greenplumn mcv 源码
greenplumn mcv 代码
文件路径：/src/backend/statistics/mcv.c
/*-------------------------------------------------------------------------
*
* mcv.c
* POSTGRES multivariate MCV lists
*
*
* Portions Copyright (c) 1996-2019, PostgreSQL Global Development Group
* Portions Copyright (c) 1994, Regents of the University of California
*
* IDENTIFICATION
* src/backend/statistics/mcv.c
*
*-------------------------------------------------------------------------
*/
#include "postgres.h"
#include <math.h>
#include "access/htup_details.h"
#include "catalog/pg_collation.h"
#include "catalog/pg_statistic_ext.h"
#include "catalog/pg_statistic_ext_data.h"
#include "fmgr.h"
#include "funcapi.h"
#include "nodes/nodeFuncs.h"
#include "optimizer/clauses.h"
#include "statistics/extended_stats_internal.h"
#include "statistics/statistics.h"
#include "utils/builtins.h"
#include "utils/bytea.h"
#include "utils/fmgroids.h"
#include "utils/fmgrprotos.h"
#include "utils/lsyscache.h"
#include "utils/syscache.h"
#include "utils/typcache.h"
/*
* Computes size of a serialized MCV item, depending on the number of
* dimensions (columns) the statistic is defined on. The datum values are
* stored in a separate array (deduplicated, to minimize the size), and
* so the serialized items only store uint16 indexes into that array.
*
* Each serialized item store (in this order):
*
* - indexes to values (ndim * sizeof(uint16))
* - null flags (ndim * sizeof(bool))
* - frequency (sizeof(double))
* - base_frequency (sizeof(double))
*
* So in total each MCV item requires this many bytes:
*
* ndim * (sizeof(uint16) + sizeof(bool)) + 2 * sizeof(double)
*/
#define ITEM_SIZE(ndims) \
MAXALIGN((ndims) * (sizeof(uint16) + sizeof(bool)) + 2 * sizeof(double))
/*
* Macros for convenient access to parts of a serialized MCV item.
*/
#define ITEM_INDEXES(item) ((uint16 *) (item))
#define ITEM_NULLS(item,ndims) ((bool *) (ITEM_INDEXES(item) + (ndims)))
#define ITEM_FREQUENCY(item,ndims) ((double *) (ITEM_NULLS(item, ndims) + (ndims)))
#define ITEM_BASE_FREQUENCY(item,ndims) ((double *) (ITEM_FREQUENCY(item, ndims) + 1))
/*
* Used to compute size of serialized MCV list representation.
*/
#define MinSizeOfMCVList \
(VARHDRSZ + sizeof(uint32) * 3 + sizeof(AttrNumber))
#define SizeOfMCVList(ndims,nitems) \
(MAXALIGN(MinSizeOfMCVList + sizeof(Oid) * (ndims)) + \
MAXALIGN((ndims) * sizeof(DimensionInfo)) + \
MAXALIGN((nitems) * ITEM_SIZE(ndims)))
static MultiSortSupport build_mss(VacAttrStats **stats, int numattrs);
static SortItem *build_distinct_groups(int numrows, SortItem *items,
MultiSortSupport mss, int *ndistinct);
static int count_distinct_groups(int numrows, SortItem *items,
MultiSortSupport mss);
/*
* get_mincount_for_mcv_list
* Determine the minimum number of times a value needs to appear in
* the sample for it to be included in the MCV list.
*
* We want to keep only values that appear sufficiently often in the
* sample that it is reasonable to extrapolate their sample frequencies to
* the entire table. We do this by placing an upper bound on the relative
* standard error of the sample frequency, so that any estimates the
* planner generates from the MCV statistics can be expected to be
* reasonably accurate.
*
* Since we are sampling without replacement, the sample frequency of a
* particular value is described by a hypergeometric distribution. A
* common rule of thumb when estimating errors in this situation is to
* require at least 10 instances of the value in the sample, in which case
* the distribution can be approximated by a normal distribution, and
* standard error analysis techniques can be applied. Given a sample size
* of n, a population size of N, and a sample frequency of p=cnt/n, the
* standard error of the proportion p is given by
* SE = sqrt(p*(1-p)/n) * sqrt((N-n)/(N-1))
* where the second term is the finite population correction. To get
* reasonably accurate planner estimates, we impose an upper bound on the
* relative standard error of 20% -- i.e., SE/p < 0.2. This 20% relative
* error bound is fairly arbitrary, but has been found empirically to work
* well. Rearranging this formula gives a lower bound on the number of
* instances of the value seen:
* cnt > n*(N-n) / (N-n+0.04*n*(N-1))
* This bound is at most 25, and approaches 0 as n approaches 0 or N. The
* case where n approaches 0 cannot happen in practice, since the sample
* size is at least 300. The case where n approaches N corresponds to
* sampling the whole the table, in which case it is reasonable to keep
* the whole MCV list (have no lower bound), so it makes sense to apply
* this formula for all inputs, even though the above derivation is
* technically only valid when the right hand side is at least around 10.
*
* An alternative way to look at this formula is as follows -- assume that
* the number of instances of the value seen scales up to the entire
* table, so that the population count is K=N*cnt/n. Then the distribution
* in the sample is a hypergeometric distribution parameterised by N, n
* and K, and the bound above is mathematically equivalent to demanding
* that the standard deviation of that distribution is less than 20% of
* its mean. Thus the relative errors in any planner estimates produced
* from the MCV statistics are likely to be not too large.
*/
static double
get_mincount_for_mcv_list(int samplerows, double totalrows)
{
double n = samplerows;
double N = totalrows;
double numer,
denom;
numer = n * (N - n);
denom = N - n + 0.04 * n * (N - 1);
/* Guard against division by zero (possible if n = N = 1) */
if (denom == 0.0)
return 0.0;
return numer / denom;
}
/*
* Builds MCV list from the set of sampled rows.
*
* The algorithm is quite simple:
*
* (1) sort the data (default collation, '<' for the data type)
*
* (2) count distinct groups, decide how many to keep
*
* (3) build the MCV list using the threshold determined in (2)
*
* (4) remove rows represented by the MCV from the sample
*
*/
MCVList *
statext_mcv_build(int numrows, HeapTuple *rows, Bitmapset *attrs,
VacAttrStats **stats, double totalrows)
{
int i,
numattrs,
ngroups,
nitems;
AttrNumber *attnums;
double mincount;
SortItem *items;
SortItem *groups;
MCVList *mcvlist = NULL;
MultiSortSupport mss;
attnums = build_attnums_array(attrs, &numattrs);
/* comparator for all the columns */
mss = build_mss(stats, numattrs);
/* sort the rows */
items = build_sorted_items(numrows, &nitems, rows, stats[0]->tupDesc,
mss, numattrs, attnums);
if (!items)
return NULL;
/* transform the sorted rows into groups (sorted by frequency) */
groups = build_distinct_groups(nitems, items, mss, &ngroups);
/*
* Maximum number of MCV items to store, based on the attribute with the
* largest stats target (and the number of groups we have available).
