greenplumn analyze 源码
greenplumn analyze 代码
文件路径：/src/backend/commands/analyze.c
/*-------------------------------------------------------------------------
*
* analyze.c
* the Postgres statistics generator
*
*
* There are a few things in Greenplum that make this more complicated
* than in upstream:
*
* Dispatching
* -----------
*
* Greenplum is an MPP system, so we need to collect the statistics from
* all the segments. The segment servers don't keep statistics (unless you
* connect to a segment in utility node and run ANALYZE directly), and
* the orchestration of ANALYZE happens in the dispatcher. The high
* level logic is the same as in upstream, but a few functions have been
* modified to gather data from the segments, instead of reading directly
* from local disk:
*
* acquire_sample_rows(), when called in the dispatcher, calls into the
* segments to acquire the sample across all segments.
* RelationGetNumberOfBlocks() calls have been replaced with a wrapper
* function, AcquireNumberOfBlocks(), which likewise calls into the
* segments, to get total relation size across all segments.
*
* AcquireNumberOfBlocks() calls pg_relation_size(), which already
* contains the logic to gather the size from all segments.
*
* Acquiring the sample rows is more tricky. When called in dispatcher,
* acquire_sample_rows() calls a helper function called gp_acquire_sample_rows()
* in the segments, to collect a sample on each segment. It then merges
* the sample rows from each segment to produce a sample of the whole
* cluster. gp_acquire_sample_rows() in turn calls acquire_sample_rows(), to
* collect the sample on the segment.
*
* One complication with collecting the sample is the way that very
* large datums are handled. We don't want to transfer multi-gigabyte
* tuples from each segment. That would slow things down, and risk
* running out of memory, if the sample contains a lot of them. They
* are not very useful for statistics, anyway; hardly anyone builds an
* index or does lookups where the histogram or MCV is meaningful for
* very large keys. PostgreSQL also ignores any datums larger than
* WIDTH_THRESHOLD (1kB) in the statistics computation, and we use the
* same limit to restrict what gets transferred from the segments.
* We substitute the very large datums with NULLs in the sample, but
* keep track separately, which datums came out as NULLs because they
* were too large, as opposed to "real" NULLs.
*
*
* Merging leaf statistics with hyperloglog
* ----------------------------------------
*
* TODO: explain how this works.
*
* Portions Copyright (c) 1996-2019, 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/genam.h"
#include "access/multixact.h"
#include "access/relation.h"
#include "access/sysattr.h"
#include "access/table.h"
#include "access/tableam.h"
#include "access/transam.h"
#include "access/tupconvert.h"
#include "access/tuptoaster.h"
#include "access/visibilitymap.h"
#include "access/xact.h"
#include "catalog/catalog.h"
#include "catalog/index.h"
#include "catalog/indexing.h"
#include "catalog/pg_collation.h"
#include "catalog/pg_inherits.h"
#include "catalog/pg_namespace.h"
#include "catalog/pg_statistic_ext.h"
#include "commands/dbcommands.h"
#include "commands/tablecmds.h"
#include "commands/vacuum.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 "postmaster/autovacuum.h"
#include "statistics/extended_stats_internal.h"
#include "statistics/statistics.h"
#include "storage/bufmgr.h"
#include "storage/lmgr.h"
#include "storage/proc.h"
#include "storage/procarray.h"
#include "utils/acl.h"
#include "utils/attoptcache.h"
#include "utils/builtins.h"
#include "utils/datum.h"
#include "utils/fmgroids.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"
#include "catalog/heap.h"
#include "catalog/pg_am.h"
#include "cdb/cdbappendonlyam.h"
#include "cdb/cdbaocsam.h"
#include "cdb/cdbdisp_query.h"
#include "cdb/cdbdispatchresult.h"
#include "cdb/cdbtm.h"
#include "cdb/cdbutil.h"
#include "cdb/cdbvars.h"
#include "commands/analyzeutils.h"
#include "executor/spi.h"
#include "funcapi.h"
#include "libpq-fe.h"
#include "utils/builtins.h"
#include "utils/faultinjector.h"
#include "utils/hyperloglog/gp_hyperloglog.h"
#include "utils/snapmgr.h"
#include "utils/typcache.h"
/*
* For Hyperloglog, we define an error margin of 0.3%. If the number of
* distinct values estimated by hyperloglog is within an error of 0.3%,
* we consider everything as distinct.
*/
#define GP_HLL_ERROR_MARGIN 0.003
/* Fix attr number of return record of function gp_acquire_sample_rows */
#define FIX_ATTR_NUM 3
/* 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;
Bitmapset **acquire_func_colLargeRowIndexes;
static void do_analyze_rel(Relation onerel,
VacuumParams *params, List *va_cols,
AcquireSampleRowsFunc acquirefunc, BlockNumber relpages,
bool inh, bool in_outer_xact, int elevel,
gp_acquire_sample_rows_context *ctx);
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, int elevel);
static int acquire_sample_rows_dispatcher(Relation onerel, bool inh, int elevel,
HeapTuple *rows, int targrows,
double *totalrows, double *totaldeadrows);
static BlockNumber acquire_index_number_of_blocks(Relation indexrel, Relation tablerel);
static int compare_rows(const void *a, const void *b);
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);
static void analyze_rel_internal(Oid relid, RangeVar *relation,
VacuumParams *params, List *va_cols,
bool in_outer_xact, BufferAccessStrategy bstrategy,
gp_acquire_sample_rows_context *ctx);
static void acquire_hll_by_query(Relation onerel, int nattrs, VacAttrStats **attrstats, int elevel);
/*
* 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, gp_acquire_sample_rows_context *ctx)
{
bool optimizerBackup;
/*
* Temporarily disable ORCA because it's slow to start up, and it
* wouldn't come up with any better plan for the simple queries that
* we run.
*/
optimizerBackup = optimizer;
optimizer = false;
PG_TRY();
{
analyze_rel_internal(relid, relation, params, va_cols,
in_outer_xact, bstrategy, ctx);
}
/* Clean up in case of error. */
PG_CATCH();
{
optimizer = optimizerBackup;
/* Carry on with error handling. */
PG_RE_THROW();
}
PG_END_TRY();
optimizer = optimizerBackup;
}
static void
analyze_rel_internal(Oid relid, RangeVar *relation,
VacuumParams *params, List *va_cols, bool in_outer_xact,
BufferAccessStrategy bstrategy, gp_acquire_sample_rows_context *ctx)
{
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;
#ifdef FAULT_INJECTOR
FaultInjector_InjectFaultIfSet(
"analyze_after_hold_lock", DDLNotSpecified,
"", RelationGetRelationName(onerel));
#endif
/*
* analyze_rel can be called in 3 different contexts: explicitly by the user
* (eg. ANALYZE, VACUUM ANALYZE), implicitly by autovacuum, or implicitly by
* autostats.
*
* In the first case, we always want to make sure the user is the owner of the
* table. In the autovacuum case, it will be called as superuser so we don't
* really care, but the ownership check should always succeed. For autostats,
* we only do the check if gp_autostats_allow_nonowner=false, otherwise we can
* proceed with the analyze.
*
* 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 relation ownership could have
* changed in-between. Make sure to generate only logs for ANALYZE in
* this case.
*/
if (!(params->auto_stats && gp_autostats_allow_nonowner))
{
if (!vacuum_is_relation_owner(RelationGetRelid(onerel),
onerel->rd_rel,
params->options & VACOPT_ANALYZE))
{
{
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 = AcquireNumberOfBlocks(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 let other backends know I'm in ANALYZE.
*/
LWLockAcquire(ProcArrayLock, LW_EXCLUSIVE);
MyPgXact->vacuumFlags |= PROC_IN_ANALYZE;
LWLockRelease(ProcArrayLock);
/*
* Do the normal non-recursive ANALYZE. We can skip this for partitioned
* tables, which don't contain any rows.
*
* On QE, when receiving ANALYZE request through gp_acquire_sample_rows.
* We should only perform do_analyze_rel for the parent table only
* or all it's children tables. Because, QD will send two acquire sample
* rows requests to QE.
* To distinguish the two requests, we check the ctx->inherited value here.
*/
if (onerel->rd_rel->relkind != RELKIND_PARTITIONED_TABLE && (!ctx || !ctx->inherited))
do_analyze_rel(onerel, params, va_cols, acquirefunc,
relpages, false, in_outer_xact, elevel, ctx);
/*
* If there are child tables, do recursive ANALYZE.
