在测试docker下postgresql性能时, 发现的一个DNAT转换后的网络性能问题.
首先附上KVM,DOCKER,本机的几组测试数据(测试数据仅供参考, 有兴趣的朋友可以自己测试一下) :
注意在测试连接超出物理核心数后, docker的性能开始严重下降. (可能有优化手段解决这个问题)
TPS 16连接IP, 16连接UNIX SOCK, 48连接UNIX SOCK, 96连接UNIX SOCK
docker DNAT 29662
docker 174089.814292 257437.255033 265112.234836 180037.521722
kvm 58692.023756 62217.477740 166212.113952 162729.867472
本机 185341.577666 271232.160555 335530.912484 303621.978815
宿主机内核 :
[root@db-172-16-3-221 ~]# uname -r
2.6.32-431.el6.x86_64
docker版本 :
[root@db-172-16-3-221 ~]# docker info
Containers: 1
Images: 99
Storage Driver: devicemapper
Pool Name: docker-8:49-17826869-pool
Data file: /var/lib/docker/devicemapper/devicemapper/data
Metadata file: /var/lib/docker/devicemapper/devicemapper/metadata
Data Space Used: 1455.9 Mb
Data Space Total: 102400.0 Mb
Metadata Space Used: 3.4 Mb
Metadata Space Total: 2048.0 Mb
Execution Driver: native-0.2
Kernel Version: 2.6.32-431.el6.x86_64
本地的images如下 :
[root@db-172-16-3-221 ~]# docker images
REPOSITORY TAG IMAGE ID CREATED VIRTUAL SIZE
postgres 9.4-beta3 68b6ddf9ca08 47 hours ago 213.6 MB
postgres 9.4 68b6ddf9ca08 47 hours ago 213.6 MB
postgres 9 935836384c52 47 hours ago 212.9 MB
postgres latest 935836384c52 47 hours ago 212.9 MB
postgres 9.3 935836384c52 47 hours ago 212.9 MB
postgres 9.3.5 935836384c52 47 hours ago 212.9 MB
postgres 9.2 2a9ab0b9fa56 47 hours ago 212.7 MB
postgres 9.2.9 2a9ab0b9fa56 47 hours ago 212.7 MB
postgres 9.1.14 ee60947f6805 47 hours ago 212.1 MB
postgres 9.1 ee60947f6805 47 hours ago 212.1 MB
postgres 9.0.18 f29be88283f6 47 hours ago 211.5 MB
postgres 9.0 f29be88283f6 47 hours ago 211.5 MB
postgres 8 c57c14beb696 47 hours ago 211 MB
postgres 8.4.22 c57c14beb696 47 hours ago 211 MB
postgres 8.4 c57c14beb696 47 hours ago 211 MB
postgres 9.4-beta2 bf872395e1d4 2 weeks ago 213.6 MB
我们可以看看一个image的网络配置 :
[root@db-172-16-3-221 ~]# docker inspect postgres:9.3.5
[{
"Architecture": "amd64",
"Author": "",
"Comment": "",
"Config": {
"AttachStderr": false,
"AttachStdin": false,
"AttachStdout": false,
"Cmd": [
"postgres"
],
"CpuShares": 0,
"Cpuset": "",
"Domainname": "",
"Entrypoint": [
"/docker-entrypoint.sh"
],
"Env": [
"PATH=/usr/lib/postgresql/9.3/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin",
"LANG=en_US.utf8",
"PG_MAJOR=9.3",
"PG_VERSION=9.3.5-1.pgdg70+1",
"PGDATA=/var/lib/postgresql/data"
],
"ExposedPorts": {
"5432/tcp": {}
},
"Hostname": "39d2c877bc4f",
"Image": "15d042248df39ca896223c9e75f0f4541996f2b52030f1f8e6619cf18f77e395",
