A Framework for Executing Complex Querying for Relational and NoSQL Databases (CQNS)

The increasing of data on the web poses major confrontations. The amount of stored data and query data sources have become needful features for huge data systems. There are a large number of platforms used to handle the NoSQL database model such as: Spark, H2O and Hadoop HDFS / MapReduce, which are suitable for controlling and managing the amount of big data. Developers of different applications impose data stores on difficult tasks by interacting with mixed data models through different APIs and queries. In this paper, a complex SQL Query and NoSQL (CQNS) framework that acts as an interpreter sends complex queries received from any data store to its corresponding executable engine called CQNS. The proposed framework supports application queries and database transformation at the same time, which in turn speeds up the process. Moreover, CQNS handles many NoSQL databases like MongoDB and Cassandra. This paper provides a spark framework that can handle SQL and NoSQL databases. This work also examines the importance of MongoDB block sharding and composition. Cassandra database deals with two types of sections vertex and edge Portioning. The four scenarios criteria datasets are used to evaluate the proposed CQNS to query the various NOSQL databases in terms of optimization performance and timing of query execution. The results show that among the comparative system, CQNS achieves optimum latency and productivity in less time.