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In this paper, we examine a number of SQL and socalled “NoSQL” data stores designed to scale simple OLTP-style application loads over many servers. Originally motivated by Web 2.0 applications, these systems are designed to scale to thousands or millions of users doing updates as well as reads, in contrast to traditional DBMSs and data warehouses. We contrast the new systems on their data model, consistency mechanisms, storage mechanisms, durability guarantees, availability, query support, and other dimensions. These systems typically sacrifice some of these dimensions, e.g. database-wide transaction consistency, in order to achieve others, e.g. higher availability and scalability. Note: Bibliographic references for systems are not listed, but URLs for more information can be found in the System References table at the end of this paper. Caveat: Statements in this paper are based on sources and documentation that may not be reliable, and the systems described are “moving targets,” so some statements may be incorrect. Verify through other sources before depending on information here. Nevertheless, we hope this comprehensive survey is useful! Check for future corrections on the author’s web site cattell.net/datastores. Disclosure: The author is on the technical advisory board of Schooner Technologies and has a consulting business advising on scalable databases.
Oracle NoSQL Database and MongoDB server are both licensed under AGPL while MongoDB has certain client drivers under the Apache 2.0 license. Oracle NoSQL Database is in many respects, as a NoSQL Database implementation leveraging BerkeleyDB in its storage layer, a commercialization of the early NoSQL implementations which lead to the adoption of this category of technology. Several of the earliest NoSQL solutions were based on BerkeleyDB and some are still to this day e.g. LinkedIn’s Voldemort. The Oracle NoSQL Database is a Java based key-value store implementation that supports a value abstraction layer currently implementing Binary and JSON types. Its key structure is designed in such a way as to facilitate large scale distribution and storage locality with range based search and retrieval. The implementation uniquely supports built in cluster load balancing and a full range of transaction semantics from ACID to relaxed eventually consistent. In addition, the technology is integrated with important open source technologies like Hadoop / MapReduce, an increasing number of Oracle software solutions and tools and can be found on Oracle Engineered Systems.
It has now been a good couple of years since the various anti-SQL proponents have gained enough momentum to come together under the wide umbrella of the term NoSQL. And it is clear that we can never go back: the typical relational database architecture is clearly insufficient for today’s dataintensive applications, and the move to distributed architectures. But is the problem in the architecture or the query language? The two are not interchangeable, though frequently confused. Some answers can be found in the following articles, which represent a progression of ideas on this very relevant topic, based on various articles published in Nati Shalom’s blog: http://natishalom.typepad.com Should Web Apps "Just Say No" to SQL? Pros and Cons of Non-SQL Patterns This paper briefly reviews what is driving the trend of adopting alternatives to the traditional SQL DB, survey alternative approaches, and discuss not only their benefits but also the risks and caveats for real-life web applications.
As companies deal with ever larger amounts of data and increasingly demanding workloads, a new class of databases has taken hold. Dubbed “NoSQL”, these databases trade some of the features used by traditional relational databases in exchange for increased performance and/or partition tolerance. But as NoSQL solutions have proliferated and differentiated themselves (into key-value stores, document databases, graph databases, and “NewSQL”), trying to evaluate the database landscape for a particular class of problem becomes more and more difficult. In this paper we attempt to answer this question for one specific, but critical, class of functionality – applications that need the highest possible raw performance for a reliable storage engine. There have been a few attempts to provide standardized tools to measure performance or other characteristics, but these have been hobbled by the lack of a clear mandate on exactly what they’re testing, plus an inability to measure load at the highest volumes. In addition, there is an implicit tradeoff between the consistency and durability requirements of an application and the maximum throughput that can be processed. What is needed is not an attempt to quantify every NoSQL solution into one artificial bucket, but a more systemic analysis of how some of these databases can achieve under assumptions that mirror real-world application needs. We attempted to provide a comprehensive answer to one specific set of use cases for NoSQL databases -- consumer-facing applications which require extremely high throughput and low latency, and whose information can be represented using a key-value schema. In particular, we look at two common scenarios.
In this paper, I describe some of the recent developments in the database management area, in particular the NoSQL phenomenon and the hoopla associated with it. The goal of the paper is not to do an exhaustive survey of NoSQL systems. The aim is to do a broad brush analysis of what these developments mean - the good and the bad aspects! Based on my more than three decades of database systems work in the research and product arenas, I will outline what are many of the pitfalls to avoid since there is currently a mad rush to develop and adopt a plethora of NoSQL systems in a segment of the IT population, including the research community. In rushing to develop these systems to overcome some of the shortcomings of the relational systems, many good principles of the latter, which go beyond the relational model and the SQL language, have been left by the wayside. Now many of the features that were initially discarded as unnecessary in the NoSQL systems are being brought in, but unfortunately in ad hoc ways. Hopefully, the lessons learnt over three decades with relational and other systems would not go to waste and we wouldn’t let history repeat itself with respect to simple minded approaches leading to enormous pain later on for developers as well as users of the NoSQL systems! Caveat: What I express in this paper are my personal opinions and they do not necessarily reflect the opinions of my employer.
Many organizations today would seemingly be content with having achieved an information architecture that features a broad-scope enterprise resource management environment feeding data in batch for reporting and analytics to a robust data warehouse environment. As a bonus, the data warehouses in this post-operational environment may consist of solid-state components and automated archival abilities. Irreversibly, the environment also has been inundated with data marts fed from original source and from the data warehouse itself. There is frequently a multidimensional database in the mix. Or a hundred. If there is any contentment with such an architecture, it will be short-lived. With information the “new gold” for companies, each shop must do everything it can to nurture, protect, make available and otherwise exploit the information asset. This will frequently mean venturing into new technology domains for the management of the asset. One may be tempted to consider the NoSQL movement as the epitome of these new technology domains. However, many possibilities have been laid on the table by the vendor community in the years prior to NoSQL. Most have merit in an enterprise today. We clearly need to get away from the winner-take-all mentality where every workload – sometimes whether it is analytical or operational – will be solved the same way as the last one. Frequently, that way was with a data integration operation with the data warehouse followed by the deployment of more reports in the business intelligence tool. Force-fitting a workload into a technology that it was not designed for creates more problems than it solves.