"Memory": 0,
"MemorySwap": 0,
"NetworkDisabled": false,
"OnBuild": [],
"OpenStdin": false,
"PortSpecs": null,
"StdinOnce": false,
"Tty": false,
"User": "",
"Volumes": {
"/var/lib/postgresql/data": {}
},
"WorkingDir": ""
},
"Container": "977043cde52cd15e070d71a67fbd8e2ec908af49123f3f773f71cda2061e9f44",
"ContainerConfig": {
"AttachStderr": false,
"AttachStdin": false,
"AttachStdout": false,
"Cmd": [
"/bin/sh",
"-c",
"#(nop) CMD [postgres]"
],
"CpuShares": 0,
"Cpuset": "",
"Domainname": "",
"Entrypoint": [
"/docker-entrypoint.sh"
],
"Env": [
"PATH=/usr/lib/postgresql/9.3/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin",
"LANG=en_US.utf8",
"PG_MAJOR=9.3",
"PG_VERSION=9.3.5-1.pgdg70+1",
"PGDATA=/var/lib/postgresql/data"
],
"ExposedPorts": {
"5432/tcp": {}
},
"Hostname": "39d2c877bc4f",
"Image": "15d042248df39ca896223c9e75f0f4541996f2b52030f1f8e6619cf18f77e395",
"Memory": 0,
"MemorySwap": 0,
"NetworkDisabled": false,
"OnBuild": [],
"OpenStdin": false,
"PortSpecs": null,
"StdinOnce": false,
"Tty": false,
"User": "",
"Volumes": {
"/var/lib/postgresql/data": {}
},
"WorkingDir": ""
},
"Created": "2014-10-21T05:54:39.659381225Z",
"DockerVersion": "1.2.0",
"Id": "935836384c524aafc0ac1c05246002c9c93343f0b4283a34e77a5f92f97f9a7c",
"Os": "linux",
"Parent": "15d042248df39ca896223c9e75f0f4541996f2b52030f1f8e6619cf18f77e395",
"Size": 0
}
启动一个container, 使用-P将端口映射出来.
[root@db-172-16-3-221 ~]# docker run -d -P postgres:9.3.5
[root@db-172-16-3-221 ~]# docker ps
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
786d815d71b0 postgres:9 /docker-entrypoint.s 2 hours ago Up 2 hours 0.0.0.0:49155->5432/tcp suspicious_wright
这个时候会自动创建nat转换条目, 可以使用iptables-save命令查看, 如下有一台DNAT的规则是端口49155的转发 :
[root@db-172-16-3-221 ~]# iptables-save
# Generated by iptables-save v1.4.7 on Thu Oct 23 14:00:48 2014
*nat
:PREROUTING ACCEPT [109:52442]
:POSTROUTING ACCEPT [90:4680]
:OUTPUT ACCEPT [90:4680]
:DOCKER - [0:0]
-A PREROUTING -m addrtype --dst-type LOCAL -j DOCKER
-A POSTROUTING -s 172.17.0.0/16 ! -d 172.17.0.0/16 -j MASQUERADE
-A POSTROUTING -s 172.17.0.0/16 ! -d 172.17.0.0/16 -j MASQUERADE
-A OUTPUT ! -d 127.0.0.0/8 -m addrtype --dst-type LOCAL -j DOCKER
-A DOCKER ! -i docker0 -p tcp -m tcp --dport 49155 -j DNAT --to-destination 172.17.0.4:5432
COMMIT
# Completed on Thu Oct 23 14:00:48 2014
同时查看这个container的网络, 也就是说, 外部可以通过宿主机的
49155
端口访问这个container的postgresql数据库.
宿主机本地则可以通过127.0.0.1: 49155 或 172.17.0.4:5432来访问.
问题来了 :
通过nat访问的性能和直接访问的性能差太多了.
