Multiple query optimization in SQL-on-Hadoop systems

摘要

To reduce the overall computation time of a batch of queries, multiple query optimization in SQL-on-Hadoop systems groups multiple MapReduce jobs converted from queries into a single one, thus avoiding redundant computations by taking sharing opportunities of data scan, map function and map output. SQL-on-Hadoop converts a query into a DAG of MapReduce jobs and each map function is a part of query plan composed of a sequence of relational operators. As each map function is a part of query plan which is usually complex and heavy, disclosed method creates a cost model to simulate the computation time which takes both I/O cost for reading/writing input file and intermediate data and CPU cost for the computation of map function into consideration. A heuristic algorithm is disclosed to find near-optimal integrated query plan for each group based on an observation that each query plan is locally optimal.