Research and Implementation of Document-Relational Data Query Execution Tech-nology

Abstract

With the arrival of the era of big data, Internet applications have produced abundant data types. Integ-rating storage, query and organization of data with various structures is a research hotspot of large data management system. The relational database and NoSQL document database are performed unified management. Two different database engines supporting structured and semi-structured data are integrated into the large data management system. The query engine ENTIA is implemented to perform query processing. Based on the global view, a unified query interface is provided to users. The end user does not need to care about the type and structure of the data, and the physical storage location. It only needs to send a request to ENTIA according to the business requirements. A large number of preliminary experiments are carried out to optimize the query based on heuristic rules. The single query is rewritten into multiple query sub-tasks that can be executed in parallel. The calculation is pushed to the appropriate database engine, which makes full use of the computing resources of the system and greatly improves the query performance of the system. Represented by two peer-to-peer engines of relational database PostgreSQL and document database MongoDB, ENTIA’s query ability for multiple data types and query optimization are realized. ENTIA can correctly execute mixed queries through functional coincidence experiments. The effectiveness of the optimization method is proven by a number of performance comparison experiments.

Keywords

• relation database
• document database
• hybrid query
• query optimization