基于非易失性内存的LSM-tree存储系统优化

Optimization of LSM-tree storage systems based on non-volatile memory

随着大数据时代的到来,金融行业产生的数据越来越多,对数据库的压力也越来越大. LevelDB是谷歌开发的一款基于LSM-tree架构的键值对数据库,有写入快和占用空间小的优点,被金融行业广泛应用.针对LSM-tree架构的写停顿、写放大、对读不友好等缺点,提出了一种基于非易失性内存和机器学习的L0层的设计方法,能够减缓甚至解决上述问题.实验结果表明,该设计能够实现较好的读写性能.

With the advent of the big data era, the financial industry has been generating increasing volumn of data, exerting pressure on database systems. LevelDB is a key-value database, developed by Google,based on the LSM-tree architecture. It offers fast writing and a small footprint, and is widely used in the financial industry. In this paper, we propose a design method for the L0 layer, based on non-volatile memory and machine learning, with the aim of addressing the shortcomings of the LSM-tree architecture,including write pause, write amplification, and unfriendly reading. The proposed solution can slow down or even solve the aforementioned problems;the experimental results demonstrate that the design can achieve better read and write performance.