"NetworkSettings": {
"Bridge": "docker0",
"Gateway": "172.17.42.1",
"IPAddress": "172.17.0.4",
"IPPrefixLen": 16,
"PortMapping": null,
"Ports": {
"5432/tcp": [
{
"HostIp": "0.0.0.0",
"HostPort": "49155"
}
]
}
详细 :
[root@db-172-16-3-221 ~]# docker inspect 786d815d71b0
[{
"Args": [
"postgres"
],
"Config": {
"AttachStderr": false,
"AttachStdin": false,
"AttachStdout": false,
"Cmd": [
"postgres"
],
"CpuShares": 0,
"Cpuset": "",
"Domainname": "",
"Entrypoint": [
"/docker-entrypoint.sh"
],
"Env": [
"PATH=/usr/lib/postgresql/9.3/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin",
"LANG=en_US.utf8",
"PG_MAJOR=9.3",
"PG_VERSION=9.3.5-1.pgdg70+1",
"PGDATA=/var/lib/postgresql/data"
],
"ExposedPorts": {
"5432/tcp": {}
},
"Hostname": "786d815d71b0",
"Image": "postgres:9.3.5",
"Memory": 0,
"MemorySwap": 0,
"NetworkDisabled": false,
"OnBuild": null,
"OpenStdin": false,
"PortSpecs": null,
"StdinOnce": false,
"Tty": false,
"User": "",
"Volumes": {
"/var/lib/postgresql/data": {}
},
"WorkingDir": ""
},
"Created": "2014-10-23T02:51:06.152941964Z",
"Driver": "devicemapper",
"ExecDriver": "native-0.2",
"HostConfig": {
"Binds": null,
"ContainerIDFile": "",
"Dns": null,
"DnsSearch": null,
"Links": null,
"LxcConf": [],
"NetworkMode": "bridge",
"PortBindings": {},
"Privileged": false,
"PublishAllPorts": true,
"VolumesFrom": null
},
"HostnamePath": "/var/lib/docker/containers/786d815d71b0dca10042e73842634483158811a9c54509e58d7a1d2afa6fd94d/hostname",
"HostsPath": "/var/lib/docker/containers/786d815d71b0dca10042e73842634483158811a9c54509e58d7a1d2afa6fd94d/hosts",
"Id": "786d815d71b0dca10042e73842634483158811a9c54509e58d7a1d2afa6fd94d",
"Image": "935836384c524aafc0ac1c05246002c9c93343f0b4283a34e77a5f92f97f9a7c",
"MountLabel": "",
"Name": "/suspicious_wright",
"NetworkSettings": {
"Bridge": "docker0",
"Gateway": "172.17.42.1",
"IPAddress": "172.17.0.4",
"IPPrefixLen": 16,
"PortMapping": null,
"Ports": {
"5432/tcp": [
{
"HostIp": "0.0.0.0",
"HostPort": "49155"
}
]
}
},
"Path": "/docker-entrypoint.sh",
"ProcessLabel": "",
"ResolvConfPath": "/etc/resolv.conf",
"State": {
"ExitCode": 0,
"FinishedAt": "0001-01-01T00:00:00Z",
"Paused": false,
"Pid": 22251,
"Running": true,
"StartedAt": "2014-10-23T02:51:06.931436989Z"
},
"Volumes": {
"/var/lib/postgresql/data": "/var/lib/docker/vfs/dir/8df8643eb4f255471b744a957a168fb9e1e57b1169ade6fba8b5f3a912681e88"
},
"VolumesRW": {
"/var/lib/postgresql/data": true
}
}
这个启动container后为container创建的接口.
# ifconfig
veth2d30 Link encap:Ethernet HWaddr 42:9B:9F:1F:09:B0
inet6 addr: fe80::409b:9fff:fe1f:9b0/64 Scope:Link
UP BROADCAST RUNNING MTU:1500 Metric:1
RX packets:6289977 errors:0 dropped:0 overruns:0 frame:0
TX packets:6290136 errors:0 dropped:0 overruns:0 carrier:0
collisions:0 txqueuelen:1000
RX bytes:786242516 (749.8 MiB) TX bytes:597549874 (569.8 MiB)
[root@db-172-16-3-221 ~]# brctl show
bridge name bridge id STP enabled interfaces
docker0 8000.429b9f1f09b0 no veth2d30
测试如下, 执行select 1; :
[root@db-172-16-3-221 ~]# cd
[root@db-172-16-3-221 ~]# psql -V
psql (PostgreSQL) 9.3.5
[root@db-172-16-3-221 ~]# vi test.sql
select 1;
通过NAT转换的性能 :
[root@db-172-16-3-221 ~]# pgbench -M prepared -n -r -f ./test.sql -h 127.0.0.1 -p 49155 -U postgres -c 16 -j 4 -T 30 postgres
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 16
number of threads: 4
duration: 30 s
number of transactions actually processed: 889901
tps = 29662.517330 (including connections establishing)
tps = 29677.580497 (excluding connections establishing)
statement latencies in milliseconds:
0.537902 select 1;
直接连接container访问的性能 :
显然使用nat转换带来的性能损失非常大, TPS下降到了17%.
