结构设计
基础架构为flask+gunicorn+负载平衡,负载平衡分为阿里云硬件负载平衡服务和软负载nginx。gunicorn应用supervisor进行治理。
应用nginx软件负载结构图
应用阿里云硬件负载平衡服务结构图
因为flask app须要在内存中保留ip树以及国家、省份、城市相干的字典,因而占用内存较高。gunicorn的1个worker须要占用300M内存,nginx的4个worker内存占用较小(不到100M),因而占用1.3G的内存(即须要一个2G内存的服务器)。当gunicorn任意一个节点挂断或者降级时,另外一个节点依然在应用,不会影响整体服务
<!–more–>
ip数据库
IP库(也叫IP地址数据库),是由业余技术人员通过长时间通过多种技术手段收集而来的,并且长期有业余人员进行更新、保护、补充。
ip数据库解析查问代码
基于二叉查找树实现
import struct
from socket import inet_aton, inet_ntoa
import os
import sys
sys.setrecursionlimit(1000000)
_unpack_V = lambda b: struct.unpack("<L", b)
_unpack_N = lambda b: struct.unpack(">L", b)
_unpack_C = lambda b: struct.unpack("B", b)
class IpTree:
def __init__(self):
self.ip_dict = {}
self.country_codes = {}
self.china_province_codes = {}
self.china_city_codes = {}
def load_country_codes(self, file_name):
try:
path = os.path.abspath(file_name)
with open(path, "rb") as f:
for line in f.readlines():
data = line.split('\t')
self.country_codes[data[0]] = data[1]
# print self.country_codes
except Exception as ex:
print "cannot open file %s: %s" % (file, ex)
print ex.message
exit(0)
def load_china_province_codes(self, file_name):
try:
path = os.path.abspath(file_name)
with open(path, "rb") as f:
for line in f.readlines():
data = line.split('\t')
provinces = data[2].split('\r')
self.china_province_codes[provinces[0]] = data[0]
# print self.china_province_codes
except Exception as ex:
print "cannot open file %s: %s" % (file, ex)
print ex.message
exit(0)
def load_china_city_codes(self, file_name):
try:
path = os.path.abspath(file_name)
with open(path, "rb") as f:
for line in f.readlines():
data = line.split('\t')
cities = data[3].split('\r')
self.china_city_codes[cities[0]] = data[0]
except Exception as ex:
print "cannot open file %s: %s" % (file, ex)
print ex.message
exit(0)
def loadfile(self, file_name):
try:
ipdot0 = 254
path = os.path.abspath(file_name)
with open(path, "rb") as f:
local_binary0 = f.read()
local_offset, = _unpack_N(local_binary0[:4])
local_binary = local_binary0[4:local_offset]
# 256 nodes
while ipdot0 >= 0:
middle_ip = None
middle_content = None
lis = []
# offset
begin_offset = ipdot0 * 4
end_offset = (ipdot0 + 1) * 4
# index
start_index, = _unpack_V(local_binary[begin_offset:begin_offset + 4])
start_index = start_index * 8 + 1024
end_index, = _unpack_V(local_binary[end_offset:end_offset + 4])
end_index = end_index * 8 + 1024
while start_index < end_index:
content_offset, = _unpack_V(local_binary[start_index + 4: start_index + 7] +
chr(0).encode('utf-8'))
content_length, = _unpack_C(local_binary[start_index + 7])
content_offset = local_offset + content_offset - 1024
content = local_binary0[content_offset:content_offset + content_length]
if middle_content != content and middle_content is not None:
contents = middle_content.split('\t')
lis.append((middle_ip, (contents[0], self.lookup_country_code(contents[0]),
contents[1], self.lookup_china_province_code(contents[1]),
contents[2], self.lookup_china_city_code(contents[2]),
contents[3], contents[4])))
middle_content, = content,
middle_ip = inet_ntoa(local_binary[start_index:start_index + 4])
start_index += 8
self.ip_dict[ipdot0] = self.generate_tree(lis)
ipdot0 -= 1
except Exception as ex:
print "cannot open file %s: %s" % (file, ex)
print ex.message
exit(0)
def lookup_country(self, country_code):
try:
for item_country, item_country_code in self.country_codes.items():
if country_code == item_country_code:
return item_country, item_country_code
return 'None', 'None'
except KeyError:
return 'None', 'None'
def lookup_country_code(self, country):
try:
return self.country_codes[country]
except KeyError:
return 'None'
def lookup_china_province(self, province_code):
try:
for item_province, item_province_code, in self.china_province_codes.items():
if province_code == item_province_code:
return item_province, item_province_code
return 'None', 'None'
except KeyError:
return 'None', 'None'
def lookup_china_province_code(self, province):
try:
return self.china_province_codes[province.encode('utf-8')]
except KeyError:
return 'None'
def lookup_china_city(self, city_code):
try:
for item_city, item_city_code in self.china_city_codes.items():
if city_code == item_city_code:
return item_city, item_city_code
return 'None', 'None'
except KeyError:
return 'None', 'None'
def lookup_china_city_code(self, city):
try:
return self.china_city_codes[city]
except KeyError:
return 'None'
def lookup(self, ip):
ipdot = ip.split('.')
