ROME: Robust Query Optimization via Parallel Multi-Plan Execution

We present a non-intrusive approach to robust query processing that can be used on top of any SQL execution engine. To reduce the risk of selecting highly sub-optimal query plans, we execute multiple plans in parallel. Query processing finishes once the first of these plans finishes execution.

Plans are selected to be complementary in terms of the intermediate results they generate. This increases robustness to cardinality estimation errors, making cost prediction hard, that concern a subset of candidate results. We present multiple cost-based approaches to selecting plans for robust execution. The first approach uses a simple cost model, based on diversity of intermediate results. The second approach features a probabilistic model, approximating expected execution overheads, given uncertainty on true intermediate result sizes. We present greedy and exhaustive algorithms to select optimal plans according to those cost models. The experiments demonstrate that executing multiple plans in parallel is preferable over executing single plans that are occasionally sub-optimal, as well as over several baselines.