DecLog: Decentralized Logging in Non-Volatile Memory for Time Series Database Systems.

Growing demands for the efficient processing of extreme-scale time
series workloads call for more capable time series database management systems (TSDBMS). Specifically, to maintain consistency and
durability of transaction processing, systems employ write-ahead
logging (WAL) whereby transactions are committed only after the
related log entries are flushed to disk. However, when faced with
massive I/O, this becomes a throughput bottleneck. Recent advances
in byte-addressable Non-Volatile Memory (NVM) provide opportunities to improve logging performance by persisting logs to NVM
instead. Existing studies typically track complex transaction dependencies and use barrier instructions of NVM to ensure log ordering.
In contrast, few studies consider the heavy-tailed characteristics of
time series workloads, where most transactions are independent of
each other. We propose DecLog, a decentralized NVM-based logging system that enables concurrent logging of TSDBMS transactions. Specifically, we propose data-driven log sequence numbering
and relaxed ordering strategies to track transaction dependencies
and resolve serialization issues. We also propose a parallel logging
method to persist logs to NVM after being compressed and aligned.
An experimental study on the YCSB-TS benchmark offers insight
into the performance properties of DecLog, showing that it improves throughput by up to 4.6× while offering lower recovery
time in comparison to the open source TSDBMS Beringei.