Browsing by Subject "NoSQL databases"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Publication
    Open Access
    A Model-Driven Approach to Generate Schemas for Object-Document Mappers
    (IEEE Access, 2019) Hernández Chillón, Alberto; Sevilla Ruiz, Diego; García Molina, Jesús J.; Feliciano Morales, Severino; Ingeniería y Tecnología de Computadores
    Many actual NoSQL systems are schemaless, that is, the structure of the data is not defined beforehand in any schema, but it is implicit in the data itself. This characteristic is very convenient when the data structure suffers frequent changes. However, the agility and flexibility achieved is at the cost of losing some important benefits, such as 1) assuring that the data stored and retrieved fits the database schema; 2) some database utilities require to know the schema, and; 3) schema visualization helps developers to write better code. In previous work, we proposed a model-based reverse engineering approach to infer schema models from NoSQL data. Model-driven engineering (MDE) techniques can be used to take advantage of extracted models with different purposes, such as schema visualization or automatic code generation. Here, in this paper, we present an MDE solution to automate the usage of Object-NoSQL mappers when the database already exists. We will focus on mappers that are available for document systems (Object-Document mappers, ODMs), but the proposed approach is mapper-independent. These mappers are emerging to provide similar functionality to Object-Relational mappers: they are in charge of the mapping of objects into NoSQL data (documents in the case of ODMs) for object-oriented applications. We show how schemas and other artifacts (e.g. validators and indexes) for ODMs can be automatically generated from inferred schemas. The solution consists of a two-step model transformation chain, where an intermediate model is generated to ease the code generation. We have applied our approach for two popular ODMs: Mongoose and Morphia and validated it with the StackOverflow dataset.
  • Thumbnail Image
    Publication
    Open Access
    A unified metamodel for NoSQL and relational databases
    (Elsevier, 2021-09-25) Fernández Candel, Carlos; Sevilla Ruiz, Diego; García Molina, Jesús J.; Ingeniería y Tecnología de Computadores
    The Database field is undergoing significant changes. Although relational systems are still predominant, the interest in NoSQL systems is continuously increasing. In this scenario, polyglot persistence is envisioned as the database architecture to be prevalent in the future. Therefore, database tools and systems are evolving to support several data models. Multi-model database tools normally use a generic or unified metamodel to represent schemas of the data model that they support. Such metamodels facilitate developing database utilities, as they can be built on a common representation. Also, the number of mappings required to migrate databases from a data model to another is reduced, and integrability is favored. In this paper, we present the U-Schema unified metamodel able to represent logical schemas for the four most popular NoSQL paradigms (columnar, document, key–value, and graph) as well as relational schemas. We will formally define the mappings between U-Schema and the data model defined for each database paradigm. How these mappings have been implemented and validated will be discussed, and some applications of U-Schema will be shown. To achieve flexibility to respond to data changes, most of NoSQL systems are “schema-on-read,” and the declaration of schemas is not required. Such an absence of schema declaration makes structural variability possible, i.e., stored data of the same entity type can have different structure. Moreover, data relationships supported by each data model are different; For example, document stores have aggregate objects but not relationship types, whereas graph stores offer the opposite. Through the paper, we will show how all these issues have been tackled in our approach. As far as we know, no proposal exists in the literature of a unified metamodel for relational and the NoSQL paradigms which describes how each individual data model is integrated and mapped. Our metamodel goes beyond the existing proposals by distinguishing entity types and relationship types, representing aggregation and reference relationships, and including the notion of structural variability. Our contributions also include developing schema extraction strategies for schemaless systems of each NoSQL data model, and tackling performance and scalability in the implementation for each store.
  • Thumbnail Image
    Publication
    Embargo
    Towards a Taxonomy of Schema Changes for NoSQL Databases: The Orion Language
    (Springer, 2021-10-16) Hernández Chillón, Alberto; Sevilla Ruiz, Diego; García Molina, Jesús J.; Ingeniería y Tecnología de Computadores
    The emergence of NoSQL databases and polyglot persistence demands to address classical research topics in the context of new data models and database systems. Schema evolution is a crucial aspect in database management to which limited attention has been paid for NoSQL systems. The definition of a taxonomy of changes is a central issue in the design of any schema evolution approach. Proposed taxonomies of changes for NoSQL databases have considered simple data models, which significantly reduce the set of considered schema change operations. In this paper, we present a unified logical data model that includes aggregation and reference relationships, and takes into account the structural variations that can occur in schemaless NoSQL stores. For this data model, we introduce a new taxonomy of changes with operations not considered in the existing proposed taxonomies for NoSQL. A schema definition language will be used to create schemas that conform to the generic data model, and a database-independent language, created to implement this taxonomy of changes, will be shown. We will show how this language can be used to automatically generate evolution scripts for a set of NoSQL stores, and validated on a case study for a real dataset.