*/
nitems = stats[0]->attr->attstattarget;
for (i = 1; i < numattrs; i++)
{
if (stats[i]->attr->attstattarget > nitems)
nitems = stats[i]->attr->attstattarget;
}
if (nitems > ngroups)
nitems = ngroups;
/*
* Decide how many items to keep in the MCV list. We can't use the same
* algorithm as per-column MCV lists, because that only considers the
* actual group frequency - but we're primarily interested in how the
* actual frequency differs from the base frequency (product of simple
* per-column frequencies, as if the columns were independent).
*
* Using the same algorithm might exclude items that are close to the
* "average" frequency of the sample. But that does not say whether the
* observed frequency is close to the base frequency or not. We also need
* to consider unexpectedly uncommon items (again, compared to the base
* frequency), and the single-column algorithm does not have to.
*
* We simply decide how many items to keep by computing minimum count
* using get_mincount_for_mcv_list() and then keep all items that seem to
* be more common than that.
*/
mincount = get_mincount_for_mcv_list(numrows, totalrows);
/*
* Walk the groups until we find the first group with a count below the
* mincount threshold (the index of that group is the number of groups we
* want to keep).
*/
for (i = 0; i < nitems; i++)
{
if (groups[i].count < mincount)
{
nitems = i;
break;
}
}
/*
* At this point we know the number of items for the MCV list. There might
* be none (for uniform distribution with many groups), and in that case
* there will be no MCV list. Otherwise construct the MCV list.
*/
if (nitems > 0)
{
int j;
/*
* Allocate the MCV list structure, set the global parameters.
*/
mcvlist = (MCVList *) palloc0(offsetof(MCVList, items) +
sizeof(MCVItem) * nitems);
mcvlist->magic = STATS_MCV_MAGIC;
mcvlist->type = STATS_MCV_TYPE_BASIC;
mcvlist->ndimensions = numattrs;
mcvlist->nitems = nitems;
/* store info about data type OIDs */
for (i = 0; i < numattrs; i++)
mcvlist->types[i] = stats[i]->attrtypid;
/* Copy the first chunk of groups into the result. */
for (i = 0; i < nitems; i++)
{
/* just pointer to the proper place in the list */
MCVItem *item = &mcvlist->items[i];
item->values = (Datum *) palloc(sizeof(Datum) * numattrs);
item->isnull = (bool *) palloc(sizeof(bool) * numattrs);
/* copy values for the group */
memcpy(item->values, groups[i].values, sizeof(Datum) * numattrs);
memcpy(item->isnull, groups[i].isnull, sizeof(bool) * numattrs);
/* groups should be sorted by frequency in descending order */
Assert((i == 0) || (groups[i - 1].count >= groups[i].count));
/* group frequency */
item->frequency = (double) groups[i].count / numrows;
/* base frequency, if the attributes were independent */
item->base_frequency = 1.0;
for (j = 0; j < numattrs; j++)
{
int count = 0;
int k;
for (k = 0; k < ngroups; k++)
{
if (multi_sort_compare_dim(j, &groups[i], &groups[k], mss) == 0)
count += groups[k].count;
}
item->base_frequency *= (double) count / numrows;
}
}
}
pfree(items);
pfree(groups);
return mcvlist;
}
/*
* build_mss
* build MultiSortSupport for the attributes passed in attrs
*/
static MultiSortSupport
build_mss(VacAttrStats **stats, int numattrs)
{
int i;
/* Sort by multiple columns (using array of SortSupport) */
MultiSortSupport mss = multi_sort_init(numattrs);
/* prepare the sort functions for all the attributes */
for (i = 0; i < numattrs; i++)
{
VacAttrStats *colstat = stats[i];
TypeCacheEntry *type;
type = lookup_type_cache(colstat->attrtypid, TYPECACHE_LT_OPR);
if (type->lt_opr == InvalidOid) /* shouldn't happen */
elog(ERROR, "cache lookup failed for ordering operator for type %u",
colstat->attrtypid);
multi_sort_add_dimension(mss, i, type->lt_opr, type->typcollation);
}
return mss;
}
/*
* count_distinct_groups
* count distinct combinations of SortItems in the array
*
* The array is assumed to be sorted according to the MultiSortSupport.
*/
static int
count_distinct_groups(int numrows, SortItem *items, MultiSortSupport mss)
{
int i;
int ndistinct;
ndistinct = 1;
for (i = 1; i < numrows; i++)
{
/* make sure the array really is sorted */
Assert(multi_sort_compare(&items[i], &items[i - 1], mss) >= 0);
if (multi_sort_compare(&items[i], &items[i - 1], mss) != 0)
ndistinct += 1;
}
return ndistinct;
}
/*
* compare_sort_item_count
* comparator for sorting items by count (frequencies) in descending order
*/
static int
compare_sort_item_count(const void *a, const void *b)
{
SortItem *ia = (SortItem *) a;
SortItem *ib = (SortItem *) b;
if (ia->count == ib->count)
return 0;
else if (ia->count > ib->count)
return -1;
return 1;
}
/*
* build_distinct_groups
* build an array of SortItems for distinct groups and counts matching items
*
* The input array is assumed to be sorted
*/
static SortItem *
build_distinct_groups(int numrows, SortItem *items, MultiSortSupport mss,
int *ndistinct)
{
int i,
j;
int ngroups = count_distinct_groups(numrows, items, mss);
SortItem *groups = (SortItem *) palloc(ngroups * sizeof(SortItem));
j = 0;
groups[0] = items[0];
groups[0].count = 1;
for (i = 1; i < numrows; i++)
{
/* Assume sorted in ascending order. */
Assert(multi_sort_compare(&items[i], &items[i - 1], mss) >= 0);
/* New distinct group detected. */
if (multi_sort_compare(&items[i], &items[i - 1], mss) != 0)
{
groups[++j] = items[i];
groups[j].count = 0;
}
groups[j].count++;
}
/* ensure we filled the expected number of distinct groups */
Assert(j + 1 == ngroups);
/* Sort the distinct groups by frequency (in descending order). */
pg_qsort((void *) groups, ngroups, sizeof(SortItem),
compare_sort_item_count);
*ndistinct = ngroups;
return groups;
}
/*
* statext_mcv_load
* Load the MCV list for the indicated pg_statistic_ext tuple
*/
MCVList *
statext_mcv_load(Oid mvoid)
{
MCVList *result;
bool isnull;
Datum mcvlist;
HeapTuple htup = SearchSysCache1(STATEXTDATASTXOID, ObjectIdGetDatum(mvoid));
if (!HeapTupleIsValid(htup))
elog(ERROR, "cache lookup failed for statistics object %u", mvoid);
mcvlist = SysCacheGetAttr(STATEXTDATASTXOID, htup,
Anum_pg_statistic_ext_data_stxdmcv, &isnull);
if (isnull)
elog(ERROR,
"requested statistic kind \"%c\" is not yet built for statistics object %u",
STATS_EXT_DEPENDENCIES, mvoid);
result = statext_mcv_deserialize(DatumGetByteaP(mcvlist));
ReleaseSysCache(htup);
return result;
}
/*
* statext_mcv_serialize
* Serialize MCV list into a pg_mcv_list value.
