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Vectorized Query Processing for CPUs using Apache Arrow

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This talk by Jacques Nadeau at Scale By The Bay discusses different types of query processing technology and focuses on the benefits of 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. Furthermore, vectorized query processing algorithms can be written in a manner that is aware of column-level encoding and can easily operate on the compressed column values in some cases. This saves CPU-memory bandwidth since we need only decompress the necessary column values. Columnar format allows us to efficiently utilize CPU and GPU cache by filling cache lines with related data (column values from an in-memory vector). With the increasing use of GPUs and FPGAs, efficient use of the smaller on-chip memory available in these architectures is especially important. In addition, Apache Arrow allows for zero-copy, shared access to buffers so that multiple processes can more efficiently operate on the same data. On the storage side, columnar representation of on-disk data makes a good case for efficient utilization of disk I/O bandwidth for analytical queries. Dremio’s query processing engine leverages columnar format of Apache Arrow and Parquet for in-memory and on-disk representations respectively. We have vectorized implementations of operators like hash join and hash aggregation to name a few.

This talk was given by Jacques Nadeau at Scale By The Bay.