*/
if (onerel->rd_rel->relhassubclass && (!ctx || ctx->inherited))
do_analyze_rel(onerel, params, va_cols, acquirefunc, relpages,
true, in_outer_xact, elevel, ctx);
/* MPP-6929: metadata tracking */
if (!vacuumStatement_IsTemporary(onerel) && (Gp_role == GP_ROLE_DISPATCH))
{
char *asubtype = "";
if (IsAutoVacuumWorkerProcess())
asubtype = "AUTO";
MetaTrackUpdObject(RelationRelationId,
RelationGetRelid(onerel),
GetUserId(),
"ANALYZE",
asubtype
);
}
/*
* 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);
/*
* Reset my PGXACT flag. Note: we need this here, and not in vacuum_rel,
* because the vacuum flag is cleared by the end-of-xact code.
*/
LWLockAcquire(ProcArrayLock, LW_EXCLUSIVE);
MyPgXact->vacuumFlags &= ~PROC_IN_ANALYZE;
LWLockRelease(ProcArrayLock);
}
/*
* 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, gp_acquire_sample_rows_context *ctx)
{
int attr_cnt,
tcnt,
i,
ind;
Relation *Irel;
int nindexes;
bool hasindex;
VacAttrStats **vacattrstats;
AnlIndexData *indexdata;
int targrows,
numrows;
double totalrows,
totaldeadrows;
HeapTuple *rows;
PGRUsage ru0;
TimestampTz starttime = 0;
MemoryContext caller_context;
Oid save_userid;
int save_sec_context;
int save_nestlevel;
Bitmapset **colLargeRowIndexes;
bool sample_needed;
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();
/* measure elapsed time iff autovacuum logging requires it */
if (IsAutoVacuumWorkerProcess() && params->log_min_duration >= 0)
{
pg_rusage_init(&ru0);
if (params->log_min_duration > 0)
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, elevel);
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, elevel);
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 (!inh)
vac_open_indexes(onerel, AccessShareLock, &nindexes, &Irel);
else
{
Irel = NULL;
nindexes = 0;
}
hasindex = (nindexes > 0);
indexdata = NULL;
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 && 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(indexpr_item);
thisdata->vacattrstats[tcnt] =
examine_attribute(Irel[ind], i + 1, indexkey, elevel);
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.)
*
* GPDB: If the caller specified the 'targrows', just use that.
*/
if (ctx)
{
targrows = ctx->targrows;
}
else /* funny indentation to avoid re-indenting upstream code */
{
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;
}
}
}
/* end of funny indentation */
/*
* Maintain information if the row of a column exceeds WIDTH_THRESHOLD
*/
colLargeRowIndexes = (Bitmapset **) palloc0(sizeof(Bitmapset *) * onerel->rd_att->natts);
if ((params->options & VACOPT_FULLSCAN) != 0)
{
if (onerel->rd_rel->relispartition)
{
acquire_hll_by_query(onerel, attr_cnt, vacattrstats, elevel);
ereport(elevel, (errmsg("HLL FULL SCAN")));
}
}
sample_needed = needs_sample(vacattrstats, attr_cnt);
if (sample_needed)
{
if (ctx)
MemoryContextSwitchTo(caller_context);
rows = (HeapTuple *) palloc(targrows * sizeof(HeapTuple));
/*
* Acquire the sample rows
*
* colLargeRowindexes is passed out-of-band, in a global variable,
* to avoid changing the function signature from upstream's.
*/
acquire_func_colLargeRowIndexes = colLargeRowIndexes;
if (inh)
numrows = acquire_inherited_sample_rows(onerel, elevel,
rows, targrows,
&totalrows, &totaldeadrows);
else
numrows = (*acquirefunc) (onerel, elevel,
rows, targrows,
&totalrows, &totaldeadrows);
acquire_func_colLargeRowIndexes = NULL;
if (ctx)
MemoryContextSwitchTo(anl_context);
}
else
{
/* If we're just merging stats from leafs, these are not needed either */
totalrows = 0;
totaldeadrows = 0;
numrows = 0;
rows = NULL;
}
if (ctx)
{
ctx->sample_rows = rows;
ctx->num_sample_rows = numrows;
ctx->totalrows = totalrows;
ctx->totaldeadrows = 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.
*
* When we have a root partition, we use the leaf partition statistics to
* derive root table statistics. In that case, we do not need to collect a
* sample. Therefore, the statistics calculation depends on root level have
* any tuples. In addition, we continue for statistics calculation if
* optimizer_analyze_root_partition or ROOTPARTITION is specified in the
* ANALYZE statement.
*/
if (numrows > 0 || !sample_needed)
{
HeapTuple *validRows = (HeapTuple *) palloc(numrows * sizeof(HeapTuple));
MemoryContext col_context,
old_context;
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];
/*
* utilize hyperloglog and merge utilities to derive
* root table statistics by directly calling merge_leaf_stats()
* if all leaf partition attributes are analyzed
*/
if(stats->merge_stats)
{
(*stats->compute_stats) (stats, std_fetch_func, 0, 0);
MemoryContextResetAndDeleteChildren(col_context);
continue;
}
Assert(sample_needed);
Bitmapset *rowIndexes = colLargeRowIndexes[stats->attr->attnum - 1];
int validRowsLength;
/* If there are too wide rows in the sample, remove them
* from the sample being sent for stats collection
*/
if (rowIndexes)
{
validRowsLength = 0;
for (int rownum = 0; rownum < numrows; rownum++)
{
/* if row is too wide, leave it out of the sample */
if (bms_is_member(rownum, rowIndexes))
continue;
validRows[validRowsLength] = rows[rownum];
validRowsLength++;
}
stats->rows = validRows;
}
else
{
stats->rows = rows;
validRowsLength = numrows;
}
AttributeOpts *aopt =
get_attribute_options(onerel->rd_id, stats->attr->attnum);
stats->tupDesc = onerel->rd_att;
if (validRowsLength > 0)
{
stats->compute_stats(stats,
std_fetch_func,
validRowsLength, // numbers of rows in sample excluding toowide if any.
totalrows);
/*
* Store HLL/HLL fullscan information for leaf partitions in
* the stats object
*/
if (onerel->rd_rel->relkind == RELKIND_RELATION && onerel->rd_rel->relispartition)
{
MemoryContext old_context;
Datum *hll_values;
old_context = MemoryContextSwitchTo(stats->anl_context);
hll_values = (Datum *) palloc(sizeof(Datum));
int16 hll_length = 0;
int16 stakind = 0;
if(stats->stahll_full != NULL)
{
hll_length = datumGetSize(PointerGetDatum(stats->stahll_full), false, -1);
hll_values[0] = datumCopy(PointerGetDatum(stats->stahll_full), false, hll_length);
stakind = STATISTIC_KIND_FULLHLL;
}
else if(stats->stahll != NULL)
{
((GpHLLCounter) (stats->stahll))->relPages = relpages;
((GpHLLCounter) (stats->stahll))->relTuples = totalrows;
hll_length = gp_hyperloglog_len((GpHLLCounter)stats->stahll);
hll_values[0] = datumCopy(PointerGetDatum(stats->stahll), false, hll_length);
stakind = STATISTIC_KIND_HLL;
}
MemoryContextSwitchTo(old_context);
if (stakind > 0)
{
stats->stakind[STATISTIC_NUM_SLOTS-1] = stakind;
stats->stavalues[STATISTIC_NUM_SLOTS-1] = hll_values;
stats->numvalues[STATISTIC_NUM_SLOTS-1] = 1;
stats->statyplen[STATISTIC_NUM_SLOTS-1] = hll_length;
}
}
}
else
{
// All the rows were too wide to be included in the sample. We cannot
// do much in that case, but at least we know there were no NULLs, and
// that every item was >= WIDTH_THRESHOLD in width.
stats->stats_valid = true;
stats->stanullfrac = 0.0;
stats->stawidth = WIDTH_THRESHOLD;
stats->stadistinct = 0.0; /* "unknown" */
}
stats->rows = rows; // Reset to original rows
/*
* If the appropriate flavor of the n_distinct option is
* specified, override with the corresponding value.
*/
aopt = get_attribute_options(onerel->rd_id, stats->attr->attnum);
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;
}
MemoryContextResetAndDeleteChildren(col_context);
}
/*
* Datums exceeding WIDTH_THRESHOLD are masked as NULL in the sample, and
* are used as is to evaluate index statistics. It is less likely to have
* indexes on very wide columns, so the effect will be minimal.
*/
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(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, totalrows, numrows, rows, attr_cnt,
vacattrstats);
}
/*
* Update pages/tuples stats in pg_class ... but not if we're doing
* inherited stats.
