PostgreSQL性能优化综合
【软件环境】
CentOS 5 x64
PostgreSQL 9.1.3
plproxy 2.3
pgbouncer 1.4.2
【测试模型】
设计一个包含INSERT, UPDATE, SELECT语句的业务模型用于本优化案例.
业务逻辑 :
【测试表】
create table user_info
(userid int,
engname text,
cnname text,
occupation text,
birthday date,
signname text,
email text,
qq numeric,
crt_time timestamp without time zone,
mod_time timestamp without time zone
);
create table user_session
(userid int,
logintime timestamp(0) without time zone,
login_count bigint default 0,
logouttime timestamp(0) without time zone,
online_interval interval default interval '0'
);
create table user_login_rec
(userid int,
login_time timestamp without time zone,
ip inet
);
create table user_logout_rec
(userid int,
logout_time timestamp without time zone,
ip inet
);
【初始化数据】
insert into user_info (userid,engname,cnname,occupation,birthday,signname,email,qq,crt_time,mod_time)
select generate_series(1,20000000),
'digoal.zhou',
'德哥',
'DBA',
'1970-01-01'
,E'公益是一辈子的事, I\'m Digoal.Zhou, Just do it!',
276732431,
clock_timestamp(),
NULL;
insert into user_session (userid) select generate_series(1,20000000);
set work_mem='2048MB';
set maintenance_work_mem='2048MB';
alter table user_info add constraint pk_user_info primary key (userid);
alter table user_session add constraint pk_user_session primary key (userid);
【业务函数】
-- 模拟用户登录的函数
create or replace function f_user_login
(i_userid int,
OUT o_userid int,
OUT o_engname text,
OUT o_cnname text,
OUT o_occupation text,
OUT o_birthday date,
OUT o_signname text,
OUT o_email text,
OUT o_qq numeric
)
as $BODY$
declare
begin
select userid,engname,cnname,occupation,birthday,signname,email,qq
into o_userid,o_engname,o_cnname,o_occupation,o_birthday,o_signname,o_email,o_qq
from user_info where userid=i_userid;
insert into user_login_rec (userid,login_time,ip) values (i_userid,now(),inet_client_addr());
update user_session set logintime=now(),login_count=login_count+1 where userid=i_userid;
return;
end;
$BODY$
language plpgsql;
-- 模拟用户退出的函数
create or replace function f_user_logout
(i_userid int,
OUT o_result int
)
as $BODY$
declare
begin
insert into user_logout_rec (userid,logout_time,ip) values (i_userid,now(),inet_client_addr());
update user_session set logouttime=now(),online_interval=online_interval+(now()-logintime) where userid=i_userid;
o_result := 0;
return;
exception
when others then
o_result := 1;
return;
end;
$BODY$
language plpgsql;
【搭建测试模型】
1. 搭建环境, 安装PostgreSQL9.1.3数据库软件并初始化数据库(略).
2. 调整数据库postgresql.conf参数. 打开日志, SQL统计, 跟踪, 以及性能参数, 便于优化过程中取证.
监听IPv4的所有IP.
listen_addresses = '0.0.0.0'
最大允许1000个连接.
max_connections = 1000
为超级用户保留3个可用连接.
superuser_reserved_connections = 3
默认的unix socket文件放在/tmp, 修改为$PGDATA, 以确保安全.
unix_socket_directory = '/pgdata/digoal/1921/data02/pg_root'
默认的访问权限是0777, 修改为0700更安全.
unix_socket_permissions = 0700
Linux下面默认是2小时.
tcp的keepalives包发送间隔以及重试次数, 如果客户端没有响应, 将主动释放对应的SOCKET.
tcp_keepalives_idle = 60
tcp_keepalives_interval = 10
tcp_keepalives_count = 6
大的shared_buffers需要大的checkpoint_segments,同时需要申请更多的System V共享内存资源.
这个值不需要设的太大, 因为PostgreSQL还依赖操作系统的cache来提高读性能, 另外, 写操作频繁的数据库这个设太大反而会增加checkpoint压力.
shared_buffers = 512MB
这个值越大, VACUUM, CREATE INDEX的操作越快, 当然大到一定程度瓶颈就不在内存了, 可能是CPU例如创建索引.
