SQL开发知识：PostgreSQL 10分区表及性能测试报告小结
作者简介：
中国比较早的postgresql使用者,2001年就开始使用postgresql,自2003年底至2014年一直担任PGSQL中国社区论坛PostgreSQL的论坛板块版主、管理员，参与Postgresql讨论和发表专题文章7000多贴.拥有15年的erp设计,开发和实施经验,开源mrp系统PostMRP就是我的作品,该应用软件是一套基于Postgresql专业的制造业管理软件系统.目前任职于–中国第一物流控股有限公司/运力宝（北京）科技有限公司,为公司的研发部经理
一、 测试环境
操作系统：CentOS 6.4
Postgresql版本号：10.0
CPU：Intel(R) Xeon(R) CPU E5-2407 v2 @ 2.40GHz 4核心 4线程
内存：32G
硬盘：2T SAS 7200
二、 编译安装PostgreSQL 10
－－编译安装及初始化
[root@ad source]# git clone git://git.postgresql.org/git/postgresql.git
[root@ad source]# cd postgresql
[root@ad source]# ./configure –prefix=/usr/local/pgsql10
[root@ad postgresql]# gmake -j 4
[root@ad postgresql]# gmake install
[root@ad postgresql]# su postgres
[postgres@ad postgresql]# /usr/local/pgsql10/bin/initdb –no-locale -E utf8 -D /home/postgres/data10/ -U postgres
[root@ad source]# cd postgresql
[root@ad source]# ./configure –prefix=/usr/local/pgsql10
[root@ad postgresql]# gmake -j 4
[root@ad postgresql]# gmake install
[root@ad postgresql]# su postgres
[postgres@ad postgresql]# /usr/local/pgsql10/bin/initdb –no-locale -E utf8 -D /home/postgres/data10/ -U postgres
－－修改一些参数
postgresql.conf
listen_addresses = ‘*’
port = 10000
shared_buffers = 8096MB
maintenance_work_mem = 512MB
effective_cache_size = 30GB
log_destination = ‘csvlog’
logging_collector = on
log_directory = ‘log’
log_filename = ‘postgresql-%Y-%m-%d_%H%M%S.log’
log_file_mode = 0600
log_checkpoints = off
log_connections = off
log_disconnections = off
log_duration = off
log_line_prefix = ‘%m %h %a %u %d %x [%p] ‘
log_statement = ‘none’
log_timezone = ‘PRC’
track_activity_query_size = 4096
max_wal_size = 32GB
min_wal_size = 2GB
checkpoint_completion_target = 0.5
listen_addresses = ‘*’
port = 10000
shared_buffers = 8096MB
maintenance_work_mem = 512MB
effective_cache_size = 30GB
log_destination = ‘csvlog’
logging_collector = on
log_directory = ‘log’
log_filename = ‘postgresql-%Y-%m-%d_%H%M%S.log’
log_file_mode = 0600
log_checkpoints = off
log_connections = off
log_disconnections = off
log_duration = off
log_line_prefix = ‘%m %h %a %u %d %x [%p] ‘
log_statement = ‘none’
log_timezone = ‘PRC’
track_activity_query_size = 4096
max_wal_size = 32GB
min_wal_size = 2GB
checkpoint_completion_target = 0.5
pg_hba.conf增加许可条目
host all all 192.168.1.0/24 trust
－－启动服务
[postgres@ad data10]$ /usr/local/pgsql10/bin/pg_ctl start -D /home/postgres/data10/
－－连接数据库
[postgres@ad data10]$ /usr/local/pgsql10/bin/psql -p 10000 -U postgres -h 127.0.0.1 -d postgres
psql (10devel)
Type “help” for help.
postgres=#
－－连接数据库
[postgres@ad data10]$ /usr/local/pgsql10/bin/psql -p 10000 -U postgres -h 127.0.0.1 -d postgres
psql (10devel)
Type “help” for help.
postgres=#
PostgreSQL的分区表跟先前版本一样，也要先建立主表，然后再建立子表，使用继承的特性，但不需要手工写规则了，这个比较赞阿。目前支持range、list分区，10正式版本发布时不知会不会支持其它方法。
range分区表
1、分区主表
create table order_range(id bigserial not null,userid integer,product text, createdate date) partition by range ( createdate );
分区主表不能建立全局约束，使用partition by range()说明分区的方式，可以是多个字段，表达式……，具体见https://www.postgresql.org/docs/devel/static/sql-createtable.html
2、分区子表
create table order_range(id bigserial not null,userid integer,product text,
createdate date not null) partition by range ( createdate );
create table order_range_201701 partition of order_range(id primary key,userid,product,
createdate) for values from (‘2017-01-01’) to (‘2017-02-01’);
create table order_range_201702 partition of order_range(id primary key,userid,product,
createdate) for values from (‘2017-02-01’) to (‘2017-03-01’);
createdate date not null) partition by range ( createdate );
create table order_range_201701 partition of order_range(id primary key,userid,product,
createdate) for values from (‘2017-01-01’) to (‘2017-02-01’);
create table order_range_201702 partition of order_range(id primary key,userid,product,
createdate) for values from (‘2017-02-01’) to (‘2017-03-01’);
- 说明：
- 建立分区表时必需指定主表。
- 分区表和主表的 列数量，定义 必须完全一致。
- 分区表的列可以单独增加Default值，或约束。
- 当用户向主表插入数据库时，系统自动路由到对应的分区，如果没有找到对应分区，则抛出错误。
- 指定分区约束的值（范围，LIST值），范围，LIST不能重叠，重叠的路由会卡壳。
- 指定分区的列必需设置成not null,如建立主表时没设置系统会自动加上。
- Range分区范围为 >=最小值 and <最大值……
- 不支持通过更新的方法把数据从一个区移动到另外一个区，这样做会报错。如果要这样做的话需要删除原来的记录，再INSERT一条新的记录。
- 修改主表的字段名，字段类型时，会自动同时修改所有的分区。
- TRUNCATE 主表时，会清除所有继承表分区的记录，如果要清除单个分区，请对分区进行操作。
- DROP主表时会把所有子表一起给DROP掉，如果drop单个分区，请对分区进行操作。
- 使用psql能查看分区表的详细定义。
postgres=# \d+ order_range
Table “public.order_range”
Column | Type | Collation | Nullable | Default | Storage | Stats target | Description
————+———+———–+———-+—————————————–+———-+————–+————-
id | bigint | | not null | nextval(‘order_range_id_seq’::regclass) | plain | |
userid | integer | | | | plain | |
product | text | | | | extended | |
createdate | date | | not null | | plain | |
Partition key: RANGE (createdate)
Partitions: order_range_201701 FOR VALUES FROM (‘2017-01-01’) TO (‘2017-02-01’),
order_range_201702 FOR VALUES FROM (‘2017-02-01’) TO (‘2017-03-01’)
Table “public.order_range”
Column | Type | Collation | Nullable | Default | Storage | Stats target | Description
————+———+———–+———-+—————————————–+———-+————–+————-
id | bigint | | not null | nextval(‘order_range_id_seq’::regclass) | plain | |
userid | integer | | | | plain | |
product | text | | | | extended | |
createdate | date | | not null | | plain | |
Partition key: RANGE (createdate)
Partitions: order_range_201701 FOR VALUES FROM (‘2017-01-01’) TO (‘2017-02-01’),
order_range_201702 FOR VALUES FROM (‘2017-02-01’) TO (‘2017-03-01’)
postgres=#
list分区表
1、分区主表
create table order_list(id bigserial not null,userid integer,product text,area text, createdate date) partition by list( area );
2、分区子表
create table order_list_gd partition of order_list(id primary key,userid,product,area,createdate) for values in (‘广东’);
create table order_list_bj partition of order_list(id primary key,userid,product,area,createdate) for values in (‘北京’);
create table order_list_bj partition of order_list(id primary key,userid,product,area,createdate) for values in (‘北京’);
多级分区表
先按地区分区，再按日期分区
1、主表
create table order_range_list(id bigserial not null,userid integer,product text,area text, createdate date) partition by list ( area );
2、一级分区表
create table order_range_list_gd partition of order_range_list for values in (‘广东’) partition by range(createdate);
create table order_range_list_bj partition of order_range_list for values in (‘北京’) partition by range(createdate);
create table order_range_list_bj partition of order_range_list for values in (‘北京’) partition by range(createdate);
3、二级分区表
create table order_range_list_gd_201701 partition of order_range_list_gd(id primary
key,userid,product,area,createdate) for values from (‘2017-01-01’) to (‘2017-02-01’);
create table order_range_list_gd_201702 partition of order_range_list_gd(id primary
key,userid,product,area,createdate) for values from (‘2017-02-01’) to (‘2017-03-01’);
key,userid,product,area,createdate) for values from (‘2017-01-01’) to (‘2017-02-01’);
create table order_range_list_gd_201702 partition of order_range_list_gd(id primary
key,userid,product,area,createdate) for values from (‘2017-02-01’) to (‘2017-03-01’);
create table order_range_list_bj_201701 partition of order_range_list_bj(id primary
key,userid,product,area,createdate) for values from (‘2017-01-01’) to (‘2017-02-01’);
create table order_range_list_bj_201702 partition of order_range_list_bj(id primary
key,userid,product,area,createdate) for values from (‘2017-02-01’) to (‘2017-03-01’);
直接操作分区也要受分区规则的约束
postgres=# insert into order_range_201702 (id,userid,product,createdate) values(1,
(random()::numeric(7,6)*1000000)::integer,md5(random()::text),('2017-01-01'));
ERROR: new row for relation "order_range_201702" violates partition constraint
DETAIL: Failing row contains (1, 322345, 51a9357a78416d11a018949a42dd2f8d, 2017-01-01).
