本文是针对单个RDS实例（同样的配置）承载6400万数据的测试。对比前面的水平分库。
创建测试表，生成测试数据。
create table userinfo(userid int,info text);
create table session (userid int,last_login timestamp);
create table login_log (userid int,db_user name,client_addr inet,
client_port int,server_addr inet,server_port int,login_time timestamp);
create table tbl_small (userid int primary key,info text);
set synchronous_commit=off;
insert into userinfo select generate_series(1,32000000);
insert into session select generate_series(1,32000000);
insert into tbl_small select generate_series(1,500000);
set maintenance_work_mem='10GB';
alter table userinfo add constraint pk_userinfo primary key (userid);
alter table session add constraint pk_session primary key (userid);
postgres=> \dt+
List of relations
Schema | Name | Type | Owner | Size | Description
--------+-----------------+-------+--------+---------+-------------
public | ha_health_check | table | aurora | 40 kB |
public | session | table | digoal | 1106 MB |
public | userinfo | table | digoal | 1106 MB |
(3 rows)
postgres=> \di+
List of relations
Schema | Name | Type | Owner | Table | Size | Description
--------+----------------------+-------+--------+-----------------+--------+-------------
public | ha_health_check_pkey | index | aurora | ha_health_check | 16 kB |
public | pk_session | index | digoal | session | 686 MB |
public | pk_userinfo | index | digoal | userinfo | 686 MB |
(3 rows)
测试中发现一个小小的惊喜，RDS限制了数据库进程的内存使用（包括shared buffers,work_mem,maintenance_work_mem, wal_buffers等限制），但是并不会限制OS层缓存的使用，也就是说我们的数据表对应的数据文件如果是热数据的话，可能被缓存好OS层缓存中，假如RDS能提供pgfincore插件就更完美了，不过在云环境中使用会造成内存争抢的情况。
下面我们看一个测试，实例只有256MB的shared buffer, 下面的查询却飞快。
postgres=> explain (analyze,verbose,timing,buffers,costs) select count(userid) from session;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------
Aggregate (cost=541593.00..541593.01 rows=1 width=4) (actual time=6574.761..6574.761 rows=1 loops=1)
Output: count(userid)
Buffers: shared hit=20229 read=121364
I/O Timings: read=227.803
-> Seq Scan on public.session (cost=0.00..461593.00 rows=32000000 width=4) (actual time=0.029..3295.744 rows=32000001 loops=1)
Output: userid, last_login
Buffers: shared hit=20229 read=121364
I/O Timings: read=227.803
Planning time: 0.044 ms
Execution time: 6574.794 ms
(10 rows)
postgres=> explain (analyze,verbose,timing,buffers,costs) select count(userid) from userinfo;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------
Aggregate (cost=541593.00..541593.01 rows=1 width=4) (actual time=6653.383..6653.383 rows=1 loops=1)
Output: count(userid)
Buffers: shared hit=64 read=141529
I/O Timings: read=265.700
-> Seq Scan on public.userinfo (cost=0.00..461593.00 rows=32000000 width=4) (actual time=0.029..3358.069 rows=32000001 loops=1)
Output: userid, info
Buffers: shared hit=64 read=141529
I/O Timings: read=265.700
Planning time: 0.046 ms
Execution time: 6653.417 ms
(10 rows)
分析这里的I/O Timings，单位毫秒，每次IO请求只需要0.0019毫秒。
这已经是内存级别的速度了。
postgres=> select 265.700/141529;
?column?
------------------------
0.00187735375788707615
(1 row)
postgres=> select 227.803/121364;
?column?