这个值是一个操作的内存使用上限, 而不是一次性分配出去的. 并且需要注意如果开启了autovacuum, 最大可能有autovacuum_max_workers*maintenance_work_mem的内存被系统消耗掉.
maintenance_work_mem = 512MB
一般设置为比系统限制的略少,ulimit -a : stack size (kbytes, -s) 10240
max_stack_depth = 8MB
手动执行vacuum操作时, 默认是没有停顿执行到底的, 为了防止VACUUM操作消耗太多数据库服务器硬件资源, 这个值是指vacuum在消耗多少资源后停顿多少时间,以便其他的操作可以使用更多的硬件资源.
vacuum_cost_delay = 10ms
#vacuum_cost_page_hit = 1 # 0-10000 credits
#vacuum_cost_page_miss = 10 # 0-10000 credits
#vacuum_cost_page_dirty = 20 # 0-10000 credits
#vacuum_cost_limit = 200 # 1-10000 credits
默认bgwriter进程执行一次后会停顿200ms再被唤醒执行下一次操作, 当数据库的写操作很频繁的时候, 200ms可能太长, 导致其他进程需要花费过多的时间来进行bgwriter的操作.
bgwriter_delay = 10ms
如果需要做数据库WAL日志备份的话至少需要设置成archive级别, 如果需要做hot_standby那么需要设置成hot_standby, 由于这个值修改需要重启数据库, 所以先设置成hot_standby比较好. 当然hot_standby意味着WAL记录得更详细, 如果没有打算做hot_standby设置得越低性能越好.
wal_level = hot_standby
wal buffers默认是-1 根据shared_buffers的设置自动调整shared_buffers*3% .最大限制是XLOG的segment_size.
wal_buffers = 16384kB
多少个xlog file产生后开始checkpoint操作, 这个值越大, 允许shared_buffer中的被频繁访问的脏数据存储得更久. 一定程度上可以提高数据库性能. 但是太大的话会导致在数据库发生checkpoint的时候需要处理更多的脏数据带来长时间的IO开销. 太小的话会导致产生更多的WAL文件(因为full page writes=on,CHECKPOINT后的第一次块的改变要写全块, checkpoint越频繁, 越多的数据更新要写全块导致产生更多WAL).
checkpoint_segments = 64
这个和checkpoint_segments的效果是一样的, 只是触发的条件是时间条件.
checkpoint_timeout = 5min
归档参数的修改也需要重启数据库, 所以就先打开吧.
archive_mode = on
这个是归档调用的命令, 我这里用date代替, 所以归档的时候调用的是输出时间而不是拷贝wal文件.
archive_command = '/bin/date'
如果要做hot standby这个必须大于0, 并且修改之后要重启数据库所以先设置为32.
max_wal_senders = 32
这是个standby 数据库参数, 为了方便角色切换, 我一般是所有的数据库都把他设置为on 的.
hot_standby = on
这个参数是说数据库中随机的PAGE访问的开销占seq_page_cost的多少倍 , seq_page_cost默认是1. 其他的开销都是seq_page_cost的倍数. 这些都用于基于成本的执行计划选择.
random_page_cost = 2.0
和上一个参数一样, 用于基于成本的执行计划选择. 不是说会用多少cache, 它只是个度量值. 表示系统有多少内存可以作为操作系统的cache. 越大的话, 数据库越倾向使用index这种适合random访问的执行计划.
effective_cache_size = 12000MB
下面是日志输出的配置.
log_destination = 'csvlog'
logging_collector = on
log_directory = '/var/applog/pg_log/digoal/1921'
log_truncate_on_rotation = on
log_rotation_age = 1d
log_rotation_size = 10MB
这个参数调整的是记录执行时间超过1秒的SQL到日志中, 一般用于跟踪哪些SQL执行时间长.
log_min_duration_statement = 1000ms
记录每一次checkpoint到日志中.
log_checkpoints = on
记录锁等待超过1秒的操作, 一般用于排查业务逻辑上的问题.
log_lock_waits = on
deadlock_timeout = 1s
记录DDL语句, 一般用于跟踪数据库中的危险操作.
log_statement = 'ddl'
这个原本是1024表示跟踪的SQL在1024的地方截断, 超过1024将无法显示全SQL. 修改为2048会消耗更多的内存(基本可以忽略), 不过可以显示更长的SQL.
track_activity_query_size = 2048
默认autovacuum就是打开的, log_autovacuum_min_duration = 0记录所有的autovacuum操作.
autovacuum = on
log_autovacuum_min_duration = 0
这个模块用于记录数据库中的最近的1000条SQL以及这些SQL的统计信息, 如执行了多少次, 总共耗时是多少. 一般用于发现业务上最频繁调用的SQL是什么, 有针对性的进行SQL优化.
shared_preload_libraries = 'pg_stat_statements'
custom_variable_classes = 'pg_stat_statements'
pg_stat_statements.max = 1000
pg_stat_statements.track = all
其他参数值默认.
这些参数的详细解释如有疑问请参考PostgreSQL官方文档.
