Implementing a Distributed Least Recently Used (LRU) Cache involves managing data across multiple nodes to ensure scalability and high availability. It is based on the LRU caching scheme, which evicts the least recently used items first.
Here's a basic outline of how you might do it:
1. Select an appropriate distributed cache system
The first step is to choose a distributed cache system that supports LRU eviction policy, like Redis or Memcached.
For example, let's take Redis. You need to enable the
allkeys-lru in your redis config file (
redis.conf). Here's how you can achieve this:
2. Implement cache sharding
Cache sharding is essential for maintaining uniform load distribution across multiple cache instances. The most common way to implement sharding is using consistent hashing, which minimizes reorganization when nodes are added or removed.
To hash your keys, you can use SHA1 or MD5 hash functions, then map these hashed keys to your Redis nodes.
3. Set up data replication
To prevent data loss and improve read performance, set up data replication across the nodes. Redis provides support for primary-replica replication.
In your redis config file (
redis.conf), you can specify a master node like this:
4. Implement client-side logic
Finally, you need to implement logic in your application to interact with your distributed cache. This includes connecting to the right node, handling failed nodes, and executing cache operations (get, set, delete, etc.).
For Python, you can use the redis-py package:
This is a very basic overview. There are many factors to consider when designing a distributed caching system, like error handling, network partitions, security, and more. Be sure to research thoroughly and test extensively.