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Enterprises may have multiple database systems spread across the organization for redundancy or for serving different applications. In such systems, query workloads can be distributed across different servers for better performance. A materialized view, or Materialized Query Table (MQT), is an auxiliary table with pre-computed data that can be used to significantly improve the performance of a database query. In this paper, we propose a framework for coordinating execution of OLAP query workloads across a database cluster with shared nothing architecture. Such coordination is complex since we need to consider (1) the time to build the MQTs, (2) the query execution impact of the MQTs, (3) whether the MQTs can fit in the disk space limitation, (4) server computation power, and (5) the effectiveness of the scheduling and placement algorithms in deriving a combination of configurations so that the workload can be completed in the shortest time period. We frame the problem as a combinatorial problem with a solution space that is exponential in the number of queries, MQTs, and servers. We provide a stochastic search heuristic that finds a near-optimal mapping of queries-to-servers and MQTs-to-servers within an arbitrarily bounded time and compare our solution with an exhaustive search and three standard greedy algorithms. Our search implementation produced schedules within 9% of the optimal found through an exhaustive search and produced better solutions than typical greedy algorithms for both TPC-H and synthetic benchmarks under a variety of experiments. For a key trial where disk space is limited, it produced 15% better results than the next best competitor, corresponding to an absolute wall clock advantage of over 10 hours.

Load distribution of analytical query workloads for database cluster architectures