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Gigaspaces: All content tagged as Gigaspaces in NoSQL databases and polyglot persistence

Main Features of In-Memory Data Grids

Good article about In-Memory Data Grids on Cubrid’s blog by Ki Sun Song.

The features of IMDG can be summarized as follows:

  • Data is distributed and stored in multiple servers.
  • Each server operates in the active mode.
  • A data model is usually object-oriented (serialized) and non-relational.
  • According to the necessity, you often need to add or reduce servers.

Even if you don’t read it all, but plan to use an IMDG solution, the first two questions you want to ask your vendor are: what the approach you are proposing to deal with the limited memory capacity and what’s the strategy for reliability. You’ll get good answers from well established products, but these answers are not necessarily the ones that provide the exact requirements your solution will need.

Original title and link: Main Features of In-Memory Data Grids (NoSQL database©myNoSQL)

via: http://www.cubrid.org/blog/dev-platform/introduction-to-in-memory-data-grid-main-features/


Spring Pet Clinic Goes Grails and Sharded on MongoDB With Cloudify

If you’re a Java programmer you must have heard of the Spring sample app Pet Clinic. To showcase Cloudify, Gigaspaces guys migrated the Pet Clinic to Grails and used MongoDB to shard it:

Grails and MongoDB Pet Clinic

Original title and link: Spring Pet Clinic Goes Grails and Sharded on MongoDB With Cloudify (NoSQL database©myNoSQL)

via: http://www.cloudifysource.org/2012/03/25/petclinic_deepdive.html


Dealing With JVM Limitations in Apache Cassandra

A couple of most notable NoSQL databases targeting large scalable systems are written in Java: Cassandra, HBase, BigCouch. Then there’s also Hadoop. Plus a series of caching and data grid solutions like Terracotta, Gigaspaces. They are all facing the same challenge: tuning the JVM garbage collector for predictable latency and throughput.

Jonathan Ellis’s slides presented at Fosdem 2012 are covering some of the problems with GC and the way Cassandra tackles them. While this is one of those presentations where the slides are not enough to understand the full picture, going through them will still give you a couple of good hints.

For those saying that Java and the JVM are not the platform for writing large concurrent systems, here’s the quote Ellis is finishing his slides with:

Cliff Click: Many concurrent algorithms are very easy to write with a GC and totally hard (to down right impossible) using explicit free.

Enjoy the slides after the break.


Cassandra and MongoDB with Gigaspaces Cloudify

There are two reasons I’m writing about Gigaspaces’s Cloudify (PR announcement):

  1. Besides MySQL, Cloudify recipes include Cassandra and MongoDB.

    Also a bit of vintage claim chowder: if you remember Mike Gaultieri’s (Forrester) NoSQL wants to be elastic caching when it grows up, this should be a clear proof he was wrong.

  2. Gigaspaces is starting to realize that it’s not really necessary to claim a NoSQL affiliation for benefitting of the NoSQL buzz. Clear market positioning and smartly showcasing it is much more useful for the potential customers. The other company showing it learned this lesson is Terracotta1.


  1. I’m probably biased on this as I was responsible for talking to Terracotta folks about this better route. 

Original title and link: Cassandra and MongoDB with Gigaspaces Cloudify (NoSQL database©myNoSQL)