We currently use Redis as our persistent cache for our web application but with it's limited memory and cost I'm starting to consider whether Table storage is a viable option.
The data we store is fairly basic json data with a clear 2 part key which we'd use for the partition and row key in table storage so I'm hoping that would mean fast querying.
I appreciate one is in memory and one is out so table storage will be a bit slower but as we scale I believe there is only one CPU serving data from a Redis cache whereas with Table storage we wouldn't have that issue as it would be down to the number of web servers we have running.
Does anyone have any experience of using Table storage in this way or comparisons between the 2.
I should add we use Redis in a very minimalist way get/set and nothing more, we evict our own data and failing that leave the eviction to Redis when it runs out of space.
While most databases store data on slower, disk-based storage, Azure Cache for Redis stores data in memory. Since memory is significantly faster than disk storage, data can be written and retrieved much faster.
Using an in-memory cache, like Azure Cache for Redis, to associate information with a user is much faster than interacting with a full relational database. Applications often add tasks to a queue when the operations associated with the request take time to execute.
1 Answer. Show activity on this post. Azure Redis Cache is a Managed Azure service, which creates and manages the Redis instance(s) (updates, automatic failover etc.) on behalf of the customer and provides the customer with TCP endpoint(s) to communicate with.
The arrangement of data across partitions affects query performance. Retrieving a records by their primary key is always very fast but Azure Tables resorts to table scans to find any data that is not in the same partition. Each scanned row counts towards that 20,000 operations per second limit.
This is a fairly broad/opinion-soliciting question. But from an objective perspective, these are the attributes you'll want to consider when deciding which to use:
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