On High-Latency Bowtie Data Streaming
Gabriel Stella, Dmitri Loguinov · 2022 IEEE International Conference on Big Data (Big Data) · 2022
In this paper, we consider applications that read sequential data from n input files and write the result into m output files, which encompasses many types of external-memory sorting, database join/group queries, and MapReduce computation. We call this I/O model bowtie streaming and develop novel algorithms for modeling its throughput, maximizing sequential run lengths, and obtaining optimal multi-pass split/merge factors under non-trivial stream-switching (i.e., seek) delay. Based on these developments, we build a platform called Tuxedo for general bowtie computation and show that it is able to perform external-memory sorting with a million times fewer attempted seeks than Hadoop and two orders of magnitude fewer than highly optimized external-memory frameworks STXXL and nsort.