*
* The MCV items may include values of various data types, and it's reasonable
* to expect redundancy (values for a given attribute, repeated for multiple
* MCV list items). So we deduplicate the values into arrays, and then replace
* the values by indexes into those arrays.
*
* The overall structure of the serialized representation looks like this:
*
* +---------------+----------------+---------------------+-------+
* | header fields | dimension info | deduplicated values | items |
* +---------------+----------------+---------------------+-------+
*
* Where dimension info stores information about type of K-th attribute (e.g.
* typlen, typbyval and length of deduplicated values). Deduplicated values
* store deduplicated values for each attribute. And items store the actual
* MCV list items, with values replaced by indexes into the arrays.
*
* When serializing the items, we use uint16 indexes. The number of MCV items
* is limited by the statistics target (which is capped to 10k at the moment).
* We might increase this to 65k and still fit into uint16, so there's a bit of
* slack. Furthermore, this limit is on the number of distinct values per column,
* and we usually have few of those (and various combinations of them for the
* those MCV list). So uint16 seems fine for now.
*
* We don't really expect the serialization to save as much space as for
* histograms, as we are not doing any bucket splits (which is the source
* of high redundancy in histograms).
*
* TODO: Consider packing boolean flags (NULL) for each item into a single char
* (or a longer type) instead of using an array of bool items.
*/
bytea *
statext_mcv_serialize(MCVList *mcvlist, VacAttrStats **stats)
{
int i;
int dim;
int ndims = mcvlist->ndimensions;
int itemsize = ITEM_SIZE(ndims);
SortSupport ssup;
DimensionInfo *info;
Size total_length;
/* allocate the item just once */
char *item = palloc0(itemsize);
/* serialized items (indexes into arrays, etc.) */
char *raw;
char *ptr;
/* values per dimension (and number of non-NULL values) */
Datum **values = (Datum **) palloc0(sizeof(Datum *) * ndims);
int *counts = (int *) palloc0(sizeof(int) * ndims);
/*
* We'll include some rudimentary information about the attribute types
* (length, by-val flag), so that we don't have to look them up while
* deserializating the MCV list (we already have the type OID in the
* header). This is safe, because when changing type of the attribute the
* statistics gets dropped automatically. We need to store the info about
* the arrays of deduplicated values anyway.
*/
info = (DimensionInfo *) palloc0(sizeof(DimensionInfo) * ndims);
/* sort support data for all attributes included in the MCV list */
ssup = (SortSupport) palloc0(sizeof(SortSupportData) * ndims);
/* collect and deduplicate values for each dimension (attribute) */
for (dim = 0; dim < ndims; dim++)
{
int ndistinct;
TypeCacheEntry *typentry;
/*
* Lookup the LT operator (can't get it from stats extra_data, as we
* don't know how to interpret that - scalar vs. array etc.).
*/
typentry = lookup_type_cache(stats[dim]->attrtypid, TYPECACHE_LT_OPR);
/* copy important info about the data type (length, by-value) */
info[dim].typlen = stats[dim]->attrtype->typlen;
info[dim].typbyval = stats[dim]->attrtype->typbyval;
/* allocate space for values in the attribute and collect them */
values[dim] = (Datum *) palloc0(sizeof(Datum) * mcvlist->nitems);
for (i = 0; i < mcvlist->nitems; i++)
{
/* skip NULL values - we don't need to deduplicate those */
if (mcvlist->items[i].isnull[dim])
continue;
/* append the value at the end */
values[dim][counts[dim]] = mcvlist->items[i].values[dim];
counts[dim] += 1;
}
/* if there are just NULL values in this dimension, we're done */
if (counts[dim] == 0)
continue;
/* sort and deduplicate the data */
ssup[dim].ssup_cxt = CurrentMemoryContext;
ssup[dim].ssup_collation = DEFAULT_COLLATION_OID;
ssup[dim].ssup_nulls_first = false;
PrepareSortSupportFromOrderingOp(typentry->lt_opr, &ssup[dim]);
qsort_arg(values[dim], counts[dim], sizeof(Datum),
compare_scalars_simple, &ssup[dim]);
/*
* Walk through the array and eliminate duplicate values, but keep the
* ordering (so that we can do bsearch later). We know there's at
* least one item as (counts[dim] != 0), so we can skip the first
* element.
*/
ndistinct = 1; /* number of distinct values */
for (i = 1; i < counts[dim]; i++)
{
/* expect sorted array */
Assert(compare_datums_simple(values[dim][i - 1], values[dim][i], &ssup[dim]) <= 0);
/* if the value is the same as the previous one, we can skip it */
if (!compare_datums_simple(values[dim][i - 1], values[dim][i], &ssup[dim]))
continue;
values[dim][ndistinct] = values[dim][i];
ndistinct += 1;
}
/* we must not exceed PG_UINT16_MAX, as we use uint16 indexes */
Assert(ndistinct <= PG_UINT16_MAX);
/*
* Store additional info about the attribute - number of deduplicated
* values, and also size of the serialized data. For fixed-length data
* types this is trivial to compute, for varwidth types we need to
* actually walk the array and sum the sizes.
*/
info[dim].nvalues = ndistinct;
if (info[dim].typlen > 0) /* fixed-length data types */
info[dim].nbytes = info[dim].nvalues * info[dim].typlen;
else if (info[dim].typlen == -1) /* varlena */
{
info[dim].nbytes = 0;
for (i = 0; i < info[dim].nvalues; i++)
{
Size len;
values[dim][i] = PointerGetDatum(PG_DETOAST_DATUM(values[dim][i]));
len = VARSIZE_ANY(values[dim][i]);
info[dim].nbytes += MAXALIGN(len);
}
}
else if (info[dim].typlen == -2) /* cstring */
{
info[dim].nbytes = 0;
for (i = 0; i < info[dim].nvalues; i++)
{
Size len;
/* c-strings include terminator, so +1 byte */
values[dim][i] = PointerGetDatum(PG_DETOAST_DATUM(values[dim][i]));
len = strlen(DatumGetCString(values[dim][i])) + 1;
info[dim].nbytes += MAXALIGN(len);
}
}
/* we know (count>0) so there must be some data */
Assert(info[dim].nbytes > 0);
}
/*
* Now we can finally compute how much space we'll actually need for the
* whole serialized MCV list (varlena header, MCV header, dimension info
* for each attribute, deduplicated values and items).
