide */
Assert(nonnull_cnt == toowide_cnt);
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;
/* Assume all too-wide values are distinct, so it's a unique column */
stats->stadistinct = -1.0 * (1.0 - stats->stanullfrac);
}
else if (null_cnt > 0)
{
/* We found only nulls; assume the column is entirely null */
stats->stats_valid = true;
stats->stanullfrac = 1.0;
if (is_varwidth)
stats->stawidth = 0; /* "unknown" */
else
stats->stawidth = stats->attrtype->typlen;
stats->stadistinct = 0.0; /* "unknown" */
}
/* We don't need to bother cleaning up any of our temporary palloc's */
}
/*
* Comparator for sorting ScalarItems
*
* Aside from sorting the items, we update the tupnoLink[] array
* whenever two ScalarItems are found to contain equal datums. The array
* is indexed by tupno; for each ScalarItem, it contains the highest
* tupno that that item's datum has been found to be equal to. This allows
* us to avoid additional comparisons in compute_scalar_stats().
*/
static int
compare_scalars(const void *a, const void *b, void *arg)
{
Datum da = ((const ScalarItem *) a)->value;
int ta = ((const ScalarItem *) a)->tupno;
Datum db = ((const ScalarItem *) b)->value;
int tb = ((const ScalarItem *) b)->tupno;
CompareScalarsContext *cxt = (CompareScalarsContext *) arg;
int compare;
compare = ApplySortComparator(da, false, db, false, cxt->ssup);
if (compare != 0)
return compare;
/*
* The two datums are equal, so update cxt->tupnoLink[].
*/
if (cxt->tupnoLink[ta] < tb)
cxt->tupnoLink[ta] = tb;
if (cxt->tupnoLink[tb] < ta)
cxt->tupnoLink[tb] = ta;
/*
* For equal datums, sort by tupno
*/
return ta - tb;
}
/*
* Comparator for sorting ScalarMCVItems by position
*/
static int
compare_mcvs(const void *a, const void *b, void *arg)
{
int da = ((const ScalarMCVItem *) a)->first;
int db = ((const ScalarMCVItem *) b)->first;
return da - db;
}
/*
* Analyze the list of common values in the sample and decide how many are
* worth storing in the table's MCV list.
*
* mcv_counts is assumed to be a list of the counts of the most common values
* seen in the sample, starting with the most common. The return value is the
* number that are significantly more common than the values not in the list,
* and which are therefore deemed worth storing in the table's MCV list.
*/
static int
analyze_mcv_list(int *mcv_counts,
int num_mcv,
double stadistinct,
double stanullfrac,
int samplerows,
double totalrows)
{
double ndistinct_table;
double sumcount;
int i;
/*
* If the entire table was sampled, keep the whole list. This also
* protects us against division by zero in the code below.
*/
if (samplerows == totalrows || totalrows <= 1.0)
return num_mcv;
/* Re-extract the estimated number of distinct nonnull values in table */
ndistinct_table = stadistinct;
if (ndistinct_table < 0)
ndistinct_table = -ndistinct_table * totalrows;
/*
* Exclude the least common values from the MCV list, if they are not
* significantly more common than the estimated selectivity they would
* have if they weren't in the list. All non-MCV values are assumed to be
* equally common, after taking into account the frequencies of all the
* values in the MCV list and the number of nulls (c.f. eqsel()).
*
* Here sumcount tracks the total count of all but the last (least common)
* value in the MCV list, allowing us to determine the effect of excluding
* that value from the list.
*
* Note that we deliberately do this by removing values from the full
* list, rather than starting with an empty list and adding values,
* because the latter approach can fail to add any values if all the most
* common values have around the same frequency and make up the majority
* of the table, so that the overall average frequency of all values is
* roughly the same as that of the common values. This would lead to any
* uncommon values being significantly overestimated.
*/
sumcount = 0.0;
for (i = 0; i < num_mcv - 1; i++)
sumcount += mcv_counts[i];
while (num_mcv > 0)
{
double selec,
otherdistinct,
N,
n,
K,
variance,
stddev;
/*
* Estimated selectivity the least common value would have if it
* wasn't in the MCV list (c.f. eqsel()).
*/
selec = 1.0 - sumcount / samplerows - stanullfrac;
if (selec < 0.0)
selec = 0.0;
if (selec > 1.0)
selec = 1.0;
otherdistinct = ndistinct_table - (num_mcv - 1);
if (otherdistinct > 1)
selec /= otherdistinct;
/*
* If the value is kept in the MCV list, its population frequency is
* assumed to equal its sample frequency. We use the lower end of a
* textbook continuity-corrected Wald-type confidence interval to
* determine if that is significantly more common than the non-MCV
* frequency --- specifically we assume the population frequency is
* highly likely to be within around 2 standard errors of the sample
* frequency, which equates to an interval of 2 standard deviations
* either side of the sample count, plus an additional 0.5 for the
* continuity correction. Since we are sampling without replacement,
* this is a hypergeometric distribution.
*
* XXX: Empirically, this approach seems to work quite well, but it
* may be worth considering more advanced techniques for estimating
* the confidence interval of the hypergeometric distribution.
*/
N = totalrows;
n = samplerows;
K = N * mcv_counts[num_mcv - 1] / n;
variance = n * K * (N - K) * (N - n) / (N * N * (N - 1));
stddev = sqrt(variance);
if (mcv_counts[num_mcv - 1] > selec * samplerows + 2 * stddev + 0.5)
{
/*
* The value is significantly more common than the non-MCV
* selectivity would suggest. Keep it, and all the other more
* common values in the list.
*/
break;
}
else
{
/* Discard this value and consider the next least common value */
num_mcv--;
if (num_mcv == 0)
break;
sumcount -= mcv_counts[num_mcv - 1];
}
}
return num_mcv;
}
View Git Blame Copy Permalink