INSERT提示违反了分区约束
postgres=# update order_range_201701 set createdate='2017-02-01' where createdate='2017-01-17';
ERROR: new row for relation "order_range_201701" violates partition constraint
DETAIL: Failing row contains (1, 163357, 7e8fbe7b632a54ba1ec401d969f3259a, 2017-02-01).
UPDATE提示违反了分区约束
如果分区表是外部表，则约束失效，后面有介绍
使用ALTER TABLE ATTACH[DETACH] PARTITION 增加或删除分区
1、移除分区
录入2条测试数据
postgres=# insert into order_range (userid,product,createdate)
values((random()::numeric(7,6)*1000000)::integer,md5(random()::text),(‘2017-01-01’::date+
(random()*31)::integer));
INSERT 0 1
Time: 25.006 ms
postgres=# insert into order_range (userid,product,createdate)
values((random()::numeric(7,6)*1000000)::integer,md5(random()::text),(‘2017-01-01’::date+
(random()*31)::integer));
INSERT 0 1
Time: 7.601 ms
postgres=# select * from order_range;
id | userid | product | createdate
—-+——–+———————————-+————
1 | 163357 | 7e8fbe7b632a54ba1ec401d969f3259a | 2017-01-17
2 | 349759 | 8095c9036295d3c800dace9069f9c102 | 2017-01-27
(2 rows)
values((random()::numeric(7,6)*1000000)::integer,md5(random()::text),(‘2017-01-01’::date+
(random()*31)::integer));
INSERT 0 1
Time: 25.006 ms
postgres=# insert into order_range (userid,product,createdate)
values((random()::numeric(7,6)*1000000)::integer,md5(random()::text),(‘2017-01-01’::date+
(random()*31)::integer));
INSERT 0 1
Time: 7.601 ms
postgres=# select * from order_range;
id | userid | product | createdate
—-+——–+———————————-+————
1 | 163357 | 7e8fbe7b632a54ba1ec401d969f3259a | 2017-01-17
2 | 349759 | 8095c9036295d3c800dace9069f9c102 | 2017-01-27
(2 rows)
删除分区
postgres=# alter table order_range detach partition order_range_201701;
ALTER TABLE
Time: 14.129 ms
ALTER TABLE
Time: 14.129 ms
查看确认分区没了
postgres=# \d+ order_range;
Table “public.order_range”
Column | Type | Collation | Nullable | Default | Storage | Stats target | Description
————+———+———–+———-+—————————————–+———-+————–+————-
id | bigint | | not null | nextval(‘order_range_id_seq’::regclass) | plain | |
userid | integer | | | | plain | |
product | text | | | | extended | |
createdate | date | | not null | | plain | |
Partition key: RANGE (createdate)
Partitions: order_range_201702 FOR VALUES FROM (‘2017-02-01’) TO (‘2017-03-01’)
postgres=#
Table “public.order_range”
Column | Type | Collation | Nullable | Default | Storage | Stats target | Description
————+———+———–+———-+—————————————–+———-+————–+————-
id | bigint | | not null | nextval(‘order_range_id_seq’::regclass) | plain | |
userid | integer | | | | plain | |
product | text | | | | extended | |
createdate | date | | not null | | plain | |
Partition key: RANGE (createdate)
Partitions: order_range_201702 FOR VALUES FROM (‘2017-02-01’) TO (‘2017-03-01’)
postgres=#
数据也查不出来了
postgres=# select * from order_range;
id | userid | product | createdate
—-+——–+———+————
(0 rows)
Time: 0.505 ms
id | userid | product | createdate
—-+——–+———+————
(0 rows)
Time: 0.505 ms
但分区表还在
postgres=# select * from order_range_201701;
id | userid | product | createdate
—-+——–+———————————-+————
1 | 163357 | 7e8fbe7b632a54ba1ec401d969f3259a | 2017-01-17
2 | 349759 | 8095c9036295d3c800dace9069f9c102 | 2017-01-27
(2 rows)
Time: 0.727 ms
id | userid | product | createdate
—-+——–+———————————-+————
1 | 163357 | 7e8fbe7b632a54ba1ec401d969f3259a | 2017-01-17
2 | 349759 | 8095c9036295d3c800dace9069f9c102 | 2017-01-27
(2 rows)
Time: 0.727 ms
2、添加分区
postgres=# alter table order_range attach partition order_range_201701 FOR VALUES FROM (‘2017-01-01’) TO (‘2017-02-01’);
ERROR: column “createdate” in child table must be marked NOT NULL
Time: 0.564 ms
ERROR: column “createdate” in child table must be marked NOT NULL
Time: 0.564 ms
增加子表里，约束需要与主表一致
postgres=# alter table order_range_201701 alter column createdate set not null;
ALTER TABLE
Time: 17.345 ms
postgres=# alter table order_range attach partition order_range_201701 FOR VALUES FROM (‘2017-01-01’) TO (‘2017-01-15’);
ERROR: partition constraint is violated by some row
Time: 1.276 ms
ALTER TABLE
Time: 17.345 ms
postgres=# alter table order_range attach partition order_range_201701 FOR VALUES FROM (‘2017-01-01’) TO (‘2017-01-15’);
ERROR: partition constraint is violated by some row
Time: 1.276 ms
加回来时可以修改其约束范围，但数据必需在约束的规则范围内
postgres=# alter table order_range attach partition order_range_201701 FOR VALUES FROM
(‘2017-01-01’) TO (‘2017-02-01’);
ALTER TABLE
Time: 18.407 ms
(‘2017-01-01’) TO (‘2017-02-01’);
ALTER TABLE
Time: 18.407 ms
分区表又加回来了
postgres=# \d+ order_range
Table “public.order_range”
Column | Type | Collation | Nullable | Default | Storage | Stats target | Description
————+———+———–+———-+—————————————–+———-+————–+————-
id | bigint | | not null | nextval(‘order_range_id_seq’::regclass) | plain | |
userid | integer | | | | plain | |
product | text | | | | extended | |
createdate | date | | not null | | plain | |
Partition key: RANGE (createdate)
Partitions: order_range_201701 FOR VALUES FROM (‘2017-01-01’) TO (‘2017-02-01’),
order_range_201702 FOR VALUES FROM (‘2017-02-01’) TO (‘2017-03-01’)
Table “public.order_range”
Column | Type | Collation | Nullable | Default | Storage | Stats target | Description
————+———+———–+———-+—————————————–+———-+————–+————-
id | bigint | | not null | nextval(‘order_range_id_seq’::regclass) | plain | |
userid | integer | | | | plain | |
product | text | | | | extended | |
createdate | date | | not null | | plain | |
Partition key: RANGE (createdate)
Partitions: order_range_201701 FOR VALUES FROM (‘2017-01-01’) TO (‘2017-02-01’),
order_range_201702 FOR VALUES FROM (‘2017-02-01’) TO (‘2017-03-01’)
postgres=# select * from order_range;
id | userid | product | createdate
—-+——–+———————————-+————
1 | 163357 | 7e8fbe7b632a54ba1ec401d969f3259a | 2017-01-17
2 | 349759 | 8095c9036295d3c800dace9069f9c102 | 2017-01-27
(2 rows)
Time: 0.627 ms
添加外部表作为分区表
－－增加一个新库，建立需要的外部表
[postgres@ad root]$ /usr/local/pgsql10/bin/psql -p 10000 -U postgres -h 127.0.0.1 -d postgres
psql (10devel)
Type “help” for help.
#建立数据库
postgres=# create database postgres_fdw;
CREATE DATABASE
postgres_fdw=# create table order_range_fdw(id bigserial not null,userid integer,product text, createdate date not null);
CREATE TABLE
postgres_fdw=#
psql (10devel)
Type “help” for help.
#建立数据库
postgres=# create database postgres_fdw;
CREATE DATABASE
postgres_fdw=# create table order_range_fdw(id bigserial not null,userid integer,product text, createdate date not null);
CREATE TABLE
postgres_fdw=#
#录入一条测试数据
postgres_fdw=# insert into order_range_fdw (userid,product,createdate)
values((random()::numeric(7,6)*1000000)::integer,md5(random()::text),(‘2017-01-01’::date-
(random()*31)::integer));
INSERT 0 1
postgres_fdw=# select * from order_range_fdw;
id | userid | product | createdate
—-+——–+———————————-+————
2 | 300686 | 55956a07742d6aebdef7ebb78c2400d7 | 2016-12-22
(1 row)
－－在postgres库中增加外部表order_range_fdw
[postgres@ad root]$ /usr/local/pgsql10/bin/psql -p 10000 -U postgres -h 127.0.0.1 -d postgres
psql (10devel)
Type “help” for help.