ipdot0 = int(ipdot[0])
if ipdot0 < 0 or ipdot0 > 255 or len(ipdot) != 4:
return None
try:
d = self.ip_dict[int(ipdot[0])]
except KeyError:
return None
if d is not None:
return self.lookup1(inet_aton(ip), d)
else:
return None
def lookup1(self, net_ip, (net_ip1, content, lefts, rights)):
if net_ip < net_ip1:
if lefts is None:
return content
else:
return self.lookup1(net_ip, lefts)
elif net_ip > net_ip1:
if rights is None:
return content
else:
return self.lookup1(net_ip, rights)
else:
return content
def generate_tree(self, ip_list):
length = len(ip_list)
if length > 1:
lefts = ip_list[:length / 2]
rights = ip_list[length / 2:]
(ip, content) = lefts[length / 2 - 1]
return inet_aton(ip), content, self.generate_tree(lefts), self.generate_tree(rights)
elif length == 1:
(ip, content) = ip_list[0]
return inet_aton(ip), content, None, None
else:
return
if __name__ == "__main__":
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
ip_tree = IpTree()
ip_tree.load_country_codes("doc/country_list.txt")
ip_tree.load_china_province_codes("doc/china_province_code.txt")
ip_tree.load_china_city_codes("doc/china_city_code.txt")
ip_tree.loadfile("doc/mydata4vipday2.dat")
print ip_tree.lookup('123.12.23.45')
http申请
提供ip查问服务的GET申请和POST申请
@ip_app.route('/api/ip_query', methods=['POST'])
def ip_query():
try:
ip = request.json['ip']
except KeyError as e:
raise InvalidUsage('bad request: no key ip in your request json body. {}'.format(e), status_code=400)
if not is_ip(ip):
raise InvalidUsage('{} is not a ip'.format(ip), status_code=400)
try:
res = ip_tree.lookup(ip)
except Exception as e:
raise InvalidUsage('internal error: {}'.format(e), status_code=500)
if res is not None:
return jsonify(res)
else:
raise InvalidUsage('no ip info in ip db for ip: {}'.format(ip), status_code=501)
@ip_app.route('/api/ip_query', methods=['GET'])
def ip_query_get():
try:
ip = request.values.get('ip')
except ValueError as e:
raise InvalidUsage('bad request: no param ip in your request. {}'.format(e), status_code=400)
if not is_ip(ip):
raise InvalidUsage('{} is not a ip'.format(ip), status_code=400)
try:
res = ip_tree.lookup(ip)
except Exception as e:
raise InvalidUsage('internal error: {}'.format(e), status_code=500)
if res is not None:
return jsonify(res)
else:
raise InvalidUsage('no ip info in ip db for ip: {}'.format(ip), status_code=501)
POST申请须要在申请体中蕴含相似上面的json字段
{
"ip": "165.118.213.9"
}
GET申请的模式如:http://127.0.0.1:5000/api/ip_query?ip=165.118.213.9
服务部署
装置依赖库
依赖的库requirements.txt如下:
certifi==2017.7.27.1
chardet==3.0.4
click==6.7
Flask==0.12.2
gevent==1.1.1
greenlet==0.4.12
gunicorn==19.7.1
idna==2.5
itsdangerous==0.24
Jinja2==2.9.6
locustio==0.7.5
MarkupSafe==1.0
meld3==1.0.2
msgpack-python==0.4.8
