Nontraditional Database Systems is the fifth volume in the Advanced Information Processing Technology series. It brings together the results of research carried out by the Japanese database research community in the field of nontraditional database systems. The book examines nontraditional types of applications, data types, systems and environments together with high-performance architecture to support nontraditional applications, such as web mining, data engineering and object processing.
Decades of research have gone into the optimization of physical designs, query execution, and related tools for relational databases. These techniques and tools make it possible for non-expert users to make effective use of relational database management systems. However, the drive for flexible data models and increased scalability has spawned a new generation of data management systems which largely eschew the relational model. These include systems such as NoSQL databases and distributed analytics frameworks such as Apache Spark which make use of a diverse set of data models. Optimization techniques and tools developed for relational data do not directly apply in this setting. This leaves developers making use of these systems with the need to become intimately familiar with system details to obtain good performance. We present techniques and tools for physical design for non-relational data systems. We explore two settings: NoSQL database systems and distributed analytics frameworks. While NoSQL databases often avoid explicit schema definitions, many choices on how to structure data remain. These choices can have a significant impact on application performance. The data structuring process normally requires expert knowledge of the underlying database. We present the NoSQL Schema Evaluator (NoSE). Given a target workload, NoSE provides an optimized physical design for NoSQL database applications which compares favourably to schemas designed by expert users. To enable existing applications to benefit from conceptual modeling, we also present an algorithm to recover a logical model from a denormalized database instance. Our second setting is distributed analytics frameworks such as Apache Spark. As is the case for NoSQL databases, expert knowledge of Spark is often required to construct efficient data pipelines. In NoSQL systems, a key challenge is how to structure stored data, while in Spark, a key challenge is how to cache intermediate results. We examine a particularly common scenario in Spark which involves performing iterative analysis on an input dataset. We show that jobs written in an intuitive manner using existing Spark APIs can have poor performance. We propose ReSpark, which automates caching decisions for iterative Spark analyses. Like NoSE, ReSpark makes it possible for non-expert users to obtain good performance from a non-relational data system.
This textbook offers a comprehensive introduction to relational (SQL) and non-relational (NoSQL) databases. The authors thoroughly review the current state of database tools and techniques and examine upcoming innovations. In the first five chapters, the authors analyze in detail the management, modeling, languages, security, and architecture of relational databases, graph databases, and document databases. Moreover, an overview of other SQL- and NoSQL-based database approaches is provided. In addition to classic concepts such as the entity and relationship model and its mapping in SQL database schemas, query languages or transaction management, other aspects for NoSQL databases such as non-relational data models, document and graph query languages (MQL, Cypher), the Map/Reduce procedure, distribution options (sharding, replication) or the CAP theorem (Consistency, Availability, Partition Tolerance) are explained. This 2nd English edition offers a new in-depth introduction to document databases with a method for modeling document structures, an overview of the document-oriented MongoDB query language MQL as well as security and architecture aspects. The topic of database security is newly introduced as a separate chapter and analyzed in detail with regard to data protection, integrity, and transactions. Texts on data management, database programming, and data warehousing and data lakes have been updated. In addition, the book now explains the concepts of JSON, JSON schema, BSON, index-free neighborhood, cloud databases, search engines and time series databases. The book includes more than 100 tables, examples and illustrations, and each chapter offers a list of resources for further reading. It conveys an in-depth comparison of relational and non-relational approaches and shows how to undertake development for big data applications. This way, it benefits students and practitioners working across the broad field of data science and applied information technology.
