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Defining NoSQL database types


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Up and Running with NoSQL Databases

with Joseph LeBlanc

Video: Defining NoSQL database types

There are numerous types of NoSQL databases. Not all NoSQL databases operate under the same principles, but many of them fall under one of many broad categories. First, they are document stores. Documents are usually stored in a structured format such as XML or JSON or some other standardized format that can be queried. Documents are usually organized into collections or databases. This allows you to group similar documents all together. Individual documents can have unique structures. You don't have to have the same fields in each document.
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Watch the Online Video Course Up and Running with NoSQL Databases
56m 57s Intermediate Nov 30, 2012

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Explore the possibilities NoSQL databases offer developers for unparalleled flexibility and performance. In this course, author Joseph LeBlanc shows how to build an entire web application with CouchDB and introduces techniques such as querying with JavaScript, working with nested data, and creating views. He also explains some of the trade-offs you might encounter with NoSQL databases, such as the complexity of partitioning data across multiple servers, and presents five commonly used NoSQL databases: CouchDB, MongoDB, Cassandra, Riak, and Redis.

Topics include:
  • What is NoSQL?
  • Installing CouchDB on Mac, Windows, and Linux
  • Storing data
  • Nesting document data
  • Specifying search criteria
  • Reducing data
  • Attaching and retrieving images
  • Deploying applications
  • Securing CouchDB
  • Understanding the CAP theorem
Subjects:
Developer IT
Software:
NoSQL CouchDB MongoDB Cassandra Riak Redis
Author:
Joseph LeBlanc

Defining NoSQL database types

There are numerous types of NoSQL databases. Not all NoSQL databases operate under the same principles, but many of them fall under one of many broad categories. First, they are document stores. Documents are usually stored in a structured format such as XML or JSON or some other standardized format that can be queried. Documents are usually organized into collections or databases. This allows you to group similar documents all together. Individual documents can have unique structures. You don't have to have the same fields in each document.

Each document usually has a specific key. This allows you to retrieve the document quickly, and normally it's possible to query a document by the specific fields contained inside. Next, there are key-value stores. Typically you have a key you can query by, and then there's a specific value at that key. However, you usually can't query by anything other than the key. Some key-value stores let you define more than one key though, which can help you get around the drawback.

Key-value stores are sometimes used alongside relational databases for caching. Next we have BigTable/tabular databases. Tabular databases are named after Google's proprietary BigTable implementation. Each row in a tabular database can have a different set of columns. Tabular databases are designed for large numbers of columns in each row, and rows are typically versioned. They're also graph databases. Graph databases are designed for data that's best represented as interconnected nodes.

So for example, if you have a series of rowed intersections that you want to keep in a database, a graph database is a good way of doing that. And then finally, there are object databases. Object databases are tightly integrated with object-oriented programming languages. Object databases act as a persistence layer. You store the objects from your programming language directly into the object database. You can also link objects together through pointers. In this course, we'll be exploring a document database.

While document databases do not necessarily enforce a specific data structure, they typically give the capability of querying and indexing against fields within each document.

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