requests==2.18.3
supervisor==3.3.3
urllib3==1.22
Werkzeug==0.12.2
装置办法:pip install -r requirements.txt
配置supervisor
vim /etc/supervisor/conf.d/ip_query_http_service.conf
,内容如下
[program:ip_query_http_service]
directory = /root/qk_python/ip_query
command = gunicorn -w10 -b0.0.0.0:8080 ip_query_app:ip_app --worker-class gevent
autostart = true
startsecs = 5
autorestart = true
startretries = 3
user = root
stdout_logfile=/root/qk_python/ip_query/log/gunicorn.log
stderr_logfile=/root/qk_python/ip_query/log/gunicorn.err
内容增加实现之后,须要创立stdout_logfile和stderr_logfile这两个目录,否则supervisor启动会报错。而后更新supervisor启动ip_query_http_service过程。
# 启动supervisor
supervisord -c /etc/supervisor/supervisord.conf
# 更新supervisor服务
supervisorctl update
对于supervisor的罕用操作参见最初面的参考资料。
装置nginx
如果是软负载的模式须要装置nginx,编译装置nginx的办法参见最初面的参考资料。
配置nginx
vim /usr/local/nginx/nginx.conf
,批改配置文件内容如下:
#user nobody;
#nginx过程数,倡议设置为等于CPU总外围数。
worker_processes 4;
#error_log logs/error.log;
#error_log logs/error.log notice;
#全局谬误日志定义类型,[ debug | info | notice | warn | error | crit ]
error_log logs/error.log info;
#过程文件
pid logs/nginx.pid;
#一个nginx过程关上的最多文件描述符数目,理论值应该是最多关上文件数(零碎的值ulimit -n)与nginx过程数相除,然而nginx调配申请并不平均,所以倡议与ulimit -n的值保持一致。
worker_rlimit_nofile 65535;
events {
#参考事件模型 linux 下应用epoll
use epoll;
#单个过程最大连接数(最大连接数=连接数*过程数)
worker_connections 65535;
}
http {
include mime.types;
default_type application/octet-stream;
log_format main '$remote_addr - $remote_user [$time_local] "$request" '
'$status $body_bytes_sent "$http_referer" '
'"$http_user_agent" "$http_x_forwarded_for"';
access_log logs/access.log main;
sendfile on;
#keepalive_timeout 0;
keepalive_timeout 65;
tcp_nopush on; #防止网络阻塞
tcp_nodelay on; #防止网络阻塞
#gzip on;
server {
#这里配置连接服务提供的代理端口.
listen 9000;
server_name localhost;
#charset koi8-r;
#access_log logs/host.access.log main;
location / {
# root html;
# index index.html index.htm;
proxy_pass http://127.0.0.1:8000;
proxy_redirect off;
proxy_set_header X-Real-IP $remote_addr;
#后端的Web服务器能够通过X-Forwarded-For获取用户实在IP
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header Host $host;
client_max_body_size 10m; #容许客户端申请的最大单文件字节数
client_body_buffer_size 128k; #缓冲区代理缓冲用户端申请的最大字节数,
proxy_buffer_size 4k; #设置代理服务器(nginx)保留用户头信息的缓冲区大小
proxy_temp_file_write_size 64k; #设定缓存文件夹大小,大于这个值,将从upstream服务器传
}
#error_page 404 /404.html;
# redirect server error pages to the static page /50x.html
#
error_page 500 502 503 504 /50x.html;
location = /50x.html {
root html;
}
}
}
压力测试
做压力测试,抉择正确的工具是前提。以下工具中,jmeter运行在windows机器较多,其余工具倡议都运行在*nix
机器上。
压力测试工具抉择
工具名称 | 优缺点 | 倡议 |
---|---|---|
ApacheBench(ab) | 命令应用简略,效率高,统计信息欠缺,施压机器内存压力小 | 举荐 |
locust | python编写,效率低,受限于GIL,须要编写python测试脚本 | 不举荐 |
wrk | 命令应用简略,效率高,统计信息精炼,坑少,少报错 | 最举荐 |
