Cache-conscious buffering for database operators with state

Database processes must be cache-efficient to effectively utilize modern hardware. In this paper, we analyze the importance of temporal locality and the resultant cache behavior in scheduling database operators for in-memory, block oriented query processing. We demonstrate how the overall performance of a workload of multiple database operators is strongly dependent on how they are interleaved with each other. Longer time slices combined with temporal locality within an operator amortize the effects of the initial compulsory cache misses needed to load the operator's state, such as a hash table, into the cache. Though running an operator to completion over all of its input results in the greatest amortization of cache misses, this is typically infeasible because of the large intermediate storage requirement to materialize all input tuples to an operator. We show experimentally that good cache performance can be obtained with smaller buffers whose size is determined at runtime. We demonstrate a low-overhead method of runtime cache miss sampling using hardware performance counters. Our evaluation considers two common database operators with state: aggregation and hash join. Sampling reveals operator temporal locality and cache miss behavior, and we use those characteristics to choose an appropriate input buffer/block size. The calculated buffer size balances cache miss amortization with buffer memory requirements.