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Today GridGain™ Systems ( GridGain.com ), provider of the leading open source In-Memory Computing (IMC) Platform, announced that Konstantin Boudnik has joined its Advisory Board. Boudnik brings 20 years of expertise in enterprise IT infrastructure management and development, and is a recognized thought leader in the open source community through his role as Vice President at the Apache Software Foundation .

“As data grows even bigger and the performance expectation becomes more demanding, the notion of scalability for technology providers is no longer a question of ‘if’ but ‘when,’” said Boudnik. “GridGain is opening the door for unprecedented innovation by removing the constraints in computing that limit its ability to evolve and keep pace with business demands.”

GridGain’s In-Memory Computing Platform is the most accessible and comprehensive of its kind, drastically accelerating computing speed and data processing scale beyond that of traditional disk-based infrastructures. In March, GridGain released its end-to-end stack under the Apache 2.0 open source license, making these performance enhancements widely available to developers of both small and large projects, whether for evaluation or full production.

“Konstantin Boudnik adds to GridGain’s leadership and vision for the future of computing, particularly as it applies to Big Data and its accelerating demand for increased computing performance and scale,” said Abe Kleinfeld, GridGain’s CEO.

Konstantin’s fluency across multiple programming languages, operating systems, and databases showcase his prowess in enterprise technology. In addition to his role as Vice President at Apache Software Foundation, Konstantin is also a director of advanced technologies at WANdisco. He was one of the original developers of Hadoop and co-founder of Apache Bigtop, the open source project that focuses on building community around creation of software stacks of Hadoop-related projects. Prior to these roles, Boudnik amassed twenty years of engineering and systems architecture experience at leading organizations like Sun Microsystems, Yahoo!, Cloudera and Karmasphere.

Boudnik holds a Master degree in Mathematics and PhD in Computer Science from Saint-Petersburg University in Russia. He has also published and maintains United States patents for several distributed systems, computer farms, and software

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:

try (Grid grid = GridGain.start()) {
    grid.compute().broadcast((GridRunnable)() -> 
        System.out.println("Hello World")).get();
}

Geospatial Indexes

GridGain allows to easily query in-memory data in SQL using in-memory indexes. Now you can extend SQL to geospatial queries. For example, query below will find all points on the map within a certain square region:

Polygon square = factory.createPolygon(new Coordinate[] {
    new Coordinate(0, 0),
    new Coordinate(0, 100),
    new Coordinate(100, 100),
    new Coordinate(100, 0),
    new Coordinate(0, 0)
});

cache.queries().
    createSqlQuery(MapPoint.class, "select * from MapPoint where location && ?").
        queryArguments(square).
        execute().get();

Near Cache in Atomic Mode

Prior to 6.1.0 GridGain supported near cache only in transactional mode. Starting with 6.1.0 near cache support was added to atomic mode as well.

Near cache allows for client-side caching (vs traditional server side caching) and renders significant performance improvements in some cases.

Fair Affinity Functions

Many know that Consistent Hashing provides a consistent distribution of data within a cluster that is resilient to server failures, but not many know that consistent hashing is not very fair. The discrepancies in distribution can be up to 20% which means that some servers will end up with 20% more data than others. This may create uneven load distribution when running cluster-enabled computations or queries.

GridGain 6.1 added two more affinity functions in addition to consistent hashing: Rendezvous and Fair.

Rendezvous affinity function works faster than consistent hashing and for smaller topologies (under 10 servers) provides a pretty fair distribution. One of the nice features here is that cache key affinity survives full cluster restarts. This means that you can back up data to disk and then reload it on restart knowing that all keys are still mapped to the same node.

Fair affinity function provides absolutely fair cache key distribution with all grid nodes holding absolutely equal amount of keys at all times. However, fair affinity function may change key-to-node assignment upon full cluster restarts.

Other Enhancements

Other fixes and enhancements involve improvements to multicast protocol for discovery and significant performance improvements for distributed cache queues.

You can download GridGain 6.1 here.

GridGain™ Systems (Gridgain.com), provider of the leading open source In-Memory Computing (IMC) Platform , today announced that Gartner has recognized it in its “Cool Vendors in In-Memory Computing Technologies, 2014” report .

“We believe that the increased attention given to In-Memory-Computing signifies the growing role that IMC is playing across all industries,” said Abe Kleinfeld, CEO, GridGain. “Companies today need to consider an in-memory computing architecture to address the hyper-scale demands of Big Data, Internet of Things (IoT) and Cloud Computing.”

