Redis Cluster Sharding Methods Explained

In a Redis cluster, data is typically sharded in the following ways:

1. Hash slicing: Hash calculation is performed based on the key value of the data, and then the data is distributed to different nodes. This slicing method ensures that the data is evenly distributed between nodes, but may result in some nodes becoming overloaded.
2. Range slicing: slicing the data according to a certain range, for example, slicing the data based on the key value range. This slicing method ensures that adjacent data is stored on adjacent nodes, but it may lead to uneven node loads.
3. Slice according to data type: slice based on the type of data, such as storing string data in one node and list data in another node. This slicing method can be optimized according to the access pattern of data, improving access efficiency.
4. Consistent hashing slicing: Distributing data to different nodes using the consistent hashing algorithm. This slicing method ensures minimal data migration when adding or removing nodes, but it may result in uneven data distribution.

Different slicing methods are suitable for different scenarios, so choosing the appropriate slicing method according to actual needs is important.