Posts Tagged ‘CAP’

Google Storage : What it really is…

Yesterday Google formally announced Google Storage to a few (5000?) of us at Google I/O. Here is the gist of this as I see it from the various discussions/talks I attended. To begin with, I have to point out that there is almost nothing new in what Google has proposed to provide. Amazon has been [...]

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You don’t have to be Google to use NoSQL

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 [...]

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Cassandra as a communication medium – A service Registry and Discovery tool

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 [...]

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SimpleDB now allows you to tweak consistency levels

We discussed Brewer’s Theorm a few days ago and how its challenging to obtain Consistency, Availability and Partition tolerance in any distributed system. We also discussed that many of the distributed datastores allow CAP to be tweaked to attain certain operational goals. Amazon SimpleDB, which was released as an “Eventually Consistent” datastore,  today launched a [...]

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Brewers CAP Theorem on distributed systems

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 [...]

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Cassandra : inverted index

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 [...]

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