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"Structured storage" redirects here. For the Microsoft technology also known as structured storage, see COM Structured Storage. A NoSQL (often interpreted as Not Only SQL[1][2]) database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Motivations for this approach include simplicity of design, horizontal scaling and finer control over availability. The data structure (e.g. key-value, graph, or document) differs from the RDBMS, and therefore some operations are faster in NoSQL and some in RDBMS. There are differences though, and the particular suitability of a given NoSQL DB depends on the problem it must solve (e.g. does the solution use graph algorithms?). History[edit] There have been various approaches to classify NoSQL databases, each with different categories and subcategories. A more detailed classification is the following, by Stephen Yen:[9] Performance[edit] Examples[edit] Graph[edit] Related:  NoSQLvoidhaze

TinkerPop Anti-database movement gains steam The meet-up in San Francisco last month had a whiff of revolution about it, like a latter-day techie version of the American Patriots planning the Boston Tea Party. The inaugural get-together of the burgeoning NoSQL community crammed 150 attendees into a meeting room at CBS Interactive. Like the Patriots, who rebelled against Britain's heavy taxes, NoSQLers came to share how they had overthrown the tyranny of slow, expensive relational databases in favor of more efficient and cheaper ways of managing data. "Relational databases give you too much. NoSQL-based alternatives "just give you what you need," Travis said. Open source rises up The movement's chief champions are Web and Java developers, many of whom learned to get by at their cash-strapped startups without Oracle by building their own data storage solutions, emulating those being built by Google Inc. and Inc., and which they subsequently released as open source. What is NoSQL (technically speaking)?

Kyoto Cabinet: a straightforward implementation of DBM Copyright (C) 2009-2012 FAL Labs Last Update: Fri, 04 Mar 2011 23:07:26 -0800 Overview Kyoto Cabinet is a library of routines for managing a database. The database is a simple data file containing records, each is a pair of a key and a value. Every key and value is serial bytes with variable length. Kyoto Cabinet runs very fast. Kyoto Cabinet is written in the C++ language, and provided as API of C++, C, Java, Python, Ruby, Perl, and Lua. Documents The following are documents of Kyoto Cabinet. Packages The following are the source packages of Kyoto Cabinet. Source Packages of the core library (C/C++) Binary Packages for Windows (C/C++/Java) Information Kyoto Cabinet was written and is maintained by FAL Labs. The following are sibling projects of Kyoto Cabinet. Remote Service (Kyoto Tycoon)

Energy Courses MongoDB MongoDB (from "humongous") is a cross-platform document-oriented database. Classified as a NoSQL database, MongoDB eschews the traditional table-based relational database structure in favor of JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster. Released under a combination of the GNU Affero General Public License and the Apache License, MongoDB is free and open-source software. First developed by the software company 10gen (now MongoDB Inc.) in October 2007 as a component of a planned platform as a service product, the company shifted to an open source development model in 2009, with 10gen offering commercial support and other services.[1] Since then, MongoDB has been adopted as backend software by a number of major websites and services, including Brave Collective, Craigslist, eBay, Foursquare, SourceForge, Viacom, and the New York Times, among others. Licensing and support[edit]

Home · tinkerpop/blueprints Wiki Blueprints is a collection of interfaces, implementations, ouplementations, and test suites for the property graph data model. Blueprints is analogous to the JDBC, but for graph databases. As such, it provides a common set of interfaces to allow developers to plug-and-play their graph database backend. Moreover, software written atop Blueprints works over all Blueprints-enabled graph databases. Pipes: A lazy, data flow framework Gremlin: A graph traversal language Frames: An object-to-graph mapper Furnace: A graph algorithms package Rexster: A graph server The documentation herein will provide information regarding the use of Blueprints.1 Please join the Gremlin users group at for all TinkerPop related discussions. Blueprints JavaDoc: 2.4.0 – 2.3.0 – 2.2.0 – 2.1.0 – 2.0.0 – 1.2 – 1.1 – 1.0 – 0.9 – 0.8 – 0.7 – 0.6 – 0.5 – 0.4 – 0.3 – 0.2 – 0.1 Blueprints WikiDoc: 2.4.0 – 2.3.0 – 2.2.0 – 2.1.0 – 2.0.0

Cassandra & Twitter There have been confirmed rumors about Twitter planning to use Cassandra for a long time. But except the mentioned post, I couldn’t find any other references. Twitter is fun by itself and we all know that NoSQL projects love Twitter. So, imagine how excited I was when after posting about Cassandra 0.5.0 release, I received a short email from Ryan King, the lead of Cassandra efforts at Twitter simply saying that he would be glad to talk about these efforts. So without further ado, here is the conversation I had with Ryan King (@rk) about Cassandra usage at Twitter: MyNoSQL: Can you please start by stating the problem that lead you to look into NoSQL? Ryan King: We have a lot of data, the growth factor in that data is huge and the rate of growth is accelerating. We have a system in place based on shared mysql + memcache but its quickly becoming prohibitively costly (in terms of manpower) to operate. Ryan King: MyNoSQL: What kind of tests have you run to evaluate these systems?

NoSQL Data Modeling Techniques « Highly Scalable Blog NoSQL databases are often compared by various non-functional criteria, such as scalability, performance, and consistency. This aspect of NoSQL is well-studied both in practice and theory because specific non-functional properties are often the main justification for NoSQL usage and fundamental results on distributed systems like the CAP theorem apply well to NoSQL systems. At the same time, NoSQL data modeling is not so well studied and lacks the systematic theory found in relational databases. In this article I provide a short comparison of NoSQL system families from the data modeling point of view and digest several common modeling techniques. I would like to thank Daniel Kirkdorffer who reviewed the article and cleaned up the grammar. To explore data modeling techniques, we have to start with a more or less systematic view of NoSQL data models that preferably reveals trends and interconnections. Key-Value storage is a very simplistic, but very powerful model. Conceptual Techniques

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