*
* GPDB_92_MERGE_FIXME: In postgres it is sufficient to check the number of
* pages that are visible with visibilitymap_count(), but in GPDB this
* needs to be the count of all pages marked all visible across the all the
* QEs. We need to gather this information from the segments and then update
* it here.
*/
if (!inh)
{
BlockNumber relallvisible;
if (RelationIsAppendOptimized(onerel))
relallvisible = 0;
else
visibilitymap_count(onerel, &relallvisible, NULL);
vac_update_relstats(onerel,
relpages,
totalrows,
relallvisible,
hasindex,
InvalidTransactionId,
InvalidMultiXactId,
in_outer_xact,
false /* isVacuum */);
}
/*
* Same for indexes. Vacuum always scans all indexes, so if we're part of
* VACUUM ANALYZE, don't overwrite the accurate count already inserted by
* VACUUM.
*/
if (!inh && !(params->options & VACOPT_VACUUM))
{
for (ind = 0; ind < nindexes; ind++)
{
AnlIndexData *thisdata = &indexdata[ind];
double totalindexrows;
BlockNumber estimatedIndexPages;
if (totalrows < 1.0)
{
/**
* If there are no rows in the relation, no point trying to estimate
* number of pages in the index.
*/
elog(elevel, "ANALYZE skipping index %s since relation %s has no rows.",
RelationGetRelationName(Irel[ind]), RelationGetRelationName(onerel));
estimatedIndexPages = 1;
}
else
{
/**
* NOTE: we don't attempt to estimate the number of tuples in an index.
* We will assume it to be equal to the estimated number of tuples in the relation.
* This does not hold for partial indexes. The number of tuples matching will be
* derived in selfuncs.c using the base table statistics.
*/
estimatedIndexPages = acquire_index_number_of_blocks(Irel[ind], onerel);
elog(elevel, "ANALYZE estimated relpages=%u for index %s",
estimatedIndexPages, RelationGetRelationName(Irel[ind]));
}
totalindexrows = ceil(thisdata->tupleFract * totalrows);
vac_update_relstats(Irel[ind],
estimatedIndexPages,
totalindexrows,
0,
false,
InvalidTransactionId,
InvalidMultiXactId,
in_outer_xact,
false /* isVacuum */);
}
}
/*
* Report ANALYZE to the stats collector, too. However, if doing
* inherited stats we shouldn't report, because the stats collector only
* tracks per-table stats. 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));
/* If this isn't part of VACUUM ANALYZE, let index AMs do cleanup */
if (!(params->options & VACOPT_VACUUM))
{
for (ind = 0; ind < nindexes; ind++)
{
IndexBulkDeleteResult *stats;
IndexVacuumInfo ivinfo;
ivinfo.index = Irel[ind];
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 (IsAutoVacuumWorkerProcess() && params->log_min_duration >= 0)
{
if (params->log_min_duration == 0 ||
TimestampDifferenceExceeds(starttime, GetCurrentTimestamp(),
params->log_min_duration))
ereport(LOG,
(errmsg("automatic analyze of table \"%s.%s.%s\" system usage: %s",
get_database_name(MyDatabaseId),
get_namespace_name(RelationGetNamespace(onerel)),
RelationGetRelationName(onerel),
pg_rusage_show(&ru0))));
}
/* 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();
/*
* 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->attr->attnum;
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];
AttributeOpts *aopt =
get_attribute_options(stats->attr->attrelid,
stats->attr->attnum);
stats->exprvals = exprvals + i;
stats->exprnulls = exprnulls + i;
stats->rowstride = attr_cnt;
stats->compute_stats(stats,
ind_fetch_func,
numindexrows,
totalindexrows);
/*
* If the n_distinct option is specified, it overrides the
* above computation. For indices, we always use just
* n_distinct, not n_distinct_inherited.
*/
if (aopt != NULL && aopt->n_distinct != 0.0)
stats->stadistinct = aopt->n_distinct;
MemoryContextResetAndDeleteChildren(col_context);
}
}
/* And clean up */
MemoryContextSwitchTo(ind_context);
ExecDropSingleTupleTableSlot(slot);
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.
*
* 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, int elevel)
{
Form_pg_attribute attr = TupleDescAttr(onerel->rd_att, attnum - 1);
HeapTuple typtuple;
VacAttrStats *stats;
int i;
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. Note that we only have a copy of the
* fixed fields of the pg_attribute tuple.
*/
stats = (VacAttrStats *) palloc0(sizeof(VacAttrStats));
stats->elevel = elevel;
stats->attr = (Form_pg_attribute) palloc(ATTRIBUTE_FIXED_PART_SIZE);
memcpy(stats->attr, attr, ATTRIBUTE_FIXED_PART_SIZE);
/*
* 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-1; i++)
{
stats->statypid[i] = stats->attrtypid;
stats->statyplen[i] = stats->attrtype->typlen;
stats->statypbyval[i] = stats->attrtype->typbyval;
stats->statypalign[i] = stats->attrtype->typalign;
}
/*
* The last slots of statistics is reserved for hyperloglog counter which
* is saved as a bytea. Therefore the type information is hardcoded for the
* bytea.
*/
stats->statypid[i] = BYTEAOID; // oid for bytea
stats->statyplen[i] = -1; // variable length type
stats->statypbyval[i] = false; // bytea is pass by reference
stats->statypalign[i] = 'i'; // INT alignment (4-byte)
/*
* 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->attr);
pfree(stats);
return NULL;
}
return stats;
}
/*
* 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.
*
* The returned list of tuples is in order by physical position in the table.
* (We will rely on this later to derive correlation estimates.)
*
* GPDB: If we are the dispatcher, then issue analyze on the segments and
* collect the statistics from them.
*/
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 */
BlockNumber totalblocks;
TransactionId OldestXmin;
BlockSamplerData bs;
ReservoirStateData rstate;
TupleTableSlot *slot;
TableScanDesc scan;
Assert(targrows > 0);
if (Gp_role == GP_ROLE_DISPATCH &&
onerel->rd_cdbpolicy && !GpPolicyIsEntry(onerel->rd_cdbpolicy))
{
/* Fetch sample from the segments. */
return acquire_sample_rows_dispatcher(onerel, false, elevel,
rows, targrows,
totalrows, totaldeadrows);
}
/*
* GPDB: Analyze does make a lot of assumptions regarding the file layout of a
* relation. These assumptions are heap specific and do not hold for AO/AOCO
* relations. In the case of AO/AOCO, what is actually needed and used instead
* of number of blocks, is number of tuples.
*
* GPDB_12_MERGE_FIXME: BlockNumber is uint32 and Number of tuples is uint64.
* That means that after row number UINT_MAX we will never analyze the table.
*/
if (RelationIsAppendOptimized(onerel))
{
BlockNumber pages;
double tuples;
double allvisfrac;
int32 attr_widths;
table_relation_estimate_size(onerel, &attr_widths, &pages,
&tuples, &allvisfrac);
if (tuples > UINT_MAX)
tuples = UINT_MAX;
totalblocks = (BlockNumber)tuples;
}
else
totalblocks = RelationGetNumberOfBlocks(onerel);
/* Need a cutoff xmin for HeapTupleSatisfiesVacuum */
OldestXmin = GetOldestXmin(onerel, PROCARRAY_FLAGS_VACUUM);
/* Prepare for sampling block numbers */
BlockSampler_Init(&bs, totalblocks, targrows, random());
/* Prepare for sampling rows */
reservoir_init_selection_state(&rstate, targrows);
scan = table_beginscan_analyze(onerel);
slot = table_slot_create(onerel, NULL);
/* Outer loop over blocks to sample */
while (BlockSampler_HasMore(&bs))
{
BlockNumber targblock = BlockSampler_Next(&bs);
vacuum_delay_point();
if (!table_scan_analyze_next_block(scan, targblock, vac_strategy))
continue;
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;
}
}
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((void *) rows, numrows, sizeof(HeapTuple), compare_rows);
/*
* 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;
}
/*
* qsort comparator for sorting rows[] array
*/
static int
compare_rows(const void *a, const void *b)
{
HeapTuple ha = *(const HeapTuple *) a;
HeapTuple hb = *(const HeapTuple *) b;
/*
* GPDB_12_MERGE_FIXME: For AO/AOCO tables, blocknumber does not have a
* meaning and is not set. The current implementation of analyze makes
* assumptions about the file layout which do not hold for these two cases.
* The compare function should maintain the row order as consrtucted, hence
* return 0;
*
* There should be no apparent and measurable perfomance hit from calling
* this function.
