github中有一个非常好的单机多开splash Docker container的方案，但是，它仅仅适用于单机，如果是集群，它就无法应付了。这篇文章主要是解决集群部署splash的方案，Google半天也没有现成的方案，自己琢磨了下搞定了。
https://github.com/TeamHG-Memex/aquarium
适用平台: windows 10, centos 7.6
技术方案：利用yaml搭建Docker Swarm集群并部署多个服务(Visualizer, splash 3.5.0, HAproxy)
为什么用yml呢？看下下面docker service create，一次只能搭建一个服务，想搭建多个就不太方便，也不容易定制一些参数
集群建设
(boss) [chen@VM_0_3_centos aquarium]$ docker service create -p 8050:8050 –replicas 6 –name splash scrapinghub/splash /bin/bash
j3qtsdci0qum515nzoxpxu7u4
overall progress: 6 out of 6 tasks
1/6: running [==================================================>]
2/6: running [==================================================>]
3/6: running [==================================================>]
4/6: running [==================================================>]
5/6: running [==================================================>]
6/6: running [==================================================>]
verify: Service converged
Prerequisite: docker-ce (Linux平台需要单独安装，只安装docker是不够的) 或者Docker for windows, windows平台是集成化。
version: “3”
services:
visualizer:
image: dockersamples/visualizer
volumes:
– “/var/run/docker.sock:/var/run/docker.sock”
ports:
– 8080:8080
deploy:
placement:
constraints: [node.role == manager]
# labels:
# – com.df.notify=true
# – com.df.serviceDomain=visualizer.youclk.com
# – com.df.port=8080
# – com.df.usersSecret=admin
yml文件如下:
splash:
image: scrapinghub/splash
ports:
- 8050:8050
deploy:
mode: replicated
replicas: 2
labels: [APP=SPLASH]
# service resource management
resources:
# Hard limit - Docker does not allow to allocate more
limits:
cpus: '0.25'
memory: 2048M
# Soft limit - Docker makes best effort to return to it
reservations:
cpus: '0.25'
memory: 2560M
# service restart policy
restart_policy:
condition: any
delay: 5s
max_attempts: 3
window: 120s
# placement constraint - in this case on 'worker' nodes only
placement:
constraints: [node.role == worker](boss) [chen@VM_0_3_centos aquarium]$ docker stack deploy -c docker-compose.yml splash
Updating service splash_visualizer (id: lcfw3l45xkvewmly5yz7ukywh)
Updating service splash_splash (id: v1s5gbl9nfzmmp1hct6c8okce)
如果之前运行过上述命令，那么重新运行时会更新服务，方便扩展。因为replicas是2，每个服务器只创建了一个splash container。
然后我把replica改成4，Visualizer监控界面就看到了4个container:
来看一下其中一个服务器,能看到两个不同时间创建的container：
(boss) [chen@VM_0_2_centos ~]$ docker ps
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
0ae3f202164d scrapinghub/splash:latest “python3 /app/bin/sp…” 3 minutes ago Up 3 minutes 8050/tcp splash_splash.1.kpj3rv7piojx4yq3h5w3vamtv
9d353fe1bd6a scrapinghub/splash:latest “python3 /app/bin/sp…” 27 minutes ago Up 26 minutes 8050/tcp splash_splash.3.bl3amsvyticoje9vv3yethp4c
那么，为什么说它是高可用呢？我们重启或者停下VM_0_2_centos下的一个container，我们看到集群重新建了一个container:
(boss) [chen@VM_0_2_centos ~]$ docker ps
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
0ae3f202164d scrapinghub/splash:latest “python3 /app/bin/sp…” 3 minutes ago Up 3 minutes 8050/tcp splash_splash.1.kpj3rv7piojx4yq3h5w3vamtv
9d353fe1bd6a scrapinghub/splash:latest “python3 /app/bin/sp…” 27 minutes ago Up 26 minutes 8050/tcp splash_splash.3.bl3amsvyticoje9vv3yethp4c
(boss) [chen@VM_0_2_centos ~]$ docker stop 0ae3f202164d
0ae3f202164d
(boss) [chen@VM_0_2_centos ~]$ docker ps
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
d9253824289f scrapinghub/splash:latest “python3 /app/bin/sp…” 39 seconds ago Up 33 seconds 8050/tcp splash_splash.1.fo051grhynfybzbh407a1hith
9d353fe1bd6a scrapinghub/splash:latest “python3 /app/bin/sp…” 30 minutes ago Up 29 minutes 8050/tcp splash_splash.3.bl3amsvyticoje9vv3yethp4c
这个集群会努力保持集群中始终有4个container,如果一个死掉或者重启了会立即重建一个container，如果一个服务器宕机了，那么会在其他服务器重建4个container。当然，前提是你的服务器没有全部GG了
文中没有细说集群建设，网络中到处是这种帖子，简单说下：
创建管理节点manager node:
docker swarm init –advertise-addr=Manager IP
运行这个命令后会提示一个token，用这个token在worker node上运行下面命令即可加入集群，非常方便，不需要考虑网络问题：
docker swarm join –token SWMTKN-1-0zhhp71dfw77axxiuoixrechl8kcd9tapi9tbqskgtvt9yxxxxxxxxxxxxxxxxxxxxxxx –advertise-addr=WORKER IP:8050
HAproxy负载均衡
虽然swarm据说也有负载均衡，但是我测试了下，它的负载均衡仅仅限于一个节点上，外面还要套一层负载均衡才能对外服务，这个可以参考Youtube上的微软工程师的视频: https://www.youtube.com/watch?v=ZfMV5JmkWCY&t=170s
