标签
PostgreSQL , dump_stat , 统计信息 , 导出导入
背景
《PostgreSQL 规格评估 - 微观、宏观、精准 多视角估算数据库性能(选型、做预算不求人)》
EXPLAIN是PG数据库用于输出SQL执行计划的语法，
1、生成的执行计划中包含COST一项。
如果校准了成本因子，COST可以和SQL实际执行时间对其。因子校对的方法如下，实际上每一种硬件，我们只需要校对一遍即可。
《优化器成本因子校对 - PostgreSQL explain cost constants alignment to timestamp》
校对因子如下：
#seq_page_cost = 1.0 # measured on an arbitrary scale
random_page_cost = 1.2 # same scale as above
#cpu_tuple_cost = 0.01 # same scale as above
#cpu_index_tuple_cost = 0.005 # same scale as above
#cpu_operator_cost = 0.0025 # same scale as above
# 以下不需要校对, 不考虑并行计算SQL
parallel_tuple_cost = 0.1 # same scale as above
parallel_setup_cost = 1000.0 # same scale as above
effective_cache_size = 10GB
2、评估COST还需要依赖统计信息柱状图：
涉及reltuples, relpages. 表示评估的记录数以及占用多少个数据块。注意源头的block_size可能和PG的不一致，占用多少个块需要转换一下。(show block_size可以查看数据块大小。)
postgres=# \d pg_class
Table "pg_catalog.pg_class"
Column | Type | Collation | Nullable | Default
---------------------+--------------+-----------+----------+---------
relname | name | | not null | -- 对象名
relnamespace | oid | | not null | -- 对象所属的schema, 对应pg_namespace.oid
reltype | oid | | not null |
reloftype | oid | | not null |
relowner | oid | | not null |
relam | oid | | not null |
relfilenode | oid | | not null |
reltablespace | oid | | not null |
relpages | integer | | not null | -- 评估的页数(单位为block_size)
reltuples | real | | not null | -- 评估的记录数
relallvisible | integer | | not null |
reltoastrelid | oid | | not null |
relhasindex | boolean | | not null |
relisshared | boolean | | not null |
relpersistence | "char" | | not null |
relkind | "char" | | not null |
relnatts | smallint | | not null |
relchecks | smallint | | not null |
relhasoids | boolean | | not null |
relhaspkey | boolean | | not null |
relhasrules | boolean | | not null |
relhastriggers | boolean | | not null |
relhassubclass | boolean | | not null |
relrowsecurity | boolean | | not null |
relforcerowsecurity | boolean | | not null |
relispopulated | boolean | | not null |
relreplident | "char" | | not null |
relispartition | boolean | | not null |
relfrozenxid | xid | | not null |
relminmxid | xid | | not null |
relacl | aclitem[] | | |
reloptions | text[] | | |
relpartbound | pg_node_tree | | |
Indexes:
"pg_class_oid_index" UNIQUE, btree (oid)
"pg_class_relname_nsp_index" UNIQUE, btree (relname, relnamespace)
"pg_class_tblspc_relfilenode_index" btree (reltablespace, relfilenode)
涉及 空值比例、平均列宽、唯一值比例或个数、高频值以及频率、柱状图分布、存储相关性、多值列（高频元素及比例、元素柱状图分布）。
-- 这个是视图：
postgres=# \d pg_stats
View "pg_catalog.pg_stats"
Column | Type | Collation | Nullable | Default
------------------------+----------+-----------+----------+---------
schemaname | name | | | -- 对象所属的schema
tablename | name | | | -- 对象名
attname | name | | | -- 列名
inherited | boolean | | | -- 是否为继承表的统计信息（false时表示当前表的统计信息，true时表示包含所有继承表的统计信息）
null_frac | real | | | -- 该列空值比例
avg_width | integer | | | -- 该列平均长度
n_distinct | real | | | -- 该列唯一值个数（-1表示唯一，小于1表示占比，大于等于1表示实际的唯一值个数）
most_common_vals | anyarray | | | -- 该列高频词
most_common_freqs | real[] | | | -- 该列高频词对应的出现频率
histogram_bounds | anyarray | | | -- 该列柱状图（表示隔出的每个BUCKET的记录数均等）