*
* One possible proper fix is to refactor analyze to use the tableam api and
* this sorting should move to the specific implementation.
*/
if (!BlockNumberIsValid(ItemPointerGetBlockNumberNoCheck(&ha->t_self)))
return 0;
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.
*/
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;
/*
* Like in acquire_sample_rows(), if we're in the QD, fetch the sample
* from segments.
*/
if (Gp_role == GP_ROLE_DISPATCH)
{
return acquire_sample_rows_dispatcher(onerel,
true, /* inherited stats */
elevel, rows, targrows,
totalrows, totaldeadrows);
}
/*
* 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);
*totalrows = 0;
*totaldeadrows = 0;
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 = AcquireNumberOfBlocks(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.)
*/
numrows = 0;
*totalrows = 0;
*totaldeadrows = 0;
for (i = 0; i < nrels; i++)
{
Relation childrel = rels[i];
AcquireSampleRowsFunc acquirefunc = acquirefuncs[i];
double childblocks = relblocks[i];
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 &&
!equalTupleDescs(RelationGetDescr(childrel),
RelationGetDescr(onerel),
false))
{
TupleConversionMap *map;
map = convert_tuples_by_name(RelationGetDescr(childrel),
RelationGetDescr(onerel),
gettext_noop("could not convert row type"));
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);
}
return numrows;
}
/*
* This function acquires the HLL counter for the entire table by
* using the hyperloglog extension gp_hyperloglog_accum().
*
* Unlike acquire_sample_rows(), this returns the HLL counter for
* the entire table, and not jsut a sample, and it stores the HLL
* counter into a separate attribute in the stats stahll_full to
* distinguish it from the HLL for sampled data. This functions scans
* the full table only once.
*/
static void
acquire_hll_by_query(Relation onerel, int nattrs, VacAttrStats **attrstats, int elevel)
{
StringInfoData str, columnStr;
int i;
int ret;
Datum *vals;
MemoryContext oldcxt;
const char *schemaName = get_namespace_name(RelationGetNamespace(onerel));
initStringInfo(&str);
initStringInfo(&columnStr);
for (i = 0; i < nattrs; i++)
{
const char *attname = quote_identifier(NameStr(attrstats[i]->attr->attname));
appendStringInfo(&columnStr, "pg_catalog.gp_hyperloglog_accum(%s)", attname);
if(i != nattrs-1)
appendStringInfo(&columnStr, ", ");
}
appendStringInfo(&str, "select %s from %s.%s as Ta ",
columnStr.data,
quote_identifier(schemaName),
quote_identifier(RelationGetRelationName(onerel)));
oldcxt = CurrentMemoryContext;
if (SPI_OK_CONNECT != SPI_connect())
ereport(ERROR,
(errcode(ERRCODE_INTERNAL_ERROR),
errmsg("unable to connect to execute internal query")));
elog(elevel, "Executing SQL: %s", str.data);
/*
* Do the query. We pass readonly==false, to force SPI to take a new
* snapshot. That ensures that we see all changes by our own transaction.
*/
ret = SPI_execute(str.data, false, 0);
Assert(ret > 0);
/*
* targrows in analyze_rel_internal() is an int,
* it's unlikely that this query will return more rows
*/
Assert(SPI_processed < 2);
int sampleTuples = (int) SPI_processed;
/* Ok, read in the tuples to *rows */
MemoryContextSwitchTo(oldcxt);
vals = (Datum *) palloc0(nattrs * sizeof(Datum));
bool isNull = false;
for (i = 0; i < sampleTuples; i++)
{
HeapTuple sampletup = SPI_tuptable->vals[i];
int j;
for (j = 0; j < nattrs; j++)
{
int tupattnum = attrstats[j]->tupattnum;
Assert(tupattnum >= 1 && tupattnum <= nattrs);
vals[tupattnum - 1] = heap_getattr(sampletup, j + 1,
SPI_tuptable->tupdesc,
&isNull);
if (isNull)
{
attrstats[j]->stahll_full = (bytea *)gp_hyperloglog_init_def();
continue;
}
int16 typlen;
bool typbyval;
get_typlenbyval(SPI_tuptable->tupdesc->tdtypeid, &typlen, &typbyval);
int hll_length = datumGetSize(vals[tupattnum-1], typbyval, typlen);
attrstats[j]->stahll_full = (bytea *)datumCopy(PointerGetDatum(vals[tupattnum - 1]), false, hll_length);
}
}
SPI_finish();
}
/*
* Compute relation size.
*
* In upstream, this is a simple RelationGetNumberOfBlocks() call.
* In GPDB if we're in the dispatcher, we need to get the size from the
* segments.
*/
BlockNumber
AcquireNumberOfBlocks(Relation onerel)
{
int64 totalbytes;
if (Gp_role == GP_ROLE_DISPATCH &&
onerel->rd_cdbpolicy && !GpPolicyIsEntry(onerel->rd_cdbpolicy))
{
/* Query the segments using pg_relation_size(<rel>). */
char relsize_sql[100];
snprintf(relsize_sql, sizeof(relsize_sql),
"select pg_catalog.pg_relation_size(%u, 'main')", RelationGetRelid(onerel));
totalbytes = get_size_from_segDBs(relsize_sql);
if (GpPolicyIsReplicated(onerel->rd_cdbpolicy))
{
/*
* pg_relation_size sums up the table size on each segment. That's
* correct for hash and randomly distributed tables. But for a
* replicated table, we want pg_class.relpages to count the data
* only once.
*/
totalbytes = totalbytes / onerel->rd_cdbpolicy->numsegments;
}
return RelationGuessNumberOfBlocksFromSize(totalbytes);
}
/* Check size on this server. */
else
{
return RelationGetNumberOfBlocks(onerel);
}
}
/*
* Compute index relation's size.
*
* Like AcquireNumberOfBlocks(), but for indexes. Indexes don't
* have a distribution policy, so we use the parent table's policy
* to determine whether we need to get the size on segments or
* locally.
*/
static BlockNumber
acquire_index_number_of_blocks(Relation indexrel, Relation tablerel)
{
int64 totalbytes;
if (Gp_role == GP_ROLE_DISPATCH &&
tablerel->rd_cdbpolicy && !GpPolicyIsEntry(tablerel->rd_cdbpolicy))
{
/* Query the segments using pg_relation_size(<rel>). */
char relsize_sql[100];
snprintf(relsize_sql, sizeof(relsize_sql),
"select pg_catalog.pg_relation_size(%u, 'main')", RelationGetRelid(indexrel));
totalbytes = get_size_from_segDBs(relsize_sql);
if (GpPolicyIsReplicated(tablerel->rd_cdbpolicy))
{
/*
* pg_relation_size sums up the table size on each segment. That's
* correct for hash and randomly distributed tables. But for a
* replicated table, we want pg_class.relpages to count the data
* only once.
*/
totalbytes = totalbytes / tablerel->rd_cdbpolicy->numsegments;
}
return RelationGuessNumberOfBlocksFromSize(totalbytes);
}
/* Check size on this server. */
else
{
return RelationGetNumberOfBlocks(indexrel);
}
}
/*
* parse_record_to_string
*
* CDB: a copy of record_in, but only parse the record string
* into separate strs for each column.
*/
static void
parse_record_to_string(char *string, TupleDesc tupdesc, char** values, bool *nulls)
{
char *ptr;
int ncolumns;
int i;
bool needComma;
StringInfoData buf;
Assert(string != NULL);
Assert(values != NULL);
Assert(nulls != NULL);
ncolumns = tupdesc->natts;
needComma = false;
/*
* Scan the string. We use "buf" to accumulate the de-quoted data for
* each column, which is then fed to the appropriate input converter.