#增加postgres_fdw模块
postgres=# create extension postgres_fdw;
CREATE EXTENSION
#建立外部服务器
postgres=# CREATE SERVER foreign_server
FOREIGN DATA WRAPPER postgres_fdw
OPTIONS (host ‘192.168.1.10’, port ‘10000’, dbname ‘postgres_fdw’);
CREATE SERVER
#建立外部服务器用户标识
postgres=# CREATE USER MAPPING FOR postgres
postgres-# SERVER foreign_server
postgres-# OPTIONS (user ‘postgres’, password ”);
CREATE USER MAPPING
#建立外部表
postgres=# CREATE FOREIGN TABLE order_range_fdw (
postgres(# id bigint not null,
postgres(# userid integer,
postgres(# product text,
postgres(# createdate date not null
postgres(# )
postgres-# SERVER foreign_server
postgres-# OPTIONS (schema_name ‘public’, table_name ‘order_range_fdw’);
CREATE FOREIGN TABLE
#查询数据
postgres=# select * from order_range_fdw;
id | userid | product | createdate
—-+——–+———————————-+————
2 | 300686 | 55956a07742d6aebdef7ebb78c2400d7 | 2016-12-22
(1 row)
－－将外部表作为分区表添加到order_range下
#添加分区表
postgres=# alter table order_range attach partition order_range_fdw FOR VALUES FROM (‘1900-01-01’) TO (‘2017-01-01’);
ALTER TABLE
#查看order_range下的所有分区表
postgres=# \d+ order_range
Table “public.order_range”
Column | Type | Collation | Nullable | Default | Storage | Stats target | Description
————+———+———–+———-+—————————————–+———-+————–+————-
id | bigint | | not null | nextval(‘order_range_id_seq’::regclass) | plain | |
userid | integer | | | | plain | |
product | text | | | | extended | |
createdate | date | | not null | | plain | |
Partition key: RANGE (createdate)
Partitions: order_range_201701 FOR VALUES FROM (‘2017-01-01’) TO (‘2017-02-01’),
order_range_201702 FOR VALUES FROM (‘2017-02-01’) TO (‘2017-03-01’),
order_range_fdw FOR VALUES FROM (‘1900-01-01’) TO (‘2017-01-01’)
#查询数据
postgres=# select * from order_range where createdate<‘2017-01-01’;
id | userid | product | createdate
—-+——–+———————————-+————
2 | 300686 | 55956a07742d6aebdef7ebb78c2400d7 | 2016-12-22
(1 row)
#查看执行计划
postgres=# explain select * from order_range where createdate<‘2017-01-01’;
QUERY PLAN
——————————————————————————–
Append (cost=100.00..131.79 rows=379 width=48)
-> Foreign Scan on order_range_fdw (cost=100.00..131.79 rows=379 width=48)
(2 rows)
#测试看看能不能更新数据
postgres=# insert into order_range (userid,product,createdate)
values((random()::numeric(7,6)*1000000)::integer,md5(random()::text),(‘2017-01-01′::date-
(random()*31)::integer));
ERROR: cannot route inserted tuples to a foreign table
postgres=# update order_range set createdate=’2016-12-01′ where createdate=’2016-12-22’;
UPDATE 1
postgres=# select * from order_range where createdate<‘2017-01-01′;
id | userid | product | createdate
—-+——–+———————————-+————
2 | 300686 | 55956a07742d6aebdef7ebb78c2400d7 | 2016-12-01
(1 row)
postgres=# delete from order_range where createdate=’2016-12-01’;
DELETE 1
postgres=# select * from order_range where createdate<‘2017-01-01’;
id | userid | product | createdate
—-+——–+———+————
(0 rows)
postgres=#
psql (10devel)
Type “help” for help.
#增加postgres_fdw模块
postgres=# create extension postgres_fdw;
CREATE EXTENSION
#建立外部服务器
postgres=# CREATE SERVER foreign_server
FOREIGN DATA WRAPPER postgres_fdw
OPTIONS (host ‘192.168.1.10’, port ‘10000’, dbname ‘postgres_fdw’);
CREATE SERVER
#建立外部服务器用户标识
postgres=# CREATE USER MAPPING FOR postgres
postgres-# SERVER foreign_server
postgres-# OPTIONS (user ‘postgres’, password ”);
CREATE USER MAPPING
#建立外部表
postgres=# CREATE FOREIGN TABLE order_range_fdw (
postgres(# id bigint not null,
postgres(# userid integer,
postgres(# product text,
postgres(# createdate date not null
postgres(# )
postgres-# SERVER foreign_server
postgres-# OPTIONS (schema_name ‘public’, table_name ‘order_range_fdw’);
CREATE FOREIGN TABLE
#查询数据
postgres=# select * from order_range_fdw;
id | userid | product | createdate
—-+——–+———————————-+————
2 | 300686 | 55956a07742d6aebdef7ebb78c2400d7 | 2016-12-22
(1 row)
－－将外部表作为分区表添加到order_range下
#添加分区表
postgres=# alter table order_range attach partition order_range_fdw FOR VALUES FROM (‘1900-01-01’) TO (‘2017-01-01’);
ALTER TABLE
#查看order_range下的所有分区表
postgres=# \d+ order_range
Table “public.order_range”
Column | Type | Collation | Nullable | Default | Storage | Stats target | Description
————+———+———–+———-+—————————————–+———-+————–+————-
id | bigint | | not null | nextval(‘order_range_id_seq’::regclass) | plain | |
userid | integer | | | | plain | |
product | text | | | | extended | |
createdate | date | | not null | | plain | |
Partition key: RANGE (createdate)
Partitions: order_range_201701 FOR VALUES FROM (‘2017-01-01’) TO (‘2017-02-01’),
order_range_201702 FOR VALUES FROM (‘2017-02-01’) TO (‘2017-03-01’),
order_range_fdw FOR VALUES FROM (‘1900-01-01’) TO (‘2017-01-01’)
#查询数据
postgres=# select * from order_range where createdate<‘2017-01-01’;
id | userid | product | createdate
—-+——–+———————————-+————
2 | 300686 | 55956a07742d6aebdef7ebb78c2400d7 | 2016-12-22
(1 row)
#查看执行计划
postgres=# explain select * from order_range where createdate<‘2017-01-01’;
QUERY PLAN
——————————————————————————–
Append (cost=100.00..131.79 rows=379 width=48)
-> Foreign Scan on order_range_fdw (cost=100.00..131.79 rows=379 width=48)
(2 rows)
#测试看看能不能更新数据
postgres=# insert into order_range (userid,product,createdate)
values((random()::numeric(7,6)*1000000)::integer,md5(random()::text),(‘2017-01-01′::date-
(random()*31)::integer));
ERROR: cannot route inserted tuples to a foreign table
postgres=# update order_range set createdate=’2016-12-01′ where createdate=’2016-12-22’;
UPDATE 1
postgres=# select * from order_range where createdate<‘2017-01-01′;
id | userid | product | createdate
—-+——–+———————————-+————
2 | 300686 | 55956a07742d6aebdef7ebb78c2400d7 | 2016-12-01
(1 row)
postgres=# delete from order_range where createdate=’2016-12-01’;
DELETE 1
postgres=# select * from order_range where createdate<‘2017-01-01’;
id | userid | product | createdate
—-+——–+———+————
(0 rows)
postgres=#
插入数据时竟然不能路由到外部表，这个是处于什么考虑呢？？？，源码中只是提示 /* We do not yet have a way to insert into a foreign partition */
还没有办法这样做，猜猜后面的版本应该能实现
下面再说说使用外部表作为分区表还有一些问题
1、无法约束向分区表插入约束外的数据，如下所示
postgres=# \d+ order_range
Table “public.order_range”
Column | Type | Collation | Nullable | Default | Storage | Stats target | Description
————+———+———–+———-+—————————————–+———-+————–+————-
id | bigint | | not null | nextval(‘order_range_id_seq’::regclass) | plain | |
userid | integer | | | | plain | |
product | text | | | | extended | |
createdate | date | | not null | | plain | |
Partition key: RANGE (createdate)
Partitions: order_range_201701 FOR VALUES FROM (‘2017-01-01’) TO (‘2017-02-01’),
order_range_201702 FOR VALUES FROM (‘2017-02-01’) TO (‘2017-03-01’),
order_range_fdw FOR VALUES FROM (‘1900-01-01’) TO (‘2017-01-01’)
postgres=#
postgres=# insert into order_range_fdw (id,userid,product,createdate) values(1,
(random()::numeric(7,6)*1000000)::integer,md5(random()::text),(‘2017-01-01′));
INSERT 0 1
postgres=# select * from order_range;
id | userid | product | createdate
—-+——–+———————————-+————
1 | 163357 | 7e8fbe7b632a54ba1ec401d969f3259a | 2017-01-17
2 | 349759 | 8095c9036295d3c800dace9069f9c102 | 2017-01-27
1 | 621895 | 5546c6e2a7006b52b5c2df55e19b3759 | 2017-02-01
4 | 313019 | 445316004208e09fb4e7eda2bf5b0865 | 2017-01-01
1 | 505836 | 6e9232c4863c82a2e97b9157996572ea | 2017-01-01
(5 rows)
postgres=# select * from order_range where createdate =’2017-01-01’;
id | userid | product | createdate
—-+——–+———+————
(0 rows)
Table “public.order_range”
Column | Type | Collation | Nullable | Default | Storage | Stats target | Description
————+———+———–+———-+—————————————–+———-+————–+————-
id | bigint | | not null | nextval(‘order_range_id_seq’::regclass) | plain | |
userid | integer | | | | plain | |
product | text | | | | extended | |
createdate | date | | not null | | plain | |
Partition key: RANGE (createdate)
Partitions: order_range_201701 FOR VALUES FROM (‘2017-01-01’) TO (‘2017-02-01’),
order_range_201702 FOR VALUES FROM (‘2017-02-01’) TO (‘2017-03-01’),
order_range_fdw FOR VALUES FROM (‘1900-01-01’) TO (‘2017-01-01’)
postgres=#
postgres=# insert into order_range_fdw (id,userid,product,createdate) values(1,
(random()::numeric(7,6)*1000000)::integer,md5(random()::text),(‘2017-01-01′));