correlation | real | | | -- 该列存储相关性（-1到1的区间），绝对值越小，存储越离散。小于0表示反向相关，大于0表示正向相关
most_common_elems | anyarray | | | -- 该列为多值类型（数组）时，多值元素的高频词
most_common_elem_freqs | real[] | | | -- 多值元素高频词的出现频率
elem_count_histogram | real[] | | | -- 多值元素的柱状图中，每个区间的非空唯一元素个数
-- 这个是实际存储的数据(也就是要导入的部分)：
-- https://www.postgresql.org/docs/10/static/catalog-pg-statistic.html
postgres=# \d pg_statistic
Table "pg_catalog.pg_statistic"
Column | Type | Collation | Nullable | Default
-------------+----------+-----------+----------+---------
starelid | oid | | not null | -- 对象OID，对应pg_class.oid
staattnum | smallint | | not null | -- 该列在表中的位置序号，对应pg_attribute.attnum
stainherit | boolean | | not null | -- 是否为继承表的统计信息（false时表示当前表的统计信息，true时表示包含所有继承表的统计信息）
stanullfrac | real | | not null | -- 空值比例
stawidth | integer | | not null | -- 平均长度
stadistinct | real | | not null | -- 唯一值个数、比例
stakind1 | smallint | | not null | -- 表示第1个SLOT的统计信息分类编号
stakind2 | smallint | | not null | -- 表示第2个SLOT的统计信息分类编号
stakind3 | smallint | | not null | -- 表示第3个SLOT的统计信息分类编号
stakind4 | smallint | | not null | -- 表示第4个SLOT的统计信息分类编号
stakind5 | smallint | | not null | -- 表示第5个SLOT的统计信息分类编号
staop1 | oid | | not null | -- 表示第1个SLOT的统计信息是用哪个operator生成的(例如统计柱状图边界，需要用到 "<" 这个操作符)
staop2 | oid | | not null | -- 表示第2个SLOT的统计信息是用哪个operator生成的(例如统计柱状图边界，需要用到 "<" 这个操作符)
staop3 | oid | | not null | -- 表示第3个SLOT的统计信息是用哪个operator生成的(例如统计柱状图边界，需要用到 "<" 这个操作符)
staop4 | oid | | not null | -- 表示第4个SLOT的统计信息是用哪个operator生成的(例如统计柱状图边界，需要用到 "<" 这个操作符)
staop5 | oid | | not null | -- 表示第5个SLOT的统计信息是用哪个operator生成的(例如统计柱状图边界，需要用到 "<" 这个操作符)
stanumbers1 | real[] | | | -- 表示第1个SLOT的以numeric[]为结果的统计信息，NULL说明这个SLOT分类没有numeric的统计信息。
stanumbers2 | real[] | | | -- 表示第2个SLOT的以numeric[]为结果的统计信息，NULL说明这个SLOT分类没有numeric的统计信息。
stanumbers3 | real[] | | | -- 表示第3个SLOT的以numeric[]为结果的统计信息，NULL说明这个SLOT分类没有numeric的统计信息。
stanumbers4 | real[] | | | -- 表示第4个SLOT的以numeric[]为结果的统计信息，NULL说明这个SLOT分类没有numeric的统计信息。
stanumbers5 | real[] | | | -- 表示第5个SLOT的以numeric[]为结果的统计信息，NULL说明这个SLOT分类没有numeric的统计信息。
stavalues1 | anyarray | | | -- 表示第1个SLOT的以anyarray[]为结果的统计信息，NULL说明这个SLOT分类没有anyarray[]统计信息。数组类型为列的元素类型，或者列本身的类型。
stavalues2 | anyarray | | | -- 表示第2个SLOT的以anyarray[]为结果的统计信息，NULL说明这个SLOT分类没有anyarray[]统计信息。数组类型为列的元素类型，或者列本身的类型。
stavalues3 | anyarray | | | -- 表示第3个SLOT的以anyarray[]为结果的统计信息，NULL说明这个SLOT分类没有anyarray[]统计信息。数组类型为列的元素类型，或者列本身的类型。
stavalues4 | anyarray | | | -- 表示第4个SLOT的以anyarray[]为结果的统计信息，NULL说明这个SLOT分类没有anyarray[]统计信息。数组类型为列的元素类型，或者列本身的类型。
stavalues5 | anyarray | | | -- 表示第5个SLOT的以anyarray[]为结果的统计信息，NULL说明这个SLOT分类没有anyarray[]统计信息。数组类型为列的元素类型，或者列本身的类型。
Indexes:
"pg_statistic_relid_att_inh_index" UNIQUE, btree (starelid, staattnum, stainherit)
statkind的定义
src/include/catalog/pg_statistic.h
/*
* Currently, five statistical slot "kinds" are defined by core PostgreSQL,
* as documented below. Additional "kinds" will probably appear in
* future to help cope with non-scalar datatypes. Also, custom data types
* can define their own "kind" codes by mutual agreement between a custom
* typanalyze routine and the selectivity estimation functions of the type's
* operators.