3. 新建数据库用户digoal, 库digoal. 并使用前面的测试模型新建表以及函数, 初始化数据.
下面的测试过程中只测登陆部分, 未测试退出部分, 因为登陆过程已经包含了INSERT, UPDATE, SELECT. 基本上可以反映整个调优过程了.
【调优阶段1】
使用pgbench进行压力测试, 发现瓶颈并合理优化.
1. pgbench用到的登陆脚本
cat login.sql
\setrandom userid 1 20000000
select userid,engname,cnname,occupation,birthday,signname,email,qq from user_info where userid=:userid;
insert into user_login_rec (userid,login_time,ip) values (:userid,now(),inet_client_addr());
update user_session set logintime=now(),login_count=login_count+1 where userid=:userid;
2. pgbench用到的退出脚本
cat logout.sql
\setrandom userid 1 20000000
insert into user_logout_rec (userid,logout_time,ip) values (:userid,now(),inet_client_addr());
update user_session set logouttime=now(),online_interval=online_interval+(now()-logintime) where userid=:userid;
3. 压力测试
pgbench -M simple -r -c 8 -f /home/postgres/test/login.sql -j 8 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal
4. 压力测试结果
transaction type: Custom query
scaling factor: 1
query mode: simple
number of clients: 8
number of threads: 8
duration: 180 s
number of transactions actually processed: 62675
tps = 348.084647 (including connections establishing)
tps = 348.100337 (excluding connections establishing)
statement latencies in milliseconds:
0.004577 \setrandom userid 1 20000000
12.963789 select userid,engname,cnname,occupation,birthday,signname,email,qq from user_info where userid=:userid;
5.540750 insert into user_login_rec (userid,login_time,ip) values (:userid,now(),inet_client_addr());
4.457834 update user_session set logintime=now(),login_count=login_count+1 where userid=:userid;
5. 瓶颈分析与优化
压力测试中查看数据库服务器的iostat -x
avg-cpu: %user %nice %system %iowait %steal %idle
0.69 0.00 0.25 24.11 0.00 74.95
Device: rrqm/s wrqm/s r/s w/s rsec/s wsec/s avgrq-sz avgqu-sz await svctm %util
cciss/c0d0 0.00 6.00 0.00 1.50 0.00 60.00 40.00 0.01 6.67 6.67 1.00
cciss/c0d0p1 0.00 6.00 0.00 1.50 0.00 60.00 40.00 0.01 6.67 6.67 1.00
cciss/c0d0p2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
cciss/c0d0p3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
cciss/c0d1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
cciss/c0d2 0.00 638.50 10.00 217.50 160.00 6444.00 29.03 152.58 707.89 4.40 100.10
cciss/c0d3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
cciss/c0d4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
cciss/c0d5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
dm-0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
dm-1 0.00 0.00 10.00 866.50 160.00 6932.00 8.09 446.26 510.49 1.14 100.10
dm-2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
dm-3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
dm-4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
操作系统的平均IO请求等待700多毫秒, PostgreSQL数据文件所处的块设备使用率100%. 存在严重的IO性能瓶颈.
使用pgfincore降低读的物理IO请求.
pgfincore的相关文章可参考如下,
《use posix_fadvise pre-cache frequency data》
http://blog.163.com/digoal@126/blog/static/163877040201062944945126/
《a powerful upgrade from pgfincore 1.0》
http://blog.163.com/digoal@126/blog/static/1638770402011630102117658/
《TOAST table with pgfincore》
http://blog.163.com/digoal@126/blog/static/16387704020120524144140/
pgfincore所起的作用类似EnterpriseDB的InfiniteCache或者熟悉Oracle的朋友可能更易于接受的KEEP BUFFER POOL.
-- 载入os cache
digoal=> select reltoastrelid from pg_class where relname='user_info';
reltoastrelid
---------------
16424
(1 row)
digoal=> select relname from pg_class where oid=16424;
relname
----------------
pg_toast_16421
(1 row)
digoal=> \c digoal postgres
seYou are now connected to database "digoal" as user "postgres".
