Chapter 21 Website Tech

21.1 Apache

https://httpd.apache.org/

  • New features in Apache HTTP Server 2.4

https://httpd.apache.org/docs/trunk/mod/event.html

互联网创业的准备——web server:apache、nginx、lighttpd与php module、fastcgi

http://www.cnblogs.com/sink_cup/archive/2012/09/14/web_server.html

虽然apache httpd 2.4 像nginx一样使用了epoll,但是性能还是比nginx低很多.

21.2 Nginx

https://nginx.org/en/

https://www.nginx.com/blog/inside-nginx-how-we-designed-for-performance-scale/

There’s one worker process per core to make efficient use of hardware resources, the ability to interleave multiple connections within a single worker process, and the capability to switch from connection to connection almost instantaneously as network traffic arrives. Put this magic together and you create the massively scalable HTTP application delivery engine that is NGINX.

  • How NGINX creates processes to make efficient use of resources
  • The use of state machines to manage traffic
  • The innovative non-blocking, event-driven architecture that allows NGINX to schedule multiple state machines simultaneously
  • How the process architecture supports non-stop, graceful updates and binary upgrades

Rolling upgrade and Socket Sharding.

https://www.nginx.com/blog/socket-sharding-nginx-release-1-9-1/?utm_source=inside-nginx-how-we-designed-for-performance-scale&utm_medium=blog&utm_campaign=Core+Product

关于Nginx session sticky
http://blog.csdn.net/yangbutao/article/details/12971037

PHP的SESSION丢失?跨页面SESSION丢失问题
http://www.111cn.net/phper/31/58529.htm

解决nginx使用proxy_pass反向代理时,session丢失的问题
http://www.cnblogs.com/zangdalei/p/6021352.html

Redis 分布式缓存,是如何实现多台服务器SESSION 实时共享的
https://segmentfault.com/q/1010000003988125

21.3 Haproxy

http://www.haproxy.org/

21.3.1 Stickiness

Session 粘连

to maintain a visitor on the same server even across various events such as server addition/removal, down/up cycles, and some methods are designed to be resistant to the distance between multiple load balancing nodes in that they don’t require any replication.

http://blog.chinaunix.net/uid-10480699-id-5179914.html

21.4 MongoDB

从百万级别数据的分析角度,Mysql,Mongodb,Hbase如何选择?

https://www.zhihu.com/question/26518864

Mongdb的坑爹指数排行:
http://xiewenwei.github.io/blog/2014/06/22/trap-in-mongodb/

MongoDB在58同城百亿量级数据下的应用实践
http://www.infoq.com/cn/articles/app-practice-of-mongodb-in-58-ten-billion-scale-data