[root@db-172-16-3-221 ~]# pgbench -M prepared -n -r -f ./test.sql -h 172.17.0.4 -p 5432 -U postgres -c 16 -j 4 -T 30 postgres
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 16
number of threads: 4
duration: 30 s
number of transactions actually processed: 5222786
tps = 174089.814292 (including connections establishing)
tps = 174177.646184 (excluding connections establishing)
statement latencies in milliseconds:
0.090595 select 1;
另外 , 附上直接访问宿主机本地的数据库的性能, 我们看到docker性能损失极少(约6%) :
相比KVM虚拟化, CPU损耗方面, DOCKER的性能损耗确实非常低
[root@db-172-16-3-221 ~]# pgbench -M prepared -n -r -f ./test.sql -h 127.0.0.1 -p 5432 -U postgres -c 16 -j 4 -T 30 postgres
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 16
number of threads: 4
duration: 30 s
number of transactions actually processed: 5560383
tps = 185341.577666 (including connections establishing)
tps = 185435.133589 (excluding connections establishing)
statement latencies in milliseconds:
0.084922 select 1;
在同一台宿主机下KVM虚拟机测试结果如下(kvm VCPU=48, 宿主机48核) :
宿主机CPU :
processor : 47
vendor_id : GenuineIntel
cpu family : 6
model : 46
model name : Intel(R) Xeon(R) CPU E7540 @ 2.00GHz
stepping : 6
cpu MHz : 1995.262
cache size : 18432 KB
physical id : 3
siblings : 12
core id : 11
cpu cores : 6
apicid : 119
initial apicid : 119
fpu : yes
fpu_exception : yes
cpuid level : 11
wp : yes
flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good xtopology nonstop_tsc aperfmperf pni dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm dca sse4_1 sse4_2 x2apic popcnt lahf_lm ida epb dts tpr_shadow vnmi flexpriority ept vpid
bogomips : 3989.44
clflush size : 64
cache_alignment : 64
address sizes : 44 bits physical, 48 bits virtual
power management:
4SOCKET
6CORE
2TH
共48核心
虚拟机CPU(问题可能出自这里) :
processor : 47
vendor_id : GenuineIntel
cpu family : 6
model : 26
model name : Intel Core i7 9xx (Nehalem Class Core i7)
stepping : 3
cpu MHz : 1995.047
cache size : 4096 KB
physical id : 3
siblings : 12
core id : 5
cpu cores : 6
apicid : 59
initial apicid : 59
fpu : yes
fpu_exception : yes
cpuid level : 4
wp : yes
flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx lm constant_tsc unfair_spinlock pni ssse3 cx16 sse4_1 sse4_2 x2apic popcnt hypervisor lahf_lm
bogomips : 3990.09
clflush size : 64
cache_alignment : 64
address sizes : 44 bits physical, 48 bits virtual
power management:
KVM进程 :
qemu 29307 1 55 15:57 ? 00:00:07 /usr/libexec/qemu-kvm -name test1 -S -M rhel6.5.0 -cpu Nehalem,+rdtscp,+x2apic,+dca,+pdcm,+xtpr,+tm2,+est,+vmx,+ds_cpl,+monitor,+dtes64,+pbe,+tm,+ht,+ss,+acpi,+ds,+vme -enable-kvm -m 8192 -realtime mlock=off -smp 48,sockets=4,cores=6,threads=2 -uuid 073dbc6f-f376-2c96-6323-e0cc488eda68 -nodefconfig -nodefaults -chardev socket,id=charmonitor,path=/var/lib/libvirt/qemu/test1.monitor,server,nowait -mon chardev=charmonitor,id=monitor,mode=control -rtc base=utc -no-shutdown -device piix3-usb-uhci,id=usb,bus=pci.0,addr=0x1.0x2 -drive file=/var/lib/libvirt/images/test1.img,if=none,id=drive-virtio-disk0,format=raw,cache=none -device virtio-blk-pci,scsi=off,bus=pci.0,addr=0x5,drive=drive-virtio-disk0,id=virtio-disk0,bootindex=1 -drive