digoal=# select * from pgfadvise_willneed('pg_toast.pg_toast_16421');
relpath | os_page_size | rel_os_pages | os_pages_free
----------------------------------------------+--------------+--------------+---------------
pg_tblspc/16385/PG_9.1_201105231/16386/16424 | 4096 | 0 | 243865
(1 row)
digoal=# select * from pgfadvise_willneed('digoal.user_info');
relpath | os_page_size | rel_os_pages | os_pages_free
------------------------------------------------+--------------+--------------+---------------
pg_tblspc/16385/PG_9.1_201105231/16386/16421 | 4096 | 262144 | 243834
pg_tblspc/16385/PG_9.1_201105231/16386/16421.1 | 4096 | 262144 | 243834
pg_tblspc/16385/PG_9.1_201105231/16386/16421.2 | 4096 | 244944 | 243834
(3 rows)
digoal=# select * from pgfadvise_willneed('digoal.user_session');
relpath | os_page_size | rel_os_pages | os_pages_free
------------------------------------------------+--------------+--------------+---------------
pg_tblspc/16385/PG_9.1_201105231/16386/16431 | 4096 | 262144 | 243834
pg_tblspc/16385/PG_9.1_201105231/16386/16431.1 | 4096 | 33640 | 243834
(2 rows)
digoal=# select reltoastrelid from pg_class where relname='user_session';
reltoastrelid
---------------
0
(1 row)
digoal=# select * from pgfadvise_willneed('digoal.pk_user_session');
relpath | os_page_size | rel_os_pages | os_pages_free
----------------------------------------------+--------------+--------------+---------------
pg_tblspc/16385/PG_9.1_201105231/16386/16438 | 4096 | 109680 | 243865
(1 row)
digoal=# select * from pgfadvise_willneed('digoal.pk_user_info');
relpath | os_page_size | rel_os_pages | os_pages_free
----------------------------------------------+--------------+--------------+---------------
pg_tblspc/16385/PG_9.1_201105231/16386/16436 | 4096 | 109680 | 235567
(1 row)
【调优阶段2】
1. 压力测试
pgbench -M simple -r -c 8 -f /home/postgres/test/login.sql -j 8 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal
2. 测试结果
transaction type: Custom query
scaling factor: 1
query mode: simple
number of clients: 8
number of threads: 8
duration: 180 s
number of transactions actually processed: 264895
tps = 1471.517096 (including connections establishing)
tps = 1471.585818 (excluding connections establishing)
statement latencies in milliseconds:
0.004226 \setrandom userid 1 20000000
0.459824 select userid,engname,cnname,occupation,birthday,signname,email,qq from user_info where userid=:userid;
2.457797 insert into user_login_rec (userid,login_time,ip) values (:userid,now(),inet_client_addr());
2.501684 update user_session set logintime=now(),login_count=login_count+1 where userid=:userid;
3. 瓶颈分析与优化
SELECT语句的延时已经通过上一个优化阶段下降到了0.45毫秒, INSERT和UPDATE语句的平均耗时也从原来的5.5和4.45下降到了2.5.原因是select的请求在内存中命中了, 因此update和insert不需要和select争抢物理io请求, 处理效率自然有一定的提高.
但是INSERT和UPDATE的语句延时还有2.5毫秒存在很大的可优化空间.
开启PostgreSQL的异步提交日志.
synchronous_commit = off
wal_writer_delay = 10ms
与Oracle的异步日志差别请参考 :
《PostgreSQL and Oracle's async commit》
http://blog.163.com/digoal@126/blog/static/16387704020121229223072/
【调优阶段3】
1. 压力测试
pgbench -M simple -r -c 8 -f /home/postgres/test/login.sql -j 8 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal
2. 测试结果
transaction type: Custom query
scaling factor: 1
query mode: simple
number of clients: 8
number of threads: 8
duration: 180 s
number of transactions actually processed: 685344
tps = 3751.377919 (including connections establishing)
tps = 3751.568948 (excluding connections establishing)
statement latencies in milliseconds:
0.003474 \setrandom userid 1 20000000
0.418716 select userid,engname,cnname,occupation,birthday,signname,email,qq from user_info where userid=:userid;
0.511601 insert into user_login_rec (userid,login_time,ip) values (:userid,now(),inet_client_addr());
1.188277 update user_session set logintime=now(),login_count=login_count+1 where userid=:userid;
3. 瓶颈分析与优化
客户端连接使用simple协议, 存在一定的可优化空间.
修改协议为extended, 查看性能提升多少.
【调优阶段4】
1. 压力测试
pgbench -M extended -r -c 8 -f /home/postgres/test/login.sql -j 8 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal
2. 测试结果
transaction type: Custom query
scaling factor: 1
query mode: extended
number of clients: 8
number of threads: 8
duration: 180 s
number of transactions actually processed: 970981
tps = 5394.015368 (including connections establishing)
tps = 5394.215477 (excluding connections establishing)
statement latencies in milliseconds:
0.003345 \setrandom userid 1 20000000
0.381675 select userid,engname,cnname,occupation,birthday,signname,email,qq from user_info where userid=:userid;
0.296300 insert into user_login_rec (userid,login_time,ip) values (:userid,now(),inet_client_addr());
0.792592 update user_session set logintime=now(),login_count=login_count+1 where userid=:userid;
3. 瓶颈分析与优化
客户端连接使用extended协议, 存在一定的可优化空间.