*
* Code reading the pg_statistic relation should not assume that a particular
* data "kind" will appear in any particular slot. Instead, search the
* stakind fields to see if the desired data is available. (The standard
* function get_attstatsslot() may be used for this.)
*/
/*
* The present allocation of "kind" codes is:
*
* 1-99: reserved for assignment by the core PostgreSQL project
* (values in this range will be documented in this file)
* 100-199: reserved for assignment by the PostGIS project
* (values to be documented in PostGIS documentation)
* 200-299: reserved for assignment by the ESRI ST_Geometry project
* (values to be documented in ESRI ST_Geometry documentation)
* 300-9999: reserved for future public assignments
*
* For private use you may choose a "kind" code at random in the range
* 10000-30000. However, for code that is to be widely disseminated it is
* better to obtain a publicly defined "kind" code by request from the
* PostgreSQL Global Development Group.
*/
/*
* In a "most common values" slot, staop is the OID of the "=" operator
* used to decide whether values are the same or not. stavalues contains
* the K most common non-null values appearing in the column, and stanumbers
* contains their frequencies (fractions of total row count). The values
* shall be ordered in decreasing frequency. Note that since the arrays are
* variable-size, K may be chosen by the statistics collector. Values should
* not appear in MCV unless they have been observed to occur more than once;
* a unique column will have no MCV slot.
*/
#define STATISTIC_KIND_MCV 1
/*
* A "histogram" slot describes the distribution of scalar data. staop is
* the OID of the "<" operator that describes the sort ordering. (In theory,
* more than one histogram could appear, if a datatype has more than one
* useful sort operator.) stavalues contains M (>=2) non-null values that
* divide the non-null column data values into M-1 bins of approximately equal
* population. The first stavalues item is the MIN and the last is the MAX.
* stanumbers is not used and should be NULL. IMPORTANT POINT: if an MCV
* slot is also provided, then the histogram describes the data distribution
* *after removing the values listed in MCV* (thus, it's a "compressed
* histogram" in the technical parlance). This allows a more accurate
* representation of the distribution of a column with some very-common
* values. In a column with only a few distinct values, it's possible that
* the MCV list describes the entire data population; in this case the
* histogram reduces to empty and should be omitted.
*/
#define STATISTIC_KIND_HISTOGRAM 2
/*
* A "correlation" slot describes the correlation between the physical order
* of table tuples and the ordering of data values of this column, as seen
* by the "<" operator identified by staop. (As with the histogram, more
* than one entry could theoretically appear.) stavalues is not used and
* should be NULL. stanumbers contains a single entry, the correlation
* coefficient between the sequence of data values and the sequence of
* their actual tuple positions. The coefficient ranges from +1 to -1.
*/
#define STATISTIC_KIND_CORRELATION 3
/*
* A "most common elements" slot is similar to a "most common values" slot,
* except that it stores the most common non-null *elements* of the column
* values. This is useful when the column datatype is an array or some other
* type with identifiable elements (for instance, tsvector). staop contains
* the equality operator appropriate to the element type. stavalues contains
* the most common element values, and stanumbers their frequencies. Unlike
* MCV slots, frequencies are measured as the fraction of non-null rows the
* element value appears in, not the frequency of all rows. Also unlike
* MCV slots, the values are sorted into the element type's default order
* (to support binary search for a particular value). Since this puts the
* minimum and maximum frequencies at unpredictable spots in stanumbers,
* there are two extra members of stanumbers, holding copies of the minimum
* and maximum frequencies. Optionally, there can be a third extra member,
* which holds the frequency of null elements (expressed in the same terms:
* the fraction of non-null rows that contain at least one null element). If
* this member is omitted, the column is presumed to contain no null elements.
*
* Note: in current usage for tsvector columns, the stavalues elements are of
* type text, even though their representation within tsvector is not
* exactly text.
*/
#define STATISTIC_KIND_MCELEM 4
/*
* A "distinct elements count histogram" slot describes the distribution of
* the number of distinct element values present in each row of an array-type
* column. Only non-null rows are considered, and only non-null elements.
* staop contains the equality operator appropriate to the element type.
