A key-value store is a very simple concept, much like a hash table: you can retrieve an item based on its key, you can insert a key/value pair, and you can delete a key/value pair. The value can just be an opaque list of bytes, or might be a structured document (most of the document databases and BigTable implementations above can also be considered to be key-value stores).

Document databases, graph databases and MapReduce introduce new data models and new ways of thinking which can be useful even in a small-scale application; you don’t need to be Google or Facebook to benefit from them. Distributed key-value stores, on the other hand, are really just about scalability. They can scale to truly vast amounts of data — much more than a single server could hold.

Distributed databases can transparently partition and replicate your data across many machines in a cluster. You don’t need to figure out a sharding scheme to decide on which server you can find a particular piece of data; the database can locate it for you. If one server dies, no problem — others can immediately take over. If you need more resources, just add servers to the cluster, and the database will automatically give them a share of the load and the data.

When choosing a key-value store you need to decide whether it should be opimised for low latency (for lightning-fast data access during your request-response cycle) or for high throughput (which is what you need for batch processing jobs).

Other than the BigTables and document databases above, Scalaris, Dynomite and Ringo provide certain data consistency guarantees while taking care of partitioning and distributing the dataset. MemcacheDB and Tokyo Cabinet (with Tokyo Tyrant for network service and LightCloud to make it distributed) focus on latency.