Ted Dziuba has a post about “I can’t wait for NoSQL to Die”. The basic argument he makes is that one has to be at the size Google is to really benefit from NoSQL. I think he is missing the point. Here are my observations. This is similar to the argument the traditional DB vendors [...]

MapReduce, Bigtable and Pregel have their origins in Google and they all deal with “large systems”. But all of them may be dwarfed in size and complexity by a new project Google is working on, which was mentioned briefly (may be un-intentionally) at an event last year. Instead of caching data closer to user, it [...]

Few weeks ago while I was mulling over what kind of service registry/discovery system to use for a scalable application deployment platform, I realized that for mid-size organizations with complex set of services, building one from scratch may be the only option. I also found out that many AWS/EC2 customers have already been using S3 [...]

So there is someone who thinks “eventual consistency is just caching”.  Though I liked the idea of discussing this, I don’t agree with Udi’s views on this. “Cache” is generally used to store data which is more expensive to obtain from the primary location. For example, caching mysql queries is ideal for queries which could [...]

Large distributed systems run into a problem which smaller systems don’t usually have to worry about. “Brewers CAP Theorem” [ Ref 1] [ Ref 2] [ Ref 3]  defines this problem in a very simple way. It states, that though its desirable to have Consistency, High-Availability and Partition-tolerance in every system, unfortunately no system can [...]

Cassandra is the only NOSQL datastore I’m aware of, which is scalable, distributed, self replicating, eventually consistent, schema-less key-value store running on java which doesn’t have a single point of failure. HBase could also match most of these requirements, but Cassandra is easier to manage due to its tiny footprint. The one thing Cassandra doesn’t [...]