The GridGain Systems In-Memory Computing Blog
In this article I’ll introduce the concept of Streaming MapReduce processing using GridGain and Scala. The choice of Scala is simply due to the fact that it provides for very concise notation and GridGain provides very effective DSL for Scala. Rest assured you can equally follow this post in Java or Groovy just as well.
The concept of streaming processing (and Streaming MapReduce in particular…
In-Memory Computing Pioneer Secures Global Investment and Accelerates Growth
FOSTER CITY, California – July 29, 2013 – GridGain™ Systems today announced a closing of $10 million in Series B venture financing. The round was led by new investor Almaz Capital, a global venture capital firm, with continued participation from previous investor RTP Ventures, the U.S. arm of ru-Net Holdings and one of…
It’s been somewhat quiet here on the GridGain front for a few months, and for good reason!
We just announced closing a $10M Series B investment and bringing an awesome new investor on board. In the last 6 months we not only closed the new round, we also rebuilt and tripled our sales and business development team, retooled our marketing, released new products, and have 3 other products in the…
As any fast growing technology In-Memory Computing has attracted a lot of interest and writing in the last couple of years. It’s bound to happen that some of the information gets stale pretty quickly - while other is simply not very accurate to being with. And thus myths are starting to grow and take hold.
I want to talk about some of the misconceptions that we are hearing almost on a daily…
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…
We have promised a while back to publish the code from live coding GridGain presentation we did at QCon London earlier this year. Since presentation was in Scala, the code we will be posting here is in Scala.First a brief intro. We all know Hadoop's counting words example which takes a file with words and then produces another file with number of occurrences next to each word.…
Today the GridGain team has announced the release of enterprise-grade GridGain In-Memory Data Fabric v. 7.5, based on Apache® Ignite™ v. 1.5. For those not familiar with GridGain or Apache Ignite, it provides the ability to distribute, cache, and compute on data in memory, including such features as in-memory data grid, compute grid, ANSI-99 in-memory SQL, real-time streaming, in-memory file…
In this blog I will describe how a large bank was able to scale a multi-geographical deployment on top of Apache Ignite™ (incubating) In-Memory Data Grid.
Problem Definition
Imagine a bank offering variety of services to its customers. The customers of the bank are located in different geo-zones (regions), and most of the operations performed by a customer are zone-local, like ATM withdrawals or…
We are pleased to announce that GridGain 6.1.0 has been released today. This is the first main upgrade since GridGain 6.0.0 was released in February and contains some cool new functionality and performance improvements:
Support for JDK8
With GridGain 6.1.0 you can execute JDK8 closures and functions in distributed fashion on the grid:
[java]
try (Grid grid = GridGain.start()) {
grid.compute…