Get the Gartner Report     |     View Press Release

GridGain Systems (GridGain.com), provider of the leading open source In-Memory Computing Platform, today announced the appointment of Max Herrmann as Executive Vice President of Marketing. Herrmann comes to GridGain from Microsoft’s cloud and enterprise marketing team and will lend his expertise to growing awareness and adoption of in-memory computing across the enterprise, cloud computing providers and the developer community.

“GridGain is poised to deliver the next generation of computing infrastructure that addresses the unprecedented demand for performance and scalability driven by big data, cloud, mobility and social networks – and as all of these technologies become more entwined with the Internet of Things,” said Herrmann. “I’m thrilled to be on the cutting-edge of this movement, which I believe is set to change computing as we know it today”.

Last month, GridGain released its In-Memory Computing Platform to open source through an Apache 2.0 license, making it the most comprehensive and accessible solution of its kind. The company has gained tremendous momentum within the last year, securing $10 million in series B funding in July, and appointing Abe Kleinfeld as its CEO in December.

“We are foremost impressed by Max’s ability to develop, educate and grow markets for emerging technologies,” said GridGain’s CEO, Abe Kleinfeld. “Through his expertise, we are confident that GridGain will accelerate adoption of in-memory computing and speed innovation across all sectors of business”.

Herrmann spent several years at Microsoft where he guided product marketing efforts for Windows Server datacenter and cloud infrastructure software. Prior to Microsoft, Herrmann was the Vice President of Marketing at Calista Technologies, where he created the company’s product and go-to-market strategy through their eventual acquisition by Microsoft. In addition, he provided marketing leadership to New Moon Systems through two acquisitions by Tarantella and Sun Microsystems in 2005, where he drove product management and product marketing for multiple desktop virtualization offerings.

Herrmann holds a master’s degree in Aerospace Engineering from the University of Stuttgart, and an MBA from the Technical University of Munich.

After five days (and eleven meetings) with new customers in Europe, Russia, and the Middle East, I think the time is right for another refinement of in-memory computing’s definition. To me, it is clear that our industry is lagging when it comes to explaining in-memory computing to potential customers and defining what in-memory computing is really about. We struggle to come up with a simple, understandable definition of what in-memory computing is all about, what problems it solves, and what uses are a good fit for the technology.

In-Memory Computing: What Is It?

In-memory computing means using a type of middleware software that allows one to store data in RAM, across a cluster of computers, and process it in parallel. Consider operational datasets typically stored in a centralized database which you can now store in “connected” RAM across multiple computers. RAM, roughly, is 5,000 times faster than traditional spinning disk. Add to the mix native support for parallel processing, and things get very fast. Really, really, fast.

RAM storage and parallel distributed processing are two fundamental pillars of in-memory computing.

RAM storage and parallel distributed processing are two fundamental pillars of in-memory computing. While in-memory data storage is expected of in-memory technology, the parallelization and distribution of data processing, which is an integral part of in-memory computing, calls for an explanation.

Parallel distributed processing capabilities of in-memory computing are… a technical necessity. Consider this: a single modern computer can hardly have enough RAM to hold a significant dataset. In fact, a typical x86 server today (mid-2014) would have somewhere between 32GB to 256GB of RAM. Although this could be a significant amount of memory for a single computer, that’s not enough to store many of today’s operational datasets that easily measure in terabytes.

To overcome this problem in-memory computing software is designed from the ground up to store data in a distributed fashion, where the entire dataset is divided into individual computers’ memory, each storing only a portion of the overall dataset. Once data is partitioned – parallel distributed processing becomes a technical necessity simply because data is stored this way.

Developing technology that enables in-memory computing and parallel processing is highly challenging and is the reason there are literally less than 10 companies in the world that have mastered the ability to produce commercially available in-memory computing middleware. But for end users of in-memory computing, they are now able to enjoy dramatic performance benefits from this “technical necessity”.

In-Memory Computing: What Is It Good For?

Let’s get this out of the way first: if one wants a 2-3x performance or scalability improvements – flash storage (SSD, Flash on PCI-E, Memory Channel Storage, etc.) can do the job. It is relatively cheap and can provide that kind of modest performance boost.

To see, however, what a difference in-memory computing can make, consider this real-live example…

Last year GridGain won an open tender for one of the largest banks in the world. The tender was for a risk analytics system to provide real-time analysis of risk for the bank’s trading desk (common use case for in-memory computing in the financial industry). In this tender GridGain software demonstrated one billion (!) business transactions per second on 10 commodity servers with the total of 1TB of RAM. The total cost of these 10 commodity servers? Less than $25K.

