The nascent noSQL market is extremely fragmented, with many competing vendors and technologies. Programming, deploying, and managing noSQL solutions requires specialized and low-level knowledge that does not easily carry over from one vendor's product to another. A necessary condition for the network effect to take off in the noSQL database market is the availability of a common abstract mathematical data model and an associated query language for noSQL that removes product differentiation at the logical level and instead shifts competition to the physical and operational level. The availability of such a common mathematical underpinning of all major noSQL databases can provide enough critical mass to convince businesses, developers, educational institutions, etc. to invest in noSQL. In this article we developed a mathematical data model for the most common form of noSQL-namely, key-value stores as the mathematical dual of SQL's foreign-/primary-key stores. Because of this deep and beautiful connection, we propose changing the name of noSQL to coSQL. Moreover, we show that monads and monad comprehensions (i.e., LINQ) provide a common query mechanism for both SQL and coSQL and that many of the strengths and weaknesses of SQL and coSQL naturally follow from the mathematics. In contrast to common belief, the question of big versus small data is orthogonal to the question of SQL versus coSQL. While the coSQL model naturally supports extreme sharding, the fact that it does not require strong typing and normalization makes it attractive for "small" data as well. On the other hand, it is possible to scale SQL databases by careful partitioning.2 What this all means is that coSQL and SQL are not in conflict, like good and evil. Instead they are two opposites that coexist in harmony and can transmute into each other like yin and yang. Because of the common query language based on monads, both can be implemented using the same principles. © 2011 ACM.
CITATION STYLE
Meijer, E., & Bierman, G. (2011). A co-relational model of data for large shared data banks. Communications of the ACM, 54(4), 49–58. https://doi.org/10.1145/1924421.1924436
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