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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Paper: Making reliable distributed systems in the presence of software errors

Joe Armstrong is a co-inventor of Erlang and general all around renaissance software tinkerer as shown by his excellent work on writing a C Compiler and his voluminous work on GitHub.

Given the success of Erlang it's probably no surprise that he wrote his thesis on the ground breaking ideas behind Erlang: Making reliable distributed systems in the presence of software errors.

Even if you have yet to join the cult of Erlang the principles behind Erlang are universal and well worth exploring for your own designs. Highly recommended.

Introduction:

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PaxosLease: Diskless Paxos for Leases

PaxosLease is a distributed algorithm for lease negotiation. It is based on Paxos, but does not require disk writes or clock synchrony. PaxosLease is used for master lease negotation in the open-source Keyspace replicated key-value store.

DyradLINQ

The goal of DryadLINQ is to make distributed computing on large compute cluster simple enough for ordinary programmers. DryadLINQ combines two important pieces of Microsoft technology: the Dryad distributed execution engine and the .NET Language Integrated Query (LINQ).

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Dyrad

The Dryad Project is investigating programming models for writing parallel and distributed programs to scale from a small cluster to a large data-center.

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Art of Distributed

Art of Distributed

Part 1: Rethinking about distributed computing models

I ‘m getting a lot of questions lately about the distributed computing, especially distributed computing model, and MapReduce, such as: What is MapReduce? Can MapReduce fit in all situations? How we can compares it with other technologies such as Grid Computing? And what is the best solution to our situation? So I decide to write about the distributed computing article in two parts. First one about the distributed computing model and what is the difference between them. In the second part I will discuss the reliability, and distributed storage systems. Download the article in PDF format. Download the article in MS Word format. I wait for your comments, and questions, and I will answer it in part two.

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