INSERT 0 1
postgres=# select * from order_range;
id | userid | product | createdate
—-+——–+———————————-+————
1 | 163357 | 7e8fbe7b632a54ba1ec401d969f3259a | 2017-01-17
2 | 349759 | 8095c9036295d3c800dace9069f9c102 | 2017-01-27
1 | 621895 | 5546c6e2a7006b52b5c2df55e19b3759 | 2017-02-01
4 | 313019 | 445316004208e09fb4e7eda2bf5b0865 | 2017-01-01
1 | 505836 | 6e9232c4863c82a2e97b9157996572ea | 2017-01-01
(5 rows)
postgres=# select * from order_range where createdate =’2017-01-01’;
id | userid | product | createdate
—-+——–+———+————
(0 rows)
如果这样操作会导致数据查询出现不匹配。
2、sql执行时无法下推
Sql执行无法下推的话对于聚集函数的执行存在很大的性能问题，使用时一定要特别的注意，如下所示
postgres=# delete from order_range_fdw;
DELETE 1
postgres=# insert into order_range_fdw (id,userid,product,createdate) values(1,
(random()::numeric(7,6)*1000000)::integer,md5(random()::text),(‘2016-01-01’));
INSERT 0 1
postgres=# insert into order_range_fdw (id,userid,product,createdate) values(1,
(random()::numeric(7,6)*1000000)::integer,md5(random()::text),(‘2016-02-01’));
INSERT 0 1
#访问order_range，基执行是
postgres=# explain analyze select count(1) from order_range where createdate<‘2017-01-01’;
QUERY PLAN
——————————————————————————————
Aggregate (cost=178.27..178.28 rows=1 width=8) (actual time=0.656..0.656 rows=1 loops=1)
-> Append (cost=100.00..175.42 rows=1138 width=0) (actual time=0.647..0.649 rows=2 loops=1)
-> Foreign Scan on order_range_fdw (cost=100.00..175.42 rows=1138 width=0) (actual
time=0.647..0.648 rows=2 loops=1)
Planning time: 0.267 ms
Execution time: 1.122 ms
(5 rows)
#直接访问外部表
postgres=# explain analyze select count(1) from order_range_fdw where createdate<‘2017-01-01’;
QUERY PLAN
——————————————————————————————-
Foreign Scan (cost=102.84..155.54 rows=1 width=8) (actual time=0.661..0.662 rows=1 loops=1)
Relations: Aggregate on (public.order_range_fdw)
Planning time: 0.154 ms
Execution time: 1.051 ms
(4 rows)
DELETE 1
postgres=# insert into order_range_fdw (id,userid,product,createdate) values(1,
(random()::numeric(7,6)*1000000)::integer,md5(random()::text),(‘2016-01-01’));
INSERT 0 1
postgres=# insert into order_range_fdw (id,userid,product,createdate) values(1,
(random()::numeric(7,6)*1000000)::integer,md5(random()::text),(‘2016-02-01’));
INSERT 0 1
#访问order_range，基执行是
postgres=# explain analyze select count(1) from order_range where createdate<‘2017-01-01’;
QUERY PLAN
——————————————————————————————
Aggregate (cost=178.27..178.28 rows=1 width=8) (actual time=0.656..0.656 rows=1 loops=1)
-> Append (cost=100.00..175.42 rows=1138 width=0) (actual time=0.647..0.649 rows=2 loops=1)
-> Foreign Scan on order_range_fdw (cost=100.00..175.42 rows=1138 width=0) (actual
time=0.647..0.648 rows=2 loops=1)
Planning time: 0.267 ms
Execution time: 1.122 ms
(5 rows)
#直接访问外部表
postgres=# explain analyze select count(1) from order_range_fdw where createdate<‘2017-01-01’;
QUERY PLAN
——————————————————————————————-
Foreign Scan (cost=102.84..155.54 rows=1 width=8) (actual time=0.661..0.662 rows=1 loops=1)
Relations: Aggregate on (public.order_range_fdw)
Planning time: 0.154 ms
Execution time: 1.051 ms
(4 rows)
3、sql查询需要访问的分区表中包含了“外部分区表”和“非外部分区表”时， 无法使用Parallel Seq Scan，如下所示
#插入100W数据到分区表中
postgres=# insert into order_range (userid,product,createdate) SELECT
(random()::numeric(7,6)*1000000)::integer,md5(random()::text),(‘2017-01-01′::date+
(random()*58)::integer) from generate_series(1,1000000);
INSERT 0 1000000
#访问所有的分区表
postgres=# explain select count(1) from order_range;
QUERY PLAN
—————————————————————————————
Aggregate (cost=24325.22..24325.23 rows=1 width=8)
-> Append (cost=0.00..21558.23 rows=1106797 width=0)
-> Seq Scan on order_range_201701 (cost=0.00..11231.82 rows=580582 width=0)
-> Seq Scan on order_range_201702 (cost=0.00..10114.02 rows=522802 width=0)
-> Foreign Scan on order_range_fdw (cost=100.00..212.39 rows=3413 width=0)
(5 rows)
#只访问“非外部分区表”
postgres=# explain select count(1) from order_range where createdate>=’2017-01-01’;
QUERY PLAN
————————————————————————————-
Finalize Aggregate (cost=17169.84..17169.85 rows=1 width=8)
-> Gather (cost=17169.62..17169.83 rows=2 width=8)
Workers Planned: 2
-> Partial Aggregate (cost=16169.62..16169.63 rows=1 width=8)
-> Append (cost=0.00..15803.52 rows=146440 width=0)
-> Parallel Seq Scan on order_range_201701 (cost=0.00..8449.86
rows=80636 width=0)
Filter: (createdate >= ‘2017-01-01’::date)
-> Parallel Seq Scan on order_range_201702 (cost=0.00..7353.66
rows=65804 width=0)
Filter: (createdate >= ‘2017-01-01’::date)
(9 rows)
postgres=#
postgres=# insert into order_range (userid,product,createdate) SELECT
(random()::numeric(7,6)*1000000)::integer,md5(random()::text),(‘2017-01-01′::date+
(random()*58)::integer) from generate_series(1,1000000);
INSERT 0 1000000
#访问所有的分区表
postgres=# explain select count(1) from order_range;
QUERY PLAN
—————————————————————————————
Aggregate (cost=24325.22..24325.23 rows=1 width=8)
-> Append (cost=0.00..21558.23 rows=1106797 width=0)
-> Seq Scan on order_range_201701 (cost=0.00..11231.82 rows=580582 width=0)
-> Seq Scan on order_range_201702 (cost=0.00..10114.02 rows=522802 width=0)
-> Foreign Scan on order_range_fdw (cost=100.00..212.39 rows=3413 width=0)
(5 rows)
#只访问“非外部分区表”
postgres=# explain select count(1) from order_range where createdate>=’2017-01-01’;
QUERY PLAN
————————————————————————————-
Finalize Aggregate (cost=17169.84..17169.85 rows=1 width=8)
-> Gather (cost=17169.62..17169.83 rows=2 width=8)
Workers Planned: 2
-> Partial Aggregate (cost=16169.62..16169.63 rows=1 width=8)
-> Append (cost=0.00..15803.52 rows=146440 width=0)
-> Parallel Seq Scan on order_range_201701 (cost=0.00..8449.86
rows=80636 width=0)
Filter: (createdate >= ‘2017-01-01’::date)
-> Parallel Seq Scan on order_range_201702 (cost=0.00..7353.66
rows=65804 width=0)
Filter: (createdate >= ‘2017-01-01’::date)
(9 rows)
postgres=#
外部分区表的应用场景
将业务库上的不再修改的冷数全部分离到另一个节点上面，然后做为外部分区表挂上来。这样可以保持业务库的容量尽可以的轻，同时也不会对业务有侵入，这一点是非常的友好。但要注意Sql执行无法下推的问题，无法使用Parallel Seq Scan问题。
如果在后面版本中能解决fdw partition insert路由问题和sql语句执行下推问题那么就可以拿来做olap应用了。
四、建立测试业务表
下面模似一个用户收支流水表
－－非分区表
create table t_pay_all (id serial not null primary key,userid integer not null,pay_money float8 not
null,createdate date not null);
create index t_pay_all_userid_idx on t_pay_all using btree(userid);
create index t_pay_all_createdate_idx on t_pay_all using btree(createdate);
null,createdate date not null);
create index t_pay_all_userid_idx on t_pay_all using btree(userid);
create index t_pay_all_createdate_idx on t_pay_all using btree(createdate);
－－分区表
生成12个分区，一个月份一个表
create table t_pay (id serial not null,userid integer not null,pay_money float8 not null,createdate
date not null) partition by range (createdate);
create table t_pay_201701 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-01-01’) to (‘2017-02-01’);
create index t_pay_201701_createdate_idx on t_pay_201701 using btree(createdate);
create index t_pay_201701_userid_idx on t_pay_201701 using btree(userid);
create table t_pay_201702 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-02-01’) to (‘2017-03-01’);
create index t_pay_201702_createdate_idx on t_pay_201702 using btree(createdate);
create index t_pay_201702_userid_idx on t_pay_201702 using btree(userid);
create table t_pay_201703 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-03-01’) to (‘2017-04-01’);
create index t_pay_201703_createdate_idx on t_pay_201703 using btree(createdate);
create index t_pay_201703_userid_idx on t_pay_201703 using btree(userid);
create table t_pay_201704 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-04-01’) to (‘2017-05-01’);
create index t_pay_201704_createdate_idx on t_pay_201704 using btree(createdate);
create index t_pay_201704_userid_idx on t_pay_201704 using btree(userid);
create table t_pay_201705 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-05-01’) to (‘2017-06-01’);
create index t_pay_201705_createdate_idx on t_pay_201705 using btree(createdate);
create index t_pay_201705_userid_idx on t_pay_201705 using btree(userid);
create table t_pay_201706 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-06-01’) to (‘2017-07-01’);