*/
ptr = string;
/* Allow leading whitespace */
while (*ptr && isspace((unsigned char) *ptr))
ptr++;
if (*ptr++ != '(')
{
ereport(ERROR,
(errcode(ERRCODE_INVALID_TEXT_REPRESENTATION),
errmsg("malformed record literal: \"%s\"", string),
errdetail("Missing left parenthesis.")));
}
initStringInfo(&buf);
for (i = 0; i < ncolumns; i++)
{
/* Ignore dropped columns in datatype, but fill with nulls */
if (TupleDescAttr(tupdesc, i)->attisdropped)
{
values[i] = NULL;
nulls[i] = true;
continue;
}
if (needComma)
{
/* Skip comma that separates prior field from this one */
if (*ptr == ',')
ptr++;
else
{
/* *ptr must be ')' */
ereport(ERROR,
(errcode(ERRCODE_INVALID_TEXT_REPRESENTATION),
errmsg("malformed record literal: \"%s\"", string),
errdetail("Too few columns.")));
}
}
/* Check for null: completely empty input means null */
if (*ptr == ',' || *ptr == ')')
{
values[i] = NULL;
nulls[i] = true;
}
else
{
/* Extract string for this column */
bool inquote = false;
resetStringInfo(&buf);
while (inquote || !(*ptr == ',' || *ptr == ')'))
{
char ch = *ptr++;
if (ch == '\0')
{
ereport(ERROR,
(errcode(ERRCODE_INVALID_TEXT_REPRESENTATION),
errmsg("malformed record literal: \"%s\"",
string),
errdetail("Unexpected end of input.")));
}
if (ch == '\\')
{
if (*ptr == '\0')
{
ereport(ERROR,
(errcode(ERRCODE_INVALID_TEXT_REPRESENTATION),
errmsg("malformed record literal: \"%s\"",
string),
errdetail("Unexpected end of input.")));
}
appendStringInfoChar(&buf, *ptr++);
}
else if (ch == '"')
{
if (!inquote)
inquote = true;
else if (*ptr == '"')
{
/* doubled quote within quote sequence */
appendStringInfoChar(&buf, *ptr++);
}
else
inquote = false;
}
else
appendStringInfoChar(&buf, ch);
}
values[i] = palloc(strlen(buf.data) + 1);
memcpy(values[i], buf.data, strlen(buf.data) + 1);
nulls[i] = false;
}
/*
* Prep for next column
*/
needComma = true;
}
if (*ptr++ != ')')
{
ereport(ERROR,
(errcode(ERRCODE_INVALID_TEXT_REPRESENTATION),
errmsg("malformed record literal: \"%s\"", string),
errdetail("Too many columns.")));
}
/* Allow trailing whitespace */
while (*ptr && isspace((unsigned char) *ptr))
ptr++;
if (*ptr)
{
ereport(ERROR,
(errcode(ERRCODE_INVALID_TEXT_REPRESENTATION),
errmsg("malformed record literal: \"%s\"", string),
errdetail("Junk after right parenthesis.")));
}
}
/*
* Collect a sample from segments.
*
* Calls the gp_acquire_sample_rows() helper function on each segment,
* and merges the results.
*/
static int
acquire_sample_rows_dispatcher(Relation onerel, bool inh, int elevel,
HeapTuple *rows, int targrows,
double *totalrows, double *totaldeadrows)
{
/*
* 'colLargeRowIndexes' is essentially an argument, but it's passed via a
* global variable to avoid changing the AcquireSampleRowsFunc prototype.
*/
Bitmapset **colLargeRowIndexes = acquire_func_colLargeRowIndexes;
TupleDesc relDesc = RelationGetDescr(onerel);
TupleDesc funcTupleDesc;
TupleDesc sampleTupleDesc;
AttInMetadata *attinmeta;
StringInfoData str;
int sampleTuples; /* 32 bit - assume that number of tuples will not > 2B */
char **funcRetValues;
bool *funcRetNulls;
char **values;
int numLiveColumns;
int perseg_targrows;
int ncolumns;
CdbPgResults cdb_pgresults = {NULL, 0};
int i;
int index = 0;
Assert(targrows > 0.0);
/*
* Acquire an evenly-sized sample from each segment.
*
* XXX: If there's a significant bias between the segments, i.e. some
* segments have a lot more rows than others, the sample will biased, too.
* Would be nice to improve that, but it's not clear how. We could issue
* another query to get the table size from each segment first, and use
* those to weigh the sample size to get from each segment. But that'd
* require an extra round-trip, which is also not good. The caller
* actually already did that, to get the total relation size, but it
* doesn't pass that down to us, let alone the per-segment sizes.
*/
if (GpPolicyIsReplicated(onerel->rd_cdbpolicy))
perseg_targrows = targrows;
else if (GpPolicyIsPartitioned(onerel->rd_cdbpolicy))
perseg_targrows = targrows / onerel->rd_cdbpolicy->numsegments;
else
elog(ERROR, "acquire_sample_rows_dispatcher() cannot be used on a non-distributed table");
/*
* Count the number of columns, excluding dropped columns. We'll need that
* later.
*/
numLiveColumns = 0;
for (i = 0; i < relDesc->natts; i++)
{
Form_pg_attribute attr = TupleDescAttr(relDesc, i);
if (attr->attisdropped)
continue;
numLiveColumns++;
}
/*
* Construct SQL command to dispatch to segments.
*
* Did not use 'select * from pg_catalog.gp_acquire_sample_rows(...) as (..);'
* here. Because it requires to specify columns explicitly which leads to
* permission check on each columns. This is not consistent with GPDB5 and
* may result in different behaviour under different acl configuration.
*/
initStringInfo(&str);
appendStringInfo(&str, "select pg_catalog.gp_acquire_sample_rows(%u, %d, '%s');",
RelationGetRelid(onerel),
perseg_targrows,
inh ? "t" : "f");
/*
* Execute it.
*/
elog(elevel, "Executing SQL: %s", str.data);
CdbDispatchCommand(str.data, DF_WITH_SNAPSHOT | DF_CANCEL_ON_ERROR, &cdb_pgresults);
/*
* Build a modified tuple descriptor for the table.
*
* Some datatypes need special treatment, so we cannot use the relation's
* original tupledesc.
*
* Also create tupledesc of return record of function gp_acquire_sample_rows.
*/
sampleTupleDesc = CreateTupleDescCopy(relDesc);
ncolumns = numLiveColumns + FIX_ATTR_NUM;
funcTupleDesc = CreateTemplateTupleDesc(ncolumns);
TupleDescInitEntry(funcTupleDesc, (AttrNumber) 1, "", FLOAT8OID, -1, 0);
TupleDescInitEntry(funcTupleDesc, (AttrNumber) 2, "", FLOAT8OID, -1, 0);
TupleDescInitEntry(funcTupleDesc, (AttrNumber) 3, "", TEXTOID, -1, 0);
for (i = 0; i < relDesc->natts; i++)
{
Form_pg_attribute attr = TupleDescAttr(relDesc, i);
Oid typid = gp_acquire_sample_rows_col_type(attr->atttypid);
TupleDescAttr(sampleTupleDesc, i)->atttypid = typid;
if (!attr->attisdropped)
{
TupleDescInitEntry(funcTupleDesc, (AttrNumber) 4 + index, "",
typid, attr->atttypmod, attr->attndims);
index++;
}
}
/* For RECORD results, make sure a typmod has been assigned */
Assert(funcTupleDesc->tdtypeid == RECORDOID && funcTupleDesc->tdtypmod < 0);
assign_record_type_typmod(funcTupleDesc);
attinmeta = TupleDescGetAttInMetadata(sampleTupleDesc);
/*
* Read the result set from each segment. Gather the sample rows *rows,
* and sum up the summary rows for grand 'totalrows' and 'totaldeadrows'.
*/
funcRetValues = (char **) palloc0(funcTupleDesc->natts * sizeof(char *));
funcRetNulls = (bool *) palloc(funcTupleDesc->natts * sizeof(bool));
values = (char **) palloc0(relDesc->natts * sizeof(char *));
sampleTuples = 0;
*totalrows = 0;
*totaldeadrows = 0;
for (int resultno = 0; resultno < cdb_pgresults.numResults; resultno++)
{
struct pg_result *pgresult = cdb_pgresults.pg_results[resultno];
bool got_summary = false;
double this_totalrows = 0;
double this_totaldeadrows = 0;
if (PQresultStatus(pgresult) != PGRES_TUPLES_OK)
{
cdbdisp_clearCdbPgResults(&cdb_pgresults);
ereport(ERROR,
(errmsg("unexpected result from segment: %d",
PQresultStatus(pgresult))));
}
if (GpPolicyIsReplicated(onerel->rd_cdbpolicy))
{
/*
* A replicated table has the same data in all segments. Arbitrarily,
* use the sample from the first segment, and discard the rest.
* (This is rather inefficient, of course. It would be better to
* dispatch to only one segment, but there is no easy API for that
* in the dispatcher.)
*/
if (resultno > 0)
continue;
}
for (int rowno = 0; rowno < PQntuples(pgresult); rowno++)
{
/*
* We cannot use record_in function to get row record here.
* Since the result row may contain just the totalrows info where the data columns
* are NULLs. Consider domain: 'create domain dnotnull varchar(15) NOT NULL;'
* NULLs are not allowed in data columns.