jmeter | 基于java,Apache开源,图形化界面,操作简便 | 举荐 |
webbench | 应用简略,然而不反对POST申请 | 个别 |
tsung | erlang编写,配置模板较多,较简单 | 不举荐 |
上述六种工具全副亲自应用过,上面抉择ab、wrk、jmeter三种工具简略阐明装置应用办法,其余工具的应用办法如有须要,自行google
ab
装置
apt-get install apache2-utils
常见options
option | 含意 |
---|---|
-r | 当接管到socket谬误的时候ab不退出 |
-t | 发送申请的最长工夫 |
-c | 并发数,一次结构的申请数量 |
-n | 发送的申请数量 |
-p | postfile,指定蕴含post数据的文件 |
-T | content-type,指定post和put发送申请时申请体的类型 |
应用
测试GET申请
ab -r -t 120 -c 5000 http://127.0.0.1:8080/api/ip_query?ip=165.118.213.9
测试POST申请
ab -r -t 120 -c 5000 -p /tmp/post_data.txt -T 'application/json' http://127.0.0.1:8080/api/ip_query
其中/tmp/post_data.txt
文件的内容为待发送的-T指定格局的数据,在此处为json格局
{"ip": "125.118.213.9"}
wrk
http://www.restran.net/2016/0…
装置
apt-get install libssl-dev
git clone https://github.com/wg/wrk.git
cd wrk
make
cp wrk /usr/sbin
常见options
option | 含意 |
---|---|
-c | 关上的连接数,即并发数 |
-d | 压力测试工夫:发送申请的最长工夫 |
-t | 施压机器应用的线程数量 |
-s | 指定要加载的lua脚本 |
–latency | 打印提早统计信息 |
应用
测试GET申请
wrk -t10 -c5000 -d120s --latency http://127.0.0.1:8080/api/ip_query?ip=165.118.213.9
测试POST申请
wrk -t50 -c5000 -d120s --latency -s /tmp/wrk_post.lua http://127.0.0.1:8080
其中/tmp/wrk_post.lua
文件的内容为待加载的lua脚本,指定post的path,header,body
request = function()
path = "/api/ip_query"
wrk.headers["Content-Type"] = "application/json"
wrk.body = "{\"ip\":\"125.118.213.9\"}"
return wrk.format("POST", path)
end
jmeter
装置
装置jmeter前须要先装置jdk1.8。而后在Apache官网能够下载jmeter,点此下载
应用
以上图片来自一个测试大牛,十分具体,残缺的xmind文件下载见:jmeter-张蓓.xmind
jmeter的入门级应用也能够参考最初面的参考资料局部:应用Apache Jmeter进行并发压力测试
压力测试后果剖析
wrk GET申请压测后果
root@ubuntu:/tmp# wrk -t10 -c5000 -d60s --latency http://127.0.0.1:8080/api/ip_query?ip=165.118.213.9
Running 1m test @ http://127.0.0.1:8080/api/ip_query?ip=165.118.213.9
10 threads and 5000 connections
Thread Stats Avg Stdev Max +/- Stdev
Latency 897.19ms 322.83ms 1.99s 70.52%
Req/Sec 318.80 206.03 2.14k 68.84%
Latency Distribution
50% 915.29ms
75% 1.11s
90% 1.29s
99% 1.57s
187029 requests in 1.00m, 51.01MB read
Socket errors: connect 0, read 0, write 0, timeout 38
Requests/sec: 3113.27
Transfer/sec: 869.53KB
ab GET申请压测后果
root@ubuntu:/tmp# ab -r -t 60 -c 5000 http://127.0.0.1:8080/api/ip_query?ip=165.118.213.9
This is ApacheBench, Version 2.3 <$Revision: 1796539 $>
Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/
Licensed to The Apache Software Foundation, https://www.apache.org/
Benchmarking 127.0.0.1 (be patient)
Completed 5000 requests
Completed 10000 requests
Completed 15000 requests
Completed 20000 requests
Completed 25000 requests
Completed 30000 requests
Completed 35000 requests
Completed 40000 requests
Completed 45000 requests
Completed 50000 requests
Finished 50000 requests
Server Software: gunicorn/19.7.1