create index t_pay_201706_createdate_idx on t_pay_201706 using btree(createdate);
create index t_pay_201706_userid_idx on t_pay_201706 using btree(userid);
create table t_pay_201707 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-07-01’) to (‘2017-08-01’);
create index t_pay_201707_createdate_idx on t_pay_201707 using btree(createdate);
create index t_pay_201707_userid_idx on t_pay_201707 using btree(userid);
create table t_pay_201708 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-08-01’) to (‘2017-09-01’);
create index t_pay_201708_createdate_idx on t_pay_201708 using btree(createdate);
create index t_pay_201708_userid_idx on t_pay_201708 using btree(userid);
create table t_pay_201709 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-09-01’) to (‘2017-10-01’);
create index t_pay_201709_createdate_idx on t_pay_201709 using btree(createdate);
create index t_pay_201709_userid_idx on t_pay_201709 using btree(userid);
create table t_pay_201710 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-10-01’) to (‘2017-11-01’);
create index t_pay_201710_createdate_idx on t_pay_201710 using btree(createdate);
create index t_pay_201710_userid_idx on t_pay_201710 using btree(userid);
create table t_pay_201711 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-11-01’) to (‘2017-12-01’);
create index t_pay_201711_createdate_idx on t_pay_201711 using btree(createdate);
create index t_pay_201711_userid_idx on t_pay_201711 using btree(userid);
create table t_pay_201712 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-12-01’) to (‘2018-01-01’);
create index t_pay_201712_createdate_idx on t_pay_201712 using btree(createdate);
create index t_pay_201712_userid_idx on t_pay_201712 using btree(userid);
date not null) partition by range (createdate);
create table t_pay_201701 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-01-01’) to (‘2017-02-01’);
create index t_pay_201701_createdate_idx on t_pay_201701 using btree(createdate);
create index t_pay_201701_userid_idx on t_pay_201701 using btree(userid);
create table t_pay_201702 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-02-01’) to (‘2017-03-01’);
create index t_pay_201702_createdate_idx on t_pay_201702 using btree(createdate);
create index t_pay_201702_userid_idx on t_pay_201702 using btree(userid);
create table t_pay_201703 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-03-01’) to (‘2017-04-01’);
create index t_pay_201703_createdate_idx on t_pay_201703 using btree(createdate);
create index t_pay_201703_userid_idx on t_pay_201703 using btree(userid);
create table t_pay_201704 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-04-01’) to (‘2017-05-01’);
create index t_pay_201704_createdate_idx on t_pay_201704 using btree(createdate);
create index t_pay_201704_userid_idx on t_pay_201704 using btree(userid);
create table t_pay_201705 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-05-01’) to (‘2017-06-01’);
create index t_pay_201705_createdate_idx on t_pay_201705 using btree(createdate);
create index t_pay_201705_userid_idx on t_pay_201705 using btree(userid);
create table t_pay_201706 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-06-01’) to (‘2017-07-01’);
create index t_pay_201706_createdate_idx on t_pay_201706 using btree(createdate);
create index t_pay_201706_userid_idx on t_pay_201706 using btree(userid);
create table t_pay_201707 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-07-01’) to (‘2017-08-01’);
create index t_pay_201707_createdate_idx on t_pay_201707 using btree(createdate);
create index t_pay_201707_userid_idx on t_pay_201707 using btree(userid);
create table t_pay_201708 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-08-01’) to (‘2017-09-01’);
create index t_pay_201708_createdate_idx on t_pay_201708 using btree(createdate);
create index t_pay_201708_userid_idx on t_pay_201708 using btree(userid);
create table t_pay_201709 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-09-01’) to (‘2017-10-01’);
create index t_pay_201709_createdate_idx on t_pay_201709 using btree(createdate);
create index t_pay_201709_userid_idx on t_pay_201709 using btree(userid);
create table t_pay_201710 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-10-01’) to (‘2017-11-01’);
create index t_pay_201710_createdate_idx on t_pay_201710 using btree(createdate);
create index t_pay_201710_userid_idx on t_pay_201710 using btree(userid);
create table t_pay_201711 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-11-01’) to (‘2017-12-01’);
create index t_pay_201711_createdate_idx on t_pay_201711 using btree(createdate);
create index t_pay_201711_userid_idx on t_pay_201711 using btree(userid);
create table t_pay_201712 partition of t_pay(id primary key,userid,pay_money,createdate) for values
from (‘2017-12-01’) to (‘2018-01-01’);
create index t_pay_201712_createdate_idx on t_pay_201712 using btree(createdate);
create index t_pay_201712_userid_idx on t_pay_201712 using btree(userid);
五、性能测试
数据导入
－－生成测试数据1000W条记录（尽可能平均分布）
postgres=# copy (select (random()::numeric(7,6)*1000000)::integer as
userid,round((random()*100)::numeric,2) as pay_money,(‘2017-01-01’::date+ (random()*364)::integer)
as createtime from generate_series(1,10000000)) to ‘/home/pg/data.txt’;
COPY 10000000
Time: 42674.548 ms (00:42.675)
userid,round((random()*100)::numeric,2) as pay_money,(‘2017-01-01’::date+ (random()*364)::integer)
as createtime from generate_series(1,10000000)) to ‘/home/pg/data.txt’;
COPY 10000000
Time: 42674.548 ms (00:42.675)
－－非分区表数据导入测试
postgres=# copy t_pay_all(userid,pay_money,createdate) from ‘/home/pg/data.txt’;
COPY 10000000
Time: 114258.743 ms (01:54.259)
COPY 10000000
Time: 114258.743 ms (01:54.259)
－－分区表数据导入测试
postgres=# copy t_pay(userid,pay_money,createdate) from ‘/home/pg/data.txt’;
COPY 10000000
Time: 186358.447 ms (03:06.358)
postgres=#
COPY 10000000
Time: 186358.447 ms (03:06.358)
postgres=#
结论：数据导入时性能相差大约是一半，所以大数据量导入时最好直接导成分区表数据，然后直接对分区表进行操作
查询某一天的数据－－直接从cache里取数据
－－非分区表
postgres=# explain (analyze,buffers) select * from t_pay_all where createdate =’2017-06-01′;
QUERY PLAN
——————————————————————————————-
Bitmap Heap Scan on t_pay_all (cost=592.06..50797.88 rows=27307 width=20) (actual
time=14.544..49.039 rows=27384 loops=1)
Recheck Cond: (createdate = ‘2017-06-01’::date)
Heap Blocks: exact=22197
Buffers: shared hit=22289
-> Bitmap Index Scan on t_pay_all_createdate_idx (cost=0.00..585.24 rows=27307 width=0)
(actual time=7.121..7.121 rows=27384 loops=1)
Index Cond: (createdate = ‘2017-06-01’::date)
Buffers: shared hit=92
Planning time: 0.153 ms
Execution time: 51.583 ms
(9 rows)
Time: 52.272 ms
QUERY PLAN
——————————————————————————————-
Bitmap Heap Scan on t_pay_all (cost=592.06..50797.88 rows=27307 width=20) (actual
time=14.544..49.039 rows=27384 loops=1)
Recheck Cond: (createdate = ‘2017-06-01’::date)
Heap Blocks: exact=22197
Buffers: shared hit=22289
-> Bitmap Index Scan on t_pay_all_createdate_idx (cost=0.00..585.24 rows=27307 width=0)
(actual time=7.121..7.121 rows=27384 loops=1)
Index Cond: (createdate = ‘2017-06-01’::date)
Buffers: shared hit=92
Planning time: 0.153 ms
Execution time: 51.583 ms
(9 rows)
Time: 52.272 ms
－－分区表
postgres=# explain (analyze,buffers) select * from t_pay where createdate =’2017-06-01′;
QUERY PLAN
———————————————————————————————-
Append (cost=608.92..6212.11 rows=27935 width=20) (actual time=4.880..27.032 rows=27384 loops=1)
Buffers: shared hit=5323
-> Bitmap Heap Scan on t_pay_201706 (cost=608.92..6212.11 rows=27935 width=20) (actual
time=4.879..21.990 rows=27384 loops=1)
Recheck Cond: (createdate = ‘2017-06-01’::date)
Heap Blocks: exact=5226
Buffers: shared hit=5323
-> Bitmap Index Scan on t_pay_201706_createdate_idx (cost=0.00..601.94 rows=27935
width=0) (actual time=3.399..3.399 rows=27384 loops=1)
Index Cond: (createdate = ‘2017-06-01’::date)
Buffers: shared hit=97
Planning time: 0.521 ms
Execution time: 30.061 ms
(11 rows)
QUERY PLAN
———————————————————————————————-
Append (cost=608.92..6212.11 rows=27935 width=20) (actual time=4.880..27.032 rows=27384 loops=1)
Buffers: shared hit=5323
-> Bitmap Heap Scan on t_pay_201706 (cost=608.92..6212.11 rows=27935 width=20) (actual
