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)
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]
Welcome to MariaDB! - MariaDB Cassandra vs MongoDB vs CouchDB vs Redis vs Riak vs HBase vs Couchbase vs Hypertable vs ElasticSearch vs Accumulo vs VoltDB vs Scalaris comparison :: Software architect Kristof Kovacs While SQL databases are insanely useful tools, their monopoly in the last decades is coming to an end. And it's just time: I can't even count the things that were forced into relational databases, but never really fitted them. (That being said, relational databases will always be the best for the stuff that has relations.) But, the differences between NoSQL databases are much bigger than ever was between one SQL database and another. In this light, here is a comparison of Open Source NOSQL databases: The most popular ones # Redis # Best used: For rapidly changing data with a foreseeable database size (should fit mostly in memory). For example: To store real-time stock prices. Cassandra # Written in: JavaMain point: Store huge datasets in "almost" SQLLicense: ApacheProtocol: CQL3 & ThriftCQL3 is very similar to SQL, but with some limitations that come from the scalability (most notably: no JOINs, no aggregate functions.)CQL3 is now the official interface. MongoDB # ElasticSearch # CouchDB #
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
CouchDB Apache CouchDB, commonly referred to as CouchDB, is an open source database that focuses on ease of use and on being "a database that completely embraces the web".[1] It is a NoSQL database that uses JSON to store data, JavaScript as its query language using MapReduce, and HTTP for an API.[1] One of its distinguishing features is multi-master replication. CouchDB was first released in 2005 and later became an Apache project in 2008. Unlike in a relational database, CouchDB does not store data and relationships in tables. Instead, each database is a collection of independent documents. Each document maintains its own data and self-contained schema. CouchDB implements a form of Multi-Version Concurrency Control (MVCC) in order to avoid the need to lock the database file during writes. Other features include document-level ACID semantics with eventual consistency, (incremental) MapReduce, and (incremental) replication. History[edit] Main features[edit] Document Storage ACID Semantics
Oracle Database 12c Release 1 (12.1) New Features The following sections describe the new business intelligence and data warehousing features for Oracle Database 12c Release 1 (12.1). 1.2.1 Oracle Advanced Analytics The following sections describe new Oracle Advanced Analytics features. 1.2.1.1 Decision Tree Mining Text Data The Decision Tree algorithm now supports nested data and can be used for text mining. Decision Tree is popular due to its transparency and prevalence, therefore, it is important to enable the algorithm to handle unstructured data. 1.2.1.2 Expectation Maximization (EM) Clustering and Density Estimation In Release 11g, Oracle Data Mining offered two clustering algorithms. In bringing analytics to applications, Oracle Data Mining provides different types of clustering capabilities currently being used by multiple applications. 1.2.1.3 Feature Extraction Using Singular Value Decomposition PCA can be viewed as a special scoring method under the SVD algorithm. 1.2.1.5 Native Double in Data Mining Functions ALTER TABLE ...
Why We Migrated from MongoDB to Riak Recommended Links Like this piece? Share it with your friends: Powering hundreds of thousands of clicks and shares and hundreds of millions of pageviews every month, Shareaholic found itself needing to scale up it's big data architecture. Learn why we chose Riak, what it does for us, and how we imported 100 gigabytes of data from a live Mongo database while maintaining data consistency and zero downtime. I'll discuss the things that went smoothly and the things I'd do differently next time. About the author: Robby leads tech at Shareaholic in Cambridge, MA.
Visual Guide to NoSQL Systems - Nathan Hurst's Blog There are so many NoSQL systems these days that it's hard to get a quick overview of the major trade-offs involved when evaluating relational and non-relational systems in non-single-server environments. I've developed this visual primer with quite a lot of help (see credits at the end), and it's still a work in progress, so let me know if you see anything misplaced or missing, and I'll fix it. Without further ado, here's what you came here for (and further explanation after the visual). Note: RDBMSs (MySQL, Postgres, etc) are only featured here for comparison purposes. As you can see, there are three primary concerns you must balance when choosing a data management system: consistency, availability, and partition tolerance. According to the CAP Theorem, you can only pick two. One of the primary goals of NoSQL systems is to bolster horizontal scalability. Now for the particulars of each CAP configuration and the systems that use each configuration: Self promotion and Credits