AB-tree: index for concurrent random sampling and updates

There has been an increasing demand for real-time data analytics. Approximate Query Processing (AQP) is a popular option for that because it can use random sampling to trade some accuracy for lower query latency. However, the state-of-the-art AQP system either relies on scan-based sampling algorithms to draw samples, which can still incur a non-trivial cost of table scan, or creates samples of the database in a preprocessing step, which are hard to update. The alternative is to use the aggregate B-tree indexes to support both random sampling and updates in database with logarithmic time. However, to the best of our knowledge, it is unknown how to design an aggregate B-tree to support highly concurrent random sampling and updates, due to the difficulty of maintaining the aggregate weights correctly and efficiently with concurrency. In this work, we identify the key challenges to achieve high concurrency and present AB-tree, an index for highly concurrent random sampling and update operations. We also conduct extensive experiments to show its efficiency and efficacy in a variety of workloads.