* stavalues is not used and should be NULL. The last member of stanumbers is
* the average count of distinct element values over all non-null rows. The
* preceding M (>=2) members form a histogram that divides the population of
* distinct-elements counts into M-1 bins of approximately equal population.
* The first of these is the minimum observed count, and the last the maximum.
*/
#define STATISTIC_KIND_DECHIST 5
/*
* A "length histogram" slot describes the distribution of range lengths in
* rows of a range-type column. stanumbers contains a single entry, the
* fraction of empty ranges. stavalues is a histogram of non-empty lengths, in
* a format similar to STATISTIC_KIND_HISTOGRAM: it contains M (>=2) range
* values that divide the column data values into M-1 bins of approximately
* equal population. The lengths are stored as float8s, as measured by the
* range type's subdiff function. Only non-null rows are considered.
*/
#define STATISTIC_KIND_RANGE_LENGTH_HISTOGRAM 6
/*
* A "bounds histogram" slot is similar to STATISTIC_KIND_HISTOGRAM, but for
* a range-type column. stavalues contains M (>=2) range values that divide
* the column data values into M-1 bins of approximately equal population.
* Unlike a regular scalar histogram, this is actually two histograms combined
* into a single array, with the lower bounds of each value forming a
* histogram of lower bounds, and the upper bounds a histogram of upper
* bounds. Only non-NULL, non-empty ranges are included.
*/
#define STATISTIC_KIND_BOUNDS_HISTOGRAM 7
那么这些统计信息如何导入导出呢？
导出导入统计信息
https://postgrespro.com/docs/postgresproee/9.6/dump-stat.html
dump_stat这个插件是PostgreSQL pro推出的兼容9.6版本的导出统计信息的插件。
代码如下：
https://github.com/postgrespro/postgrespro/tree/PGPRO9_6
https://github.com/postgrespro/postgrespro/tree/PGPRO9_6/contrib/dump_stat
导出
通过dump_stat导出（导出的结构已经是SQL形式），然后通过SQL导入。
$ psql test -A
test=# \t
test=# \o dump_stat.sql
test=# select dump_statistic();
pg_statistic的每一条记录，产生一条如下SQL：
WITH upsert as (
UPDATE pg_catalog.pg_statistic SET column_name = expression [, ...]
WHERE to_schema_qualified_relation(starelid) = t_relname
AND to_attname(t_relname, staattnum) = t_attname
AND to_atttype(t_relname, staattnum) = t_atttype
AND stainherit = t_stainherit
RETURNING *)
ins as (
SELECT expression [, ...]
WHERE NOT EXISTS (SELECT * FROM upsert)
AND to_attnum(t_relname, t_attname) IS NOT NULL
AND to_atttype(t_relname, t_attname) = t_atttype)
INSERT INTO pg_catalog.pg_statistic SELECT * FROM ins;
where expression can be one of:
array_in(array_text, type_name::regtype::oid, -1)
value::type_name
stat导入的实际例子
(表示public.test表的info列的统计信息，如果存在则更新，不存在则插入。)
WITH
upsert as (
UPDATE pg_catalog.pg_statistic
SET
stanullfrac = 0, stawidth = 4, stadistinct = -1, stakind1 = 2, stakind2 = 3, stakind3 = 0, stakind4 = 0, stakind5 = 0, staop1 = 'pg_catalog.<(pg_catalog.text, pg_catalog.text)'::regoperator, staop2 = 'pg_catalog.<(pg_catalog.text, pg_catalog.text)'::regoperator, staop3 = '0'::regoperator, staop4 = '0'::regoperator, staop5 = '0'::regoperator, stanumbers1 = NULL::real[], stanumbers2 = '{-0.5}'::real[], stanumbers3 = NULL::real[], stanumbers4 = NULL::real[], stanumbers5 = NULL::real[], stavalues1 = array_in('{abc,cde,test}', 'pg_catalog.text'::regtype, -1)::anyarray, stavalues2 = NULL::anyarray, stavalues3 = NULL::anyarray, stavalues4 = NULL::anyarray, stavalues5 = NULL::anyarray
WHERE to_schema_qualified_relation(starelid) = 'public.test' AND to_attname('public.test', staattnum) = 'info' AND to_atttype('public.test', staattnum) = 'pg_catalog.text' AND stainherit = false
