Azure Redis cache latency

Question

I am working on an application having web job and azure function app. Web job generates the redis cache for function app to consume. Cache size is around 10 Mega Bytes. I am using lazy loading and all as per the recommendation. I still find that the overall cache operation is slow. Depending upon the size of the file i am processing, i may end up calling Redis cache upto 100,000 times . Wondering if I need to hold the cache data in a local variabke instead of reading it every time from redis. Has anyone experienced any latency in accessing Redis? Does it makes sense to create a singletone object in c# function app and refresh it based on some timer or other logic?

Answer 1

could you consider this points in your usage this is some good practices of azure redis cashe

• Redis works best with smaller values, so consider chopping up bigger data into multiple keys. In this Redis discussion, 100kb is considered "large". Read this article for an example problem that can be caused by large values.

• Use Standard or Premium Tier for Production systems. The Basic Tier is a single node system with no data replication and no SLA. Also, use at least a C1 cache. C0 caches are really meant for simple dev/test scenarios since they have a shared CPU core, very little memory, are prone to "noisy neighbor", etc.

• Remember that Redis is an In-Memory data store. so that you are aware of scenarios where data loss can occur.

• Reuse connections - Creating new connections is expensive and increases latency, so reuse connections as much as possible. If you choose to create new connections, make sure to close the old connections before you release them (even in managed memory languages like .NET or Java).

• Locate your cache instance and your application in the same region. Connecting to a cache in a different region can significantly increase latency and reduce reliability. Connecting from outside of Azure is supported, but not recommended especially when using Redis as a cache (as opposed to a key/value store where latency may not be the primary concern).

• Redis works best with smaller values, so consider chopping up bigger data into multiple keys.

• Configure your maxmemory-reserved setting to improve system responsiveness under memory pressure conditions, especially for write-heavy workloads or if you are storing larger values (100KB or more) in Redis. I would recommend starting with 10% of the size of your cache, then increase if you have write-heavy loads. See some considerations when selecting a value.

• Avoid Expensive Commands - Some redis operations, like the "KEYS" command, are VERY expensive and should be avoided.

• Configure your client library to use a "connect timeout" of at least 10 to 15 seconds, giving the system time to connect even under higher CPU conditions. If your client or server tend to be under high load, use an even larger value. If you use a large number of connections in a single application, consider adding some type of staggered reconnect logic to prevent a flood of connections hitting the server at the same time.