*
* The header fields are copied one by one, so that we don't need any
* explicit alignment (we copy them while deserializing). All fields after
* this need to be properly aligned, for direct access.
*/
total_length = MAXALIGN(VARHDRSZ + (3 * sizeof(uint32))
+ sizeof(AttrNumber) + (ndims * sizeof(Oid)));
/* dimension info */
total_length += MAXALIGN(ndims * sizeof(DimensionInfo));
/* add space for the arrays of deduplicated values */
for (i = 0; i < ndims; i++)
total_length += MAXALIGN(info[i].nbytes);
/*
* And finally the items (no additional alignment needed, we start at
* proper alignment and the itemsize formula uses MAXALIGN)
*/
total_length += mcvlist->nitems * itemsize;
/*
* Allocate space for the whole serialized MCV list (we'll skip bytes, so
* we set them to zero to make the result more compressible).
*/
raw = palloc0(total_length);
SET_VARSIZE(raw, total_length);
ptr = VARDATA(raw);
/* copy the MCV list header fields, one by one */
memcpy(ptr, &mcvlist->magic, sizeof(uint32));
ptr += sizeof(uint32);
memcpy(ptr, &mcvlist->type, sizeof(uint32));
ptr += sizeof(uint32);
memcpy(ptr, &mcvlist->nitems, sizeof(uint32));
ptr += sizeof(uint32);
memcpy(ptr, &mcvlist->ndimensions, sizeof(AttrNumber));
ptr += sizeof(AttrNumber);
memcpy(ptr, mcvlist->types, sizeof(Oid) * ndims);
ptr += (sizeof(Oid) * ndims);
/* the header may not be exactly aligned, so make sure it is */
ptr = raw + MAXALIGN(ptr - raw);
/* store information about the attributes */
memcpy(ptr, info, sizeof(DimensionInfo) * ndims);
ptr += MAXALIGN(sizeof(DimensionInfo) * ndims);
/* Copy the deduplicated values for all attributes to the output. */
for (dim = 0; dim < ndims; dim++)
{
/* remember the starting point for Asserts later */
char *start PG_USED_FOR_ASSERTS_ONLY = ptr;
for (i = 0; i < info[dim].nvalues; i++)
{
Datum value = values[dim][i];
if (info[dim].typbyval) /* passed by value */
{
Datum tmp;
/*
* For values passed by value, we need to copy just the
* significant bytes - we can't use memcpy directly, as that
* assumes little endian behavior. store_att_byval does
* almost what we need, but it requires properly aligned
* buffer - the output buffer does not guarantee that. So we
* simply use a static Datum variable (which guarantees proper
* alignment), and then copy the value from it.
*/
store_att_byval(&tmp, value, info[dim].typlen);
memcpy(ptr, &tmp, info[dim].typlen);
ptr += info[dim].typlen;
}
else if (info[dim].typlen > 0) /* passed by reference */
{
/* no special alignment needed, treated as char array */
memcpy(ptr, DatumGetPointer(value), info[dim].typlen);
ptr += info[dim].typlen;
}
else if (info[dim].typlen == -1) /* varlena */
{
int len = VARSIZE_ANY(value);
memcpy(ptr, DatumGetPointer(value), len);
ptr += MAXALIGN(len);
}
else if (info[dim].typlen == -2) /* cstring */
{
Size len = strlen(DatumGetCString(value)) + 1; /* terminator */
memcpy(ptr, DatumGetCString(value), len);
ptr += MAXALIGN(len);
}
/* no underflows or overflows */
Assert((ptr > start) && ((ptr - start) <= info[dim].nbytes));
}
/* we should get exactly nbytes of data for this dimension */
Assert((ptr - start) == info[dim].nbytes);
/* make sure the pointer is aligned correctly after each dimension */
ptr = raw + MAXALIGN(ptr - raw);
}
/* Serialize the items, with uint16 indexes instead of the values. */
for (i = 0; i < mcvlist->nitems; i++)
{
MCVItem *mcvitem = &mcvlist->items[i];
/* don't write beyond the allocated space */
Assert(ptr <= raw + total_length - itemsize);
/* reset the item (we only allocate it once and reuse it) */
memset(item, 0, itemsize);
for (dim = 0; dim < ndims; dim++)
{
Datum *value;
/* do the lookup only for non-NULL values */
if (mcvitem->isnull[dim])
continue;
value = (Datum *) bsearch_arg(&mcvitem->values[dim], values[dim],
info[dim].nvalues, sizeof(Datum),
compare_scalars_simple, &ssup[dim]);
Assert(value != NULL); /* serialization or deduplication error */
/* compute index within the array */
ITEM_INDEXES(item)[dim] = (uint16) (value - values[dim]);
/* check the index is within expected bounds */
Assert(ITEM_INDEXES(item)[dim] < info[dim].nvalues);
}
/* copy NULL and frequency flags into the item */
memcpy(ITEM_NULLS(item, ndims), mcvitem->isnull, sizeof(bool) * ndims);
memcpy(ITEM_FREQUENCY(item, ndims), &mcvitem->frequency, sizeof(double));
memcpy(ITEM_BASE_FREQUENCY(item, ndims), &mcvitem->base_frequency, sizeof(double));
/* copy the serialized item into the array */
memcpy(ptr, item, itemsize);
ptr += itemsize;
}
/* at this point we expect to match the total_length exactly */
Assert((ptr - raw) == total_length);
pfree(item);
pfree(values);
pfree(counts);
return (bytea *) raw;
}
/*
* statext_mcv_deserialize
* Reads serialized MCV list into MCVList structure.
*
* All the memory needed by the MCV list is allocated as a single chunk, so
* it's possible to simply pfree() it at once.
*/
MCVList *
statext_mcv_deserialize(bytea *data)
{
int dim,
i;
Size expected_size;
MCVList *mcvlist;
char *raw;
char *ptr;
int ndims,
nitems;
DimensionInfo *info = NULL;
/* local allocation buffer (used only for deserialization) */
Datum **map = NULL;
/* MCV list */
Size mcvlen;
/* buffer used for the result */
Size datalen;
char *dataptr;
char *valuesptr;
char *isnullptr;
if (data == NULL)
return NULL;
/*
* We can't possibly deserialize a MCV list if there's not even a complete
* header. We need an explicit formula here, because we serialize the
* header fields one by one, so we need to ignore struct alignment.