time=4.879..21.990 rows=27384 loops=1)
Recheck Cond: (createdate = ‘2017-06-01’::date)
Heap Blocks: exact=5226
Buffers: shared hit=5323
-> Bitmap Index Scan on t_pay_201706_createdate_idx (cost=0.00..601.94 rows=27935
width=0) (actual time=3.399..3.399 rows=27384 loops=1)
Index Cond: (createdate = ‘2017-06-01’::date)
Buffers: shared hit=97
Planning time: 0.521 ms
Execution time: 30.061 ms
(11 rows)
结论：分区表的Planning time时间明显比非分区表要高，但比起Execution time基本可以忽略。
查询某个时间范围的数据
1、时间范围落在同一个分区内
－－非分区表
postgres=# explain (analyze,buffers)select * from t_pay_all where createdate >=’2017-06-01′
AND createdate<‘2017-07-01’;
QUERY PLAN
——————————————————————————————
Bitmap Heap Scan on t_pay_all (cost=19802.01..95862.00 rows=819666 width=20) (actual
time=115.210..459.547 rows=824865 loops=1)
Recheck Cond: ((createdate >= ‘2017-06-01’::date) AND (createdate < ‘2017-07-01’::date))
Heap Blocks: exact=63701
Buffers: shared read=66578
-> Bitmap Index Scan on t_pay_all_createdate_idx (cost=0.00..19597.10 rows=819666 width=0)
(actual time=101.453..101.453 rows=825865 loops=1)
Index Cond: ((createdate >= ‘2017-06-01’::date) AND (createdate < ‘2017-07-01′::date))
Buffers: shared read=2877
Planning time: 0.166 ms
Execution time: 504.297 ms
(9 rows)
AND createdate<‘2017-07-01’;
QUERY PLAN
——————————————————————————————
Bitmap Heap Scan on t_pay_all (cost=19802.01..95862.00 rows=819666 width=20) (actual
time=115.210..459.547 rows=824865 loops=1)
Recheck Cond: ((createdate >= ‘2017-06-01’::date) AND (createdate < ‘2017-07-01’::date))
Heap Blocks: exact=63701
Buffers: shared read=66578
-> Bitmap Index Scan on t_pay_all_createdate_idx (cost=0.00..19597.10 rows=819666 width=0)
(actual time=101.453..101.453 rows=825865 loops=1)
Index Cond: ((createdate >= ‘2017-06-01’::date) AND (createdate < ‘2017-07-01′::date))
Buffers: shared read=2877
Planning time: 0.166 ms
Execution time: 504.297 ms
(9 rows)
Time: 505.021 ms
postgres=# explain (analyze,buffers)select count(1) from t_pay_all where createdate >=’2017-06-01’
AND createdate<‘2017-07-01’;
QUERY PLAN
———————————————————————————————-
Finalize Aggregate (cost=90543.96..90543.97 rows=1 width=8) (actual time=335.334..335.335
rows=1 loops=1)
Buffers: shared hit=351 read=66593
-> Gather (cost=90543.74..90543.95 rows=2 width=8) (actual time=334.988..335.327 rows=3
loops=1)
Workers Planned: 2
Workers Launched: 2
Buffers: shared hit=351 read=66593
-> Partial Aggregate (cost=89543.74..89543.75 rows=1 width=8) (actual
time=330.796..330.797 rows=1 loops=3)
Buffers: shared read=66578
-> Parallel Bitmap Heap Scan on t_pay_all (cost=19802.01..88689.92 rows=341528
width=0) (actual time=124.126..303.125 rows=274955 loops=3)
Recheck Cond: ((createdate >= ‘2017-06-01’::date) AND (createdate <
‘2017-07-01’::date))
Heap Blocks: exact=25882
Buffers: shared read=66578
-> Bitmap Index Scan on t_pay_all_createdate_idx (cost=0.00..19597.10
rows=819666 width=0) (actual time=111.233..111.233 rows=825865 loops=1)
Index Cond: ((createdate >= ‘2017-06-01’::date) AND (createdate <
‘2017-07-01’::date))
Buffers: shared read=2877
Planning time: 0.213 ms
Execution time: 344.013 ms
(17 rows)
Time: 344.759 ms
postgres=#
－－分区表
postgres=# explain (analyze,buffers)select * from t_pay where createdate >=’2017-06-01′ AND
createdate<‘2017-07-01’;
QUERY PLAN
——————————————————————————————-
Append (cost=0.00..17633.97 rows=824865 width=20) (actual time=0.020..272.926 rows=824865
loops=1)
Buffers: shared hit=5261
-> Seq Scan on t_pay_201706 (cost=0.00..17633.97 rows=824865 width=20) (actual
time=0.019..170.128 rows=824865 loops=1)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate < ‘2017-07-01′::date))
Buffers: shared hit=5261
Planning time: 0.779 ms
Execution time: 335.351 ms
(7 rows)
createdate<‘2017-07-01’;
QUERY PLAN
——————————————————————————————-
Append (cost=0.00..17633.97 rows=824865 width=20) (actual time=0.020..272.926 rows=824865
loops=1)
Buffers: shared hit=5261
-> Seq Scan on t_pay_201706 (cost=0.00..17633.97 rows=824865 width=20) (actual
time=0.019..170.128 rows=824865 loops=1)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate < ‘2017-07-01′::date))
Buffers: shared hit=5261
Planning time: 0.779 ms
Execution time: 335.351 ms
(7 rows)
Time: 336.676 ms
postgres=# explain (analyze,buffers)select count(1) from t_pay where createdate >=’2017-06-01’
AND createdate<‘2017-07-01’;
QUERY PLAN
——————————————————————————————–
Finalize Aggregate (cost=12275.86..12275.87 rows=1 width=8) (actual time=144.023..144.023
rows=1 loops=1)
Buffers: shared hit=5429
-> Gather (cost=12275.64..12275.85 rows=2 width=8) (actual time=143.966..144.016 rows=3
loops=1)
Workers Planned: 2
Workers Launched: 2
Buffers: shared hit=5429
-> Partial Aggregate (cost=11275.64..11275.65 rows=1 width=8) (actual
time=140.230..140.230 rows=1 loops=3)
Buffers: shared hit=5261
-> Append (cost=0.00..10416.41 rows=343694 width=0) (actual time=0.022..106.973
rows=274955 loops=3)
Buffers: shared hit=5261
-> Parallel Seq Scan on t_pay_201706 (cost=0.00..10416.41 rows=343694
width=0) (actual time=0.020..68.952 rows=274955 loops=3)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate <
‘2017-07-01’::date))
Buffers: shared hit=5261
Planning time: 0.760 ms
Execution time: 145.289 ms
(15 rows)
Time: 146.610 ms
在同一个分区内查询优势明显
2、不在同一个分区内
－－非分区表
postgres=# explain (analyze,buffers)select count(1) from t_pay_all where createdate >=’2017-06-01′
AND createdate<‘2017-12-01’;
QUERY PLAN
——————————————————————————————-
Finalize Aggregate (cost=132593.42..132593.43 rows=1 width=8) (actual time=717.848..717.848
rows=1 loops=1)
Buffers: shared hit=33571 read=30446 dirtied=9508 written=4485
-> Gather (cost=132593.20..132593.41 rows=2 width=8) (actual time=717.782..717.841 rows=3
loops=1)
Workers Planned: 2
Workers Launched: 2
Buffers: shared hit=33571 read=30446 dirtied=9508 written=4485
-> Partial Aggregate (cost=131593.20..131593.21 rows=1 width=8) (actual
time=714.096..714.097 rows=1 loops=3)
Buffers: shared hit=33319 read=30446 dirtied=9508 written=4485
-> Parallel Seq Scan on t_pay_all (cost=0.00..126330.64 rows=2105024 width=0)
(actual time=0.059..545.016 rows=1675464 loops=3)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate <
‘2017-12-01’::date))
Rows Removed by Filter: 1661203
Buffers: shared hit=33319 read=30446 dirtied=9508 written=4485
Planning time: 0.178 ms
Execution time: 721.822 ms
(14 rows)
AND createdate<‘2017-12-01’;
QUERY PLAN
——————————————————————————————-
Finalize Aggregate (cost=132593.42..132593.43 rows=1 width=8) (actual time=717.848..717.848
rows=1 loops=1)
Buffers: shared hit=33571 read=30446 dirtied=9508 written=4485
-> Gather (cost=132593.20..132593.41 rows=2 width=8) (actual time=717.782..717.841 rows=3
loops=1)
Workers Planned: 2
Workers Launched: 2
Buffers: shared hit=33571 read=30446 dirtied=9508 written=4485
-> Partial Aggregate (cost=131593.20..131593.21 rows=1 width=8) (actual
time=714.096..714.097 rows=1 loops=3)
Buffers: shared hit=33319 read=30446 dirtied=9508 written=4485
-> Parallel Seq Scan on t_pay_all (cost=0.00..126330.64 rows=2105024 width=0)
(actual time=0.059..545.016 rows=1675464 loops=3)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate <
‘2017-12-01’::date))
Rows Removed by Filter: 1661203
Buffers: shared hit=33319 read=30446 dirtied=9508 written=4485
Planning time: 0.178 ms
Execution time: 721.822 ms
(14 rows)
Time: 722.521 ms
－－分区表
postgres=# explain (analyze,buffers)select count(1) from t_pay where createdate >=’2017-06-01′
AND createdate<‘2017-12-01’;
QUERY PLAN
——————————————————————————————
Finalize Aggregate (cost=69675.98..69675.99 rows=1 width=8) (actual time=714.560..714.560 rows=1
loops=1)
Buffers: shared hit=27002 read=5251
-> Gather (cost=69675.77..69675.98 rows=2 width=8) (actual time=714.426..714.551 rows=3
loops=1)
Workers Planned: 2
Workers Launched: 2
Buffers: shared hit=27002 read=5251
-> Partial Aggregate (cost=68675.77..68675.78 rows=1 width=8) (actual
time=710.416..710.416 rows=1 loops=3)
Buffers: shared hit=26774 read=5251
-> Append (cost=0.00..63439.94 rows=2094330 width=0) (actual time=0.023..536.033
rows=1675464 loops=3)
Buffers: shared hit=26774 read=5251