修改协议为prepared, 查看性能提升多少.
参见 :
《PostgreSQL prepared statement: SPI_prepare, prepare|execute COMMAND, PL/pgsql STYLE: custom & generic plan cache》
http://blog.163.com/digoal@126/blog/static/1638770402012112452432251/
【调优阶段5】
1. 压力测试
pgbench -M prepared -r -c 8 -f /home/postgres/test/login.sql -j 8 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal
2. 测试结果
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 8
number of threads: 8
duration: 180 s
number of transactions actually processed: 1044186
tps = 5800.589330 (including connections establishing)
tps = 5800.902491 (excluding connections establishing)
statement latencies in milliseconds:
0.003465 \setrandom userid 1 20000000
0.319665 select userid,engname,cnname,occupation,birthday,signname,email,qq from user_info where userid=:userid;
0.266931 insert into user_login_rec (userid,login_time,ip) values (:userid,now(),inet_client_addr());
0.777822 update user_session set logintime=now(),login_count=login_count+1 where userid=:userid;
3. 瓶颈分析与优化
压力测试的脚本中使用的是普通的SQL语句, 未使用初始化时用到的登陆函数和退出函数. 使用普通SQL显然比使用函数多了交互的次数以及每次发送的数据包的大小.
使用函数看看性能能提升多少.
【调优阶段6】
1. 登陆脚本
cat login.sql
\setrandom userid 1 20000000
SELECT f_user_login(:userid);
2. 退出脚本
cat logout.sql
\setrandom userid 1 20000000
SELECT f_user_logout(:userid);
3. 压力测试
pgbench -M prepared -r -c 8 -f /home/postgres/test/login.sql -j 8 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal
4. 测试结果
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 8
number of threads: 8
duration: 180 s
number of transactions actually processed: 1616746
tps = 8981.596290 (including connections establishing)
tps = 8981.995800 (excluding connections establishing)
statement latencies in milliseconds:
0.004012 \setrandom userid 1 20000000
0.881060 SELECT f_user_login(:userid);
5. 瓶颈分析与优化
到这个时候看起来好像没什么好优化的了, 其实不然.
我们知道在整个登陆过程中用到了SELECT, UPDATE, INSERT.
其中UPDATE的表有一个PK索引, 每次更新需要修改数据表的同时还需要更新索引. 所以理论上这个更新操作表越小性能越高.
我们可以通过拆表来提升性能. 如下 :
拆表 :
create table user_info_0 (like user_info including all);
create table user_info_1 (like user_info including all);
create table user_info_2 (like user_info including all);
create table user_info_3 (like user_info including all);
create table user_info_4 (like user_info including all);
create table user_session_0 (like user_session including all);
create table user_session_1 (like user_session including all);
create table user_session_2 (like user_session including all);
create table user_session_3 (like user_session including all);
create table user_session_4 (like user_session including all);
插入初始化数据 :
insert into user_info_0 (userid,engname,cnname,occupation,birthday,signname,email,qq,crt_time,mod_time)
select generate_series(1,4000000),
'digoal.zhou',
'德哥',
'DBA',
'1970-01-01'
,E'公益是一辈子的事, I\'m Digoal.Zhou, Just do it!',
276732431,
clock_timestamp(),
NULL;
insert into user_info_1 (userid,engname,cnname,occupation,birthday,signname,email,qq,crt_time,mod_time)
select generate_series(4000001,8000000),
'digoal.zhou',
'德哥',
'DBA',
'1970-01-01'
,E'公益是一辈子的事, I\'m Digoal.Zhou, Just do it!',
276732431,
clock_timestamp(),
NULL;
insert into user_info_2 (userid,engname,cnname,occupation,birthday,signname,email,qq,crt_time,mod_time)
select generate_series(8000001,12000000),
'digoal.zhou',
'德哥',
'DBA',
'1970-01-01'
,E'公益是一辈子的事, I\'m Digoal.Zhou, Just do it!',
276732431,
clock_timestamp(),
NULL;
insert into user_info_3 (userid,engname,cnname,occupation,birthday,signname,email,qq,crt_time,mod_time)
select generate_series(12000001,16000000),
'digoal.zhou',
'德哥',
'DBA',
'1970-01-01'
,E'公益是一辈子的事, I\'m Digoal.Zhou, Just do it!',
276732431,
clock_timestamp(),
NULL;
insert into user_info_4 (userid,engname,cnname,occupation,birthday,signname,email,qq,crt_time,mod_time)
select generate_series(16000001,20000000),
'digoal.zhou',
'德哥',
'DBA',
'1970-01-01'
,E'公益是一辈子的事, I\'m Digoal.Zhou, Just do it!',
276732431,
clock_timestamp(),
NULL;
insert into user_session_0 (userid) select generate_series(1,4000000);
insert into user_session_1 (userid) select generate_series(4000001,8000000);
insert into user_session_2 (userid) select generate_series(8000001,12000000);
insert into user_session_3 (userid) select generate_series(12000001,16000000);
insert into user_session_4 (userid) select generate_series(16000001,20000000);
set work_mem='2048MB';
set maintenance_work_mem='2048MB';
alter table user_info_0 add primary key (userid);
alter table user_info_1 add primary key (userid);
alter table user_info_2 add primary key (userid);
alter table user_info_3 add primary key (userid);
alter table user_info_4 add primary key (userid);
alter table user_session_0 add primary key (userid);
alter table user_session_1 add primary key (userid);
alter table user_session_2 add primary key (userid);
alter table user_session_3 add primary key (userid);
alter table user_session_4 add primary key (userid);
同样通过pgfincore把他们加载到内存中, 这里不详细描述.
