Accelerating Deletion Interventions on OLAP Workload

Deletion based view maintenance is a building block in many query explanation and analytics applications, such as sensitivity analysis, what-if analysis, data cleaning, and probabilistic databases, which require exploring how a query's output result changes when excluding (deleting) subsets of input relations (referred to as “deletion interventions”). These applications' ability to remain interactive is limited by how fast they can recompute output results, especially over complex queries and large data. A critical limitation in existing engines is their performance. To remain interactive, existing approaches either restrict the types of aggregation functions, require explanations to be pre-specified, or apply sampling and approximation. We show how to build an interactive deletion intervention evaluation engine for SPJA queries, by Ieveraging recent advances in fast fine-grained provenance that captures input/output relationship for each physical operator in a denormalized compact pointer-free format. We use provenance to propagate the deletion status of tuples per operator, in a tight loop that leads to improvement in instruction and data locality. In addition, this representation is amenable to a variety of physical optimizations, such as dictionary-encoding, bit-packing, vectorization, and parallelization. Operators' output tuples can be evenly split across threads, and their deletion status can be computed independently. Our preliminary results show that on the TPC-H workload, our engine takes on average 9ms to evaluate 1K deletion interventions at a time, achieving on average wins of 3 orders of magnitude over DBToaster, a state-of-the art IVM based engine, and wins of 4 orders of magnitude against existing provenance based approaches. Utilizing tight loops and a cache-friendly provenance and intervention representation enable us to evaluate thousands of deletion interventions at interactive speed, and enable interactive time query explanations over more complex queries and data than previously possible.