In Redis, both sharding and multiple databases are methodologies to segregate and organize data. However, they serve different purposes and use cases.
Sharding in Redis is a method of splitting and storing data across several servers or clusters. This technique is used when the amount of data you have exceeds the storage capacity of a single Redis node. With sharding, you can distribute your data across multiple nodes, which allows for horizontal scaling. Each shard operates independently, enabling simultaneous operations on different shards, leading to better performance. Here's an example with Python's
On the other hand, multiple databases in Redis refer to logical partitions within a single Redis instance. By default, Redis offers 16 numbered databases (indexed from 0 to 15). You can use these separate databases to store different types of data within the same Redis server. However, it should be noted that these databases don't provide any isolation benefits, meaning a command that affects the entire server (like FLUSHALL) will affect all databases.
In conclusion, sharding and multiple databases are two different approaches for data segregation in Redis. The choice between them depends on your specific use case and requirements. Sharding is more about horizontal scaling and performance optimization, while multiple databases can help logically separate different types of data within the same Redis instance.