A Comparison Of NoSQL Database Management Systems And Models A previous version of this article was written by O.S. Tezer. Introduction When most people think of a database, they often envision the traditional relational database model that involves tables made up of rows and columns. While relational database management systems still handle the lion’s share of data on the internet, alternative data models have become more common in recent years as developers have sought workarounds to the relational model’s limitations. This article will introduce you to a few of the more commonly used NoSQL database models. Relational Databases and Their Limitations Databases are logically modeled clusters of information, or data. All database management systems have an underlying model that structures how data is stored and accessed. Historically, the relational model has been the most widely used approach for managing data, and to this day many of the most popular database management systems implement the relational model. About NoSQL Key-value Databases
www.BenStopford.com » Blog Archive » Shared Nothing v.s. Shared Disk Architectures: An Independent View The Shared Nothing Architecture is a relatively old pattern that has had a resurgence of late in data storage technologies, particularly in the NoSQL, Data Warehousing and Big Data spaces. As architectures go it’s there are fairly dramatic performance tradeoffs across the two. This article contrasts Shared Nothing with Shared Disk Architectures, which is largely equivalent to the tradeoffs between sharding and replication. Shared Disk and Shared Nothing? Shared Nothing is a data architecture for distributed data storage in a clustered environment. By comparison Shared Disk is exactly what it says; disk accessible from all cluster nodes. In the Shared Disk any node can access any piece of data and any single piece of data has no dedicated owner. Understanding the Trade-offs for Writing When persisting data in a Shared Disk architecture writes can be performed against any node. To explain this a little further consider the case described by the below diagram. So Which Should You Use?
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 #
Document Databases Compared: CouchDB, MongoDB, RavenDB Brian Ritchie has two posts (☞ here and ☞ here) covering three document databases: CouchDB, MongoDB, and RavenDB concluding with the matrix below: But before using this as a reference material there are a couple of corrections needed: They have some special characteristics that make them kick some serious SQL.Objects can be stored as documents: The relational database impedance mismatch is gone. Judging by the growing number of document database mapping tools, I’m not sure impedance mismatch is really gone (related to 1st point above)Using embedded format is not always the best solution for mapping relationships and other more complex data structures. Related to the matrix comparison: Versioning is not supported by either MongoDB and CouchDB. Original title and link for this post: Document Databases Compared: CouchDB, MongoDB, RavenDB (published on the NoSQL blog: myNoSQL) by Alex Popescu & Ana-Maria Bacalu Most read Latest
Thoughts on development using CouchDB with Node.js | Tim Branyen @tbranyen I was recently on a client facing project for Bocoup and needed to store millions of tweets that contained sentiment analysis of the text. From this sentiment gathered along with other various metadata, I was tasked with creating a scalable API that could query/filter the data retrieved. This post will discuss the Why and the How behind my first experience with CouchDB and how I created a better development environment in order to deliver a polished product. Why CouchDB? First and foremost, I have a "background" with MySQL, SQLite and SQL Server from previous employment and college. I had very simple requirements: Easily put data into the store && easily get data out of the store. I made the decision for CouchDB after having conversations with peers and feeling comfortable that I could complete this project with: The abundant support via the communityThe rich feature set for queryingThe local cluster hosting provider Cloudant Community The easiest sell for CouchDB, to me, was the community.
Traditional database design vs. key-value table Consider the following two scenarios: Scenario no. 1 – traditional database design method The DBA will use the following SQL statements in order to create two additional columns: In our example, we will update the data that already exists in the table with the following update sql statements: This will make our table look like this: We might want to be able to make a new descriptive change without altering the database schema structure. Scenario no. 2 – use of key-value table The following scenario describes a generic approach to the challenge. The DBA can create an additional table which will hold any additional information that describes the employee better. Updating existing data in our example can be done using the following insert sql statements: This would yield the following table: This information is stored in a more generic structure, allowing the programmer to add employee attributes on his own as well as create a mechanism that adds employee attributes at run-time.
Add search features to your application, try Elasticsearch part 3 : attaching indexation to events « Diving deep into JEE Now that we are able to index, we should think of when whe should trigger indexing tasks. A simple answer would be : whenever some indexed data has changed. “Changed” means change cardinality (add/remove) or change existing data. Either we invoke indexing tasks whenever we code an action that changes data or we use an event model which listens to precise events. 1 – JPA event model If you use JPA as a persistence mechanism you can take advantage of its elegant mechanism. Below, an example: That model, although very elegant, doesn’t suit you if you use Spring because the persistence can’t use a bean instance. 2 – Hibernate event model When using Hibernate, without JPA, with Spring you can register instances, not only classes. PostCommitDeleteEventListener source code: This is one of the most non intrusive solution. 3 – Spring event model When you’re stuck with JPA you can use Spring event model. Example of ApplicationEventPublisher call: Example of event listener: 4 – Elasticsearch river
NoSQL Relational Database Management System: Home Page A Relational Database Management System NoSQL is a fast, portable, relational database management system without arbitrary limits, (other than memory and processor speed) that runs under, and interacts with, the UNIX1 Operating System. It uses the "Operator-Stream Paradigm" described in "Unix Review", March, 1991, page 24, entitled "A 4GL Language". What is NoSQL NoSQL, which I personally like to pronounce as noseequel2, is a derivative of the RDB database system. Other major contributors to the original RDB system, besides Walter Hobbs, are: Chuck BushDon EmersonJudy LenderRoy GatesRae Starr People who helped with turning RDB into NoSQL: Vincenzo (Vicky) BelloliDavid FreyGiuseppe PaternòMaurizio (Masar) SartoriPaul LussierSeth LaForgeMicah StetsonThomas MillerMichael SomosAgustín Ferrin The NoSQL logo was kindly provided by Kyle Hart. As its name implies, NoSQL is not an SQL database but rather a shell-level tool, as explained in Philosophy of NoSQL. What NoSQL is not How to get NoSQL Support