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Can you have your ACID cake and eat your distributed database too? Yes explains Daniel Abadi, Assistant Professor of Computer Science at Yale University, in an epic post, The problems with ACID, and how to fix them without going NoSQL, coauthored with Alexander Thomson, on their paper The Case for
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Misco: A MapReduce Framework for Mobile Systems is a very exciting paper to me because it's really one of the first explorations of some of the ideas in Building Super Scalable Systems: Blade Runner Meets Autonomic Computing in the Ambient Cloud. What they are trying to do is
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If Google was a boxer then MapReduce would be a probing right hand that sets up the massive left hook that is Dremel, Google's—scalable (thousands of CPUs, petabytes of data, trillions of rows), SQL based, columnar, interactive (results returned in seconds), ad-hoc—analytics system. If Google was
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In the never ending quest to figure out how to do something useful with never ending streams of data, GraphLab: A New Framework For Parallel Machine Learning wants to go beyond low-level programming, MapReduce, and dataflow languages with a new parallel framework for ML (machine learning) which exploits the sparse
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The Declarative Imperative: Experiences and Conjectures in Distributed Logic is written by UC Berkeley's Joseph Hellerstein for a keynote speech he gave at PODS. The video version of the talk is here. You may have heard about Mr. Hellerstein through the Berkeley Orders Of Magnitude project (BOOM), whose
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Alexey Radul in his fascinating 174 page dissertation Propagation Networks: A Flexible and Expressive Substrate for Computation, offers to help us break free of the tyranny of linear time by arranging computation as a network of autonomous but interconnected machines. We can do this by organizing computation as a network
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Cloud computing promises a number of advantages for the deployment of data-intensive applications. Most prominently, these include reducing cost with a pay-as-you-go pricing model and (virtually) unlimited throughput by adding servers if the workload increases. At the Systems Group, ETH Zurich, we did an extensive end-to-end performance study to compare
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Imagine a single search request coursing through Google's massive infrastructure. A single request can run across thousands of machines and involve hundreds of different subsystems. And oh by the way, you are processing more requests per second than any other system in the world. How do you debug
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Parallel Information Retrieval is a sample chapter in what appears to be a book-in-progress titled Information Retrieval Implementing and Evaluation Search Engines by Stefan Büttcher, Google Inc and Charles L. A. Clarke, Gordon V. Cormack, both of the University of Waterloo. The full table of contents is on-line and looks
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In A Brief History of the Internet it was revealed that the Internet was based on the idea that there would be multiple independent networks of rather arbitrary design. The Internet as we now know it embodies a key underlying technical idea, namely that of open architecture networking. In this
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This excellent survey of the field was written by Ian Thomas Varley as part of his Master of Science in Engineering program. The aim of this paper is to explore the conceptual design space of non-relational databases as compared to traditional relational databases. It is clear that the design needs
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We saw in Why are Facebook, Digg, and Twitter so hard to scale? scaling social networks is a lot harder than you might think. This paper, Scaling Online Social Networks without Pains, from a team at Telefonica Research in Spain hopes to meet the challenge of status distribution, user generated