Group-by Query Process in Middleware of Large Scale Data Intensive Systems

Large scale data intensive systems are available in many fields in recent years, and itpsilas a severe challenge for group-by query of large volume of data in a cluster based on shared-nothing architecture. This paper proposes a design of a parallel query engine (PQE) and its asynchronous improvement (APQE) for group-by queries. PQE and APQE support for pipelined query processing and develop maximum degree of pipeline parallelism. APQE further eliminates the synchronous overhead of multi nodes parallelism, and returns part of final result as early as possible if no data dependency exists. Experimental results demonstrate that, compared to previous 2-step query engine, PQE and APQE can make a significant performance improvement for group-by query of large data sets in a shared-nothing cluster system, as well as obviously better scalability.