Server Hostname: 127.0.0.1
Server Port: 8080
Document Path: /api/ip_query?ip=165.118.213.9
Document Length: 128 bytes
Concurrency Level: 5000
Time taken for tests: 19.617 seconds
Complete requests: 50000
Failed requests: 2
(Connect: 0, Receive: 0, Length: 1, Exceptions: 1)
Total transferred: 14050000 bytes
HTML transferred: 6400000 bytes
Requests per second: 2548.85 [#/sec] (mean)
Time per request: 1961.668 [ms] (mean)
Time per request: 0.392 [ms] (mean, across all concurrent requests)
Transfer rate: 699.44 [Kbytes/sec] received
Connection Times (ms)
min mean[+/-sd] median max
Connect: 0 597 1671.8 4 15500
Processing: 4 224 201.4 173 3013
Waiting: 4 223 200.1 172 2873
Total: 7 821 1694.4 236 15914
Percentage of the requests served within a certain time (ms)
50% 236
66% 383
75% 1049
80% 1155
90% 1476
95% 3295
98% 7347
99% 7551
100% 15914 (longest request)
jmeter GET申请压测后果
后果剖析
以上三个工具的压测后果大体雷同,RPS(Requests per second)大抵在3000左右,此时机器配置为4核4G内存,并且gunicorn开了10个worker,内存占用3.2G。单台机器只有3000并发,对于此配置的机器来说,须要进一步剖析起因。后续再弄一台机器,负载平衡后能达到5000以上能力满足应用要求。
压力测试注意事项
文件关上数
压力测试时对施压机器的文件关上数个别有要求,远不止1024个open files,须要减少linux零碎的文件关上数,减少办法:
# 文件关上数
ulimit -a
# 批改文件关上数
ulimit -n 500000
SYN洪水攻打爱护
linux零碎中有一个参数:/etc/sysctl.conf
配置文件中的net.ipv4.tcp_syncookies
字段。这个字段值默认为1,示意零碎会检测SYN洪水攻打,并开启爱护。因而压测时,如果发送大量重复性数据的申请,受压机器SYN队列溢出之后启用SYN cookie,导致会有大量申请超时失败。阿里云的负载平衡是有SYN洪水攻打检测和DDos攻打检测性能的,因而在做压力测试时须要留神两点:
- 测试时适当敞开负载平衡机器的 net.ipv4.tcp_syncookies 字段
- 造数据时应该尽量避免大量重复性数据,免得被辨认为攻打。
gunicorn简介及调优
对于gunicorn的抉择能够参考测试报告:Python WSGI Server 性能剖析
在选定gunicorn作为WSGI server之后,须要依据机器抉择相应的worker数量以及每个worker的worker-class。
worker数量抉择
每一个worker都是作为一个独自的子过程来运行,都持有一份独立的内存数据,每减少或缩小一个worker,零碎内存显著的成倍数的扭转。最后单台机器gunicorn开启3个worker,零碎只反对1000RPS的并发。当把worker扩大为9个之后,零碎反对3000RPS的并发。因而在内存足够的时候,能够适当减少worker数量。
worker-class抉择
能够参考尾部的参考资料中的gunicorn罕用settings和Gunicorn 几种 Worker class 性能测试比拟这两篇文章。
将gunicorn启动时的worker-class从默认的sync改成gevent之后,零碎RPS间接翻倍。
worker-class | worker数量 | ab测试的RPS |
---|---|---|
sync | 3 | 573.90 |
gevent | 3 | 1011.84 |
gevent依赖:gevent >= 0.13。因而须要先应用pip装置。对应的gunicorn启动flask利用的命令须要批改为:
gunicorn -w10 -b0.0.0.0:8080 ip_query_app:ip_app --worker-class gevent
改良点
改良ip数据库准确性
损失效率换取准确性:应用繁多ip数据库会存在一些ip无奈查问出后果的状况,并且国外ip个别只能准确到国家。能够均衡几家ip数据库的准确度和覆盖率,当无奈查问出精确的地址信息时去查问另外几个ip数据库。
进步单台机器并发量
从发动申请,到WSGI服务器解决,到利用接口,到ip查问每个过程都须要独自剖析每秒可执行量,进而剖析零碎瓶颈,从根本上进步单机并发量。
参考资料
- 寰球 IPv4 地址归属地数据库(IPIP.NET 版)
- 应用flask开发RESTful架构的api服务器端(5)–部署flask利用到nginx
- python web 部署:nginx + gunicorn + supervisor + flask 部署笔记
- flowsnow-nginx编译装置
- supervisor举荐教程-应用 supervisor 治理过程
- 维基-二叉查找树
- 简书-wrk压力测试post接口
- 应用Apache Jmeter进行并发压力测试
- gunicorn罕用settings
- Gunicorn 几种 Worker class 性能测试比拟
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