Speculative Query Execution in Relational Databases with Graph Modelling.

Relational databases are holding the maximum amount of data underpinning the web. They show excellent record of convenience and efficiency in repository, optimized query execution, scalability, security and accuracy. Recently graph databases are seen as an good replacement for relational database. When compared to the relational data model, graph data model is more vivid, strong and data expressed in it models relationships among data properly. An important requirement is to increase the vast quantities of data stored in RDB into web. In this situation, migration from relational to graph format is very advantageous. Both databases have advantages and limitations depending on the form of queries. Thus, this paper converts relational to graph database by utilizing the schema in order to develop a dual database system through migration, which merges the capability of both relational db and graph db. The experimental results are provided to demonstrate the practicability of the method and query response time over the target database. The proposed concept is proved by implementing it on MySQL and Neo4j

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An adaptive spark-based framework for querying large-scale NoSQL and relational databases

PLoS ONE ◽

10.1371/journal.pone.0255562 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0255562

Author(s):

Eman Khashan ◽

Ali Eldesouky ◽

Sally Elghamrawy

Keyword(s):

Big Data ◽

Data Storage ◽

Relational Databases ◽

Large Scale ◽

Query Languages ◽

Heterogeneous Data ◽

Query Execution ◽

Database Queries ◽

Nosql Databases ◽

Complex Queries

The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneous data models via numerous APIs and query languages imposes challenging tasks on multi-data processing developers. Indeed, complex queries concerning homogenous data structures cannot currently be performed in a declarative manner when found in single data storage applications and therefore require additional development efforts. Many models were presented in order to address complex queries Via multistore applications. Some of these models implemented a complex unified and fast model, while others’ efficiency is not good enough to solve this type of complex database queries. This paper provides an automated, fast and easy unified architecture to solve simple and complex SQL and NoSQL queries over heterogeneous data stores (CQNS). This proposed framework can be used in cloud environments or for any big data application to automatically help developers to manage basic and complicated database queries. CQNS consists of three layers: matching selector layer, processing layer, and query execution layer. The matching selector layer is the heart of this architecture in which five of the user queries are examined if they are matched with another five queries stored in a single engine stored in the architecture library. This is achieved through a proposed algorithm that directs the query to the right SQL or NoSQL database engine. Furthermore, CQNS deal with many NoSQL Databases like MongoDB, Cassandra, Riak, CouchDB, and NOE4J databases. This paper presents a spark framework that can handle both SQL and NoSQL Databases. Four scenarios’ benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. The results show that, the CQNS achieves best latency and throughput in less time among the compared systems.

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