Defining NoSQL

NoSQL is literally a combination of two words: No and SQL. The implication is that NoSQL is a technology or product that counters SQL. The creators and early adopters of the buzzword NoSQL probably wanted to say No RDBMS or No relational but were infatuated by the nicer sounding NoSQL and stuck to it. In due course, some have proposed NonRel as an alternative to NoSQL. A few others have tried to salvage the original term by proposing that NoSQL is actually an acronym that expands to “Not Only SQL.” Whatever the literal meaning, NoSQL is used today as an umbrella term for all databases and data stores that don’t follow the popular and well established RDBMS principles and often relate to large data sets accessed and manipulated on a Web scale. This means NoSQL is not a single product or even a single technology. It represents a class of products and a collection of diverse, and sometimes related, concepts about data storage and manipulation.


Context and a Bit of History
Before I start with details on the NoSQL types and the concepts involved, it’s important to set the context in which NoSQL emerged. Non-relational databases are not new. In fact, the fi rst non-relational stores go back in time to when the first set of computing machines were invented. Non-relational databases thrived through the advent of mainframes and have existed in specialized and specific domains — for example, hierarchical directories for storing authentication and authorization credentials — through the years. However, the non-relational stores those have appeared in the world of NoSQL are a new incarnation, which were born in the world of massively scalable Internet applications. These non-relational NoSQL stores, for the most part, were conceived in the world of distributed and parallel computing.

Starting out with Inktomi, which could be thought of as the first true search engine, and culminating with Google, it is clear that the widely adopted relational database management system (RDBMS) has its own set of problems when applied to massive amounts of data. The problems relate to efficient processing, effective parallelization, scalability, and costs.

Google has, over the past few years, built out a massively scalable infrastructure for its search engine and other applications, including Google Maps, Google Earth, GMail, Google Finance, and Google Apps. Google’s approach was to solve the problem at every level of the application stack. The goal was to build a scalable infrastructure for parallel processing of large amounts of data. Google therefore created a full mechanism that included a distributed file system, a column-family-oriented data store, a distributed coordination system, and a MapReduce-based parallel algorithm execution environment. Graciously enough, Google published and presented a series of papers explaining some of the key pieces of its infrastructure. The most important of these publications are as follows:

» Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung. “The Google File System”; pub. 19th ACM Symposium on Operating Systems Principles, Lake George, NY, October 2003. URL: http://labs.google.com/papers/gfs.html

» Jeffrey Dean and Sanjay Ghemawat. “MapReduce: Simplifi ed Data Processing on Large Clusters”; pub. OSDI’04: Sixth Symposium on Operating System Design and
Implementation, San Francisco, CA, December 2004. URL: http://labs.google.com/papers/mapreduce.html

» Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, and Robert E. Gruber. “Bigtable: A Distributed Storage System for Structured Data”; pub. OSDI’06: Seventh Symposium on Operating System Design and Implementation, Seattle, WA, November 2006. URL: http://labs.google.com/papers/bigtable.html

» Mike Burrows. “The Chubby Lock Service for Loosely-Coupled Distributed Systems”; pub. OSDI’06: Seventh Symposium on Operating System Design and Implementation, Seattle, WA, November 2006. URL: http://labs.google.com/papers/chubby.html

The release of Google’s papers to the public spurred a lot of interest among open-source developers. The creators of the open-source search engine, Lucene, were the first to develop an open-source version that replicated some of the features of Google’s infrastructure. Subsequently, the core Lucene developers joined Yahoo, where with the help of a host of other contributors, they created a parallel universe that mimicked all the pieces of the Google distributed computing stack. This open-source alternative is Hadoop, its sub-projects, and its related projects. You can find more information, code, and documentation on Hadoop at http://adoop.apache.org.

Without getting into the exact timeline of Hadoop’s development, somewhere toward the first of its releases emerged the idea of NoSQL. The history of who coined the term NoSQL and when is irrelevant, but it’s important to note that the emergence of Hadoop laid the groundwork for the rapid growth of NoSQL. Also, it’s important to consider that Google’s success helped propel a healthy adoption of the new-age distributed computing concepts, the Hadoop project, and NoSQL.

A year after the Google papers had catalyzed interest in parallel scalable processing and nonrelational distributed data stores, Amazon decided to share some of its own success story. In 2007, Amazon presented its ideas of a distributed highly available and eventually consistent data store named Dynamo. You can read more about Amazon Dynamo in a research paper, the details of which are as follows: Giuseppe DeCandia, Deniz Hastorun, Madan Jampani, Gunavardhan Kakulapati, Avinash Lakshman, Alex Pilchin, Swami Sivasubramanian, Peter Vosshall, and Werner Vogels, “Dynamo: Amazon’s Highly Available Key/value Store,” in the Proceedings of the 21st ACM
Symposium on Operating Systems Principles, Stevenson, WA, October 2007. Werner Vogels, the Amazon CTO, explained the key ideas behind Amazon Dynamo in a blog post accessible online at www.allthingsdistributed.com/2007/10/amazons_dynamo.html.

With endorsement of NoSQL from two leading web giants — Google and Amazon — several new products emerged in this space. A lot of developers started toying with the idea of using these methods in their applications and many enterprises, from startups to large corporations, became amenable to learning more about the technology and possibly using these methods. In less than 5 years, NoSQL and related concepts for managing big data have become widespread and use cases have emerged from many well-known companies, including Facebook, Netfl ix, Yahoo, EBay, Hulu, IBM, and many more. Many of these companies have also contributed by open sourcing their extensions and newer products to the world.

Source of Information : NoSQL

Defining NoSQL