Making cost-based query optimization asymmetry-aware

The architecture and algorithms of database systems have been built around the properties of existing hardware technologies. Many such elementary design assumptions are 20--30 years old. Over the last five years we witness multiple new I/O technologies (e.g. Flash SSDs, NV-Memories) that have the potential of changing these assumptions. Some of the key technological differences to traditional spinning disk storage are: (i) asymmetric read/write performance; (ii) low latencies; (iii) fast random reads; (iv) endurance issues. Cost functions used by traditional database query optimizers are directly influenced by these properties. Most cost functions estimate the cost of algorithms based on metrics such as sequential and random I/O costs besides CPU and memory consumption. These do not account for asymmetry or high random read and inferior random write performance, which represents a significant mismatch. In the present paper we show a new asymmetry-aware cost model for Flash SSDs with adapted cost functions for algorithms such as external sort, hash-join, sequential scan, index scan, etc. It has been implemented in PostgreSQL and tested with TPC-H. Additionally we describe a tool that automatically finds good settings for the base coefficients of cost models. After tuning the configuration of both the original and the asymmetry-aware cost model with that tool, the optimizer with the asymmetry-aware cost model selects faster execution plans for 14 out of the 22 TPC-H queries (the rest being the same or negligibly worse). We achieve an overall performance improvement of 48% on SSD.