------------------------
0.00187702284038100260
(1 row)
离散扫描测试：
postgres=> set enable_seqscan=off;
SET
postgres=> explain (analyze,verbose,timing,buffers,costs) select count(userid) from userinfo;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------
--------------------------
Aggregate (cost=1052572.56..1052572.57 rows=1 width=4) (actual time=10343.801..10343.801 rows=1 loops=1)
Output: count(userid)
Buffers: shared read=229028
I/O Timings: read=674.634
-> Index Only Scan using pk_userinfo on public.userinfo (cost=0.56..972572.56 rows=32000000 width=4) (actual time=0.082..7277.8
18 rows=32000001 loops=1)
Output: userid
Heap Fetches: 32000001
Buffers: shared read=229028
I/O Timings: read=674.634
Planning time: 0.035 ms
Execution time: 10343.851 ms
(11 rows)
postgres=> explain (analyze,verbose,timing,buffers,costs) select count(userid) from session;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------
------------------------
Aggregate (cost=1052572.56..1052572.57 rows=1 width=4) (actual time=10321.901..10321.901 rows=1 loops=1)
Output: count(userid)
Buffers: shared read=229028
I/O Timings: read=633.969
-> Index Only Scan using pk_session on public.session (cost=0.56..972572.56 rows=32000000 width=4) (actual time=0.080..7268.908
rows=32000001 loops=1)
Output: userid
Heap Fetches: 32000001
Buffers: shared read=229028
I/O Timings: read=633.969
Planning time: 0.056 ms
Execution time: 10321.935 ms
(11 rows)
分析这里的I/O Timings，单位毫秒，每次IO请求只需要0.0028毫秒。
postgres=> select 633.969/229028;
?column?
------------------------
0.00276808512496288663
(1 row)
postgres=> select 674.634/229028;
?column?
------------------------
0.00294563983443072463
(1 row)
如果这些数据不是在内存中，那么有这样IOPS能力的块设备，那也是怪兽级别的了(8K的数据块，离散读IOPS达到36万，未考虑read ahead，考虑的话一般默认预读是256个扇区，真实IOPS能力会略低)。
我个人的判断还是倾向阿里的RDS未限制OS层CACHE，也就是随你用。
创建测试函数：
CREATE OR REPLACE FUNCTION query_pk(IN i_userid int, OUT userid int, OUT info text)
RETURNS record
LANGUAGE plpgsql
STRICT
AS $function$
declare
begin
select t.userid,t.info into userid,info from userinfo t where t.userid=i_userid;
return;
end;
$function$;
CREATE OR REPLACE FUNCTION insert_log(IN i_userid int)
RETURNS void
LANGUAGE plpgsql
STRICT
AS $function$
declare
begin
set synchronous_commit=off;
insert into login_log (userid,db_user,client_addr,client_port,server_addr,server_port,login_time)
values (i_userid,current_user,inet_client_addr(),inet_client_port(),inet_server_addr(),inet_server_port(),now());
end;
$function$;
CREATE OR REPLACE FUNCTION query_insert(IN i_userid int, OUT userid int, OUT info text)
RETURNS record
LANGUAGE plpgsql
STRICT
AS $function$
declare
begin
set synchronous_commit=off;
select t.userid,t.info into userid,info from userinfo t where t.userid=i_userid;
insert into login_log (userid,db_user,client_addr,client_port,server_addr,server_port,login_time)
values (i_userid,current_user,inet_client_addr(),inet_client_port(),inet_server_addr(),inet_server_port(),now());
return;
end;
$function$;
CREATE OR REPLACE FUNCTION update_pk(IN i_userid int)
RETURNS void
LANGUAGE plpgsql
STRICT
AS $function$
declare
begin
set synchronous_commit=off;
update session t set last_login=now() where t.userid=i_userid;
end;
$function$;
CREATE OR REPLACE FUNCTION query_update_insert(IN i_userid int, OUT userid int, OUT info text)
RETURNS record
LANGUAGE plpgsql
STRICT
AS $function$
declare
begin
set synchronous_commit=off;
select t.userid,t.info into userid,info from userinfo t where t.userid=i_userid;
insert into login_log (userid,db_user,client_addr,client_port,server_addr,server_port,login_time)
values (i_userid,current_user,inet_client_addr(),inet_client_port(),inet_server_addr(),inet_server_port(),now());
update session t set last_login=now() where t.userid=i_userid;
return;
end;
$function$;
CREATE OR REPLACE FUNCTION query_smalltbl(IN i_userid int, OUT userid int, OUT info text)
RETURNS record
