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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Infinispan narrows the gap between open source and commercial data caches

Recently I attended a lecture presented by Manik Surtani , JBoss Cache & Infinispan project lead. The goal of the talk was to provide a technical overview of both products and outline Infinispan's road-map. Infinispan is the successor to the open-source JBoss Cache. JBoss Cache was originally targeted at simple web page caching and Infinispan builds on this to take it into the Cloud paradigm.

Why did I attend? Well, over the past few years I have worked on projects that have used commercial distributed caching (aka data grid) technologies such as GemFire, GigaSpaces XAP or Oracle Coherence . These projects required more functionality than is currently provided by open-source solutions such as memcached or EHCache. Looking at the road-map for Infinispan, I was struck by its ambition – will it provide the functionality that I need?

Read more at: http://bigdatamatters.com/bigdatamatters/2009/09/infinispan-vs-gigaspaces.html

Data grid comparison: Oracle Coherence vs Gigaspaces XAP

A short summary of differences between Oracle Coherence and GigaSpaces XAP.

Scaling Out MySQL

This post covers two main options for scaling-out MySql and compare between them. The first is based on data-base clustering and the second is based on In Memory clustering a.k.a Data Grid. A special emphasis is given to a pattern which shows how to scale our existing data base without changing it through a combination of Data Grid and data base as a background service. This pattern is referred to as Persistency as a Service (PaaS). It also address many of the fequently asked question related to how performance, reliability and scalability is achieved with this pattern.

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