Now, read the previous paragraph again: one billion financial transactions per second on $25K worth of hardware. That is the in-memory computing difference — not just 2-3x times faster; more than 100x faster than theoretically possible even with the most expensive flash-based storage available on today’s market (forget about spinning disks). And 1TB of flash-based storage alone would cost 10x of entire hardware setup mentioned.

Importantly, that performance translates directly into the clear business value:

  • you can use less hardware to support the required performance and throughput SLAs, get better data center consolidation, and significantly reduce capital costs, as well as operational and infrastructure overhead, and
  • you can also significantly extend the lifetime of your existing hardware and software by getting increased performance and improve its ROI by using what you already have longer and making it go faster.

And that’s what makes in-memory computing such a hot topic these days: the demand to process ever growing datasets in real-time can now be fulfilled with the extraordinary performance and scale of in-memory computing, with economics so compelling that the business case becomes clear and obvious.

In-Memory Computing: What Are The Best Use Cases?

I can only speak for GridGain here but our user base is big enough to be statistically significant. GridGain has production customers in a wide variety of industries:

  • Investment banking
  • Insurance claim processing & modeling
  • Real-time ad platforms
  • Real-time sentiment analysis
  • Merchant platform for online games
  • Hyper-local advertising
  • Geospatial/GIS processing
  • Medical imaging processing
  • Natural language processing & cognitive computing
  • Real-time machine learning
  • Complex event processing of streaming sensor data

And we’re also seeing our solutions deployed for more mundane use cases, like speeding the response time of a student registration system from 45 seconds to under a half-second.

By looking at this list it becomes pretty obvious that the best use cases are defined not by specific industry but by the underlying technical need, i.e. the need to get the ultimate best and uncompromised performance and scalability for a given task.

In many of these real-life deployments in-memory computing was an enabling technology, the technology that made these particular systems possible to consider and ultimately possible to implement.

The bottom line is that in-memory computing is beginning to unleash a wave of innovation that’s not built on Big Data per se, but on Big Ideas, ideas that are suddenly attainable. It’s blowing up the costly economics of traditional computing that frankly can’t keep up with either the growth of information or the scale of demand.

As the Internet expands from connecting people to connecting things, devices like refrigerators, thermostats, light bulbs, jet engines and even heart rate monitors are producing streams of information that will not just inform us, but also protect us, make us healthier and help us live richer lives. We’ll begin to enjoy conveniences and experiences that only existed in science fiction novels. The technology to support this transformation exists today – and it’s called in-memory computing.

Gordon E. Moore’s famously predicted tech explosion was prophetic, but it may have hit a snag. While the number of transistors on integrated circuits has doubled approximately every two years since his 1965 paper, the ability to process and transact on data hasn’t. We’re now ingesting data faster than we can make sense of it, leaving computing at an impasse. Without a new approach, the innovation promised by the combination of big data and internet scale may be like the flying cars we thought we’d see by 2014. Fortunately, this is is not the case, as in-memory computing offers a way to bridge this impasse.

Keeping up with Moore’s law requires computing orders of magnitude faster than allowed by traditional methods, and at a reasonable cost. In-memory computing achieves just this. It’s already well-established that in-memory computing is much, much faster and scalable than traditional methods. Furthermore, the dropping cost of memory has made it economical.

Despite this, there’s a lingering misperception that in-memory computing resides in the realm of supercomputers. Most people don’t realize just how fast and affordable it really is. To offer some perspective, GridGain recently demonstrated one billion transactions per second using our In-Memory Data Grid on just $25K worth of commodity hardware. In short, it’s now economical for organizations of all sizes.

Opening the doors to mass adoption through open source in-memory technology

In-memory computing is definitely entering the mainstream (http://www.gartner.com/newsroom), however, achieving mass innovation with any technology requires mass adoption. One of the best ways to accomplish this is by offering technology through an open source license, enabling users to begin to work with it without necessarily committing to it. This allows developers the flexibility to use the technology in new and interesting ways, and to address very specific challenges.

With GridGain offering a complete In-Memory Computing Platform through an Apache 2.0 license, all the barriers to adoption are removed. The high performance computing capabilities of in-memory technology are now fully part of the public domain, meaning that developers have full freedom to experiment with it, test its capabilities and try out new ideas.

Unifying the cloud, big data and real-time analytics to accelerate innovation

Now that developers have access to computing power commensurate with their creativity it’ll be exciting to see what they come up with. While we can’t predict the future, one thing is for certain — the new level of computing power afforded by in-memory technology will enable developers to create a new class of applications that combine the cloud, Big Data and real-time analytics. Once you can do that, the genie is out of the bottle.

