Dynamic Configurable Write-Ahead Logging Framework for Memory Table

Abstract

Normally, the NoSQL database management systems’ databases or data partitions fixedly write their write-ahead logging (WAL) into one or more log files after they are started up, forming a strong-coupled rela-tionship. Since the database logical model and partition configuration are determined by the application business and computing environments, with the write-ahead logging tightly coupled, the database management systems cannot optimize performance via simply configuring parameters such as the number and size of the write-ahead logging. In response to this problem, this paper proposes a dynamic configurable write-ahead logging framework for memory table. This framework records Redo log, and memory tables can be dynamically allocated to different write-ahead logging queues, supporting mutable relationship and decoupling write-ahead logging and applications. This paper implements this framework on the time series database Apache IoTDB, and relevant experiments are conducted. Experimental results show that, compared with strong-coupled write-ahead logging, this dynamic configurable write-ahead logging framework can find a better configuration and improve the write performance by 8% to 19%, indicating that this framework can achieve dynamic performance tuning for different computing environments and application loads.

Keywords

• write-ahead logging (wal); redo log; log-structured merge-tree (lsm); time series database