In Azure, you can scale out a Redis Cache by implementing sharding. Sharding involves splitting your data into multiple smaller Redis instances and distributing the data across these instances.
Partitioning: Split your data into multiple Redis instances. There are different ways to do this like range partitioning or hash partitioning. The choice largely depends on your application and its requirements.
Implement client-side code changes: Modify your application code to distribute data across the shards. This usually involves creating a hash function that consistently maps each key to a specific shard.
Azure also offers Redis Cache as a managed service, where many of these scaling concerns are handled automatically. In terms of vertical scaling, Azure allows for choosing from various tiers of Redis Cache, which offer different levels of memory and performance characteristics.
For example, if you need to upgrade your cache to a larger size, you can use the Azure portal, Azure CLI, or Powershell to scale-up your cache. Here's how you could do it with Azure CLI:
This command would upgrade the 'myCache' instance to a 'Premium' tier with a 'P4' size.
Remember, scaling should be implemented carefully to ensure optimal performance while minimizing costs. Always monitor your application's behavior closely when scaling and adjust resources as needed.