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Graph Databases by Emil Eifrem, Jim Webber, Ian Robinson

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Appendix A. NOSQL Overview

Recent years have seen a meteoric rise in popularity of a family of data storage technologies known as NOSQL (a cheeky acronym for Not Only SQL, or more confrontationally, No to SQL). But NOSQL as a term defines what those data stores are not—they’re not SQL-centric relational databases—rather than what they are, which is an interesting and useful set of storage technologies whose operational, functional, and architectural characteristics are many and varied.

Why were these new databases created? What problems do they address? Here we’ll discuss some of the new data challenges that have emerged in the past decade. We’ll then look at four families of NOSQL databases, including graph databases.

The Rise of NOSQL

Historically, most enterprise-level web apps ran on top of a relational database. But in the past decade, we’ve been faced with data that is bigger in volume, changes more rapidly, and is more structurally varied than can be dealt with by traditional RDBMS deployments. The NOSQL movement has arisen in response to these challenges.

It’s no surprise that as storage has increased dramatically, volume has become the principal driver behind the adoption of NOSQL stores by organizations. Volume may be defined simply as the size of the stored data.

As is well known, large datasets become unwieldy when stored in relational databases; in particular, query execution times increase as the size of tables and the number of joins grow (so-called join pain). This isn’t ...

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