新建登陆和退出函数
create or replace function f_user_login_0
(i_userid int,
OUT o_userid int,
OUT o_engname text,
OUT o_cnname text,
OUT o_occupation text,
OUT o_birthday date,
OUT o_signname text,
OUT o_email text,
OUT o_qq numeric
)
as $BODY$
declare
begin
select userid,engname,cnname,occupation,birthday,signname,email,qq
into o_userid,o_engname,o_cnname,o_occupation,o_birthday,o_signname,o_email,o_qq
from user_info_0 where userid=i_userid;
insert into user_login_rec (userid,login_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_0 set logintime=now(),login_count=login_count+1 where userid=i_userid;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_login_1
(i_userid int,
OUT o_userid int,
OUT o_engname text,
OUT o_cnname text,
OUT o_occupation text,
OUT o_birthday date,
OUT o_signname text,
OUT o_email text,
OUT o_qq numeric
)
as $BODY$
declare
begin
select userid,engname,cnname,occupation,birthday,signname,email,qq
into o_userid,o_engname,o_cnname,o_occupation,o_birthday,o_signname,o_email,o_qq
from user_info_1 where userid=i_userid;
insert into user_login_rec (userid,login_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_1 set logintime=now(),login_count=login_count+1 where userid=i_userid;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_login_2
(i_userid int,
OUT o_userid int,
OUT o_engname text,
OUT o_cnname text,
OUT o_occupation text,
OUT o_birthday date,
OUT o_signname text,
OUT o_email text,
OUT o_qq numeric
)
as $BODY$
declare
begin
select userid,engname,cnname,occupation,birthday,signname,email,qq
into o_userid,o_engname,o_cnname,o_occupation,o_birthday,o_signname,o_email,o_qq
from user_info_2 where userid=i_userid;
insert into user_login_rec (userid,login_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_2 set logintime=now(),login_count=login_count+1 where userid=i_userid;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_login_3
(i_userid int,
OUT o_userid int,
OUT o_engname text,
OUT o_cnname text,
OUT o_occupation text,
OUT o_birthday date,
OUT o_signname text,
OUT o_email text,
OUT o_qq numeric
)
as $BODY$
declare
begin
select userid,engname,cnname,occupation,birthday,signname,email,qq
into o_userid,o_engname,o_cnname,o_occupation,o_birthday,o_signname,o_email,o_qq
from user_info_3 where userid=i_userid;
insert into user_login_rec (userid,login_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_3 set logintime=now(),login_count=login_count+1 where userid=i_userid;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_login_4
(i_userid int,
OUT o_userid int,
OUT o_engname text,
OUT o_cnname text,
OUT o_occupation text,
OUT o_birthday date,
OUT o_signname text,
OUT o_email text,
OUT o_qq numeric
)
as $BODY$
declare
begin
select userid,engname,cnname,occupation,birthday,signname,email,qq
into o_userid,o_engname,o_cnname,o_occupation,o_birthday,o_signname,o_email,o_qq
from user_info_4 where userid=i_userid;
insert into user_login_rec (userid,login_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_4 set logintime=now(),login_count=login_count+1 where userid=i_userid;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_logout_0
(i_userid int,
OUT o_result int
)
as $BODY$
declare
begin
insert into user_logout_rec (userid,logout_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_0 set logouttime=now(),online_interval=online_interval+(now()-logintime) where userid=i_userid;
o_result := 0;
return;
exception
when others then
o_result := 1;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_logout_1
(i_userid int,
OUT o_result int
)
as $BODY$
declare
begin
insert into user_logout_rec (userid,logout_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_1 set logouttime=now(),online_interval=online_interval+(now()-logintime) where userid=i_userid;
o_result := 0;
return;
exception
when others then
o_result := 1;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_logout_2
(i_userid int,
OUT o_result int
)
as $BODY$
declare
begin
insert into user_logout_rec (userid,logout_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_2 set logouttime=now(),online_interval=online_interval+(now()-logintime) where userid=i_userid;
o_result := 0;
return;
exception
when others then
o_result := 1;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_logout_3
