When your application's data grows beyond the capacity of a single Redis instance, or when you need to distribute data across multiple instances to increase throughput, you may want to consider scaling out.
Scaling out in Redis can be achieved through two primary methods:
Here is a basic python example of sharding:
This code creates connections to two Redis instances (on ports 6379 and 6380). The
get_redis_server() function uses simple consistent hashing to pick which server should store a particular key.
Here is an example of how you can set up a Redis slave:
First, configure the Redis master instance. Edit the redis configuration file.
Then, configure the Redis slave instance. Edit the redis configuration file like so:
After this setup, all data written to the master Redis will also be written to the slave.
Remember, Sharding increases your write throughput and storage capacity by spreading data across nodes. Replication, on the other hand, improves read throughput and provides high availability.
The choice between these or a combination depends on the characteristics of your specific workload, your tolerance for complexity, and your needs in terms of data consistency.