*/
char * rowStr = PQgetvalue(pgresult, rowno, 0);
if (rowStr == NULL)
elog(ERROR, "got NULL pointer from return value of gp_acquire_sample_rows");
parse_record_to_string(rowStr, funcTupleDesc, funcRetValues, funcRetNulls);
if (!funcRetNulls[0])
{
/* This is a summary row. */
if (got_summary)
elog(ERROR, "got duplicate summary row from gp_acquire_sample_rows");
this_totalrows = DatumGetFloat8(DirectFunctionCall1(float8in,
CStringGetDatum(funcRetValues[0])));
this_totaldeadrows = DatumGetFloat8(DirectFunctionCall1(float8in,
CStringGetDatum(funcRetValues[1])));
got_summary = true;
}
else
{
/* This is a sample row. */
if (sampleTuples >= targrows)
elog(ERROR, "too many sample rows received from gp_acquire_sample_rows");
/* Read the 'toolarge' bitmap, if any */
if (colLargeRowIndexes && !funcRetNulls[2])
{
char *toolarge;
toolarge = funcRetValues[2];
if (strlen(toolarge) != numLiveColumns)
elog(ERROR, "'toolarge' bitmap has incorrect length");
index = 0;
for (i = 0; i < relDesc->natts; i++)
{
Form_pg_attribute attr = TupleDescAttr(relDesc, i);
if (attr->attisdropped)
continue;
if (toolarge[index] == '1')
colLargeRowIndexes[i] = bms_add_member(colLargeRowIndexes[i], sampleTuples);
index++;
}
}
/* Process the columns */
index = 0;
for (i = 0; i < relDesc->natts; i++)
{
Form_pg_attribute attr = TupleDescAttr(relDesc, i);
if (attr->attisdropped)
continue;
if (funcRetNulls[3 + index])
values[i] = NULL;
else
values[i] = funcRetValues[3 + index];
index++; /* Move index to the next result set attribute */
}
rows[sampleTuples] = BuildTupleFromCStrings(attinmeta, values);
sampleTuples++;
/*
* note: we don't set the OIDs in the sample. ANALYZE doesn't
* collect stats for them
*/
}
}
if (!got_summary)
elog(ERROR, "did not get summary row from gp_acquire_sample_rows");
if (resultno >= onerel->rd_cdbpolicy->numsegments)
{
/*
* This result is for a segment that's not holding any data for this
* table. Should get 0 rows.
*/
if (this_totalrows != 0)
elog(WARNING, "table \"%s\" contains rows in segment %d, which is outside the # of segments for the table's policy (%d segments)",
RelationGetRelationName(onerel), resultno, onerel->rd_cdbpolicy->numsegments);
}
(*totalrows) += this_totalrows;
(*totaldeadrows) += this_totaldeadrows;
}
for (i = 0; i < funcTupleDesc->natts; i++)
{
if (funcRetValues[i])
pfree(funcRetValues[i]);
}
pfree(funcRetValues);
pfree(funcRetNulls);
pfree(values);
cdbdisp_clearCdbPgResults(&cdb_pgresults);
return sampleTuples;
}
/*
* 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;
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->attr->attnum);
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]);
/* XXX knows more than it should about type float4: */
arry = construct_array(numdatums, nnum,
FLOAT4OID,
sizeof(float4), FLOAT4PASSBYVAL, 'i');
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->attr->attnum),
BoolGetDatum(inh));
if (HeapTupleIsValid(oldtup))
{
/* Yes, replace it */
stup = heap_modify_tuple(oldtup,
RelationGetDescr(sd),
values,
nulls,
replaces);
ReleaseSysCache(oldtup);
CatalogTupleUpdate(sd, &stup->t_self, stup);
}
else
{
/* No, insert new tuple */
stup = heap_form_tuple(RelationGetDescr(sd), values, nulls);
CatalogTupleInsert(sd, stup);
}
heap_freetuple(stup);
}
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.
*
*==========================================================================
*/
/*
* In PostgreSQL, WIDTH_THRESHOLD is here, but we've moved it to vacuum.h in
* GPDB.
*/
#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 void merge_leaf_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);
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)
{
Form_pg_attribute attr = stats->attr;
Oid ltopr;
Oid eqopr;
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;
/* Look for default "<" and "=" operators for column's type */
get_sort_group_operators(stats->attrtypid,
false, false, false,
<opr, &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;
stats->merge_stats = false;
/*
* Determine which standard statistics algorithm to use
*/
List *va_cols = list_make1(makeString(NameStr(stats->attr->attname)));
if (get_rel_relkind(attr->attrelid) == RELKIND_PARTITIONED_TABLE &&
!get_rel_relispartition(attr->attrelid) &&
leaf_parts_analyzed(stats->attr->attrelid, InvalidOid, va_cols, stats->elevel) &&
((!OidIsValid(eqopr)) || op_hashjoinable(eqopr, stats->attrtypid)))
{
stats->merge_stats = true;
stats->compute_stats = merge_leaf_stats;
stats->minrows = 300 * attr->attstattarget;
}
else
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 * attr->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 * attr->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 * attr->attstattarget;
}
list_free(va_cols);
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();
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->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);
stats->stahll = (bytea *)gp_hyperloglog_init_def();
ereport(DEBUG2,
(errmsg("Computing Minimal Stats for column %s",
get_attname(stats->attr->attrelid, stats->attr->attnum, false))));
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++;
stats->stahll = (bytea *)gp_hyperloglog_add_item((GpHLLCounter) stats->stahll, value, stats->attr->attlen, stats->attr->attbyval, stats->attr->attalign);
/*
* 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;
}
((GpHLLCounter) (stats->stahll))->nmultiples = nmultiple;
((GpHLLCounter) (stats->stahll))->ndistinct = track_cnt;
((GpHLLCounter) (stats->stahll))->samplerows = samplerows;
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->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));
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);
/* Initialize HLL counter to be stored in stats */
stats->stahll = (bytea *)gp_hyperloglog_init_def();
ereport(DEBUG2,
(errmsg("Computing Scalar Stats for column %s",
get_attname(stats->attr->attrelid, stats->attr->attnum, false))));
/* 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++;
stats->stahll = (bytea *)gp_hyperloglog_add_item((GpHLLCounter) stats->stahll, value, stats->attr->attlen, stats->attr->attbyval, stats->attr->attalign);
/*
* 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_arg((void *) values, values_cnt, sizeof(ScalarItem),
compare_scalars, (void *) &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;
// interpolate NDV calculation based on the hll distinct count
// for each column in leaf partitions which will be used later
// to merge root stats
((GpHLLCounter) (stats->stahll))->nmultiples = nmultiple;
((GpHLLCounter) (stats->stahll))->ndistinct = ndistinct;
((GpHLLCounter) (stats->stahll))->samplerows = samplerows;
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);
}
/*
* For FULLSCAN HLL, get ndistinct from the GpHLLCounter
* instead of computing it
*/
if (stats->stahll_full != NULL)
{
GpHLLCounter hLLFull = (GpHLLCounter) DatumGetByteaP(stats->stahll_full);
GpHLLCounter hllFull_copy = gp_hll_copy(hLLFull);
stats->stadistinct = round(gp_hyperloglog_estimate(hllFull_copy));
pfree(hllFull_copy);
if ((fabs(totalrows - stats->stadistinct) / (float) totalrows) < 0.05)
{
stats->stadistinct = -1;
}
}
/*
* 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((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));
/*
* 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" */
}
else
{
/*
* ORCA complains if a column has no statistics whatsoever, so store
* either the best we can figure out given what we have, or zero in
* case we don't have enough.
*/
stats->stats_valid = true;
if (samplerows)
stats->stanullfrac = (double) null_cnt / (double) samplerows;
else
stats->stanullfrac = 0.0;
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 */
}
/*
* merge_leaf_stats() -- merge leaf stats for the root
*
* This is only used when the relation is the root partition and merges
* the statistics available in pg_statistic for the leaf partitions.
*
* We use this for two scenarios:
*
* 1. When we can find "=" and "<" operators for the datatype, and the
* "=" operator is hashjoinable. In this case, we determine the fraction
* of non-null rows, the average width, the most common values, the
* (estimated) number of distinct values, the distribution histogram.
*
* 2. When we can find neither "=" nor "<" operator for the data type. In
* this case, we only determine the fraction of non-null rows and the
* average width.
