Redis versus Cassandra(Bigtable data model)

Question

Suppose I need to do the following operations intensively:
put(key, value)
where value is a map of <column name, column value>.
I havn’t known NoSQL for long, what I know is that both Cassandra insert(which conform the api defined in Bigtable paper) and Redis “HSET” command could do that. But what’s the pros and cons of both way? Any performance and scalability difference there?

EDIT :

My requirement is something like an IM server --- I need to store session data , and I want all of them to be in memory so that low latency can be easily achieved. The session last for at most 2 hours. No consistency requirement to consider yet. And disk is only for fail-over. Lost of data is not terrible. All i need is lower latency. Operations per second --- the more, the better.

Answer 1

Both redis and cassandra can be used as a key value store. The difference is in speed, scale and reliability.

Redis works best as a single server, where the entire data set resides in memory.

Cassandra can handle data sets that don't fit in memory, and data sets that don't fit on a single machine. As part of distributing over multiple machines, cassandra is much more reliable. Cassandra can handle machine failures, rebuilding machines, adding capacity to the cluster when needed.

Because redis is entirely in memory, and reads/writes are served by a single machine (a single cassandra write will typically talk to multiple machines), redis will most likely be faster.

If your primary goal is speed, and you don't need to store data reliably, and your data set fits in memory, then redis would probably be a better solution.