论文标题
用于自动驾驶数据库的自适应柱压缩家族
An Adaptive Column Compression Family for Self-Driving Databases
论文作者
论文摘要
现代的内存数据库通常用于高性能工作负载，因此必须同时对小记忆足迹和高查询速度进行优化。数据压缩有可能减少内存需求，但通常也会降低查询速度。在本文中，我们提出了一个新颖的自适应压缩机，它为这些维度提供了新的权衡点，与LZ4相比，在接近现有最快的段编码器的同时，达到了比LZ4更好的压缩。我们通过隔离和TPC-H的合成数据评估压缩机，并加入订单基准，并集成到现代的关系柱商店Hyrise中。
Modern in-memory databases are typically used for high-performance workloads, therefore they have to be optimized for small memory footprint and high query speed at the same time. Data compression has the potential to reduce memory requirements but often reduces query speed too. In this paper we propose a novel, adaptive compressor that offers a new trade-off point of these dimensions, achieving better compression than LZ4 while reaching query speeds close to the fastest existing segment encoders. We evaluate our compressor both with synthetic data in isolation and on the TPC-H and Join Order Benchmarks, integrated into a modern relational column store, Hyrise.
