Studying the effect of multi-query functionality on a correlation-aware SQL-to-MapReduce translator

摘要

Real-time operational tools for the mining of massive datasets should provide fault tolerance, scalability, and support for complex query processing in relational database management systems. Data mining analytical tools that operate in a clustered environment such as Hadoop can preserve data integrity during a breakdown and support the scalability of query processing in nonrelational database systems. But how should the multifaceted semantics of queries in structured query language (SQL) be accurately translated for optimal processing in nonrelational systems? Amirthalingam and Springer investigate this nontrivial question by exploring the effectiveness of a "correlation-aware SQL-to-MapReduce" translator. They design experiments for recognizing and optimizing the relationships among multiple SQL queries processing in environments that support parallel and distributed transaction processing.