(i_userid int,
OUT o_result int
)
as $BODY$
declare
begin
insert into user_logout_rec (userid,logout_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_3 set logouttime=now(),online_interval=online_interval+(now()-logintime) where userid=i_userid;
o_result := 0;
return;
exception
when others then
o_result := 1;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_logout_4
(i_userid int,
OUT o_result int
)
as $BODY$
declare
begin
insert into user_logout_rec (userid,logout_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_4 set logouttime=now(),online_interval=online_interval+(now()-logintime) where userid=i_userid;
o_result := 0;
return;
exception
when others then
o_result := 1;
return;
end;
$BODY$
language plpgsql;
【调优阶段7】
1. 登陆脚本
cat login*.sql
\setrandom userid 1 4000000
SELECT f_user_login_0(:userid);
\setrandom userid 4000001 8000000
SELECT f_user_login_1(:userid);
\setrandom userid 8000001 12000000
SELECT f_user_login_2(:userid);
\setrandom userid 12000001 16000000
SELECT f_user_login_3(:userid);
\setrandom userid 16000001 20000000
SELECT f_user_login_4(:userid);
2. 退出脚本
cat logout*.sql
\setrandom userid 1 4000000
SELECT f_user_logout_0(:userid);
\setrandom userid 4000001 8000000
SELECT f_user_logout_1(:userid);
\setrandom userid 8000001 12000000
SELECT f_user_logout_2(:userid);
\setrandom userid 12000001 16000000
SELECT f_user_logout_3(:userid);
\setrandom userid 16000001 20000000
SELECT f_user_logout_4(:userid);
3. 压力测试
pgbench -M prepared -r -c 1 -f /home/postgres/test/login0.sql -j 1 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal >./log.login0 &
pgbench -M prepared -r -c 1 -f /home/postgres/test/login1.sql -j 1 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal >./log.login1 &
pgbench -M prepared -r -c 2 -f /home/postgres/test/login2.sql -j 2 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal >./log.login2 &
pgbench -M prepared -r -c 2 -f /home/postgres/test/login3.sql -j 2 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal >./log.login3 &
pgbench -M prepared -r -c 2 -f /home/postgres/test/login4.sql -j 2 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal >./log.login4 &
4. 测试结果
cat log.log*
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 1
number of threads: 1
duration: 180 s
number of transactions actually processed: 233348
tps = 1281.818097 (including connections establishing)
tps = 1281.837109 (excluding connections establishing)
statement latencies in milliseconds:
0.003492 \setrandom userid 1 4000000
0.771932 SELECT f_user_login_0(:userid);
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 1
number of threads: 1
duration: 180 s
number of transactions actually processed: 233466
tps = 1282.514774 (including connections establishing)
tps = 1282.573500 (excluding connections establishing)
statement latencies in milliseconds:
0.003546 \setrandom userid 4000001 8000000
0.771399 SELECT f_user_login_1(:userid);
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 2
number of threads: 2
duration: 180 s
number of transactions actually processed: 475466
tps = 2612.200783 (including connections establishing)
tps = 2612.281526 (excluding connections establishing)
statement latencies in milliseconds:
0.003605 \setrandom userid 8000001 12000000
0.757312 SELECT f_user_login_2(:userid);
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 2
number of threads: 2
duration: 180 s
number of transactions actually processed: 468904
tps = 2576.380443 (including connections establishing)
tps = 2576.488485 (excluding connections establishing)
statement latencies in milliseconds:
0.003587 \setrandom userid 12000001 16000000
0.767869 SELECT f_user_login_3(:userid);
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 2
number of threads: 2
duration: 180 s
number of transactions actually processed: 439381
tps = 2414.347086 (including connections establishing)
tps = 2414.425600 (excluding connections establishing)
statement latencies in milliseconds:
0.004431 \setrandom userid 16000001 20000000
0.817879 SELECT f_user_login_4(:userid);
总计 :
tps = 10167.261183 (including connections establishing)
tps = 10167.261183 (excluding connections establishing)
5. 瓶颈分析与优化
到这里我们还没有关注过表空间, 其实这些表拆分后它们还在同一个表空间里面. 把它们放在不同的表空间可以扩展它们整体的IO吞吐能力.