LANGUAGE plpgsql
STRICT
AS $function$
declare
begin
select t.userid,t.info into userid,info from tbl_small t where t.userid=i_userid;
return;
end;
$function$;
测试结果：
vi test.sql
\setrandom id 1 32000000
select query_pk(:id);
pgbench -M prepared -n -r -f ./test.sql -P 1 -c 88 -j 88 -T 20 -h xxxx.pg.rds.aliyuncs.com -p 3433 -U digoal postgres
progress: 1.1 s, 1938.5 tps, lat 30.861 ms stddev 63.730
progress: 2.1 s, 3397.7 tps, lat 26.197 ms stddev 43.067
progress: 3.0 s, 3293.2 tps, lat 25.744 ms stddev 36.761
progress: 4.2 s, 3477.7 tps, lat 26.012 ms stddev 44.032
progress: 5.1 s, 3448.3 tps, lat 25.291 ms stddev 39.993
progress: 6.0 s, 3581.1 tps, lat 24.386 ms stddev 53.515
progress: 7.0 s, 3669.4 tps, lat 23.736 ms stddev 43.620
progress: 8.1 s, 3635.0 tps, lat 24.333 ms stddev 54.772
progress: 9.0 s, 3625.6 tps, lat 24.457 ms stddev 39.071
progress: 10.0 s, 3708.4 tps, lat 23.017 ms stddev 41.434
vi test.sql
\setrandom id 1 32000000
select insert_log(:id);
pgbench -M prepared -n -r -f ./test.sql -P 1 -c 88 -j 88 -T 20 -h xxxx.pg.rds.aliyuncs.com -p 3433 -U digoal postgres
progress: 1.1 s, 2194.8 tps, lat 26.288 ms stddev 51.427
progress: 2.0 s, 3841.0 tps, lat 22.859 ms stddev 37.456
progress: 3.0 s, 3745.8 tps, lat 23.536 ms stddev 46.164
progress: 4.0 s, 3843.2 tps, lat 22.481 ms stddev 37.077
progress: 5.0 s, 3676.9 tps, lat 24.256 ms stddev 45.177
progress: 6.1 s, 3838.0 tps, lat 22.898 ms stddev 38.825
progress: 7.0 s, 3890.9 tps, lat 22.836 ms stddev 38.612
progress: 8.0 s, 3590.9 tps, lat 24.565 ms stddev 43.551
progress: 9.0 s, 3675.0 tps, lat 24.210 ms stddev 38.266
progress: 10.1 s, 3812.7 tps, lat 22.507 ms stddev 36.516
vi test.sql
\setrandom id 1 32000000
select query_insert(:id);
pgbench -M prepared -n -r -f ./test.sql -P 1 -c 88 -j 88 -T 20 -h xxxx.pg.rds.aliyuncs.com -p 3433 -U digoal postgres
progress: 1.1 s, 1269.2 tps, lat 45.745 ms stddev 89.929
progress: 2.1 s, 2700.4 tps, lat 33.356 ms stddev 58.091
progress: 3.0 s, 2654.6 tps, lat 35.314 ms stddev 54.011
progress: 4.0 s, 2673.0 tps, lat 31.859 ms stddev 48.704
progress: 5.0 s, 2762.7 tps, lat 31.759 ms stddev 51.929
progress: 6.1 s, 2667.7 tps, lat 32.047 ms stddev 55.966
progress: 7.1 s, 2688.7 tps, lat 32.407 ms stddev 58.218
progress: 8.2 s, 2785.4 tps, lat 30.795 ms stddev 65.419
progress: 9.0 s, 2789.9 tps, lat 35.547 ms stddev 58.010
progress: 10.0 s, 2879.6 tps, lat 30.196 ms stddev 53.233
vi test.sql
\setrandom id 1 32000000
select update_pk(:id);
pgbench -M prepared -n -r -f ./test.sql -P 1 -c 88 -j 88 -T 20 -h xxxx.pg.rds.aliyuncs.com -p 3433 -U digoal postgres
progress: 2.5 s, 282.4 tps, lat 218.387 ms stddev 495.226
progress: 5.8 s, 94.8 tps, lat 787.358 ms stddev 1325.987
progress: 5.8 s, 15727.4 tps, lat 150.434 ms stddev 668.515
progress: 5.9 s, 945.4 tps, lat 769.080 ms stddev 1374.084
progress: 16.1 s, 93.2 tps, lat 833.108 ms stddev 1856.263
progress: 16.2 s, 2598.5 tps, lat 665.837 ms stddev 1693.883
progress: 17.2 s, 71.7 tps, lat 1571.432 ms stddev 1858.991
progress: 22.2 s, 29.9 tps, lat 3003.451 ms stddev 2389.133
vi test.sql
\setrandom id 1 32000000
select query_update_insert(:id);
pgbench -M prepared -n -r -f ./test.sql -P 1 -c 88 -j 88 -T 20 -h xxxx.pg.rds.aliyuncs.com -p 3433 -U digoal postgres
progress: 5.7 s, 144.2 tps, lat 563.075 ms stddev 1426.395
progress: 5.8 s, 1292.3 tps, lat 133.407 ms stddev 609.956
progress: 5.8 s, 1028.1 tps, lat 29.967 ms stddev 37.131
progress: 11.3 s, 25.5 tps, lat 2265.784 ms stddev 2573.469
progress: 11.3 s, 6079.0 tps, lat 9.619 ms stddev 9.293
progress: 11.3 s, 4787.2 tps, lat 624.805 ms stddev 1740.448
progress: 16.9 s, 98.1 tps, lat 867.968 ms stddev 1989.390
progress: 17.1 s, 1313.4 tps, lat 870.720 ms stddev 2098.172
progress: 17.1 s, 13863.8 tps, lat 65.169 ms stddev 56.996
progress: 17.1 s, 11670.3 tps, lat 20.520 ms stddev 35.188
postgres=> \timing
Timing is on.
