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BigDebug: debugging primitives for interactive big data processing in spark

Authors:

Muhammad Ali Gulzar,

Matteo Interlandi,

Sai Deep Tetali,

Todd Millstein,

Miryung KimAuthors Info & Claims

ICSE '16: Proceedings of the 38th International Conference on Software Engineering

Pages 784 - 795

https://doi.org/10.1145/2884781.2884813

Published: 14 May 2016 Publication History

Abstract

Developers use cloud computing platforms to process a large quantity of data in parallel when developing big data analytics. Debugging the massive parallel computations that run in today's datacenters is time consuming and error-prone. To address this challenge, we design a set of interactive, real-time debugging primitives for big data processing in Apache Spark, the next generation data-intensive scalable cloud computing platform. This requires rethinking the notion of step-through debugging in a traditional debugger such as gdb, because pausing the entire computation across distributed worker nodes causes significant delay and naively inspecting millions of records using a watchpoint is too time consuming for an end user.

First, BigDebug's simulated breakpoints and on-demand watchpoints allow users to selectively examine distributed, intermediate data on the cloud with little overhead. Second, a user can also pinpoint a crash-inducing record and selectively resume relevant sub-computations after a quick fix. Third, a user can determine the root causes of errors (or delays) at the level of individual records through a fine-grained data provenance capability. Our evaluation shows that BigDebug scales to terabytes and its record-level tracing incurs less than 25% overhead on average. It determines crash culprits orders of magnitude more accurately and provides up to 100% time saving compared to the baseline replay debugger. The results show that BigDebug supports debugging at interactive speeds with minimal performance impact.

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Huang YWang ZLi CBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Demonstration of Udon: Line-by-line Debugging of User-Defined Functions in Data WorkflowsCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654756(476-479)Online publication date: 9-Jun-2024
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Index Terms

BigDebug: debugging primitives for interactive big data processing in spark
1. Software and its engineering
  1. Software creation and management
    1. Software development techniques
      1. Error handling and recovery
    2. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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cover image ACM Conferences

ICSE '16: Proceedings of the 38th International Conference on Software Engineering

May 2016

1235 pages

ISBN:9781450339001

DOI:10.1145/2884781

General Chair:
Laura Dillon
Michigan State University
,
Program Chairs:
Willem Visser
Stellenbosch University, South Africa
,
Laurie Williams
North Carolina State University

Copyright © 2016 ACM.

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IEEE-CS\TCSE: TC on Software Engineering
IEEE-CS\DATC: IEEE Computer Society

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New York, NY, United States

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Published: 14 May 2016

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ICSE '16: 38th International Conference on Software Engineering

May 14 - 22, 2016

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https://dl.acm.org/doi/10.1145/3650203.3663333
Huang YWang ZLi CBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Demonstration of Udon: Line-by-line Debugging of User-Defined Functions in Data WorkflowsCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654756(476-479)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3654756
Morán JBertolino Ade la Riva CTuya J(2024)Automatic Debugging of Design Faults in MapReduce ApplicationsIEEE Transactions on Software Engineering10.1109/TSE.2024.336976650:4(956-978)Online publication date: Apr-2024
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