PG空闲连接对性能的影响
该系列的第一篇为:PG空闲连接的资源消耗:https://amazonaws-china.com/cn/blogs/database/resources-consumed-by-idle-postgresql-connections/讨论PG如何管理连接以及空闲连接如何消耗内存和CPU。本文讨论空闲连接对PG性能的影响。
事务率影响
PG获取数据的时候,首先看请求页在没在共享内存。如果共享内存没有请求页,则从操作系统缓存取,如果也没有,则需要请求磁盘上的数据页。共享内存最快,操作系统缓存次之,磁盘最慢。随着PG连接的增长,操作系统缓存的可用内存就会减小,从而从操作系统缓存中移除数据页。下次再进行数据页查询时就会从磁盘上请求,因此性能变得更慢。
如果PG实例的空闲内存处于低水位,就会使用swap。这也是位于磁盘上,因此也很慢。使用swap空间可帮助释放一些内存,但是如果swapped 页再次被OS请求时,会被读回,导致IO的增加。更多信息请查看swap管理:https://www.kernel.org/doc/gorman/html/understand/understand014.html
可用内存对性能的影响取决于工作负载、数据集、总共的可用内存。如果数据集比总可用内存小,空闲内存的减少不会有明显影响,若数据集比总可用内存还大,就会产生巨大影响。
性能测试
下面小节显示了通过pgbench进行的性能测试。测试中Amazon RDS for PG实例为db.m5.large,2vCPU,8GB内存。1个EBS的IO为3000IOPS。
每个测试都有两个阶段,第一阶段pgbench执行1个小时,没有其他工作负载。这个提供了一个基准事务率。
第二个阶段,再次执行pgbench前打开1000个连接,每个连接从information_schema表获取一行数据。下面是步骤:
1)打开一个连接
2)获取所有表名及information_schema视图:
SELECT table_schema||'.'||table_name as relname from information_schema.tables WHERE table_schema='information_schema';
3)循环执行select:
SELECT * FROM information_schema.columns LIMIT 1;
4)对于1000个连接重复以上步骤
5)事务提交后不进行断开,保持空闲状态
重启实例后,内存中没有缓存任何数据页。第一次执行pgbench会加载请求的数据页到内存,随后再次执行pgbench,cache中的数据页可以重用,此时不再需要从磁盘加载。
为了最小化页缓存的影响,在执行测试案例前执行一个初始步骤。下图显示了打开1000个连接时,实例内存时如何从4.88GB下降到90MB的。
正如前系列介绍,虽然连接是空闲的,他们也会消耗内存和CPU资源。这个结果显示空闲连接对性能的影响。
事务率测试1:标准pgbench
第一个测试中,使用标准配置执行100个客户端连接,结果:
transaction type: <builtin: TPC-B (sort of)> scaling factor: 1000 query mode: simple number of clients: 100 number of threads: 2 duration: 600 s number of transactions actually processed: 749572 latency average = 80.058 ms tps = 1249.096708 (including connections establishing) tps = 1249.116996 (excluding connections establishing)
1000个连接下,结果:
transaction type: <builtin: TPC-B (sort of)> scaling factor: 1000 query mode: simple number of clients: 100 number of threads: 2 duration: 600 s number of transactions actually processed: 684434 latency average = 87.686 ms tps = 1140.430155 (including connections establishing) tps = 1140.449899 (excluding connections establishing)
结果表明,TPS从1249下降到1140,有8.7%的下降。
事务率测试2:select-only
因为空闲连接消耗了内存减小了页缓存可用内存,所以这些空闲连接对读的影响尤为明显。为测试这点,使用-S配置运行pgbench,使用内置的select only脚本。结果:
transaction type: <builtin: select only> scaling factor: 1000 query mode: simple number of clients: 100 number of threads: 2 duration: 600 s number of transactions actually processed: 1181937 latency average = 50.778 ms tps = 1969.344251 (including connections establishing) tps = 1969.377751 (excluding connections establishing)
1000个空闲连接下:
transaction type: <builtin: select only> scaling factor: 1000 query mode: simple number of clients: 100 number of threads: 2 duration: 600 s number of transactions actually processed: 966656 latency average = 62.095 ms tps = 1610.440842 (including connections establishing) tps = 1610.470585 (excluding connections establishing)
TPS从1969下降到1610,有18.2%的下降。
事务率测试3:custom pgbench
执行脚本:
\set nbranches :scale \set naccounts 100000 * :scale \set aid random(1, :naccounts) \set bid random(1, :nbranches) BEGIN; SELECT * FROM pgbench_accounts WHERE aid >= :aid AND aid < (:aid + 5000) AND bid=:bid LIMIT 1; END;
脚本中每个事物从pgbench_accounts表读取5000行数据,然后仅返回1条。结果:
transaction type: pgbench_script.sql scaling factor: 5000 query mode: simple number of clients: 100 number of threads: 2 duration: 600 s number of transactions actually processed: 227484 latency average = 264.140 ms tps = 378.586790 (including connections establishing) tps = 378.592772 (excluding connections establishing)