postgres=# \db+
List of tablespaces
Name | Owner | Location | Access privileges | Description
------------+----------+---------------------------------------------+---------------------+-------------
digoal | postgres | /pgdata/digoal/1921/data02/pg_tbs/digoal | postgres=C/postgres+|
| | | digoal=C/postgres |
digoal_01 | postgres | /pgdata/digoal/1921/data03/pg_tbs/digoal_01 | postgres=C/postgres+|
| | | digoal=C/postgres |
digoal_02 | postgres | /pgdata/digoal/1921/data04/pg_tbs/digoal_02 | postgres=C/postgres+|
| | | digoal=C/postgres |
digoal_03 | postgres | /pgdata/digoal/1921/data05/pg_tbs/digoal_03 | postgres=C/postgres+|
| | | digoal=C/postgres |
digoal_04 | postgres | /pgdata/digoal/1921/data06/pg_tbs/digoal_04 | postgres=C/postgres+|
| | | digoal=C/postgres |
digoal=> alter table user_info_0 set tablespace digoal_04;
ALTER TABLE
digoal=> alter table user_info_2 set tablespace digoal_01;
ALTER TABLE
digoal=> alter table user_info_3 set tablespace digoal_02;
ALTER TABLE
digoal=> alter table user_info_4 set tablespace digoal_03;
digoal=> alter index user_info_0_pkey set tablespace digoal_04;
ALTER INDEX
digoal=> alter index user_info_2_pkey set tablespace digoal_01;
ALTER INDEX
digoal=> alter index user_info_3_pkey set tablespace digoal_02;
ALTER INDEX
digoal=> alter index user_info_4_pkey set tablespace digoal_03;
digoal=> alter table user_session_0 set tablespace digoal_04;
ALTER TABLE
digoal=> alter table user_session_2 set tablespace digoal_01;
ALTER TABLE
digoal=> alter table user_session_3 set tablespace digoal_02;
ALTER TABLE
digoal=> alter table user_session_4 set tablespace digoal_03;
digoal=> alter index user_session_0_pkey set tablespace digoal_04;
ALTER INDEX
digoal=> alter index user_session_2_pkey set tablespace digoal_01;
ALTER INDEX
digoal=> alter index user_session_3_pkey set tablespace digoal_02;
ALTER INDEX
digoal=> alter index user_session_4_pkey set tablespace digoal_03;
重新把它们加载到内存.
下节 :
http://blog.163.com/digoal@126/blog/static/163877040201221333411196/
【附】
pgbench simple|extended|prepare 部分源码 :
1. pgbench.c
if (commands[st->state]->type == SQL_COMMAND)
{
const Command *command = commands[st->state];
int r;
if (querymode == QUERY_SIMPLE)
{
char *sql;
sql = xstrdup(command->argv[0]);
sql = assignVariables(st, sql);
if (debug)
fprintf(stderr, "client %d sending %s\n", st->id, sql);
r = PQsendQuery(st->con, sql);
free(sql);
}
else if (querymode == QUERY_EXTENDED)
{
const char *sql = command->argv[0];
const char *params[MAX_ARGS];
getQueryParams(st, command, params);
if (debug)
fprintf(stderr, "client %d sending %s\n", st->id, sql);
r = PQsendQueryParams(st->con, sql, command->argc - 1,
NULL, params, NULL, NULL, 0);
}
else if (querymode == QUERY_PREPARED)
{
char name[MAX_PREPARE_NAME];
const char *params[MAX_ARGS];
if (!st->prepared[st->use_file])
{
int j;
for (j = 0; commands[j] != NULL; j++)
{
PGresult *res;
char name[MAX_PREPARE_NAME];
if (commands[j]->type != SQL_COMMAND)
continue;
preparedStatementName(name, st->use_file, j);
res = PQprepare(st->con, name,
commands[j]->argv[0], commands[j]->argc - 1, NULL);
if (PQresultStatus(res) != PGRES_COMMAND_OK)
fprintf(stderr, "%s", PQerrorMessage(st->con));
PQclear(res);
}
st->prepared[st->use_file] = true;
}
getQueryParams(st, command, params);
preparedStatementName(name, st->use_file, st->state);
if (debug)
fprintf(stderr, "client %d sending %s\n", st->id, name);
r = PQsendQueryPrepared(st->con, name, command->argc - 1,
params, NULL, NULL, 0);
}