*/
if (VARSIZE_ANY(data) < MinSizeOfMCVList)
elog(ERROR, "invalid MCV size %zd (expected at least %zu)",
VARSIZE_ANY(data), MinSizeOfMCVList);
/* read the MCV list header */
mcvlist = (MCVList *) palloc0(offsetof(MCVList, items));
/* pointer to the data part (skip the varlena header) */
ptr = VARDATA_ANY(data);
raw = (char *) data;
/* get the header and perform further sanity checks */
memcpy(&mcvlist->magic, ptr, sizeof(uint32));
ptr += sizeof(uint32);
memcpy(&mcvlist->type, ptr, sizeof(uint32));
ptr += sizeof(uint32);
memcpy(&mcvlist->nitems, ptr, sizeof(uint32));
ptr += sizeof(uint32);
memcpy(&mcvlist->ndimensions, ptr, sizeof(AttrNumber));
ptr += sizeof(AttrNumber);
if (mcvlist->magic != STATS_MCV_MAGIC)
elog(ERROR, "invalid MCV magic %u (expected %u)",
mcvlist->magic, STATS_MCV_MAGIC);
if (mcvlist->type != STATS_MCV_TYPE_BASIC)
elog(ERROR, "invalid MCV type %u (expected %u)",
mcvlist->type, STATS_MCV_TYPE_BASIC);
if (mcvlist->ndimensions == 0)
elog(ERROR, "invalid zero-length dimension array in MCVList");
else if ((mcvlist->ndimensions > STATS_MAX_DIMENSIONS) ||
(mcvlist->ndimensions < 0))
elog(ERROR, "invalid length (%d) dimension array in MCVList",
mcvlist->ndimensions);
if (mcvlist->nitems == 0)
elog(ERROR, "invalid zero-length item array in MCVList");
else if (mcvlist->nitems > STATS_MCVLIST_MAX_ITEMS)
elog(ERROR, "invalid length (%u) item array in MCVList",
mcvlist->nitems);
nitems = mcvlist->nitems;
ndims = mcvlist->ndimensions;
/*
* Check amount of data including DimensionInfo for all dimensions and
* also the serialized items (including uint16 indexes). Also, walk
* through the dimension information and add it to the sum.
*/
expected_size = SizeOfMCVList(ndims, nitems);
/*
* Check that we have at least the dimension and info records, along with
* the items. We don't know the size of the serialized values yet. We need
* to do this check first, before accessing the dimension info.
*/
if (VARSIZE_ANY(data) < expected_size)
elog(ERROR, "invalid MCV size %zd (expected %zu)",
VARSIZE_ANY(data), expected_size);
/* Now copy the array of type Oids. */
memcpy(mcvlist->types, ptr, sizeof(Oid) * ndims);
ptr += (sizeof(Oid) * ndims);
/* ensure alignment of the pointer (after the header fields) */
ptr = raw + MAXALIGN(ptr - raw);
/* Now it's safe to access the dimension info. */
info = (DimensionInfo *) ptr;
ptr += MAXALIGN(ndims * sizeof(DimensionInfo));
/* account for the value arrays */
for (dim = 0; dim < ndims; dim++)
{
/*
* XXX I wonder if we can/should rely on asserts here. Maybe those
* checks should be done every time?
*/
Assert(info[dim].nvalues >= 0);
Assert(info[dim].nbytes >= 0);
expected_size += MAXALIGN(info[dim].nbytes);
}
/*
* Now we know the total expected MCV size, including all the pieces
* (header, dimension info. items and deduplicated data). So do the final
* check on size.
*/
if (VARSIZE_ANY(data) != expected_size)
elog(ERROR, "invalid MCV size %zd (expected %zu)",
VARSIZE_ANY(data), expected_size);
/*
* We need an array of Datum values for each dimension, so that we can
* easily translate the uint16 indexes later. We also need a top-level
* array of pointers to those per-dimension arrays.
*
* While allocating the arrays for dimensions, compute how much space we
* need for a copy of the by-ref data, as we can't simply point to the
* original values (it might go away).
*/
datalen = 0; /* space for by-ref data */
map = (Datum **) palloc(ndims * sizeof(Datum *));
for (dim = 0; dim < ndims; dim++)
{
map[dim] = (Datum *) palloc(sizeof(Datum) * info[dim].nvalues);
/* space needed for a copy of data for by-ref types */
if (!info[dim].typbyval)
datalen += MAXALIGN(info[dim].nbytes);
}
/*
* Now resize the MCV list so that the allocation includes all the data
* Allocate space for a copy of the data, as we can't simply reference the
* original data - it may disappear while we're still using the MCV list,
* e.g. due to catcache release. Only needed for by-ref types.
*/
mcvlen = MAXALIGN(offsetof(MCVList, items) + (sizeof(MCVItem) * nitems));
/* arrays of values and isnull flags for all MCV items */
mcvlen += nitems * MAXALIGN(sizeof(Datum) * ndims);
mcvlen += nitems * MAXALIGN(sizeof(bool) * ndims);
/* we don't quite need to align this, but it makes some asserts easier */
mcvlen += MAXALIGN(datalen);
/* now resize the deserialized MCV list, and compute pointers to parts */
mcvlist = repalloc(mcvlist, mcvlen);
/* pointer to the beginning of values/isnull arrays */
valuesptr = (char *) mcvlist
+ MAXALIGN(offsetof(MCVList, items) + (sizeof(MCVItem) * nitems));
isnullptr = valuesptr + (nitems * MAXALIGN(sizeof(Datum) * ndims));
dataptr = isnullptr + (nitems * MAXALIGN(sizeof(bool) * ndims));
/*
* Build mapping (index => value) for translating the serialized data into
* the in-memory representation.