RETURNING *
),
ins as (
SELECT
'public.test'::regclass,
to_attnum('public.test', 'info'),
'false'::boolean,
0::real,
4::integer,
-1::real,
2, -- stakind=2 表示柱状图
3, -- stakind=3 表示相关性
0,
0,
0,
'pg_catalog.<(pg_catalog.text, pg_catalog.text)'::regoperator,
'pg_catalog.<(pg_catalog.text, pg_catalog.text)'::regoperator,
'0'::regoperator,
'0'::regoperator,
'0'::regoperator,
NULL::real[],
'{-0.5}'::real[],
NULL::real[],
NULL::real[],
NULL::real[],
array_in('{abc,cde,test}', 'pg_catalog.text'::regtype, -1)::anyarray,
NULL::anyarray,
NULL::anyarray,
NULL::anyarray,
NULL::anyarray
WHERE NOT EXISTS (SELECT * FROM upsert) AND to_attnum('public.test', 'info') IS NOT NULL AND to_atttype('public.test', 'info') = 'pg_catalog.text'
)
INSERT INTO pg_catalog.pg_statistic SELECT * FROM ins;
导入
1、修改postgresql.conf，允许修改系统表，重启数据库生效配置
vi postgresql.conf
allow_system_table_mods=on
pg_ctl restart -m fast
2、导入统计信息
-- 1 pg_class
update pg_class set reltuples=?, relpages=? where oid=?;
-- 2 pg_statistic
WITH upsert as (
UPDATE pg_catalog.pg_statistic SET column_name = expression [, ...]
WHERE to_schema_qualified_relation(starelid) = t_relname
AND to_attname(t_relname, staattnum) = t_attname
AND to_atttype(t_relname, staattnum) = t_atttype
AND stainherit = t_stainherit
RETURNING *)
ins as (
SELECT expression [, ...]
WHERE NOT EXISTS (SELECT * FROM upsert)
AND to_attnum(t_relname, t_attname) IS NOT NULL
AND to_atttype(t_relname, t_attname) = t_atttype)
INSERT INTO pg_catalog.pg_statistic SELECT * FROM ins;
where expression can be one of:
array_in(array_text, type_name::regtype::oid, -1)
value::type_name
3、导入完成后，将allow_system_table_mods设置为off，重启数据库。
dump_statistic代码
CREATE FUNCTION dump_statistic(relid oid) RETURNS SETOF TEXT AS $$
DECLARE
result text;
cmd text; -- main query
in_args text; -- args for insert
up_args text; -- args for upsert
fstaop text := '%s::regoperator';
arr_in text := 'array_in(%s, %s::regtype, -1)::anyarray';
stacols text[] = ARRAY['stakind', 'staop',
'stanumbers', 'stavalues' ];
r record;
i int;
j text;
ncols int := 26; -- number of columns in pg_statistic
stanum text[]; -- stanumbers{1, 5}
staval text[]; -- stavalues{1, 5}
staop text[]; -- staop{1, 5}
relname text; -- quoted relation name
attname text; -- quoted attribute name
atttype text; -- quoted attribute type
BEGIN
for r in
select * from pg_catalog.pg_statistic
where starelid = relid
and get_namespace(starelid) != to_namespace('information_schema')
and get_namespace(starelid) != to_namespace('pg_catalog') loop
relname := to_schema_qualified_relation(r.starelid);
attname := quote_literal(to_attname(relname, r.staattnum));
atttype := quote_literal(to_atttype(relname, r.staattnum));
relname := quote_literal(relname); -- redefine relname
in_args := '';
up_args = 'stanullfrac = %s, stawidth = %s, stadistinct = %s, ';
cmd := 'WITH upsert as ( ' ||
'UPDATE pg_catalog.pg_statistic SET %s ' ||
'WHERE to_schema_qualified_relation(starelid) = ' || relname || ' '
'AND to_attname(' || relname || ', staattnum) = ' || attname || ' '
'AND to_atttype(' || relname || ', staattnum) = ' || atttype || ' '
'AND stainherit = ' || r.stainherit || ' ' ||
'RETURNING *), ' ||
'ins as ( ' ||
'SELECT %s ' ||
'WHERE NOT EXISTS (SELECT * FROM upsert) ' ||
'AND to_attnum(' || relname || ', ' || attname || ') IS NOT NULL '
'AND to_atttype(' || relname || ', ' || attname || ') = ' || atttype || ') '