*/
static void
merge_leaf_stats(VacAttrStatsP stats,
AnalyzeAttrFetchFunc fetchfunc,
int samplerows,
double totalrows)
{
List *all_children_list;
List *oid_list;
StdAnalyzeData *mystats = (StdAnalyzeData *) stats->extra_data;
int numPartitions;
ListCell *lc;
float *relTuples;
float *nDistincts;
float *nMultiples;
int relNum;
float totalTuples = 0;
float nmultiple = 0; // number of values that appeared more than once
bool allDistinct = false;
int slot_idx = 0;
int sampleCount = 0;
Oid ltopr = mystats->ltopr;
Oid eqopr = mystats->eqopr;
ereport(DEBUG2,
(errmsg("Merging leaf partition stats to calculate root partition stats : column %s",
get_attname(stats->attr->attrelid, stats->attr->attnum, false))));
/* GPDB_12_MERGE_FIXME: what's the appropriate lock level? AccessShareLock
* is enough to scan the table, but are we updating them, too? If not,
* NoLock might be enough?
*/
all_children_list = find_all_inheritors(stats->attr->attrelid, AccessShareLock, NULL);
oid_list = NIL;
foreach (lc, all_children_list)
{
Oid pkrelid = lfirst_oid(lc);
/* skip intermediate partitions, we're only interested in leaves */
if (get_rel_relkind(pkrelid) != RELKIND_RELATION)
continue;
oid_list = lappend_oid(oid_list, pkrelid);
}
numPartitions = list_length(oid_list);
relTuples = (float *) palloc0(sizeof(float) * numPartitions);
nDistincts = (float *) palloc0(sizeof(float) * numPartitions);
nMultiples = (float *) palloc0(sizeof(float) * numPartitions);
relNum = 0;
foreach (lc, oid_list)
{
Oid pkrelid = lfirst_oid(lc);
relTuples[relNum] = get_rel_reltuples(pkrelid);
totalTuples = totalTuples + relTuples[relNum];
relNum++;
}
if (totalTuples == 0.0)
return;
MemoryContext old_context;
HeapTuple *heaptupleStats =
(HeapTuple *) palloc(numPartitions * sizeof(HeapTuple));
// NDV calculations
float4 colAvgWidth = 0;
float4 nullCount = 0;
GpHLLCounter *hllcounters = (GpHLLCounter *) palloc0(numPartitions * sizeof(GpHLLCounter));
GpHLLCounter *hllcounters_fullscan = (GpHLLCounter *) palloc0(numPartitions * sizeof(GpHLLCounter));
GpHLLCounter *hllcounters_copy = (GpHLLCounter *) palloc0(numPartitions * sizeof(GpHLLCounter));
GpHLLCounter finalHLL = NULL;
GpHLLCounter finalHLLFull = NULL;
int i = 0;
double ndistinct = 0.0;
int fullhll_count = 0;
int samplehll_count = 0;
int totalhll_count = 0;
foreach (lc, oid_list)
{
Oid leaf_relid = lfirst_oid(lc);
int32 stawidth = 0;
float4 stanullfrac = 0.0;
const char *attname = get_attname(stats->attr->attrelid, stats->attr->attnum, false);
/*
* fetch_leaf_attnum and fetch_leaf_att_stats retrieve leaf partition
* table's pg_attribute tuple and pg_statistic tuple through index scan
* instead of system catalog cache. Since if using system catalog cache,
* the total tuple entries insert into the cache will up to:
* (number_of_leaf_tables * number_of_column_in_this_table) pg_attribute tuples
* +
* (number_of_leaf_tables * number_of_column_in_this_table) pg_statistic tuples
* which could use extremely large memroy in CacheMemoryContext.
* This happens when all of the leaf tables are analyzed. And the current function
* will execute for all columns.
*
* fetch_leaf_att_stats copy the original tuple, so remember to free it.
*
* As a side-effect, ANALYZE same root table serveral times in same session is much
* more slower than before since we don't rely on system catalog cache.
*
* But we still using the tuple descriptor in system catalog cache to retrieve
* attribute in fetched tuples. See get_attstatsslot.
*/
AttrNumber child_attno = fetch_leaf_attnum(leaf_relid, attname);
heaptupleStats[i] = fetch_leaf_att_stats(leaf_relid, child_attno);
// if there is no colstats, we can skip this partition's stats
if (!HeapTupleIsValid(heaptupleStats[i]))
{
i++;
continue;
}
stawidth = ((Form_pg_statistic) GETSTRUCT(heaptupleStats[i]))->stawidth;
stanullfrac = ((Form_pg_statistic) GETSTRUCT(heaptupleStats[i]))->stanullfrac;
colAvgWidth = colAvgWidth + (stawidth > 0 ? stawidth : 0) * relTuples[i];
nullCount = nullCount + (stanullfrac > 0.0 ? stanullfrac : 0.0) * relTuples[i];
AttStatsSlot hllSlot;
(void) get_attstatsslot(&hllSlot, heaptupleStats[i], STATISTIC_KIND_FULLHLL,
InvalidOid, ATTSTATSSLOT_VALUES);
if (hllSlot.nvalues > 0)
{
hllcounters_fullscan[i] = (GpHLLCounter) DatumGetByteaP(hllSlot.values[0]);
GpHLLCounter finalHLLFull_intermediate = finalHLLFull;
finalHLLFull = gp_hyperloglog_merge_counters(finalHLLFull_intermediate, hllcounters_fullscan[i]);
if (NULL != finalHLLFull_intermediate)
{
pfree(finalHLLFull_intermediate);
}
free_attstatsslot(&hllSlot);
fullhll_count++;
totalhll_count++;
}
(void) get_attstatsslot(&hllSlot, heaptupleStats[i], STATISTIC_KIND_HLL,
InvalidOid, ATTSTATSSLOT_VALUES);
if (hllSlot.nvalues > 0)
{
hllcounters[i] = (GpHLLCounter) DatumGetByteaP(hllSlot.values[0]);
nDistincts[i] = (float) hllcounters[i]->ndistinct;
nMultiples[i] = (float) hllcounters[i]->nmultiples;
sampleCount += hllcounters[i]->samplerows;
hllcounters_copy[i] = gp_hll_copy(hllcounters[i]);
GpHLLCounter finalHLL_intermediate = finalHLL;
finalHLL = gp_hyperloglog_merge_counters(finalHLL_intermediate, hllcounters[i]);
if (NULL != finalHLL_intermediate)
{
pfree(finalHLL_intermediate);
}
free_attstatsslot(&hllSlot);
samplehll_count++;
totalhll_count++;
}
i++;
}
if (totalhll_count == 0)
{
/*
* If neither HLL nor HLL Full scan stats are available,
* continue merging stats based on the defaults, instead
* of reading them from HLL counter.
*/
}
else
{
/*
* If all partitions have HLL full scan counters,
* merge root NDV's based on leaf partition HLL full scan
* counter
*/
if (fullhll_count == totalhll_count)
{
ndistinct = gp_hyperloglog_estimate(finalHLLFull);
pfree(finalHLLFull);
/*
* For fullscan the ndistinct is calculated based on the entire table scan
* so if it's within the marginal error, we consider everything as distinct,
* else the ndistinct value will provide the actual value and we do not ,
* need to do any additional calculation for the nmultiple
*/
if ((fabs(totalTuples - ndistinct) / (float) totalTuples) < GP_HLL_ERROR_MARGIN)
{
allDistinct = true;
}
nmultiple = ndistinct;
}
/*
* Else if all partitions have HLL counter based on sampled data,
* merge root NDV's based on leaf partition HLL counter on
* sampled data
*/
else if (finalHLL != NULL && samplehll_count == totalhll_count)
{
ndistinct = gp_hyperloglog_estimate(finalHLL);
pfree(finalHLL);
/*
* For sampled HLL counter, the ndistinct calculated is based on the
* sampled data. We consider everything distinct if the ndistinct
* calculated is within marginal error, else we need to calculate
* the number of distinct values for the table based on the estimator
* proposed by Haas and Stokes, used later in the code.
*/
if ((fabs(sampleCount - ndistinct) / (float) sampleCount) < GP_HLL_ERROR_MARGIN)
{
allDistinct = true;
}
else
{
/*
* The gp_hyperloglog_estimate() utility merges the number of
* distnct values accurately, but for the NDV estimator used later
* in the code, we also need additional information for nmultiples,
* i.e., the number of values that appeared more than once.