*/
for (dim = 0; dim < ndims; dim++)
{
/* remember start position in the input array */
char *start PG_USED_FOR_ASSERTS_ONLY = ptr;
if (info[dim].typbyval)
{
/* for by-val types we simply copy data into the mapping */
for (i = 0; i < info[dim].nvalues; i++)
{
Datum v = 0;
memcpy(&v, ptr, info[dim].typlen);
ptr += info[dim].typlen;
map[dim][i] = fetch_att(&v, true, info[dim].typlen);
/* no under/overflow of input array */
Assert(ptr <= (start + info[dim].nbytes));
}
}
else
{
/* for by-ref types we need to also make a copy of the data */
/* passed by reference, but fixed length (name, tid, ...) */
if (info[dim].typlen > 0)
{
for (i = 0; i < info[dim].nvalues; i++)
{
memcpy(dataptr, ptr, info[dim].typlen);
ptr += info[dim].typlen;
/* just point into the array */
map[dim][i] = PointerGetDatum(dataptr);
dataptr += info[dim].typlen;
}
}
else if (info[dim].typlen == -1)
{
/* varlena */
for (i = 0; i < info[dim].nvalues; i++)
{
Size len = VARSIZE_ANY(ptr);
memcpy(dataptr, ptr, len);
ptr += MAXALIGN(len);
/* just point into the array */
map[dim][i] = PointerGetDatum(dataptr);
dataptr += MAXALIGN(len);
}
}
else if (info[dim].typlen == -2)
{
/* cstring */
for (i = 0; i < info[dim].nvalues; i++)
{
Size len = (strlen(ptr) + 1); /* don't forget the \0 */
memcpy(dataptr, ptr, len);
ptr += MAXALIGN(len);
/* just point into the array */
map[dim][i] = PointerGetDatum(dataptr);
dataptr += MAXALIGN(len);
}
}
/* no under/overflow of input array */
Assert(ptr <= (start + info[dim].nbytes));
/* no overflow of the output mcv value */
Assert(dataptr <= ((char *) mcvlist + mcvlen));
}
/* check we consumed input data for this dimension exactly */
Assert(ptr == (start + info[dim].nbytes));
/* ensure proper alignment of the data */
ptr = raw + MAXALIGN(ptr - raw);
}
/* we should have also filled the MCV list exactly */
Assert(dataptr == ((char *) mcvlist + mcvlen));
/* deserialize the MCV items and translate the indexes to Datums */
for (i = 0; i < nitems; i++)
{
uint16 *indexes = NULL;
MCVItem *item = &mcvlist->items[i];
item->values = (Datum *) valuesptr;
valuesptr += MAXALIGN(sizeof(Datum) * ndims);
item->isnull = (bool *) isnullptr;
isnullptr += MAXALIGN(sizeof(bool) * ndims);
/* just point to the right place */
indexes = ITEM_INDEXES(ptr);
memcpy(item->isnull, ITEM_NULLS(ptr, ndims), sizeof(bool) * ndims);
memcpy(&item->frequency, ITEM_FREQUENCY(ptr, ndims), sizeof(double));
memcpy(&item->base_frequency, ITEM_BASE_FREQUENCY(ptr, ndims), sizeof(double));
/* translate the values */
for (dim = 0; dim < ndims; dim++)
if (!item->isnull[dim])
item->values[dim] = map[dim][indexes[dim]];
ptr += ITEM_SIZE(ndims);
/* check we're not overflowing the input */
Assert(ptr <= (char *) raw + VARSIZE_ANY(data));
}
/* check that we processed all the data */
Assert(ptr == raw + VARSIZE_ANY(data));
/* release the buffers used for mapping */
for (dim = 0; dim < ndims; dim++)
pfree(map[dim]);
pfree(map);
return mcvlist;
}
/*
* SRF with details about buckets of a histogram:
*
* - item ID (0...nitems)
* - values (string array)
* - nulls only (boolean array)
* - frequency (double precision)
* - base_frequency (double precision)
*
* The input is the OID of the statistics, and there are no rows returned if
* the statistics contains no histogram.
*/
Datum
pg_stats_ext_mcvlist_items(PG_FUNCTION_ARGS)
{
FuncCallContext *funcctx;
int call_cntr;
int max_calls;
AttInMetadata *attinmeta;
/* stuff done only on the first call of the function */
if (SRF_IS_FIRSTCALL())
{
MemoryContext oldcontext;
MCVList *mcvlist;
TupleDesc tupdesc;
/* create a function context for cross-call persistence */
funcctx = SRF_FIRSTCALL_INIT();
/* switch to memory context appropriate for multiple function calls */
oldcontext = MemoryContextSwitchTo(funcctx->multi_call_memory_ctx);
mcvlist = statext_mcv_deserialize(PG_GETARG_BYTEA_P(0));
funcctx->user_fctx = mcvlist;
/* total number of tuples to be returned */
funcctx->max_calls = 0;
if (funcctx->user_fctx != NULL)
funcctx->max_calls = mcvlist->nitems;
/* Build a tuple descriptor for our result type */
if (get_call_result_type(fcinfo, NULL, &tupdesc) != TYPEFUNC_COMPOSITE)
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("function returning record called in context "
"that cannot accept type record")));
/*
* generate attribute metadata needed later to produce tuples from raw
* C strings
*/
attinmeta = TupleDescGetAttInMetadata(tupdesc);
funcctx->attinmeta = attinmeta;
MemoryContextSwitchTo(oldcontext);
}
/* stuff done on every call of the function */
funcctx = SRF_PERCALL_SETUP();
call_cntr = funcctx->call_cntr;
max_calls = funcctx->max_calls;
attinmeta = funcctx->attinmeta;
if (call_cntr < max_calls) /* do when there is more left to send */
{
char **values;
HeapTuple tuple;
Datum result;
StringInfoData itemValues;
StringInfoData itemNulls;
int i;
Oid *outfuncs;
FmgrInfo *fmgrinfo;
MCVList *mcvlist;
MCVItem *item;
mcvlist = (MCVList *) funcctx->user_fctx;
Assert(call_cntr < mcvlist->nitems);
item = &mcvlist->items[call_cntr];
/*
* Prepare a values array for building the returned tuple. This should
* be an array of C strings which will be processed later by the type
* input functions.
*/
values = (char **) palloc0(5 * sizeof(char *));
values[0] = (char *) palloc(64 * sizeof(char)); /* item index */
values[3] = (char *) palloc(64 * sizeof(char)); /* frequency */
values[4] = (char *) palloc(64 * sizeof(char)); /* base frequency */
outfuncs = (Oid *) palloc0(sizeof(Oid) * mcvlist->ndimensions);
fmgrinfo = (FmgrInfo *) palloc0(sizeof(FmgrInfo) * mcvlist->ndimensions);
for (i = 0; i < mcvlist->ndimensions; i++)
{
bool isvarlena;
getTypeOutputInfo(mcvlist->types[i], &outfuncs[i], &isvarlena);
fmgr_info(outfuncs[i], &fmgrinfo[i]);
}
/* build the arrays of values / nulls */
initStringInfo(&itemValues);
initStringInfo(&itemNulls);
appendStringInfoChar(&itemValues, '{');
appendStringInfoChar(&itemNulls, '{');
for (i = 0; i < mcvlist->ndimensions; i++)
{
Datum val,
valout;
if (i > 0)
{
appendStringInfoString(&itemValues, ", ");
appendStringInfoString(&itemNulls, ", ");
}
if (item->isnull[i])
valout = CStringGetDatum("NULL");
else
{
val = item->values[i];
valout = FunctionCall1(&fmgrinfo[i], val);
}
appendStringInfoString(&itemValues, DatumGetCString(valout));
appendStringInfoString(&itemNulls, item->isnull[i] ? "t" : "f");
}
appendStringInfoChar(&itemValues, '}');
appendStringInfoChar(&itemNulls, '}');
snprintf(values[0], 64, "%d", call_cntr);
snprintf(values[3], 64, "%f", item->frequency);
snprintf(values[4], 64, "%f", item->base_frequency);
values[1] = itemValues.data;
values[2] = itemNulls.data;
/* build a tuple */
tuple = BuildTupleFromCStrings(attinmeta, values);
/* make the tuple into a datum */
result = HeapTupleGetDatum(tuple);
/* clean up (this is not really necessary) */
pfree(itemValues.data);
pfree(itemNulls.data);
pfree(values[0]);
pfree(values[3]);
pfree(values[4]);
pfree(values);
SRF_RETURN_NEXT(funcctx, result);
}
else /* do when there is no more left */
{
SRF_RETURN_DONE(funcctx);
}
}
/*
* pg_mcv_list_in - input routine for type pg_mcv_list.