-> Parallel Seq Scan on t_pay_201706 (cost=0.00..10416.41 rows=343694
width=0) (actual time=0.021..67.935 rows=274955 loops=3)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate <
‘2017-12-01’::date))
Buffers: shared hit=5261
-> Parallel Seq Scan on t_pay_201707 (cost=0.00..10728.06 rows=354204
width=0) (actual time=0.007..54.999 rows=283363 loops=3)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate <
‘2017-12-01’::date))
Buffers: shared hit=5415
-> Parallel Seq Scan on t_pay_201708 (cost=0.00..10744.08 rows=354738
width=0) (actual time=0.007..55.117 rows=283791 loops=3)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate <
‘2017-12-01’::date))
Buffers: shared hit=5423
-> Parallel Seq Scan on t_pay_201709 (cost=0.00..10410.71 rows=343714
width=0) (actual time=0.007..53.402 rows=274971 loops=3)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate <
‘2017-12-01’::date))
Buffers: shared hit=5255
-> Parallel Seq Scan on t_pay_201710 (cost=0.00..10737.41 rows=354494
width=0) (actual time=0.007..55.475 rows=283595 loops=3)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate <
‘2017-12-01’::date))
Buffers: shared hit=5420
-> Parallel Seq Scan on t_pay_201711 (cost=0.00..10403.29 rows=343486
width=0) (actual time=0.036..57.635 rows=274789 loops=3)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate <
‘2017-12-01’::date))
Buffers: shared read=5251
Planning time: 1.217 ms
Execution time: 718.372 ms
(30 rows)
AND createdate<‘2017-12-01’;
QUERY PLAN
——————————————————————————————
Finalize Aggregate (cost=69675.98..69675.99 rows=1 width=8) (actual time=714.560..714.560 rows=1
loops=1)
Buffers: shared hit=27002 read=5251
-> Gather (cost=69675.77..69675.98 rows=2 width=8) (actual time=714.426..714.551 rows=3
loops=1)
Workers Planned: 2
Workers Launched: 2
Buffers: shared hit=27002 read=5251
-> Partial Aggregate (cost=68675.77..68675.78 rows=1 width=8) (actual
time=710.416..710.416 rows=1 loops=3)
Buffers: shared hit=26774 read=5251
-> Append (cost=0.00..63439.94 rows=2094330 width=0) (actual time=0.023..536.033
rows=1675464 loops=3)
Buffers: shared hit=26774 read=5251
-> Parallel Seq Scan on t_pay_201706 (cost=0.00..10416.41 rows=343694
width=0) (actual time=0.021..67.935 rows=274955 loops=3)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate <
‘2017-12-01’::date))
Buffers: shared hit=5261
-> Parallel Seq Scan on t_pay_201707 (cost=0.00..10728.06 rows=354204
width=0) (actual time=0.007..54.999 rows=283363 loops=3)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate <
‘2017-12-01’::date))
Buffers: shared hit=5415
-> Parallel Seq Scan on t_pay_201708 (cost=0.00..10744.08 rows=354738
width=0) (actual time=0.007..55.117 rows=283791 loops=3)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate <
‘2017-12-01’::date))
Buffers: shared hit=5423
-> Parallel Seq Scan on t_pay_201709 (cost=0.00..10410.71 rows=343714
width=0) (actual time=0.007..53.402 rows=274971 loops=3)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate <
‘2017-12-01’::date))
Buffers: shared hit=5255
-> Parallel Seq Scan on t_pay_201710 (cost=0.00..10737.41 rows=354494
width=0) (actual time=0.007..55.475 rows=283595 loops=3)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate <
‘2017-12-01’::date))
Buffers: shared hit=5420
-> Parallel Seq Scan on t_pay_201711 (cost=0.00..10403.29 rows=343486
width=0) (actual time=0.036..57.635 rows=274789 loops=3)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate <
‘2017-12-01’::date))
Buffers: shared read=5251
Planning time: 1.217 ms
Execution time: 718.372 ms
(30 rows)
跨分区查询，大约在跨一半分区时性能相当。
查询某个月里某个用户数据－－直接从cache里取数据
1、数据都落在所在分区，并且数据量极少
－－非分区表
postgres=# explain (analyze,buffers) select * from t_pay_all where createdate>=’2017-06-01′
AND createdate<‘2017-07-01’ and userid=268460;
QUERY PLAN
——————————————————————————————–
Index Scan using t_pay_all_userid_idx on t_pay_all (cost=0.43..48.68 rows=1 width=20)
(actual time=0.053..0.071 rows=7 loops=1)
Index Cond: (userid = 268460)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate < ‘2017-07-01’::date))
Rows Removed by Filter: 10
Buffers: shared hit=20
Planning time: 0.149 ms
Execution time: 0.101 ms
(7 rows)
AND createdate<‘2017-07-01’ and userid=268460;
QUERY PLAN
——————————————————————————————–
Index Scan using t_pay_all_userid_idx on t_pay_all (cost=0.43..48.68 rows=1 width=20)
(actual time=0.053..0.071 rows=7 loops=1)
Index Cond: (userid = 268460)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate < ‘2017-07-01’::date))
Rows Removed by Filter: 10
Buffers: shared hit=20
Planning time: 0.149 ms
Execution time: 0.101 ms
(7 rows)
Time: 0.676 ms
－－分区表
postgres=# explain (analyze,buffers) select * from t_pay where createdate >=’2017-06-01′
AND createdate<‘2017-07-01’ and userid=268460;
QUERY PLAN
——————————————————————————————
Append (cost=0.42..12.47 rows=2 width=20) (actual time=0.019..0.032 rows=7 loops=1)
Buffers: shared hit=10
-> Index Scan using t_pay_201706_userid_idx on t_pay_201706 (cost=0.42..12.47 rows=2 width=20)
(actual time=0.018..0.029 rows=7 loops=1)
Index Cond: (userid = 268460)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate < ‘2017-07-01’::date))
Buffers: shared hit=10
Planning time: 0.728 ms
Execution time: 0.064 ms
(8 rows)
AND createdate<‘2017-07-01’ and userid=268460;
QUERY PLAN
——————————————————————————————
Append (cost=0.42..12.47 rows=2 width=20) (actual time=0.019..0.032 rows=7 loops=1)
Buffers: shared hit=10
-> Index Scan using t_pay_201706_userid_idx on t_pay_201706 (cost=0.42..12.47 rows=2 width=20)
(actual time=0.018..0.029 rows=7 loops=1)
Index Cond: (userid = 268460)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate < ‘2017-07-01’::date))
Buffers: shared hit=10
Planning time: 0.728 ms
Execution time: 0.064 ms
(8 rows)
Time: 1.279 ms
在返回记录极少的情况下由于分布表的Planning time开销较大，所以非分区表有优势
2、数据落在其它分区，并且数据量比较大
－－非分区表
postgres=# explain (analyze,buffers) select * from t_pay_all where createdate >=’2017-06-01′
AND createdate<‘2017-07-01’ and userid=302283 ;
QUERY PLAN
———————————————————————————————
Bitmap Heap Scan on t_pay_all (cost=19780.69..22301.97 rows=683 width=20) (actual
time=91.778..91.803 rows=2 loops=1)
Recheck Cond: ((userid = 302283) AND (createdate >= ‘2017-06-01’::date) AND (createdate < ‘2017-07-01’::date))
Heap Blocks: exact=9
Buffers: shared hit=2927
-> BitmapAnd (cost=19780.69..19780.69 rows=683 width=0) (actual time=91.767..91.767 rows=0 loops=1)
Buffers: shared hit=2918
-> Bitmap Index Scan on t_pay_all_userid_idx (cost=0.00..183.00 rows=8342 width=0)
(actual time=0.916..0.916 rows=11013 loops=1)
Index Cond: (userid = 302283)
Buffers: shared hit=41
-> Bitmap Index Scan on t_pay_all_createdate_idx (cost=0.00..19597.10 rows=819666
width=0) (actual time=90.837..90.837 rows=825865 loops=1)
Index Cond: ((createdate >= ‘2017-06-01’::date) AND (createdate < ‘2017-07-01’::date))
Buffers: shared hit=2877
Planning time: 0.172 ms
Execution time: 91.851 ms
(14 rows)
AND createdate<‘2017-07-01’ and userid=302283 ;
QUERY PLAN
———————————————————————————————
Bitmap Heap Scan on t_pay_all (cost=19780.69..22301.97 rows=683 width=20) (actual
time=91.778..91.803 rows=2 loops=1)
Recheck Cond: ((userid = 302283) AND (createdate >= ‘2017-06-01’::date) AND (createdate < ‘2017-07-01’::date))
Heap Blocks: exact=9
Buffers: shared hit=2927
-> BitmapAnd (cost=19780.69..19780.69 rows=683 width=0) (actual time=91.767..91.767 rows=0 loops=1)
Buffers: shared hit=2918
-> Bitmap Index Scan on t_pay_all_userid_idx (cost=0.00..183.00 rows=8342 width=0)
(actual time=0.916..0.916 rows=11013 loops=1)
Index Cond: (userid = 302283)
Buffers: shared hit=41
-> Bitmap Index Scan on t_pay_all_createdate_idx (cost=0.00..19597.10 rows=819666
width=0) (actual time=90.837..90.837 rows=825865 loops=1)
Index Cond: ((createdate >= ‘2017-06-01’::date) AND (createdate < ‘2017-07-01’::date))
Buffers: shared hit=2877
Planning time: 0.172 ms
Execution time: 91.851 ms
(14 rows)
Time: 92.534 ms
－－分区表
postgres=# explain (analyze,buffers) select * from t_pay where createdate >=’2017-06-01′
AND createdate<‘2017-07-01’ and userid=302283 ;
QUERY PLAN
——————————————————————————————-
Append (cost=0.42..12.47 rows=2 width=20) (actual time=0.042..0.046 rows=2 loops=1)
