A debugging approach for live Big Data applications

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Many frameworks exist for programmers to develop and deploy Big Data applications such as Hadoop Map/Reduce and Apache Spark. However, very little debugging support is currently provided in those frameworks. When an error occurs, developers are lost in trying to understand what has happened from the information provided in log files. Recently, new solutions allow developers to record & replay the application execution, but replaying is not always affordable when hours of computation need to be re-executed. In this paper, we present an online approach that allows developers to debug Big Data applications in isolation by moving the debugging session to an external process when a halting point is reached. We introduce IDRAMR, our prototype implementation in Pharo. IDRAMR centralizes the debugging of parallel applications by introducing novel debugging concepts, such as composite debugging events, and the ability to dynamically update both the code of the debugged application and the same configuration of the running framework. We validate our approach by debugging both application and configuration failures for two driving scenarios. The scenarios are implemented and executed using Port, our Map/Reduce framework for Pharo, also introduced in this paper.
Original languageEnglish
Article number102460
Number of pages32
JournalScience of Computer Programming
Publication statusPublished - 1 Aug 2020

Bibliographical note

Funding Information:
We would like to thank Clément Béra for his help in the early stages of this work ([17]). We would also like to thank the anonymous reviewers for their useful feedback. Matteo Marra is a SB PhD Fellow at the FWO - Research Foundation - Flanders. Project: 1S63418N.

Publisher Copyright:
© 2020 Elsevier B.V.

Copyright 2020 Elsevier B.V., All rights reserved.


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