Using SSD as a Foundation for New Generations of Flash Databases - Nati Shalom

“You just can't have it all” is a phrase that most of us are accustomed to hearing and that many still believe to be true when discussing the speed, scale and cost of processing data. To reach high speed data processing, it is necessary to utilize more memory resources which increases cost. This occurs because price increases as memory, on average, tends to be more expensive than commodity disk drive. The idea of data systems being unable to reliably provide you with both memory and fast access—not to mention at the right cost—has long been debated, though the idea of such limitations was cemented by computer scientist, Eric Brewer, who introduced us to the CAP theorem.

The CAP Theorem and Limitations for Distributed Computer Systems

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PaaS on OpenStack - Run Applications on Any Cloud, Any Time Using Any Thing

Yesterday, I had a session during the OpenStack Summit where I tried to present a more general view on how we should be thinking about PaaS in the context of OpenStack.

The key takeaway :

The main goal of PaaS is to drive productivity into the process by which we can deliver new applications.

Most of the existing PaaS solutions take a fairly extreme approach with their abstraction of the underlying infrastructure and therefore fit a fairly small number of extremely simple applications and thus miss the real promise of PaaS.

Amazon's Elastic Beanstalk took a more bottom up approach giving us better set of tradeoffs between the abstraction and control which makes it more broadly applicable to a larger set of applications.

The fact that OpenStack is opensource allows us to think differently on the things we can do at the platform layer. We can create a tighter integration between the PaaS and IaaS layers and thus come up with better set of tradeoffs into the way we drive productivity without giving up control. Specifically that means that:

• Anyone should be able to:
  • Build their own PaaS in a snap
  • Run on any cloud (public/private)
  • Gain multi-tenancy, elasticity… Without code changes.
• Provide a significantly higher degree of control without adding substantial complexity over our:
  • Language choice
  • Operating System
  • Middleware stack
• Should come pre-integrated with a popular stack:
  • Spring,Tomcat, DevOps, NoSQL, Hadoop...
  • Designed to run the most demanding mission-critical app

You can read the full story and see the demo here

Scaling Social Ecommerce Architecture Case study

A recent study showed that over 92 percent of executives from leading retailers are focusing their marketing efforts on Facebook and subsequent applications. Furthermore, over 71 percent of users have confirmed they are more likely to make a purchase after “liking” a brand they find online. (source)

Sears Architect Tomer Gabel provides an insightful overview on how they built a Social Ecommerce solution for Sears.com that can handle complex relationship quires in real time. Tomer goes through:

• the architectural considerations behind their solution
• why they chose memory over disk
• how they partitioned the data to gain scalability
• why they chose to execute code with the data using GigaSpaces Map/Reduce execution framework
• how they integrated with Facebook
• why they chose GigaSpaces over Coherence and Terracotta for in-memory caching and scale

In this post I tried to summarize the main takeaway from the interview.

You can also watch the full interview (highly recomended).

Read the full story here

Getting ready for the cloud

This presentation illustrates how one can scale EXISTING JEE application and deploy it on Amazon cloud using GigaSpaces as the scale-out application server while: * Not having to re-write your application * Preventing lock-in to specific cloud provider * Enabling seamless portability between your local environment to cloud environment o No code or configuration change is required between the two environments o Develop local - test on the cloud o Built for iterative development

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Risk Analysis on the Cloud (Using Excel and GigaSpaces)

Every day brings news of either more failures of the financial systems or out-right fraud, with the $50 billion Bernard Madoff Ponzi scheme being the latest, breaking all records. This post provide a technical overview of a solution that was implemented for one of the largest banks in China. The solution illustrate how one can use Excel as a front end client and at the same time leverage cloud computing model and mapreduce as well as other patterns to scale-out risk calculations. I'm hoping that this type of approach will reduce the chances for seeing this type of fraud from happening in the future.

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