PDL Abstract

End-to-end Tracing in HDFS

Carnegie Mellon University School of Computer Science Technical Report (Masters Thesis)
CMU-CS-11-120, July 2011.

William Wang

School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213

Debugging performance problems in distributed systems is difficult. Thus many debugging tools are being developed to aid diagnosis. Many of the most interesting new tools require information from end-to-end tracing in order to perform their analysis. This paper describes the development of an end-to-end tracing framework for the Hadoop Distributed File System. The approach to instrumentation in this implementation differs from previous ones as it focuses on detailed low-level instrumentation. Such instrumentation encounters the problems of large request flow graphs and a large number of different kinds of graphs, impeding the effectiveness of the diagnosis tools that use them. This report describes how to instrument at a fine granularity and explain techniques to handle the resulting challenges. The current implementation is evaluated in terms of performance, scalability, the data the instrumentation generates, and its ability to be used to solve performance problems.

KEYWORDS: computer science, HDFS, end-to-end tracing, Hadoop, performance diagnosis