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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)

Big Data Analytics and BI Strategies: Five Words To Avoid

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 the decay. Beware: some measure of hyperbole is used below to make the points more contrast… “Batch” This is probably getting obvious for the most of industry insiders but still worth while to mention. If you have “b... (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)

Streaming MapReduce with GridGain & Scala

GridGain will present at Twin Cities Java JUG about “Streaming MapReduce with GridGain and Scala”. As always, JUG is hosted by Intertech. Live coding, good introduction into in-memory computing and data processing and plenty of… Scala. Come and stop by for good pizza too! All information is on JUG’s website. ... (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)