NOSQL Databases. Couchbase: Server Features. Flexible Data Model JSON Support Couchbase Server 2.0 has native support for JSON documents.
Each JSON document can have a different structure, and multiple documents with different structures can be stored in the same Couchbase bucket. Document structure can be changed at any time, without changing other documents in the database, making it faster to add features to your application. Indexing and Querying With Couchbase Server 2.0, you can create primary and secondary indexes on JSON documents. Incremental Map Reduce Incremental map reduce provides powerful aggregates for real-time analytics, even with large datasets. Easy Scalability Clone to Grow with Auto-Sharding With Couchbase Server, you can add (or remove) servers in your cluster with the click of a button, easily keeping resources in step with the changing needs of your application. Auto-sharding distributes data uniformly across servers, enabling direct routing of requests to the appropriate server without any application changes.
Cassandra vs MongoDB vs CouchDB vs Redis vs Riak vs HBase comparison. (Yes it's a long title, since people kept asking me to write about this and that too :) I do when it has a point.)
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.
This means that it is a bigger responsibility on software architects to choose the appropriate one for a project right at the beginning. In this light, here is a comparison of Cassandra, Mongodb, CouchDB, Redis, Riak, Couchbase (ex-Membase), Hypertable, ElasticSearch, Accumulo, VoltDB, Kyoto Tycoon, Scalaris, Neo4j and HBase: The most popular ones MongoDB (2.2) Written in: C++ Main point: Retains some friendly properties of SQL. Riak (V1.2) FAQ. Why Redis is different compared to other key-value stores?
There are two main reasons. Redis is a different evolution path in the key-value DBs where values can contain more complex data types, with atomic operations defined against those data types. Redis data types are closely related to fundamental data structures and are exposed to the programmer as such, without additional abstraction layers. Redis is an in-memory but persistent on disk database, so it represents a different trade off where very high write and read speed is achieved with the limitation of data sets that can't be larger than memory. Another advantage of in memory databases is that the memory representation of complex data structure is much simpler to manipulate compared to the same data structure on disk, so Redis can do a lot with little internal complexity.
What's the Redis memory footprint? With large keys/values the ratio is much better of course. Is there something I can do to lower the Redis memory usage? Redis Benchmarks on FusionIO. December 10, 2009 by Ryan Lowe7 Comments Peter took a look at Redis some time ago; and now, with the impending 1.2 release and a slew of new features, I thought it time to look again.
One of the more interesting features in 1.2 is the ability to operate in “append-only file persistence mode”, meaning Redis has graduated from a semi-persistent to a fully-persistent system! Using the redis-benchmark script included, I ran the following command in five modes: 1 – In-Memory I set “save 900000000 900000000″ so nothing would be written to disk during the tests. 2 – Semi-Persistent I set “save 1 1″ so that changes would be flushed to disk every second (assuming there was at least one change the previous second). 3 – Fully Persistent I set appendonly yes and appendfsync always, which calls an fsync for every operation. 4 – Semi-Persistent I set appendonly yes and appendfsync no, which is about as persistent as MyISAM.
I am quite surprised at the performance penalty here for full durability. 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. Also, some of these systems can vary their features by configuration (I use the default configuration here, but will try to delve into others later).
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. NoSQL. "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) 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 Twitter/flockdb - GitHub. Neo4j: NOSQL For the Enterprise.
Memcached - a distributed memory object caching system. 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. MongoDB.