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Monday, July 7, 2014
Arun Selvaganapathy's invitation is awaiting your response
Tuesday, July 1, 2014
Arun Selvaganapathy's invitation is awaiting your response
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Saturday, June 28, 2014
Find out why I love LinkedIn
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Monday, August 23, 2010
Distributed data grids: Foundation for future cloud computing?
The tantalizing prospect of cloud computing is changing the way people in IT think. Instead of massive and ever growing data centers, it may now be possible to simply tap into potentially unlimited resources residing externally, in the "cloud." Of course, these visions have already begun to take tangible form in cloud services such as Amazon EC2 and Microsoft Azure. However, according to analysts and others, the potential of the cloud -- at least for data-intensive applications -- will be limited without the application of a crucial enabling technology -- distributed data grids.
A distributed data grid, also called a distributed data cache, operates between the database and the in-memory of an application and provides a temporary repository for data, enhancing performance by improving access and eliminating bottlenecks.
Many people associate cloud computing with scale, notes Gualtieri. Certainly the cloud allows you to scale instances of machines -- but you can't easily scale applications and data in the cloud because applications and data haven't been architected to take advantage of the "extra horsepower."
Likewise, if you think of a typical relational database packed with customer or order information, when it comes to the cloud, that database becomes your bottleneck. "If you are getting more and more transactions against that database you can try to speed it up by adding five more servers, but how do you split the data? You can't," says Gualtieri. "So elastic caching is really interesting because it has a huge impact for the cloud -- it is a solution for scaling data," he adds.
Because of its elasticity, nodes can be added in real time; if you start with four servers and add four more, these platforms will rebalance the data fairly evenly across the nodes and if any node goes down you are not down because they replicate the data. "So elastic caching also provides fault tolerance and high availability at a fraction of the cost of what it would take just to re-architect a database," he adds.
According to Gualtieri, the quest to deliver cloud scalability has also spawned a few other variations, notably the NO SQL movement. "On first glance it sounds like an attempt to get rid of SQL but the term actually stands for Not Only SQL," he says. Of course, he notes, traditional relational database are great at transactional integrity; they always provide consistent data.
By contrast, notes Gualtieri, the NO SQL crowd talks about a concept called eventual consistency. For example, when someone does an update on Twitter or Facebook, it isn't absolutely necessary that every user on the internet sees it that second -- as long as it arrives eventually. "It isn't like decrementing $100, you need a relational database for that," says Gualtieri.
For all the data that doesn't need absolute timeliness or consistency, NO SQL can provide that eventual consistency. "You give up some of the transactional integrity but what you get is an inexpensive way to scale a large amount of non-transactional data," he says.
Historically, notes Gualtieri, NO SQL grew out of the attempts by Amazon and EBay to master issues of scale. "What has happened over the years is that these technologies and similar ones have made their way into open source projects," one of which -- Cassandra -- is an open source NO SQL "that is very much like elastic caching in that the data is distributed, spread across multiple nodes, and it is fault tolerant," he explains. However, he adds, in general, NO SQL is not as well defined or developed as elastic caching -- and most NO SQL products are open source.
A distributed data grid, also called a distributed data cache, operates between the database and the in-memory of an application and provides a temporary repository for data, enhancing performance by improving access and eliminating bottlenecks.
Many people associate cloud computing with scale, notes Gualtieri. Certainly the cloud allows you to scale instances of machines -- but you can't easily scale applications and data in the cloud because applications and data haven't been architected to take advantage of the "extra horsepower."
Likewise, if you think of a typical relational database packed with customer or order information, when it comes to the cloud, that database becomes your bottleneck. "If you are getting more and more transactions against that database you can try to speed it up by adding five more servers, but how do you split the data? You can't," says Gualtieri. "So elastic caching is really interesting because it has a huge impact for the cloud -- it is a solution for scaling data," he adds.
Because of its elasticity, nodes can be added in real time; if you start with four servers and add four more, these platforms will rebalance the data fairly evenly across the nodes and if any node goes down you are not down because they replicate the data. "So elastic caching also provides fault tolerance and high availability at a fraction of the cost of what it would take just to re-architect a database," he adds.
According to Gualtieri, the quest to deliver cloud scalability has also spawned a few other variations, notably the NO SQL movement. "On first glance it sounds like an attempt to get rid of SQL but the term actually stands for Not Only SQL," he says. Of course, he notes, traditional relational database are great at transactional integrity; they always provide consistent data.
By contrast, notes Gualtieri, the NO SQL crowd talks about a concept called eventual consistency. For example, when someone does an update on Twitter or Facebook, it isn't absolutely necessary that every user on the internet sees it that second -- as long as it arrives eventually. "It isn't like decrementing $100, you need a relational database for that," says Gualtieri.
For all the data that doesn't need absolute timeliness or consistency, NO SQL can provide that eventual consistency. "You give up some of the transactional integrity but what you get is an inexpensive way to scale a large amount of non-transactional data," he says.
Historically, notes Gualtieri, NO SQL grew out of the attempts by Amazon and EBay to master issues of scale. "What has happened over the years is that these technologies and similar ones have made their way into open source projects," one of which -- Cassandra -- is an open source NO SQL "that is very much like elastic caching in that the data is distributed, spread across multiple nodes, and it is fault tolerant," he explains. However, he adds, in general, NO SQL is not as well defined or developed as elastic caching -- and most NO SQL products are open source.
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