Data Dissemination and Parallel Processing Techniques Research Based on Massively Parallel Processing

In the current distributed database system architecture enterprise-class, the massively parallel processing architecture is used frequently. This method can be used to carry out large-scale analysis of data through distributed across multiple nodes and storage and query process, from its scope of application produce simple reports to perform complex analytics workloads. However, due to the characteristics of shared-nothing MPP technology, to carry out large-scale data analysis query and maintain data consistency there are some difficulties. In this paper, a relational SQL-based query parsing distributed MPP data distribution and parallel processing technology, the goal is to maintain and improve the consistency of distributed data query speed. First SQL query analysis section, according to the syntax analysis, semantic analysis and sentence parsing steps such order; in the form of work distribution node/ data node in the data distribution phase, all tasks emanating from the work of a distribution node, all need to treated results are returned to the node; when parallel processing, each node needs to store a copy of the lookup table, and on each node concurrent execution of SQL statements for each query. Experimental results show that the proposed MPP data distribution and parallel processing scheme can support large volume of data processing, ensuring data consistency in the premise of improving query processing speed.