Redis: Partitioning
Explore Redis partitioning concepts to understand how splitting data across multiple instances supports horizontal scaling. Learn key techniques like range and hash partitioning, their advantages and challenges, and how to manage data in distributed Redis environments for efficient scaling and reliability.
What is partitioning?
In the previous lesson, we discussed replication. With replication, we make multiple copies of the data, and each server contains the complete data. But if the data is too large and can’t be saved on a single instance? In that case, we may need to split the data and store it in different instances. This is called partitioning the data.
Partitioning the data will not solve the problem of data safety. If a node goes down, the data stored in that node will be lost. So, replication is required in addition to partitioning. Although it will increase the amount of data that needs to be stored, the data will not be lost in the case of a failure.
Advantages of partitioning
- Partitioning helps in horizontal scaling of the data. If the data is spread across different servers, then the traffic can be routed to different servers, and and each server will have less load.
- Sometimes it may