Buffers: shared hit=7
-> Index Scan using t_pay_201706_userid_idx on t_pay_201706 (cost=0.42..12.47 rows=2 width=20)
(actual time=0.041..0.045 rows=2 loops=1)
Index Cond: (userid = 302283)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate < ‘2017-07-01’::date))
Buffers: shared hit=7
Planning time: 0.818 ms
Execution time: 0.096 ms
(8 rows)
AND createdate<‘2017-07-01’ and userid=302283 ;
QUERY PLAN
——————————————————————————————-
Append (cost=0.42..12.47 rows=2 width=20) (actual time=0.042..0.046 rows=2 loops=1)
Buffers: shared hit=7
-> Index Scan using t_pay_201706_userid_idx on t_pay_201706 (cost=0.42..12.47 rows=2 width=20)
(actual time=0.041..0.045 rows=2 loops=1)
Index Cond: (userid = 302283)
Filter: ((createdate >= ‘2017-06-01’::date) AND (createdate < ‘2017-07-01’::date))
Buffers: shared hit=7
Planning time: 0.818 ms
Execution time: 0.096 ms
(8 rows)
Time: 1.499 ms
这是分区表最大的优势体现了，性能提升不是一般的大
索引维护
－－非分区表
postgres=# REINDEX INDEX t_pay_all_createdate_idx;
REINDEX
Time: 11827.344 ms (00:11.827)
REINDEX
Time: 11827.344 ms (00:11.827)
－－分区表
postgres=# REINDEX INDEX t_pay_201706_createdate_idx;
REINDEX
Time: 930.439 ms
postgres=#
REINDEX
Time: 930.439 ms
postgres=#
这个也是分区表的优势，可以针对某个分区的索引进行重建。
删除整个分区数据
－－非分区表
postgres=# delete from t_pay_all where createdate >=’2017-06-01′ and createdate<‘2017-07-01’;
DELETE 824865
Time: 5775.545 ms (00:05.776)
DELETE 824865
Time: 5775.545 ms (00:05.776)
－－分区表
postgres=# truncate table t_pay_201706;
TRUNCATE TABLE
Time: 177.809 ms
TRUNCATE TABLE
Time: 177.809 ms
个也是分区表的优势，可以对某个分区直接truncate
全表扫描
－－非分区表
postgres=# explain analyze select count(1) from t_pay;
QUERY PLAN
———————————————————————————————
Finalize Aggregate (cost=107370.96..107370.97 rows=1 width=8) (actual time=971.561..971.561 rows=1 loops=1)
-> Gather (cost=107370.75..107370.96 rows=2 width=8) (actual time=971.469..971.555 rows=3 loops=1)
Workers Planned: 2
Workers Launched: 2
-> Partial Aggregate (cost=106370.75..106370.76 rows=1 width=8) (actual
time=967.378..967.378 rows=1 loops=3)
-> Append (cost=0.00..96800.40 rows=3828141 width=0) (actual time=0.019..698.882 rows=3061712 loops=3)
-> Parallel Seq Scan on t_pay_201701 (cost=0.00..8836.14 rows=349414
width=0) (actual time=0.017..48.716 rows=279531 loops=3)
-> Parallel Seq Scan on t_pay_201702 (cost=0.00..8119.94 rows=321094
width=0) (actual time=0.007..33.072 rows=256875 loops=3)
-> Parallel Seq Scan on t_pay_201703 (cost=0.00..9079.47 rows=359047
width=0) (actual time=0.006..37.153 rows=287238 loops=3)
-> Parallel Seq Scan on t_pay_201704 (cost=0.00..8672.67 rows=342968
width=0) (actual time=0.006..35.317 rows=274374 loops=3)
-> Parallel Seq Scan on t_pay_201705 (cost=0.00..8975.23 rows=354923
width=0) (actual time=0.006..36.571 rows=283938 loops=3)
-> Parallel Seq Scan on t_pay_201706 (cost=0.00..20.00 rows=1000 width=0)
(actual time=0.000..0.000 rows=0 loops=3)
-> Parallel Seq Scan on t_pay_201707 (cost=0.00..8957.04 rows=354204
width=0) (actual time=0.006..36.393 rows=283363 loops=3)
-> Parallel Seq Scan on t_pay_201708 (cost=0.00..8970.38 rows=354738
width=0) (actual time=0.006..37.015 rows=283791 loops=3)
-> Parallel Seq Scan on t_pay_201709 (cost=0.00..8692.14 rows=343714
width=0) (actual time=0.006..35.187 rows=274971 loops=3)
-> Parallel Seq Scan on t_pay_201710 (cost=0.00..8964.94 rows=354494
width=0) (actual time=0.006..36.566 rows=283595 loops=3)
-> Parallel Seq Scan on t_pay_201711 (cost=0.00..8685.86 rows=343486
width=0) (actual time=0.006..35.198 rows=274789 loops=3)
-> Parallel Seq Scan on t_pay_201712 (cost=0.00..8826.59 rows=349059
width=0) (actual time=0.006..36.523 rows=279247 loops=3)
Planning time: 0.706 ms
Execution time: 977.364 ms
(20 rows)
QUERY PLAN
———————————————————————————————
Finalize Aggregate (cost=107370.96..107370.97 rows=1 width=8) (actual time=971.561..971.561 rows=1 loops=1)
-> Gather (cost=107370.75..107370.96 rows=2 width=8) (actual time=971.469..971.555 rows=3 loops=1)
Workers Planned: 2
Workers Launched: 2
-> Partial Aggregate (cost=106370.75..106370.76 rows=1 width=8) (actual
time=967.378..967.378 rows=1 loops=3)
-> Append (cost=0.00..96800.40 rows=3828141 width=0) (actual time=0.019..698.882 rows=3061712 loops=3)
-> Parallel Seq Scan on t_pay_201701 (cost=0.00..8836.14 rows=349414
width=0) (actual time=0.017..48.716 rows=279531 loops=3)
-> Parallel Seq Scan on t_pay_201702 (cost=0.00..8119.94 rows=321094
width=0) (actual time=0.007..33.072 rows=256875 loops=3)
-> Parallel Seq Scan on t_pay_201703 (cost=0.00..9079.47 rows=359047
width=0) (actual time=0.006..37.153 rows=287238 loops=3)
-> Parallel Seq Scan on t_pay_201704 (cost=0.00..8672.67 rows=342968
width=0) (actual time=0.006..35.317 rows=274374 loops=3)
-> Parallel Seq Scan on t_pay_201705 (cost=0.00..8975.23 rows=354923
width=0) (actual time=0.006..36.571 rows=283938 loops=3)
-> Parallel Seq Scan on t_pay_201706 (cost=0.00..20.00 rows=1000 width=0)
(actual time=0.000..0.000 rows=0 loops=3)
-> Parallel Seq Scan on t_pay_201707 (cost=0.00..8957.04 rows=354204
width=0) (actual time=0.006..36.393 rows=283363 loops=3)
-> Parallel Seq Scan on t_pay_201708 (cost=0.00..8970.38 rows=354738
width=0) (actual time=0.006..37.015 rows=283791 loops=3)
-> Parallel Seq Scan on t_pay_201709 (cost=0.00..8692.14 rows=343714
width=0) (actual time=0.006..35.187 rows=274971 loops=3)
-> Parallel Seq Scan on t_pay_201710 (cost=0.00..8964.94 rows=354494
width=0) (actual time=0.006..36.566 rows=283595 loops=3)
-> Parallel Seq Scan on t_pay_201711 (cost=0.00..8685.86 rows=343486
width=0) (actual time=0.006..35.198 rows=274789 loops=3)
-> Parallel Seq Scan on t_pay_201712 (cost=0.00..8826.59 rows=349059
width=0) (actual time=0.006..36.523 rows=279247 loops=3)
Planning time: 0.706 ms
Execution time: 977.364 ms
(20 rows)
Time: 978.705 ms
postgres=#
－－分区表
postgres=# explain analyze select count(1) from t_pay_all;
QUERY PLAN
————————————————————————————————-
Finalize Aggregate (cost=116900.63..116900.64 rows=1 width=8) (actual time=644.093..644.093
rows=1 loops=1)
-> Gather (cost=116900.42..116900.63 rows=2 width=8) (actual time=644.035..644.087 rows=3 loops=1)
Workers Planned: 2
Workers Launched: 2
-> Partial Aggregate (cost=115900.42..115900.43 rows=1 width=8) (actual
time=640.587..640.587 rows=1 loops=3)
-> Parallel Seq Scan on t_pay_all (cost=0.00..105473.33 rows=4170833 width=0)
(actual time=0.344..371.965 rows=3061712 loops=3)
Planning time: 0.164 ms
Execution time: 645.438 ms
(8 rows)
QUERY PLAN
————————————————————————————————-
Finalize Aggregate (cost=116900.63..116900.64 rows=1 width=8) (actual time=644.093..644.093
rows=1 loops=1)
-> Gather (cost=116900.42..116900.63 rows=2 width=8) (actual time=644.035..644.087 rows=3 loops=1)
Workers Planned: 2
Workers Launched: 2
-> Partial Aggregate (cost=115900.42..115900.43 rows=1 width=8) (actual
time=640.587..640.587 rows=1 loops=3)
-> Parallel Seq Scan on t_pay_all (cost=0.00..105473.33 rows=4170833 width=0)
(actual time=0.344..371.965 rows=3061712 loops=3)
Planning time: 0.164 ms
Execution time: 645.438 ms
(8 rows)
Time: 646.027 ms
全扫描时分区表落后，但还基本上能接收。
增加新的分区并导入数据
－－生成新的分区数据
copy (select userid,pay_money,createdate+31 as createdate from t_pay_201712) to ‘/home/pg/201801.txt’;
－－建立新的分区
create table t_pay_201801 partition of t_pay(id primary key,userid,pay_money,createdate) for
values from (‘2018-01-01’) to (‘2018-02-01’);
create index t_pay_201801_createdate_idx on t_pay_201801 using btree(createdate);
create index t_pay_201801_userid_idx on t_pay_201801 using btree(userid);
values from (‘2018-01-01’) to (‘2018-02-01’);
create index t_pay_201801_createdate_idx on t_pay_201801 using btree(createdate);
create index t_pay_201801_userid_idx on t_pay_201801 using btree(userid);
－－非分区表
postgres=# copy t_pay_all(userid,pay_money,createdate) from ‘/home/pg/201801.txt’;
COPY 837741
Time: 18105.024 ms (00:18.105)
COPY 837741
Time: 18105.024 ms (00:18.105)
－－分区表
postgres=# copy t_pay(userid,pay_money,createdate) from ‘/home/pg/201801.txt’;
COPY 837741
Time: 13864.950 ms (00:13.865)
postgres=#
COPY 837741
Time: 13864.950 ms (00:13.865)
postgres=#
新的分区数据导入保持优势
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