  Hadoop is emerging as the primary data hub in enterprises, and SQL represents
the de facto language for data analysis. This combination has led to the
development of a variety of SQL-on-Hadoop systems in use today. While the
various SQL-on-Hadoop systems target the same class of analytical workloads,
their different architectures, design decisions and implementations impact
query performance. In this work, we perform a comparative analysis of four
state-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 the
Amazon EC2 cloud platform. The TPC-H experiment results show that, although
Impala outperforms other systems (4.41x - 6.65x) in the text format, trade-offs
exists in the parquet format, with each system performing best on subsets of
queries. A comprehensive analysis of execution profiles expands upon the
performance results to provide insights into performance variations,
performance bottlenecks and query execution characteristics.