'INSERT INTO pg_catalog.pg_statistic SELECT * FROM ins;';
for i in 1..ncols loop
in_args := in_args || '%s';
if i != ncols then
in_args := in_args || ', ';
end if;
end loop;
for j in 1..4 loop
for i in 1..5 loop
up_args := up_args || format('%s%s = %%s', stacols[j], i);
if i * j != 20 then
up_args := up_args || ', ';
end if;
end loop;
end loop;
cmd := format(cmd, up_args, in_args); --prepare template for main query
staop := array[format(fstaop, quote_literal(to_schema_qualified_operator(r.staop1))),
format(fstaop, quote_literal(to_schema_qualified_operator(r.staop2))),
format(fstaop, quote_literal(to_schema_qualified_operator(r.staop3))),
format(fstaop, quote_literal(to_schema_qualified_operator(r.staop4))),
format(fstaop, quote_literal(to_schema_qualified_operator(r.staop5)))];
stanum := array[r.stanumbers1::text,
r.stanumbers2::text,
r.stanumbers3::text,
r.stanumbers4::text,
r.stanumbers5::text];
for i in 1..5 loop
if stanum[i] is null then
stanum[i] := 'NULL::real[]';
else
stanum[i] := '''' || stanum[i] || '''::real[]';
end if;
end loop;
if r.stavalues1 is not null then
staval[1] := format(arr_in, quote_literal(r.stavalues1),
quote_literal(
to_schema_qualified_type(
anyarray_elemtype(r.stavalues1))));
else
staval[1] := 'NULL::anyarray';
end if;
if r.stavalues2 is not null then
staval[2] := format(arr_in, quote_literal(r.stavalues2),
quote_literal(
to_schema_qualified_type(
anyarray_elemtype(r.stavalues2))));
else
staval[2] := 'NULL::anyarray';
end if;
if r.stavalues3 is not null then
staval[3] := format(arr_in, quote_literal(r.stavalues3),
quote_literal(
to_schema_qualified_type(
anyarray_elemtype(r.stavalues3))));
else
staval[3] := 'NULL::anyarray';
end if;
if r.stavalues4 is not null then
staval[4] := format(arr_in, quote_literal(r.stavalues4),
quote_literal(
to_schema_qualified_type(
anyarray_elemtype(r.stavalues4))));
else
staval[4] := 'NULL::anyarray';
end if;
if r.stavalues5 is not null then
staval[5] := format(arr_in, quote_literal(r.stavalues5),
quote_literal(
to_schema_qualified_type(
anyarray_elemtype(r.stavalues5))));
else
staval[5] := 'NULL::anyarray';
end if;
--DEBUG
--staop := array['{arr}', '{arr}', '{arr}', '{arr}', '{arr}'];
--stanum := array['{num}', '{num}', '{num}', '{num}', '{num}'];
--staval := array['{val}', '{val}', '{val}', '{val}', '{val}'];
result := format(cmd,
r.stanullfrac,
r.stawidth,
r.stadistinct,
-- stakind
r.stakind1, r.stakind2, r.stakind3, r.stakind4, r.stakind5,
-- staop
staop[1], staop[2], staop[3], staop[4], staop[5],
-- stanumbers
stanum[1], stanum[2], stanum[3], stanum[4], stanum[5],
-- stavalues
staval[1], staval[2], staval[3], staval[4], staval[5],
-- first 6 columns
format('%s::regclass', relname),
format('to_attnum(%s, %s)', relname, attname),
'''' || r.stainherit || '''::boolean',
r.stanullfrac || '::real',
r.stawidth || '::integer',
r.stadistinct || '::real',
-- stakind
r.stakind1, r.stakind2, r.stakind3, r.stakind4, r.stakind5,
-- staop
staop[1], staop[2], staop[3], staop[4], staop[5],
-- stanumbers
stanum[1], stanum[2], stanum[3], stanum[4], stanum[5],
-- stavalues
staval[1], staval[2], staval[3], staval[4], staval[5]);
return next result;
end loop;
return;
END;
$$ LANGUAGE plpgsql;
我们甚至可以将Oracle数据库的统计信息，平移到PG数据库，对齐需要的元素即可：
记录数、占用多少个数据块。每列的空值比例、平均列宽、唯一值比例或个数、高频值以及频率、柱状图分布、存储相关性、多值列（高频元素及比例、元素柱状图分布）。
好处：在迁移ORACLE数据时，可以关闭autovacuumm（提高导入速度），通过这种方法来导入统计信息。（只要元素对应即可，当然有些元素可能是ORACLE中不采集的，比如多值列的统计信息）。
参考
https://github.com/postgrespro/postgrespro/tree/PGPRO9_6/contrib/dump_stat
