Matrix-Query: A Distributed SQL-Like Query Processing Model for Large Database Clusters

Along with the development of distributed computation and the rapid growth of data, scientific research increasingly requires the support of high-efficiency relational data processing framework. According to the characteristics of scientific data, for example bulk inserts and unfrequented change, this paper proposes a streaming processing model called Matrix-Query with the matching data storage architecture for relational query. Through transforming the original relational schema to entities and key-value indexing, the data storage solution provides more localization operation and data positioning. Compare to traditional Map-Reduce model, the Matrix-Query isolates the influence between subtasks to ensure execution in a streaming and parallel manner and reduces negative impacts of writing intermediate file. We also optimize the data structure and subtask management to improve the performance of Matrix-Query. The experimental results demonstrate performance advantage of Matrix-query compared to two famous data processing systems, Hive and HadoopDB, which build on the top of Map-Reduce model.