Redis failover and Partitioning?

I am using client side partitioning on a 4 node redis setup. The writes and reads are distributed among the nodes. Redis is used as a persistence layer for volatile data as well as a cache by different parts of application. We also have a cassandra deployment for persisting non-volatile data.

On redis we peak at nearly 1k ops/sec (instantaneous_ops_per_sec). The load is expected to increase with time. There are many operations where we query for a non-existent key to check whether data is present for that key.

I want to achieve following things:

Writes should failover to something when a redis node goes down.
There should be a backup for reading the data lost when the redis node went down.
If we add more redis nodes in the future (or a dead node comes back up), reads and writes should be re-distributed consistently.

I am trying to figure out suitable design to handle the above scenario. I have thought of the following options:

Create hot slaves for the existing nodes and swap them as and when a master goes down. This will not address the third point.
Write a Application layer to persist data in both redis and cassandra allowing a lazy load path for reads when a redis node goes down. This approach will have an overhead of writing to two stores.

Which is a better approach? Is there a suitable alternative to the above approaches?

Solution

A load of 1k ops/s is far below the capabilities of Redis. You would need to increase by up to two or more orders of magnitude before you come close to overloading it. If you aren't expecting to exceed 50-70,000 ops/second and are not exceeding your available single/0-node memory I really wouldn't bother with sharding your data as it is more effort than it is worth.

That said, I wouldn't do sharding for this client-side. I'd look at something like Twemproxy/Nutcracker to do it do you. This provides a path to a Redis Cluster as well as the ability to scale out connections and proved transparent client-side support for failover scenarios.

To handle failover in the client you would want to set up two instances per slot (in your description a write node) with one shaved to the other. Then you would run a Sentinel Constellation to manage the failover.

Then you would need to have your client code connect to sentinel to get the current master connectivity for each slot. This also means client code which can reconnect to the newly promoted master when a failover occurs. If you have load Balancers available you can place your Redis nodes behind one or more (preferably two with failover) and eliminated client reconnection requirements, but you would then need to implement a sentinel script or monitor to update the load balancer configuration on failover.

For the Sentinel Constellation a standard 3 node setup will work fine. If you do your load balancing with software in nodes you control it would be best to have at least two sentinel nodes on the load Balancers to provide natural connectivity tests.

Given your description I would test out running a single master with multiple read slaves, and instead of hashing in client code, distribute reads to slaves and writes to master. This will provide a much simpler setup and likely less complex code on the client side. Scaling read slaves is easier and simpler, and as you describe it the vast majority if ops will be read requests so it fits your described usage pattern precisely.

You would still need to use Sentinel to manage failover, but that complexity will still exist, resulting in a net decrease in code and code complexity. For a single master, sentinel is almost trivial so setup; the caveats being code to either manage a load balancer or Virtual IP or to handle sentinel discovery in the client code.