What is load balancing

When the number of visits per unit time of a server is greater, the pressure on the server will be greater. When it exceeds its own capacity, the server will will crash. In order to avoid server crashes and provide users with a better experience, we use load balancing to share server pressure.

We can build many servers to form a server cluster. When a user accesses the website, he first visits an intermediate server, and then lets the intermediate server choose a server with less pressure in the server cluster, and then transfers the server to the server cluster. Access requests are directed to the server. In this way, every visit by a user will ensure that the pressure of each server in the server cluster tends to be balanced, sharing the server pressure and avoiding server crash.

Load balancing is implemented using the principle of reverse proxy.

Several common methods of load balancing

1. Polling (default)
Each requestis assignedto different servers one by one in chronological order The end server can be automatically eliminated if the backend server goes down.

upstream backserver { server 192.168.0.14; server 192.168.0.15; }

2. Weight
Specifies the polling probability, weight is proportional to the access ratio, and is used for
situations where back-end server performance is uneven.

upstream backserver { server 192.168.0.14 weight=3; server 192.168.0.15 weight=7; }

The higher the weight, the greater the probability of being accessed. As in the above example, they are 30% and 70% respectively.

3. There is a problem with the above method, that is, in the load balancing system, if the user logs in to a certain server, then when the user makes a second request, because we are a load balancing system, every time Every request will be redirected to one of the server clusters. If a user who has logged in to a server is redirected to another server, his login information will be lost. This is obviously inappropriate.

We can use the ip_hash instruction to solve this problem. If the customer has already visited a certain server, when the user visits again, the request will be automatically located to the server through the hash algorithm.

Each request is allocated according to the hash result of the access IP, so that each visitor has fixed access to a back-end server, which can solve the session problem.

upstream backserver { ip_hash; server 192.168.0.14:88; server 192.168.0.15:80; }

4. fair (third party)
Requests are allocated according to the response time of the backend server, and those with short response times are allocated first.

upstream backserver { server server1; server server2; fair; }

5. url_hash (third party)
Distribute requests according to the hash result of the accessed URL, so that each URL is directed to the same back-end server. It is more effective when the back-end server is cached.

upstream backserver { server squid1:3128; server squid2:3128; hash $request_uri; hash_method crc32; }

The status of each device is set to:

1.down means that the previous server will not participate in the load temporarily
2.weight The default is 1. The greater the weight, the weight of the load The bigger it gets.
3.max_fails: The number of allowed request failures is 1 by default. When the maximum number is exceeded, the error defined by theproxy_next_upstreammodule is returned
4.fail_timeout: The time to pause after max_failsfailures.
5.backup: When all other non-backup machines are down or busy, request the backup machine. So this machine will have the least pressure.

Configuration example:

#user nobody;worker_processes 4; events { # 最大并发数 worker_connections 1024; } http{ # 待选服务器列表 upstream myproject{ # ip_hash指令，将同一用户引入同一服务器。 ip_hash; server 125.219.42.4 fail_timeout=60s; server 172.31.2.183; } server{ # 监听端口 listen 80; # 根目录下 location / { # 选择哪个服务器列表 proxy_pass http://myproject; } } }

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