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
－－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
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=#
三、分区表介绍
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(xxx)说明分区的方式，xxx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');
- 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') 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');
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);
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'); 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 xxx 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)
删除分区
postgres=# alter table order_range detach partition order_range_201701; 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=#
数据也查不出来了
postgres=# select * from order_range; 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
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
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
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
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') 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=# #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=#
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)
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)
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=#
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);
－－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);
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)
－－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)
－－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=#
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
－－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)
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) 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) 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) 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)
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) 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) 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) 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) 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)
－－分区表
postgres=# REINDEX INDEX t_pay_201706_createdate_idx; 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)
－－分区表
postgres=# truncate table t_pay_201706; 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) 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) 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);
－－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)
－－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=#
新的分区数据导入保持优势