if=none,media=cdrom,id=drive-ide0-1-0,readonly=on,format=raw -device ide-drive,bus=ide.1,unit=0,drive=drive-ide0-1-0,id=ide0-1-0 -netdev tap,fd=21,id=hostnet0,vhost=on,vhostfd=22 -device virtio-net-pci,netdev=hostnet0,id=net0,mac=52:54:00:d6:b2:07,bus=pci.0,addr=0x3 -chardev pty,id=charserial0 -device isa-serial,chardev=charserial0,id=serial0 -device usb-tablet,id=input0 -vnc 127.0.0.1:0 -vga cirrus -device intel-hda,id=sound0,bus=pci.0,addr=0x4 -device hda-duplex,id=sound0-codec0,bus=sound0.0,cad=0 -device virtio-balloon-pci,id=balloon0,bus=pci.0,addr=0x6
KVM性能(损耗68%) :
[root@db-172-16-3-221 ~]# pgbench -M prepared -n -r -f ./test.sql -h 172.17.42.222 -p 5432 -U postgres -c 16 -j 4 -T 30 postgres
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 16
number of threads: 4
duration: 30 s
number of transactions actually processed: 1760790
tps = 58692.023756 (including connections establishing)
tps = 58743.201940 (excluding connections establishing)
statement latencies in milliseconds:
0.271061 select 1;
计算密集型的话docker有明显优势.
最后再来一组unix socket的测试结果 :
本机 :
[root@db-172-16-3-221 ~]# pgbench -M prepared -n -r -f ./test.sql -h /data01/pgdata/pg_root -p 5432 -U postgres -c 16 -j 4 -T 30 postgres
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 16
number of threads: 4
duration: 30 s
number of transactions actually processed: 8137047
tps = 271232.160555 (including connections establishing)
tps = 271348.434446 (excluding connections establishing)
statement latencies in milliseconds:
0.057746 select 1;
docker (性能损耗5%)
[root@db-172-16-3-221 ~]# docker run -t -i postgres:9.3.5 /bin/bash
root@48f8decd3ccb:/# more docker-entrypoint.sh
#!/bin/bash
set -e
if [ "$1" = 'postgres' ]; then
chown -R postgres "$PGDATA"
if [ -z "$(ls -A "$PGDATA")" ]; then
gosu postgres initdb
sed -ri "s/^#(listen_addresses\s*=\s*)\S+/\1'*'/" "$PGDATA"/postgresql.conf
{ echo; echo 'host all all 0.0.0.0/0 trust'; } >> "$PGDATA"/pg_hba.conf
if [ -d /docker-entrypoint-initdb.d ]; then
for f in /docker-entrypoint-initdb.d/*.sh; do
[ -f "$f" ] && . "$f"
done
fi
fi
exec gosu postgres "$@"
fi
exec "$@"
root@48f8decd3ccb:/# ./docker-entrypoint.sh postgres
root@48f8decd3ccb:/# echo "select 1;" > ./test.sql
root@48f8decd3ccb:/# pgbench -M prepared -n -r -f ./test.sql -h /var/run/postgresql -p 5432 -U postgres -c 16 -j 4 -T 30 postgres transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 16
number of threads: 4
duration: 30 s
number of transactions actually processed: 7723173
tps = 257437.255033 (including connections establishing)
tps = 257554.115083 (excluding connections establishing)
statement latencies in milliseconds:
0.060943 select 1;
kvm (性能损耗77%)
postgres@localhost-> pgbench -M prepared -n -r -f ./test.sql -h /home/postgres/pgdata/pg_root -p 5432 -U postgres -c 16 -j 4 -T 30 postgres
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 16
number of threads: 4
duration: 30 s
number of transactions actually processed: 1866859
tps = 62217.477740 (including connections establishing)
tps = 62248.936343 (excluding connections establishing)
statement latencies in milliseconds:
0.254873 select 1;
加大连接后, kvm有性能提升. 但是到顶也只能达到16.6W左右, 性能损失46.4%左右.