World’s fastest, most scalable In-Memory Computing Platform now available under Apache 2.0 license

FOSTER CITY, Calif., March 3, 2014 /PRNewswire/ — Today GridGain (www.gridgain.org) officially released its industry leading In-Memory Computing Platform through an Apache 2.0 open source license, offering the world access to its technology for a broad range of real-time data processing applications. GridGain’s open source software provides immediate, unhindered freedom to develop with the most mature, complete and tested in-memory computing platform on the market, enabling computation and transactions orders of magnitude faster than traditional technologies allow.

“The promised advances of big data combined with internet scale simply can’t happen without a transformative change in computing performance,” said Abe Kleinfeld, CEO of GridGain. “In-memory computing is enabling this change by offering a major leap forward in computing power, opening doors to a new era of innovation.”

In-memory computing on commodity hardware is no longer a dream. The falling cost of RAM has made in-memory computing broadly accessible to organizations of all sizes. In a recent customer engagement, GridGain demonstrated one billion financial transactions per second using its In-Memory Data Grid software on just $25,000 of commodity hardware. According to GridGain, a performance increase of this magnitude will allow organizations to achieve goals they would not have previously considered pursuing.

“Organizations that do not consider adopting in-memory application infrastructure technologies risk being out-innovated by competitors that are early mainstream users of these capabilities,” said Massimo Pezzini, Gartner Press Release, Gartner Says In-Memory Computing Is Racing Towards Mainstream Adoption, April 3, 2013, http://www.gartner.com/newsroom/id/2405315.

GridGain’s In-Memory Computing Platform is used in a broad range of applications around the world including:

  • Financial trading systems
  • Online gaming
  • Bioinformatics
  • Hyperlocal advertising
  • Cognitive computing
  • Geospatial analysis

Developers can download GridGain’s In-Memory Computing Platform at www.gridgain.org.

About GridGain™

GridGain’s complete In-Memory Computing Platform enables organizations to conquer challenges that traditional technology can’t approach. While most organizations now ingest infinitely more data than they can possibly make sense of, GridGain’s customers leverage a new level of real-time computing power that allows them to easily innovate ahead of the accelerating pace of business. Built from the ground up, GridGain’s product line delivers all the high performance benefits of in-memory computing in a simple, intuitive package. From high performance computing, streaming and data grid to an industry first in-memory Hadoop accelerator, GridGain provides a complete end-to-end stack for low-latency, high performance computing for each and every category of payload and data processing requirements. Fortune 500 companies, top government agencies and innovative mobile and web companies use GridGain to achieve unprecedented performance and business insight. GridGain is headquartered in Foster City, California. Learn more at http://www.gridgain.com.

If you are attending the Big Data Innovation Summit in Las Vegas on January 22-23, be sure to stop by our booth (we’re in BOOTH #1–you can’t miss us!) to meet the GridGain team and learn about how we can accelerate your Big Data projects.  Whether your project involves a Data Grid, Hadoop, or virtually any other  structured or unstructured data source, our In-Memory Computing platform can help you achieve the scalability and real-time processing you need.

While you’re at our booth, be sure to enter our drawing for a really cool Jawbone Mini Jambox wireless speaker.  We’ll be giving out one each day, so you’ve got a great chance of winning one of the hottest tech gadgets on the market!

We’ll also be exhibiting at the Hadoop Innovation Summit in San Diego on February 19-20. So mark your calendars for that, too.  It’s not too late to sign up if you haven’t yet.

And also look for GridGain at a Meetup near you, as we are currently setting dates to present talks on Data Grids, Hadoop acceleration and In-Memory computing in various Meetups across the country.

Meanwhile, feel free to contact us any time at info@gridgain.com if you’d like to discuss how our suite of In-Memory Computing solutions can work for you.

Former nCircle CEO Brings Over 30 Years of Tech Leadership
to In-Memory Computing
Pioneer

FOSTER CITY, California – December 06, 2013 – GridGainTM Systems (Gridgain.com), the company that provides a complete In-Memory Computing infrastructure stack, today announced the appointment of Abe Kleinfeld as Chief Executive Officer.

Kleinfeld comes to GridGain from nCircle, a company which he led to achieve $40M in annual sales. Under Kleinfeld’s guidance, GridGain plans to make the benefits of In-Memory computing available to all organizations, enabling them to implement strategy based on data that would otherwise be incomprehensible due its volume and velocity.

“Now more than ever, organizations must harness both structured and unstructured data and deliver actionable business intelligence – this can only be accomplished with In-Memory technology,” said Kleinfeld. “I’m excited to lead GridGain in its journey to help companies innovate ahead of the business cycle and conquer challenges far beyond the capabilities of traditional computing technology.”

