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A debugging approach for live Big Data applications

Matteo Marra 1 Guillermo Polito 2 Elisa Gonzalez Boix 1 
2 RMOD - Analyses and Languages Constructs for Object-Oriented Application Evolution
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : 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 IDRA MR , our prototype implementation in Pharo. IDRA MR 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.
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Submitted on : Wednesday, September 29, 2021 - 4:18:33 PM
Last modification on : Tuesday, November 22, 2022 - 2:26:16 PM
Long-term archiving on: : Thursday, December 30, 2021 - 7:38:10 PM


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  • HAL Id : hal-03358830, version 1


Matteo Marra, Guillermo Polito, Elisa Gonzalez Boix. A debugging approach for live Big Data applications. Science of Computer Programming, In press. ⟨hal-03358830⟩



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