Dive into the dynamic and evolving world of NoSQL databases with "NoSQL Essentials: Navigating the World of Non-Relational Databases." This comprehensive guide is your passport to understanding the intricacies and power of NoSQL technology, a crucial tool in managing and interpreting the vast ocean of data in today's digital landscape. Traditional relational databases have been the backbone of data storage and retrieval for decades. However, with the explosion of big data, the limitations of these systems have become increasingly apparent. Enter NoSQL - a flexible, scalable, and efficient alternative. This book demystifies the NoSQL paradigm, offering insights into its diverse types, including document stores like MongoDB, key-value stores like Redis, wide-column stores like Cassandra, and graph databases like Neo4j. Authored by a seasoned expert in database technologies, "NoSQL Essentials" begins with a historical overview of data storage systems, leading up to the emergence of NoSQL. It provides a solid foundation for understanding the challenges faced by traditional databases and the solutions offered by NoSQL. The core chapters delve into the architectural principles of NoSQL databases, discussing their advantages in scalability, flexibility, and performance. With detailed explanations and practical examples, the book guides you through the nuances of data modeling in a NoSQL context, highlighting how it differs from relational models. One of the book's key strengths is its hands-on approach. It offers practical advice on selecting the right NoSQL database for specific project needs and provides step-by-step guidance on setup, configuration, and optimization. The book also covers advanced topics such as data sharding, replication, and consistency models, ensuring that readers are equipped with a comprehensive understanding of NoSQL technologies. "NoSQL Essentials" is rich with real-world scenarios, case studies, and best practices, making it an invaluable resource for IT professionals, software developers, and anyone involved in database design or big data. Whether you're new to the world of NoSQL or looking to deepen your existing knowledge, this book is an essential tool in navigating the ever-changing database landscape. Embrace the future of data management and unlock the potential of NoSQL with "NoSQL Essentials: Navigating the World of Non-Relational Databases."
This paper investigates a new class of database systems loosely referred to as "non-relational databases," which offer a subset of traditional relational database functionality, in exchange for improved scalability, performance, and / or simplicity. We explore the differences in conceptual modeling techniques, and examine both the advantages and limitations of several classes of currently available systems, using running examples of real-world problems as implemented in both a traditional relational database model, as well as several non-relational models.
This volume is the first in a series which aims to contribute to the wider dissemination of the results of research and development in database systems for non-traditional applications and non-traditional machine organizations. It contains updated versions of selected papers from the First International Symposium on Database Systems for Advanced Applications.
Design great databases—from logical data modeling through physical schema definition. You will learn a framework that finally cracks the problem of merging data and process models into a meaningful and unified design that accounts for how data is actually used in production systems. Key to the framework is a method for taking the logical data model that is a static look at the definition of the data, and merging that static look with the process models describing how the data will be used in actual practice once a given system is implemented. The approach solves the disconnect between the static definition of data in the logical data model and the dynamic flow of the data in the logical process models. The design framework in this book can be used to create operational databases for transaction processing systems, or for data warehouses in support of decision support systems. The information manager can be a flat file, Oracle Database, IMS, NoSQL, Cassandra, Hadoop, or any other DBMS. Usage-Driven Database Design emphasizes practical aspects of design, and speaks to what works, what doesn’t work, and what to avoid at all costs. Included in the book are lessons learned by the author over his 30+ years in the corporate trenches. Everything in the book is grounded on good theory, yet demonstrates a professional and pragmatic approach to design that can come only from decades of experience. Presents an end-to-end framework from logical data modeling through physical schema definition. Includes lessons learned, techniques, and tricks that can turn a database disaster into a success. Applies to all types of database management systems, including NoSQL such as Cassandra and Hadoop, and mainstream SQL databases such as Oracle and SQL Server What You'll Learn Create logical data models that accurately reflect the real world of the user Create usage scenarios reflecting how applications will use a new database Merge static data models with dynamic process models to create resilient yet flexible database designs Support application requirements by creating responsive database schemas in any database architecture Cope with big data and unstructured data for transaction processing and decision support systems Recognize when relational approaches won’t work, and when to turn toward NoSQL solutions such as Cassandra or Hadoop Who This Book Is For System developers, including business analysts, database designers, database administrators, and application designers and developers who must design or interact with database systems
One of the primary assumptions used in the relational model is that all relations must be in first normal form; that is, all values must be non-decomposable units. This assumption unduly constrains our ability to model data, especially for the non-traditional applications which are taxing our current database systems. This research extends relational database theory by relaxing the assumption that all relations in the database must be in first normal form. Relations containing attributes which may be atomic-valued or relation-valued are said to be in non-first normal form (non-1NF). In this context, we develop a non-1NF model and an extended formal query language based on the relational calculus, and prove its equivalence to a relational algebra extended with nest and unnest operators to deal with non-1NF relations. We define a property which non-1NF relations should satisfy, called partitioned normal form (PNF), and develop a set of extended algebra operators to manipulate non-1NF relations and maintain the PNF property. Our model and the extended operators are then further extended to deal with null values and empty nested relations. We present a user-oriented non-1NF query language, called SQL/NF, which is based on the SQL commercial database language and a proposed relational database language standard.