GridGain, which secured $10M in Series B venture financing, provides the first and only end-to-end In-Memory technology stack. Its enterprise offering includes high performance computing, streaming, and In-Memory Database, as well as an industry first In-Memory accelerator for Hadoop.

“Over the course of more than three decades Abe Kleinfeld has successfully guided companies from the point of startup all the way through their IPOs’,”

Co-founder of GridGain. “We believe Abe’s experience will enable GridGain to advance said Nikita Ivanov, its position as a market leader in high performance computing for the enterprise.”

Abe Kleinfeld led nCircle, a company with over 6,500 enterprise customers globally, through 10 consecutive years of revenue growth to achieve five successive years of profitability and two acquisitions. Prior to nCircle, Abe Kleinfeld was president and CEO of Eloquent, a leading provider of rich media communications solutions, where he built a sophisticated management team and implemented a high-growth strategy that drove the company through a successful $83M IPO in February 2000. Before Eloquent, Kleinfeld co-founded document management leader Odesta Systems Corporation, where he played a key role in building the company from startup in 1991 through its merger with Open Text Corporation in 1995 and subsequent IPO in 1996.

Kleinfeld’s four-decade career began at Raytheon Data Systems, and includes marketing and sales management roles at Wang Laboratories and business development at Oracle Corporation. He holds a bachelor’s in Computer Science from State University of NY at Oswego.

From Cache To In-Memory Data Grids

Posted by on Tuesday, November 19, 2013
 For Your Information

I would like to clarify definitions for the following technologies:

  • In-Memory Distributed Cache
  • In-Memory Data Grid

These terms are, surprisingly, often used interchangeably and yet technically and historically they represent very different products and serve different, sometimes very different, use cases.

It’s also important to note that there’s no specifications or industry standards on what cache, or data grid should be (unlike java application servers and JEE, for example). There was and still is an attempt to standardize caching via JSR107 and JSR 347 but it has been years (almost a decade for JSR 107) in the making and they are both hopelessly outdated by now (I’m on the expert group for JSR 107).

Tricycle vs. Motorcycle

First of all, let me clarify that I am discussing caches and data grids in the context of in-memory, distributed architectures. Traditional disk-based databases and non-distributed in-memory caches or databases are out of scope for this article.

cache_grid_platform

Chronologically, caches and data grids were developed in that order from simple caching to more complex data grids. The first distributed caches appeared in the late 1990s, and data grids emerged around 2003-2005.

Both of these technologies are enjoying a significant boost in interest in the last couple years thanks to explosive growth in-memory computing in general fueled by 30% YoY price reduction for DRAM and cheaper Flash storage.

Despite the fact that I believe that distributed caching is rapidly going away, I still think it’s important to place it in its proper historical and technical context along with data grids and databases.

In-Memory Distributed Caching

The primary use case for caching is to keep frequently accessed data in process memory to avoid constantly fetching this data from disk, which leads to the High Availability (HA) of that data to the application running in that process space (hence, “in-memory” caching).

Most of the caches were built as distributed in-memory key/value stores that supported a simple set of ‘put’ and ‘get’ operations and optionally some sort of read-through and write-through behavior for writing and reading values to and from underlying disk-based storage such as an RDBMS. Depending on the product, additional features like ACID transactions, eviction policies, replication vs. partitioning, active backups, etc. also became available as the products matured.

These fundamental data management capabilities of distributed caches formed the foundation for the technologies that came later and were built on top of them such as In-Memory Data Grids.

In-Memory Data Grid

The feature of data grids that distinguishes them from distributed caches the most was their ability to support co-location of computations with data in a distributed context and consequently provided the ability to move computation to data. This capability was the key innovation that addressed the demands of rapidly growing data sets that made moving data to the application layer increasing impractical. Most of the data grids provide some basic capabilities to move the computations to the data.

Another uniquely new characteristic of in-memory data grids is the addition of distributed MPP processing based on standard SQL and/or MapReduce, that allows to effectively compute over data stored in-memory across the cluster.

Just as distributed caches were developed in response to a growing need for data HA, in-memory data grids were developed to respond to the growing complexities of data processing. It was no longer enough to have simple key-based access. Distributed SQL, complex indexing and MapReduce-based processing across TBs of data in-memory are necessary tools for today’s demanding data processing.

Adding distributed SQL and/or MapReduce type processing required a complete re-thinking of distributed caches, as focus has shifted from pure data management to hybrid data and compute management.

This new and very disruptive capability of in-memory data grids also marked the start of the in-memory computing revolution.

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