Abstract
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.
Highlights
The popularity of NoSQL systems is caused by their efficiency in handling unstructured data and backing up effective design schemes that give the system users supreme flexibility and scalability
complex query SQL and NoSQL (CQNS)’s upload performance averaged 0.2, 10.2, 1.7, and 20.23 times higher than HBase, Cassandra, MongoDB, and Haery, to continue. the Cassandra with CQNS framework achieved better results than Haery [11], but the results of Haery using Mongo database are relatively better than CQNS results with no Sharding
The experiments conducted provided a comparison between CQNS and ODBPI to measure the cost time based on the number of joins
Summary
The popularity of NoSQL systems is caused by their efficiency in handling unstructured data and backing up effective design schemes that give the system users supreme flexibility and scalability. Every NoSQL database has a special query language and does not support the criteria of other systems. The main problem that many researches focused on, is that there is no standard method to execute complex queries across NoSQL Databases [1,2,3,4,5,6,7,8]. As a result of the variety and changes in the data models [9] of various databases, there is no standard way to solve the problem of implementing queries for various NoSQL data stores. The reason is due to a lack of a combined access model for diversified data stores. The programmers must challenge themselves with the execution of these queries, which are hard to optimize.
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