We are happy to announce that GridGain 4.2 is released!
This release includes several new exciting feature as well as the host of
performance optimizations that we’ve included. This release is 100%
backward compatible with 4.x product line and we recommend anyone on 4.x
version to update as soon as possible.
Now – let’s talk about new features…
In GridGain 4.2 we’ve introduced support for delayed preloading. Dmitriy
Setrakyan wrote an excellent blog detailing this new functionality.
Essentially, whenever a new node joins the grid or an existing node leaves
the grid, cluster repartitioning happens. This basically means that, in case
of new node, it has to take responsibility for some of the data cached on
other nodes, and in case of node leaving the grid, other nodes have to take
responsibility for the data cached on that node. Essentially this res... (more)
Over the last 12 months I’ve accumulated plenty of “conversations”
where we’ve discussed big data analytics and BI strategies with our
customers and potential users. These 5 points below represent some of the key
take-away points about current state of analytics/BI field, why it is by in
large a sore state of affairs and what some of the obvious tell-tale signs of
Beware: some measure of hyperbole is used below to make the points more
This is probably getting obvious for the most of industry insiders but still
worth while to mention. If you have “b... (more)
Few days ago I blogged about how GridGain easily supports starting many
GridGain nodes in the single JVM – which is a huge productivity boost
during the development. I’ve got a lot of requests to show the code – so
here it is (next page).
This is an example that we are shipping with upcoming 4.3 release (entire
import org.gridgain.grid.*; import org.gridgain.grid.spi.discovery.tcp.*;
import org.gridgain.grid.spi.discovery.tcp.ipfinder.*; import
org.gridgain.grid.typedef.*; import javax.swing.*; import
GridGain is Java-based middleware for in-memory processing of big data in a
distributed environment. It is based on high performance in-memory data
platform that integrates fast In-Memory MapReduce implementation with
In-Memory Data Grid technology delivering easy to use and easy to scale
software. Using GridGain you can process terabytes of data, on 1000s of nodes
in under a second.
GridGain typically resides between business, analytics, transactional or BI
applications and long term data storage such as RDBMS, ERP or Hadoop HDFS,
and provides in-memory data platform for high p... (more)
Wikibon produced an interesting material (looks like paid by Aerospike, NoSQL
database recently emerged by resurrecting failed CitrusLeaf and acquihiring
AlchemyDB, which product, of course, was recommended in the end) that
compares NoSQL databases based on storing data in flash-based SSD vs. storing
data in DRAM.
There are number of factual problems with that paper and I want to point them
Note that Wikibon doesn’t mention GridGain in this study (we are not a
NoSQL datastore per-se after all) so I don’t have any bone in this game
other than annoyance with biased and factu... (more)