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Hadoop is emerging as the primary data hub in enterprises, and SQL representsthe de facto language for data analysis. This combination has led to thedevelopment of a variety of SQL-on-Hadoop systems in use today. While thevarious SQL-on-Hadoop systems target the same class of analytical workloads,their different architectures, design decisions and implementations impactquery performance. In this work, we perform a comparative analysis of fourstate-of-the-art SQL-on-Hadoop systems (Impala, Drill, Spark SQL and Phoenix)using the Web Data Analytics micro benchmark and the TPC-H benchmark on theAmazon EC2 cloud platform. The TPC-H experiment results show that, althoughImpala outperforms other systems (4.41x - 6.65x) in the text format, trade-offsexists in the parquet format, with each system performing best on subsets ofqueries. A comprehensive analysis of execution profiles expands upon theperformance results to provide insights into performance variations,performance bottlenecks and query execution characteristics.

A comparative analysis of state-of-the-art SQL-on-Hadoop systems for interactive analytics