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Thomas Krafft

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Top Stories by Thomas Krafft

Recently, at one of the customer meetings, I was asked whether GridGain comes with its own database. Naturally my reaction was – why? GridGain easily integrates pretty much with any persistent store you wish, including any RDBMS, NoSql, or HDFS stores. However, then I thought, why not? We already have cache swap space (disk overflow) storage based on Google LevelDB key-value database implementation, so why not have the same for data store. Here is how easy it was to add LevelDB based data store implementation for GridGain cache – literally took me 20 minutes to do, including unit tests. The store is based on GridGain swap space, but since swap space is based on LevelDB, you essentially get LevelDB local store for your cached data. public class GridCacheSwapSpaceStore extends GridCacheStoreAdapter { private ClassLoader dfltLdr = getClass().getClassLoader();... (more)

Are Hadoop's Days Numbered?

Interesting article at GigaOm: http://bit.ly/OINpfr I won’t repeat the main points – but basically it says that since Hadoop is disk/ETL/batch based it won’t fit for real time processing of frequently changing data. Author correctly points out that real time processing (i.e. perceptual real time meaning sub-second to few seconds response time) is becoming a HUGE trend that’s impossible to ignore. He points to Google that moved away from Hadoop MapReduce-like approach towards massively distributed in-memory platform for its various projects like Precolator and Dremel… So, What’s ... (more)

GridGain 4.2 Released!

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… Delayed Preloading 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 th... (more)

Micro Cloud in Your JVM: Code Example

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 source code): import org.gridgain.grid.*; import org.gridgain.grid.spi.discovery.tcp.*; import org.gridgain.grid.spi.discovery.tcp.ipfinder.*; import org.gridgain.grid.spi.discovery.tcp.ipfinder.vm.*; import org.gridgain.grid.typedef.*; import javax.swing.*; import java... (more)

GridGain and Hadoop: Differences and Synergies

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)