Vectorizing an in situ query engine

Database systems serve a wide range of use cases efficiently, but require data to be loaded and adapted to the system's execution engine. This pre-processing step is a bottleneck to the analysis of the increasingly large and heterogeneous datasets. Therefore, numerous research efforts advocate for querying each dataset in situ, i.e., without pre-loading it in a DBMS. On the other hand, performing analysis over raw data entails numerous overheads because of the potentially inefficient data representations. In this paper, we investigate the effect of vector processing on raw data querying. We enhance the operators of a query engine to use SIMD operations. Specifically, we examine the effect of SIMD on two different cases: the scan operators that perform the CPU-intensive task of input parsing, and the part of the query pipeline that performs a selection and computes an aggregate. We show that a vectorized approach has a lot of potential to improve performance, which nevertheless comes with trade-offs.