视频一共三个，第三个提到了负载均衡提供对外服务。这里我用一个性能很弱的腾讯云服务器做的负载均衡，也是docker集群的管理节点。
# HAProxy 1.7 config for Splash. It assumes Splash instances are executed
# on the same machine and connected to HAProxy using Docker links.
global
# raise it if necessary
maxconn 512
# required for stats page
stats socket /tmp/haproxy
userlist users
user USER insecure-password PASSWD
defaults
log global
mode http
# remove requests from a queue when clients disconnect;
# see https://cbonte.github.io/haproxy-dconv/1.7/configuration.html#4.2-option%20abortonclose
option abortonclose
# gzip can save quite a lot of traffic with json, html or base64 data
# compression algo gzip
compression type text/html text/plain application/json
# increase these values if you want to
# allow longer request queues in HAProxy
timeout connect 3600s
timeout client 3600s
timeout server 3600s
# visit 0.0.0.0:8036 to see HAProxy stats page
listen stats
bind *:8036
mode http
stats enable
stats hide-version
stats show-legends
stats show-desc Splash Cluster
stats uri /
stats refresh 10s
stats realm Haproxy\ Statistics
stats auth admin:adminpass
# Splash Cluster configuration
# 代理服务器监听全局的8050端口
frontend http-in
bind *:8050
# 如果你需要开启Splash的访问认证
# 则注释default_backend splash-cluster
# 并放开其余default_backend splash-cluster 之上的其余注释
# 账号密码为user userpass
acl auth_ok http_auth(users)
http-request auth realm Splash if !auth_ok
http-request allow if auth_ok
http-request deny
acl staticfiles path_beg /_harviewer/
acl misc path / /info /_debug /debug
use_backend splash-cluster if auth_ok !staticfiles !misc
use_backend splash-misc if auth_ok staticfiles
use_backend splash-misc if auth_ok misc
default_backend splash-cluster
backend splash-cluster
option httpchk GET /
balance leastconn
# try another instance when connection is dropped
retries 2
option redispatch
# 将下面IP地址替换为你自己的Splash服务IP和端口
# 按照以下格式一次增加其余的Splash服务器
server splash-0 SPLASH0_IP:8050 check maxconn 50 inter 2s fall 10 observe layer4
server splash-1 SPLASH1_IP:8050 check maxconn 50 inter 2s fall 10 observe layer4
backend splash-misc
balance roundrobin
# 将下面IP地址替换为你自己的Splash服务IP和端口
# 按照以下格式一次增加其余的Splash服务器
server splash-0 SPLASH0_IP:8050 check fall 15
server splash-1 SPLASH1_IP:8050 check fall 15
重磅： scrapy crawl SPIDER -a http_user=’USER’ -a http_pass=’PASSWD’
splash加密访问之后，如何在scrapy项目中利用是个问题，这个命令行解决了一大难题。网络上多数帖子都是抄袭的，这种关键问题却是没几个人提到。如果像我一样你想用多进程，一个服务器开多个scrapy进程，那么下面的脚本能解决你的问题:
from scrapy.crawler import CrawlerProcess
from scrapy.utils.project import get_project_settings
from multiprocessing import Pool
import os, time, random, multiprocessing
def long_time_task(name):
# 如下代码中加入异常捕捉后可以正常返回结果，防止主进程一直被阻塞
pid=os.getpid()
try:
print('pid:%d'%pid)
print('Run task %s (%s)...' % (name, os.getpid()))
start = time.time()
process = CrawlerProcess(get_project_settings())
process.crawl('lagouspd', domain='lagou.com', http_user='USER', http_pass='PASSWD')
process.start(stop_after_crawl = False) # the script will block here until the crawling is finished
end = time.time()
print('Task %s runs %0.2f seconds.' % (name, (end - start)))
time.sleep(2)
except KeyboardInterrupt:
print('进程%d被中断...'%pid)
if __name__=='__main__':
print('Parent process %s.' % os.getpid())
try:
p = Pool(multiprocessing.cpu_count()*4)
for i in range(multiprocessing.cpu_count()*4):
p.apply_async(long_time_task, args=(i,))
time.sleep(random.random() * 3)
print('Waiting for all subprocesses done...')
p.close()
p.join()
print('All subprocesses done.')
except KeyboardInterrupt:
print('catch keyboardinterupterror')
pid=os.getpid()
os.popen('taskkill.exe /f /pid:%d'%pid) #在unix下无需此命令，但在windows下需要此命令强制退出当前进程
except Exception as e:
print(e)
else:
print('quit normally') 脚本中的用户名密码就是上面的USER, PASSWD，不然splash没法使用。该脚本还解决了multiprocessing多进程用进程池时没法CTRL C暂停爬虫的问题