DOCKER的话, 超出核数后性能下降严重, 在达到物理核限制极限时, 损失12.7% 左右.
KVM加大连接(CPU使用率1200%) :
top - 17:54:56 up 43 days, 7:58, 3 users, load average: 4.72, 1.43, 0.53
Tasks: 1115 total, 1 running, 1114 sleeping, 0 stopped, 0 zombie
Cpu(s): 24.9%us, 0.2%sy, 0.0%ni, 74.9%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st
Mem: 99045564k total, 13146680k used, 85898884k free, 272772k buffers
Swap: 1048568k total, 0k used, 1048568k free, 9688200k cached
PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
30008 qemu 20 0 11.8g 815m 5828 S 1204.4 0.8 20:56.29 /usr/libexec/qemu-kvm -name test1 -S -M rhel6.5.0 -cpu Nehalem,+rdtsc
postgres@localhost-> pgbench -M prepared -n -r -f ./test.sql -h /home/postgres/pgdata/pg_root -p 5432 -U postgres -c 48 -j 12 -T 30 postgres
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 48
number of threads: 12
duration: 30 s
number of transactions actually processed: 4988101
tps = 166212.113952 (including connections establishing)
tps = 166348.747712 (excluding connections establishing)
statement latencies in milliseconds:
0.285856 select 1;
postgres@localhost-> pgbench -M prepared -n -r -f ./test.sql -h /home/postgres/pgdata/pg_root -p 5432 -U postgres -c 96 -j 12 -T 30 postgres
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 96
number of threads: 12
duration: 30 s
number of transactions actually processed: 4885569
tps = 162729.867472 (including connections establishing)
tps = 163017.799292 (excluding connections establishing)
statement latencies in milliseconds:
0.585876 select 1;
本地加大连接 :
[root@db-172-16-3-221 ~]# pgbench -M prepared -n -r -f ./test.sql -h /data01/pgdata/pg_root -p 5432 -U postgres -c 48 -j 12 -T 30 postgres
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 48
number of threads: 12
duration: 30 s
number of transactions actually processed: 10068364
tps = 335530.912484 (including connections establishing)
tps = 335757.764533 (excluding connections establishing)
statement latencies in milliseconds:
0.141343 select 1;
[root@db-172-16-3-221 ~]# pgbench -M prepared -n -r -f ./test.sql -h /data01/pgdata/pg_root -p 5432 -U postgres -c 96 -j 12 -T 30 postgres
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 96
number of threads: 12
duration: 30 s
number of transactions actually processed: 9113013
tps = 303621.978815 (including connections establishing)
tps = 304050.332175 (excluding connections establishing)
statement latencies in milliseconds:
0.313762 select 1;
docker加大连接 :
root@48f8decd3ccb:/# pgbench -M prepared -n -r -f ./test.sql -h /var/run/postgresql -p 5432 -U postgres -c 96 -j 12 -T 30 postgres
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 96
number of threads: 12
duration: 30 s
number of transactions actually processed: 5403085
tps = 180037.521722 (including connections establishing)
tps = 180248.827373 (excluding connections establishing)
statement latencies in milliseconds:
0.527325 select 1;
root@48f8decd3ccb:/# pgbench -M prepared -n -r -f ./test.sql -h /var/run/postgresql -p 5432 -U postgres -c 40 -j 10 -T 30 postgres
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 40
number of threads: 10
duration: 30 s
number of transactions actually processed: 7955103
tps = 265112.234836 (including connections establishing)
tps = 265278.332675 (excluding connections establishing)
statement latencies in milliseconds:
0.147930 select 1;
[参考]
1. http://docs.docker.com/userguide/
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