*
* pg_mcv_list is real enough to be a table column, but it has no operations
* of its own, and disallows input too
*/
Datum
pg_mcv_list_in(PG_FUNCTION_ARGS)
{
/*
* pg_mcv_list stores the data in binary form and parsing text input is
* not needed, so disallow this.
*/
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("cannot accept a value of type %s", "pg_mcv_list")));
PG_RETURN_VOID(); /* keep compiler quiet */
}
/*
* pg_mcv_list_out - output routine for type pg_mcv_list.
*
* MCV lists are serialized into a bytea value, so we simply call byteaout()
* to serialize the value into text. But it'd be nice to serialize that into
* a meaningful representation (e.g. for inspection by people).
*
* XXX This should probably return something meaningful, similar to what
* pg_dependencies_out does. Not sure how to deal with the deduplicated
* values, though - do we want to expand that or not?
*/
Datum
pg_mcv_list_out(PG_FUNCTION_ARGS)
{
return byteaout(fcinfo);
}
/*
* pg_mcv_list_recv - binary input routine for type pg_mcv_list.
*/
Datum
pg_mcv_list_recv(PG_FUNCTION_ARGS)
{
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("cannot accept a value of type %s", "pg_mcv_list")));
PG_RETURN_VOID(); /* keep compiler quiet */
}
/*
* pg_mcv_list_send - binary output routine for type pg_mcv_list.
*
* MCV lists are serialized in a bytea value (although the type is named
* differently), so let's just send that.
*/
Datum
pg_mcv_list_send(PG_FUNCTION_ARGS)
{
return byteasend(fcinfo);
}
/*
* mcv_get_match_bitmap
* Evaluate clauses using the MCV list, and update the match bitmap.
*
* A match bitmap keeps match/mismatch status for each MCV item, and we
* update it based on additional clauses. We also use it to skip items
* that can't possibly match (e.g. item marked as "mismatch" can't change
* to "match" when evaluating AND clause list).
*
* The function also returns a flag indicating whether there was an
* equality condition for all attributes, the minimum frequency in the MCV
* list, and a total MCV frequency (sum of frequencies for all items).
*
* XXX Currently the match bitmap uses a bool for each MCV item, which is
* somewhat wasteful as we could do with just a single bit, thus reducing
* the size to ~1/8. It would also allow us to combine bitmaps simply using
* & and |, which should be faster than min/max. The bitmaps are fairly
* small, though (thanks to the cap on the MCV list size).
*/
static bool *
mcv_get_match_bitmap(PlannerInfo *root, List *clauses,
Bitmapset *keys, MCVList *mcvlist, bool is_or)
{
int i;
ListCell *l;
bool *matches;
/* The bitmap may be partially built. */
Assert(clauses != NIL);
Assert(list_length(clauses) >= 1);
Assert(mcvlist != NULL);
Assert(mcvlist->nitems > 0);
Assert(mcvlist->nitems <= STATS_MCVLIST_MAX_ITEMS);
matches = palloc(sizeof(bool) * mcvlist->nitems);
memset(matches, (is_or) ? false : true,
sizeof(bool) * mcvlist->nitems);
/*
* Loop through the list of clauses, and for each of them evaluate all the
* MCV items not yet eliminated by the preceding clauses.
*/
foreach(l, clauses)
{
Node *clause = (Node *) lfirst(l);
/* if it's a RestrictInfo, then extract the clause */
if (IsA(clause, RestrictInfo))
clause = (Node *) ((RestrictInfo *) clause)->clause;
/*
* Handle the various types of clauses - OpClause, NullTest and
* AND/OR/NOT
*/
if (is_opclause(clause))
{
OpExpr *expr = (OpExpr *) clause;
bool varonleft = true;
bool ok;
FmgrInfo opproc;
/* get procedure computing operator selectivity */
RegProcedure oprrest = get_oprrest(expr->opno);
fmgr_info(get_opcode(expr->opno), &opproc);
ok = (NumRelids(clause) == 1) &&
(is_pseudo_constant_clause(lsecond(expr->args)) ||
(varonleft = false,
is_pseudo_constant_clause(linitial(expr->args))));
if (ok)
{
TypeCacheEntry *typecache;
FmgrInfo gtproc;
Var *var;
Const *cst;
bool isgt;
int idx;
/* extract the var and const from the expression */
var = (varonleft) ? linitial(expr->args) : lsecond(expr->args);
cst = (varonleft) ? lsecond(expr->args) : linitial(expr->args);
isgt = (!varonleft);
/* strip binary-compatible relabeling */
if (IsA(var, RelabelType))
var = (Var *) ((RelabelType *) var)->arg;
/* match the attribute to a dimension of the statistic */
idx = bms_member_index(keys, var->varattno);
/* get information about the >= procedure */
typecache = lookup_type_cache(var->vartype, TYPECACHE_GT_OPR);
fmgr_info(get_opcode(typecache->gt_opr), >proc);
/*
* Walk through the MCV items and evaluate the current clause.
* We can skip items that were already ruled out, and
* terminate if there are no remaining MCV items that might
* possibly match.
*/
for (i = 0; i < mcvlist->nitems; i++)
{
bool mismatch = false;
MCVItem *item = &mcvlist->items[i];
/*
* For AND-lists, we can also mark NULL items as 'no
* match' (and then skip them). For OR-lists this is not
* possible.
*/
if ((!is_or) && item->isnull[idx])
matches[i] = false;
/* skip MCV items that were already ruled out */
if ((!is_or) && (matches[i] == false))
continue;
else if (is_or && (matches[i] == true))
continue;
switch (oprrest)
{
case F_EQSEL:
case F_NEQSEL:
/*
* We don't care about isgt in equality, because
* it does not matter whether it's (var op const)
* or (const op var).
