Scale By The Bay 2019: Jacques Nadeau, Vectorized Query Processing for CPUs using Apache Arrow

ai.bythebay.io Nov 2025, Oakland, full-stack AI conference Title: Vectorized Query Processing for CPUs and GPUs using Apache Arrow Query processing technology has seen rapid development since the iconic C-Store paper was published in 2005. The focus has been on designing query processing algorithms and data structures that efficiently utilize CPU and leverage the changing trends in hardware to deliver optimal performance. In this talk we will explore different types of vectorized query processing in Dremio using Apache Arrow. Abstract Columnar data has become the de facto format for building high performance query engines that run analytical workloads. Apache Arrow is an in-memory columnar data format that houses canonical in-memory representations for both flat and nested data structures. It is a natural complement to on-disk formats like Apache Parquet and Apache ORC. Data stored in a columnar format is amenable to processing using vectorized instructions (SIMD) available on all modern architectures. Query processing algorithms can implement simple and efficient code that operates on the columnar values in a tight-loop, providing fast and CPU cache-friendly access patterns. Operations like SUM, FILTER, COUNT, MIN, MAX etc on columnar data can be made more efficient by leveraging the data-level parallelism property of SIMD instructions. Columnar data can be encoded using lightweight algorithms like dictionary encoding, run length encoding, bit packing and delta encoding that are far more CPU efficient than general purpose compression algorithms like LZO and ZLIB. Further…