postgres=> select count(*) from login_log;
count
--------
140456
(1 row)
Time: 28.747 ms
postgres=> select count(*) from userinfo;
count
----------
32000001
(1 row)
Time: 3141.289 ms
vi test.sql
\setrandom id 1 32000000
select query_smalltbl(:id);
pgbench -M prepared -n -r -f ./test.sql -P 1 -c 88 -j 88 -T 20 -h xxxx.pg.rds.aliyuncs.com -p 3433 -U digoal postgres
progress: 1.0 s, 2420.4 tps, lat 23.557 ms stddev 45.623
progress: 2.0 s, 4337.3 tps, lat 19.923 ms stddev 37.168
progress: 3.0 s, 4555.2 tps, lat 20.154 ms stddev 35.738
progress: 4.0 s, 4362.4 tps, lat 20.094 ms stddev 40.591
progress: 5.1 s, 4203.5 tps, lat 20.386 ms stddev 36.220
progress: 6.0 s, 4484.5 tps, lat 19.888 ms stddev 36.724
progress: 7.0 s, 4551.6 tps, lat 19.634 ms stddev 39.959
progress: 8.0 s, 4041.8 tps, lat 21.195 ms stddev 40.362
progress: 9.1 s, 4557.6 tps, lat 19.758 ms stddev 37.218
progress: 10.0 s, 4349.1 tps, lat 20.254 ms stddev 34.562
测试结果与使用plproxy分布式处理的对比：
性能提升非常明显。
再报几个可能遇到的问题（现在这些问题以及都修复了）：
1. 当容量超出时（例如执行一个大的插入，我在生成测试数据时遇到），数据库会被KILL掉，数据库重启并恢复。（原因是单个SQL需要申请的内存超出了购买的规格，触发了OOM。）
恢复时间有点长，约30分钟，(恢复过程中建议不要限制IOPS，尽快恢复才是王道)并且恢复后，还会有很长一段时间处于recovery状态。
postgres=> select pg_is_in_recovery();
pg_is_in_recovery
-------------------
t
(1 row)
同时这点过于暴力，是不是可以给用户提个醒，和用户协商一下呢？给用户一个时间窗口，让用户自己处理。（现在已经改为比较温柔的做法了，感兴趣的童鞋可以测试一下）
2. OS层缓存，这个已经说了，阿里云RDS目前可能没有限制OS层缓存，所以尽情享受吧。
3. IOPS限制间隔，在测试UPDATE时，性能非常不稳，可能是IOPS限制间隔或者手段造成的，当然也可能是FPW造成的，因为没有RDS所在服务器的权限，没有办法调试，所以基本靠猜。
4. 为什么我们的plproxy没有看到所有测试的线性性能提升(目测某些只有5到8倍的性能提升，某些有超过16倍的性能提升)，因为阿里云RDS并没有限制CPU的使用率，只限制了共享内存和IOPS，那么有些节点所在的机器可能CPU资源较空，有些较忙，我们的测试虽然是随机的分发到各个节点，但是因测试线程是共享的，所以单个实例如果比较慢，对总体测试结果有一定的影响。
而对于IO类的测试，性能提升是达到16倍的。
排除这些影响，使用plproxy是线性提升的，我在以前的分享中有数据可供查看，有兴趣的朋友可以看我以前的一些分享。
先写到这里，下一篇来讲讲如何增加或减少数据节点。
[参考]
