mongdb性能压力测试，随机查询，数据量1亿条记录
操作系统centos6.4x64位
从测试结果看，当mongodb将数据全部载入到内存后，查询速度根据文档的大小，性能瓶颈通常会是在网络流量和CPU的处理性能（该次测试中当数据全部在内存后，纯粹的查询速度可以稳定在10W/S左右，系统load可以维持在1以下，由于此时CPU已经被使用到极限了，当并发再大时load值会直线飙升，性能急剧下降）。
压力生成服务器与Mongodb服务器基本配置
cpu型号：Intel(R) Xeon(R) CPU E5-2630 0 @ 2.30GHz
内存：64G
硬盘：10K转速 6块做RAID0，raid卡cache 1G（磁盘的IO性能决定MONGODB将非热点数据载入到内存的速度，当热点数据全部在内存后，IO性能可以忽略）
网卡速度：接入千兆交互机，速度1000MB
数据结构，user_id为索引键值为0-99999999，数据总大小约37G左右，全部载入内存后约32G
> db.thing.findOne();
{
"_id" : ObjectId("51fd6830c6db9d15676f89ef"),
"ip" : "192.168.168.254",
"g_roup" : "kiwi",
"mac" : "of:fd:67:8c:2f:8f",
"address" : "hongmei1801num",
"user_id" : 10000000,
"name" : "user10000000",
"title" : "system",
"database" : "mongodb",
"telphone" : NumberLong("15718441234"),
"mail" : "yj@chinapnr.com",
"os" : "win7",
"company" : "chinapnr"
}
压力生成脚本开启40个进程，并发随机查询
[root@mdb-166 app]# cat mselectmongodb.py
import time,pymongo,multiprocessing,random,string
class SqlToMongo:
def m_sql(self,x,y):
server="mongodb://python:oracle@192.168.4.167:27017/syslog"
conn=pymongo.Connection(server)
db=conn.syslog
col=db.thing
start=x*y
end=start+x
for i in xrange(start,end):
d=random.randint(start,end)
val=col.find({"user_id":d})
a=list(val)
def gen_load(x,taskid):
task=SqlToMongo()
print "task %s start!" % taskid
task.m_sql(x,taskid)
if __name__ == "__main__":
inser_number=2500000
pro_pool = multiprocessing.Pool(processes=101)
print time.strftime('%Y-%m-%d:%H-%M-%S',time.localtime(time.time()))
start_time=time.time()
manager = multiprocessing.Manager()
for i in xrange(40):
taskid=i
pro_pool.apply_async(gen_load,args=(inser_number,taskid))
pro_pool.close()
pro_pool.join()
elapsed = time.time()-start_time
print elapsed
time.sleep(1)
print "Sub-process(es) done."
压力生成服务器负载，同时开启80个并发时抗不住了
top - 17:12:59 up 2 days, 7:34, 4 users, load average: 35.87, 29.41, 18.16
Tasks: 487 total, 64 running, 423 sleeping, 0 stopped, 0 zombie
Cpu0 : 88.1%us, 10.6%sy, 0.0%ni, 1.3%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st
Cpu1 : 87.7%us, 10.9%sy, 0.0%ni, 1.3%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st
Cpu2 : 87.7%us, 11.0%sy, 0.0%ni, 1.3%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st
Cpu3 : 88.0%us, 10.3%sy, 0.0%ni, 1.7%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st
Cpu4 : 87.7%us, 11.0%sy, 0.0%ni, 1.3%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st
Cpu5 : 88.7%us, 9.9%sy, 0.0%ni, 1.3%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st
Cpu6 : 40.2%us, 3.3%sy, 0.0%ni, 0.3%id, 0.0%wa, 0.0%hi, 56.1%si, 0.0%st
Cpu7 : 89.7%us, 8.9%sy, 0.0%ni, 1.3%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st
Cpu8 : 88.1%us, 10.6%sy, 0.0%ni, 1.3%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st
Cpu9 : 89.7%us, 9.3%sy, 0.0%ni, 1.0%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st
Cpu10 : 88.7%us, 9.9%sy, 0.0%ni, 1.3%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st
Cpu11 : 88.4%us, 10.2%sy, 0.0%ni, 1.3%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st
Mem: 65909128k total, 5409148k used, 60499980k free, 90680k buffers
Swap: 8388600k total, 0k used, 8388600k free, 4392420k cached
PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
13342 root 20 0 178m 9056 1480 R 15.9 0.0 5:15.79 python mselectmongodb.py
13430 root 20 0 178m 8976 1468 R 15.9 0.0 3:05.26 python mselectmongodb.py