1000个空闲连接下,结果为:
transaction type: pgbench_script.sql scaling factor: 5000 query mode: simple number of clients: 100 number of threads: 2 duration: 600 s number of transactions actually processed: 124114 latency average = 484.485 ms tps = 206.404854 (including connections establishing) tps = 206.507645 (excluding connections establishing)
结果显示TPS从378下降到206,有46%的下降。通过Amazon RDS Performance Insights可以看到引擎wait events详细信息。下面两个图显示了DataFileRead等待事件中耗费时间最多的。即等待从表数据文件中读取数据。
下图显示了Amazon CloudWatch指标中的读负载:
第一次执行时读为87MB/s,第二次1000个连接下,增长到117MB/s。空闲连接消耗了操作系统内存,导致OS cache变小。因此需要从磁盘读取更多数据页,从而导致性能的衰减。
连接池
连接池可帮助减小数据库连接带来的影响。可以使用pgbouncer或者Amazon RDS Proxy。这些连接池可以限制连接数量。
Pgbouncer
Pgbouncer是轻量级的连接池组件,支持下面三种模式:
Session mode:每个应用连接绑定到一个数据库连接上。如果连接处于空闲状态,pgbouncer不能将它给其他应用连接重用。
Transaction mode:一个事务完成后,该连接就可以重用
Statement mode:一个SQL语句完成后就可以将该连接给其他客户端重用。
大多数应用中,使用transaction mode可以提供最优结果。下面测试pgbouncer配置了最大5000客户端连接,但我们的测试中最大连接设置为200.pgbench运行在pgbouncer pool中。结果:
transaction type: pgbench_script.sql scaling factor: 5000 query mode: simple number of clients: 100 number of threads: 2 duration: 600 s number of transactions actually processed: 227064 latency average = 264.600 ms tps = 377.928241 (including connections establishing) tps = 377.928476 (excluding connections establishing)
运行过程中,可以查看连接状态:
pgbouncer=# show pools; -[ RECORD 1 ]----------- database | pgbench user | postgres cl_active | 100 cl_waiting | 0 sv_active | 100 sv_idle | 0 sv_used | 0 sv_tested | 0 sv_login | 0 maxwait | 0 maxwait_us | 0 pool_mode | transaction
Pool状态显示有100个客户端连接(cl_active)从而有100个活跃server连接(sv_active)。第二次执行,打开1000个连接,并处于空闲状态。Pooler不需要维护任何服务端连接:
pgbouncer=# show pools; -[ RECORD 1 ]----------- database | pgbench user | postgres cl_active | 1000 cl_waiting | 0 sv_active | 0 sv_idle | 1 sv_used | 0 sv_tested | 0 sv_login | 0 maxwait | 0 maxwait_us | 0 pool_mode | transaction
1000个空闲连接下,执行pgbench:
transaction type: pgbench_sc transaction type: pgbench_script.sql scaling factor: 5000 query mode: simple number of clients: 100 number of threads: 2 duration: 600 s number of transactions actually processed: 226827 latency average = 264.935 ms tps = 377.451418 (including connections establishing) tps = 377.451655 (excluding connections establishing)
下面显示使用连接池是,性能没有影响:
pgbouncer=# show pools; -[ RECORD 1 ]----------- database | pgbench user | postgres cl_active | 1100 cl_waiting | 0 sv_active | 100 sv_idle | 0 sv_used | 0 sv_tested | 0 sv_login | 0 maxwait | 0 maxwait_us | 0 pool_mode | transaction
总共有1100个客户端连接,但是仅有100个服务端连接活跃。
该测试,RDS实例有2个CPU,因此100个进程并行执行,导致大量上下文切换,从而造成性能衰减。Pgbouncer配置最多20个数据连接下性能:
transaction type: pgbench_script.sql scaling factor: 5000 query mode: simple number of clients: 100 number of threads: 2 duration: 600 s number of transactions actually processed: 256267 latency average = 234.286 ms tps = 426.828543 (including connections establishing) tps = 426.828801 (excluding connections establishing)
得到了个更高的TPS,状态:
pgbouncer=# show pools; -[ RECORD 1 ]----------- database | pgbench user | postgres cl_active | 20 cl_waiting | 80 sv_active | 20 sv_idle | 0 sv_used | 0 sv_tested | 0 sv_login | 0 maxwait | 0 maxwait_us | 125884 pool_mode | transaction
只有20个客户端连接活跃。剩下的80个连接等待被分配。更多的连接并不意味着更多的吞吐量。较少的客户端连接有助于上下文切换和资源争用,从而提高总体性能。
总结
连接数多并不意味着高吞吐。增加连接数,会增加上下文切换和资源争用,从而影响性能。
PG连接即使空闲状态,也会消耗资源。空闲连接不会影响性能的假设不正确。
应用设计的时候需要考虑不要有太多连接。
原文
https://amazonaws-china.com/cn/blogs/database/performance-impact-of-idle-postgresql-connections/