I’m at that critical point in the lifecycle of my consumer mobile and web
software projects and ideas: I need to build something in order to get
funding, but I need funding in order to build something.
Ugh. I’ve gotten as far as I can in my own projects without developers or
investors. This is a common problem, particularly for founders who aren’t
able to write their own code, so I don’t feel too depressed right now. My
ideas are solid. And I have 15+ years of experience in and around this space
– enough to know the pitfalls and issues to avoid, and which questions to
ask myself as early as possible. I know once I get funding, I’ll put it to
good use. I’m confident I’ll be one of the 5% that actually survive and
succeed – finding some sliver of sunshine through this meat grinder of a
gut wrenchingly terrible infliction, often referred to as
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 ... (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)
I’m pleased to announce that today we released GridGain 4.0 – latest
edition of our platform for Real Time Big Data processing. I’m proud that
our team set this final deadline almost 5 months ago and we were able to hit
without a single delay.
I’m especially proud of this fact because of the enormous complexity of the
development process involved in making software like GridGain – dozens of
production clients, testing on serious massively distributed environments,
set of new features, and the usual array of setbacks that we had to go
through to get here.
Needless to say that w... (more)