How to realize Memcached service distributed cluster?

How to realize Memcached service distributed cluster?

　　In a distributed system, in order to improve availability, performance and scalability, multiple servers are usually used to build a Memcached cluster. Memcached is a high-performance distributed memory caching system that can be used to cache data to reduce the load on databases or other back-end storage.

　　Let's look at a specific example below, showing how to use Python's memcached library to implement a simple Memcached distributed cluster. In the actual production environment, we need more configuration and consider more factors to ensure reliability and high availability, but this example can let us understand the basic implementation method.

　　Let's say we have 3 servers with IP addresses 192.168.1.101, 192.168.1.102 and 192.168.1.103.

　　First, we need to install Python's memcached library:

pip install python-memcached

　　Next, we write a Python program to implement the Memcached distributed cluster. We use consistent hashing algorithm to distribute data to different servers.

import memcache

# 定义服务器列表
servers = ['192.168.1.101:11211', '192.168.1.102:11211', '192.168.1.103:11211']

# 创建一个Memcached客户端，并使用一致性哈希算法
client = memcache.Client(servers, hash_fn=hash)

# 存储数据的函数
def store_data(key, value):
    client.set(key, value)

# 获取数据的函数
def get_data(key):
    return client.get(key)

# 删除数据的函数
def delete_data(key):
    client.delete(key)

# 演示使用
if __name__ == "__main__":
    # 存储数据
    store_data('user:1', 'John Doe')
    store_data('user:2', 'Jane Smith')
    store_data('user:3', 'Bob Johnson')

    # 获取数据
    print(get_data('user:1'))
    print(get_data('user:2'))
    print(get_data('user:3'))

    # 删除数据
    delete_data('user:1')
    delete_data('user:2')
    delete_data('user:3')

　　In this example, we distribute the data across different servers through a consistent hashing algorithm. When adding or removing servers, only part of the data needs to be remapped. This is where consistent hashing comes in.

　　It should be noted that the above is just an example, and the actual Memcached cluster requires more configuration and considerations. In a production environment, we need to consider data backup, failover, dynamic addition and deletion of server nodes, etc. Also, ensure network stability and security, as well as proper monitoring and operational measures.