Dynamic File Placing Control for Improving the I/O Performance in the Reduce Phase of Hadoop

Hadoop is a popular open-source MapReduce implementation. In the cases of jobs wherein all the output files of all the relevant Map tasks are transmitted and consolidated into a single Reduce task, such as in TeraSort, the single Reduce task is the bottleneck task and is I/O bounded for processing many large output files. In most cases, including TeraSort, the intermediate data, which include the output files of the Map tasks, are large and accessed sequentially. For improving the performance of these jobs, it is important to increase the sequential access performance. In this paper, we focus on Hadoop sample job TeraSort, which is a single-Reduce-tasked job, and discuss a method for improving its performance. First, we perform TeraSort and demonstrate that the single Reduce task is the bottleneck task and is I/O bounded. Second, we show the sequential I/O speed of each zone of an HDD. Third, we introduce a static method for improving the performance of such single-Reduce-tasked jobs. The method statically controls block bitmaps of the filesystem and places the intermediate files in a faster zone, i.e., the outer range, of the HDD. Forth, we propose to improve this static method by controlling block bitmap dynamically. Lastly, we present performance evaluation of the proposed method and demonstrate that our method improves the performance.