* At this point we have the information for nmultiples for each
* partition, but the nmultiples in one partition can be accounted as
* a distinct value in some other partition. In order to merge the
* approximate nmultiples better, we extract unique values in each
* partition as follows,
* P1 -> ndistinct1 , nmultiple1
* P2 -> ndistinct2 , nmultiple2
* P3 -> ndistinct3 , nmultiple3
* Root -> ndistinct(Root) (using gp_hyperloglog_estimate)
* nunique1 = ndistinct(Root) - gp_hyperloglog_estimate(P2 & P3)
* nunique2 = ndistinct(Root) - gp_hyperloglog_estimate(P1 & P3)
* nunique3 = ndistinct(Root) - gp_hyperloglog_estimate(P2 & P1)
* And finally once we have unique values in individual partitions,
* we can get the nmultiples on the ROOT as seen below,
* nmultiple(Root) = ndistinct(Root) - (sum of uniques in each partition)
*/
/*
* hllcounters_left array stores the merged hll result of all the
* hll counters towards the left of index i and excluding the hll
* counter at index i
*/
GpHLLCounter *hllcounters_left = (GpHLLCounter *) palloc0(numPartitions * sizeof(GpHLLCounter));
/*
* hllcounters_right array stores the merged hll result of all the
* hll counters towards the right of index i and excluding the hll
* counter at index i
*/
GpHLLCounter *hllcounters_right = (GpHLLCounter *) palloc0(numPartitions * sizeof(GpHLLCounter));
hllcounters_left[0] = gp_hyperloglog_init_def();
hllcounters_right[numPartitions - 1] = gp_hyperloglog_init_def();
/*
* The following loop populates the left and right array by accumulating the merged
* result of all the hll counters towards the left/right of the given index i excluding
* the counter at index i.
* Note that there might be empty values for some partitions, in which case the
* corresponding element in the left/right arrays will simply be the value
* of its neighbor.
* For E.g If the hllcounters_copy array is 1, null, 2, 3, null, 4
* the left and right arrays will be as follows:
* hllcounters_left: default, 1, 1, (1,2), (1,2,3), (1,2,3)
* hllcounters_right: (2,3,4), (2,3,4), (3,4), 4, 4, default
*/
/*
* The first and the last element in the left and right arrays
* are default values since there is no element towards
* the left or right of them
*/
for (i = 1; i < numPartitions; i++)
{
/* populate left array */
if (nDistincts[i - 1] == 0)
{
hllcounters_left[i] = gp_hll_copy(hllcounters_left[i - 1]);
}
else
{
GpHLLCounter hllcounter_temp1 = gp_hll_copy(hllcounters_copy[i - 1]);
GpHLLCounter hllcounter_temp2 = gp_hll_copy(hllcounters_left[i - 1]);
hllcounters_left[i] = gp_hyperloglog_merge_counters(hllcounter_temp1, hllcounter_temp2);
pfree(hllcounter_temp1);
pfree(hllcounter_temp2);
}
/* populate right array */
if (nDistincts[numPartitions - i] == 0)
{
hllcounters_right[numPartitions - i - 1] = gp_hll_copy(hllcounters_right[numPartitions - i]);
}
else
{
GpHLLCounter hllcounter_temp1 = gp_hll_copy(hllcounters_copy[numPartitions - i]);
GpHLLCounter hllcounter_temp2 = gp_hll_copy(hllcounters_right[numPartitions - i]);
hllcounters_right[numPartitions - i - 1] = gp_hyperloglog_merge_counters(hllcounter_temp1, hllcounter_temp2);
pfree(hllcounter_temp1);
pfree(hllcounter_temp2);
}
}
int nUnique = 0;
for (i = 0; i < numPartitions; i++)
{
/* Skip if statistics are missing for the partition */
if (nDistincts[i] == 0)
continue;
GpHLLCounter hllcounter_temp1 = gp_hll_copy(hllcounters_left[i]);
GpHLLCounter hllcounter_temp2 = gp_hll_copy(hllcounters_right[i]);
GpHLLCounter final = NULL;
final = gp_hyperloglog_merge_counters(hllcounter_temp1, hllcounter_temp2);
pfree(hllcounter_temp1);
pfree(hllcounter_temp2);
if (final != NULL)
{
float nUniques = ndistinct - gp_hyperloglog_estimate(final);
nUnique += nUniques;
nmultiple += nMultiples[i] * (nUniques / nDistincts[i]);
pfree(final);
}
else
{
nUnique = ndistinct;
break;
}
}
// nmultiples for the ROOT
nmultiple += ndistinct - nUnique;
if (nmultiple < 0)
nmultiple = 0;
pfree(hllcounters_left);
pfree(hllcounters_right);
}
}
else
{
/* Else error out due to incompatible leaf HLL counter merge */
pfree(hllcounters);
pfree(hllcounters_fullscan);
pfree(hllcounters_copy);
pfree(nDistincts);
pfree(nMultiples);
ereport(ERROR,
(errmsg("ANALYZE cannot merge since not all non-empty leaf partitions have consistent hyperloglog statistics for merge"),
errhint("Re-run ANALYZE or ANALYZE FULLSCAN")));
}
}
pfree(hllcounters);
pfree(hllcounters_fullscan);
pfree(hllcounters_copy);
pfree(nDistincts);
pfree(nMultiples);
if (allDistinct)
{
/* If we found no repeated values, assume it's a unique column */
ndistinct = -1.0;
}
else if (!OidIsValid(eqopr) && !OidIsValid(ltopr))
{
/* If operators are not available, NDV is unknown. */
ndistinct = 0;
}
else if ((int) nmultiple >= (int) ndistinct)
{
/*
* Every value in the sample appeared more than once. Assume the
* column has just these values.
*/
}
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;
int d = f1 + nmultiple;
double numer, denom, stadistinct;
numer = (double) sampleCount * (double) d;
denom = (double) (sampleCount - f1) +
(double) f1 * (double) sampleCount / totalTuples;
stadistinct = numer / denom;
/* Clamp to sane range in case of roundoff error */
if (stadistinct < (double) d)
stadistinct = (double) d;
if (stadistinct > totalTuples)
stadistinct = totalTuples;
ndistinct = floor(stadistinct + 0.5);
}
ndistinct = round(ndistinct);
if (ndistinct > 0.1 * totalTuples)
ndistinct = -(ndistinct / totalTuples);
// finalize NDV calculation
stats->stadistinct = ndistinct;
stats->stats_valid = true;
stats->stawidth = colAvgWidth / totalTuples;
stats->stanullfrac = (float4) nullCount / (float4) totalTuples;
// MCV calculations
MCVFreqPair **mcvpairArray = NULL;
int rem_mcv = 0;
int num_mcv = 0;
if (ndistinct > -1 && OidIsValid(eqopr))
{
if (ndistinct < 0)
{
ndistinct = -ndistinct * totalTuples;
}
old_context = MemoryContextSwitchTo(stats->anl_context);
void *resultMCV[2];
mcvpairArray = aggregate_leaf_partition_MCVs(
stats->attr->attrelid, stats->attr->attnum,
numPartitions, heaptupleStats, relTuples,
stats->attr->attstattarget, ndistinct, &num_mcv, &rem_mcv,
resultMCV);
MemoryContextSwitchTo(old_context);
if (num_mcv > 0)
{
stats->stakind[slot_idx] = STATISTIC_KIND_MCV;
stats->staop[slot_idx] = mystats->eqopr;
stats->stavalues[slot_idx] = (Datum *) resultMCV[0];
stats->numvalues[slot_idx] = num_mcv;
stats->stanumbers[slot_idx] = (float4 *) resultMCV[1];
stats->numnumbers[slot_idx] = num_mcv;
slot_idx++;
}
}
// Histogram calculation
if (OidIsValid(eqopr) && OidIsValid(ltopr))
{
old_context = MemoryContextSwitchTo(stats->anl_context);
void *resultHistogram[1];
int num_hist = aggregate_leaf_partition_histograms(
stats->attr->attrelid, stats->attr->attnum,
numPartitions, heaptupleStats, relTuples,
stats->attr->attstattarget, mcvpairArray + num_mcv,
rem_mcv, resultHistogram);
MemoryContextSwitchTo(old_context);
if (num_hist > 0)
{
stats->stakind[slot_idx] = STATISTIC_KIND_HISTOGRAM;
stats->staop[slot_idx] = mystats->ltopr;
stats->stavalues[slot_idx] = (Datum *) resultHistogram[0];
stats->numvalues[slot_idx] = num_hist;
slot_idx++;
}
}
for (i = 0; i < numPartitions; i++)
{
if (HeapTupleIsValid(heaptupleStats[i]))
heap_freetuple(heaptupleStats[i]);
}
if (num_mcv > 0)
pfree(mcvpairArray);
pfree(heaptupleStats);
pfree(relTuples);
}
/*
* qsort_arg 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;
}
/*
* qsort comparator for sorting ScalarMCVItems by position
*/
static int
compare_mcvs(const void *a, const void *b)
{
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;
}
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