*/
mismatch = !DatumGetBool(FunctionCall2Coll(&opproc,
DEFAULT_COLLATION_OID,
cst->constvalue,
item->values[idx]));
break;
case F_SCALARLTSEL: /* column < constant */
case F_SCALARLESEL: /* column <= constant */
case F_SCALARGTSEL: /* column > constant */
case F_SCALARGESEL: /* column >= constant */
/*
* First check whether the constant is below the
* lower boundary (in that case we can skip the
* bucket, because there's no overlap).
*/
if (isgt)
mismatch = !DatumGetBool(FunctionCall2Coll(&opproc,
DEFAULT_COLLATION_OID,
cst->constvalue,
item->values[idx]));
else
mismatch = !DatumGetBool(FunctionCall2Coll(&opproc,
DEFAULT_COLLATION_OID,
item->values[idx],
cst->constvalue));
break;
}
/*
* XXX The conditions on matches[i] are not needed, as we
* skip MCV items that can't become true/false, depending
* on the current flag. See beginning of the loop over MCV
* items.
*/
if ((is_or) && (!mismatch))
{
/* OR - was not a match before, matches now */
matches[i] = true;
continue;
}
else if ((!is_or) && mismatch)
{
/* AND - was a match before, does not match anymore */
matches[i] = false;
continue;
}
}
}
}
else if (IsA(clause, NullTest))
{
NullTest *expr = (NullTest *) clause;
Var *var = (Var *) (expr->arg);
/* match the attribute to a dimension of the statistic */
int idx = bms_member_index(keys, var->varattno);
/*
* Walk through the MCV items and evaluate the current clause. We
* can skip items that were already ruled out, and terminate if
* there are no remaining MCV items that might possibly match.
*/
for (i = 0; i < mcvlist->nitems; i++)
{
bool match = false; /* assume mismatch */
MCVItem *item = &mcvlist->items[i];
/* if the clause mismatches the MCV item, update the bitmap */
switch (expr->nulltesttype)
{
case IS_NULL:
match = (item->isnull[idx]) ? true : match;
break;
case IS_NOT_NULL:
match = (!item->isnull[idx]) ? true : match;
break;
}
/* now, update the match bitmap, depending on OR/AND type */
if (is_or)
matches[i] = Max(matches[i], match);
else
matches[i] = Min(matches[i], match);
}
}
else if (is_orclause(clause) || is_andclause(clause))
{
/* AND/OR clause, with all subclauses being compatible */
int i;
BoolExpr *bool_clause = ((BoolExpr *) clause);
List *bool_clauses = bool_clause->args;
/* match/mismatch bitmap for each MCV item */
bool *bool_matches = NULL;
Assert(bool_clauses != NIL);
Assert(list_length(bool_clauses) >= 2);
/* build the match bitmap for the OR-clauses */
bool_matches = mcv_get_match_bitmap(root, bool_clauses, keys,
mcvlist, is_orclause(clause));
/*
* Merge the bitmap produced by mcv_get_match_bitmap into the
* current one. We need to consider if we're evaluating AND or OR
* condition when merging the results.
*/
for (i = 0; i < mcvlist->nitems; i++)
{
/* Is this OR or AND clause? */
if (is_or)
matches[i] = Max(matches[i], bool_matches[i]);
else
matches[i] = Min(matches[i], bool_matches[i]);
}
pfree(bool_matches);
}
else if (is_notclause(clause))
{
/* NOT clause, with all subclauses compatible */
int i;
BoolExpr *not_clause = ((BoolExpr *) clause);
List *not_args = not_clause->args;
/* match/mismatch bitmap for each MCV item */
bool *not_matches = NULL;
Assert(not_args != NIL);
Assert(list_length(not_args) == 1);
/* build the match bitmap for the NOT-clause */
not_matches = mcv_get_match_bitmap(root, not_args, keys,
mcvlist, false);
/*
* Merge the bitmap produced by mcv_get_match_bitmap into the
* current one.
*/
for (i = 0; i < mcvlist->nitems; i++)
{
/*
* When handling a NOT clause, we need to invert the result
* before merging it into the global result.
*/
if (not_matches[i] == false)
not_matches[i] = true;
else
not_matches[i] = false;
/* Is this OR or AND clause? */
if (is_or)
matches[i] = Max(matches[i], not_matches[i]);
else
matches[i] = Min(matches[i], not_matches[i]);
}
pfree(not_matches);
}
else if (IsA(clause, Var))
{
/* Var (has to be a boolean Var, possibly from below NOT) */
Var *var = (Var *) (clause);
/* match the attribute to a dimension of the statistic */
int idx = bms_member_index(keys, var->varattno);
Assert(var->vartype == BOOLOID);
/*
* Walk through the MCV items and evaluate the current clause. We
* can skip items that were already ruled out, and terminate if
* there are no remaining MCV items that might possibly match.
*/
for (i = 0; i < mcvlist->nitems; i++)
{
MCVItem *item = &mcvlist->items[i];
bool match = false;
/* if the item is NULL, it's a mismatch */
if (!item->isnull[idx] && DatumGetBool(item->values[idx]))
match = true;
/* now, update the match bitmap, depending on OR/AND type */
if (is_or)
matches[i] = Max(matches[i], match);
else
matches[i] = Min(matches[i], match);
}
}
else
{
elog(ERROR, "unknown clause type: %d", clause->type);
}
}
return matches;
}
/*
* mcv_clauselist_selectivity
* Return the selectivity estimate computed using an MCV list.
*
* First builds a bitmap of MCV items matching the clauses, and then sums
* the frequencies of matching items.
*
* It also produces two additional interesting selectivities - total
* selectivity of all the MCV items (not just the matching ones), and the
* base frequency computed on the assumption of independence.
*/
Selectivity
mcv_clauselist_selectivity(PlannerInfo *root, StatisticExtInfo *stat,
List *clauses, int varRelid,
JoinType jointype, SpecialJoinInfo *sjinfo,
RelOptInfo *rel,
Selectivity *basesel, Selectivity *totalsel)
{
int i;
MCVList *mcv;
Selectivity s = 0.0;
/* match/mismatch bitmap for each MCV item */
bool *matches = NULL;
/* load the MCV list stored in the statistics object */
mcv = statext_mcv_load(stat->statOid);
/* build a match bitmap for the clauses */
matches = mcv_get_match_bitmap(root, clauses, stat->keys, mcv, false);
/* sum frequencies for all the matching MCV items */
*basesel = 0.0;
*totalsel = 0.0;
for (i = 0; i < mcv->nitems; i++)
{
*totalsel += mcv->items[i].frequency;
if (matches[i] != false)
{
/* XXX Shouldn't the basesel be outside the if condition? */
*basesel += mcv->items[i].base_frequency;
s += mcv->items[i].frequency;
}
}
return s;
}
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