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A User-friendly and Powerful R Analysis of Large-scale Datasets
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BigDebug: Debugging Primitives for Interactive Big Data Processing in Spark.

Muhammad Ali Gulzar1, Matteo Interlandi1, Seunghyun Yoo1

  • 1University of California, Los Angeles.

Proceedings - International Conference on Software Engineering. International Conference on Software Engineering
|July 9, 2016
PubMed
Summary
This summary is machine-generated.

Debugging big data analytics on cloud computing platforms is challenging. BIGDEBUG introduces interactive, real-time debugging primitives for Apache Spark, enabling efficient, record-level error analysis with minimal overhead.

Keywords:
Debuggingbig data analyticsdata-intensive scalable computing (DISC)fault localization and recoveryinteractive tools

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Area of Science:

  • Computer Science
  • Software Engineering
  • Distributed Systems

Background:

  • Big data analytics on cloud platforms involve massive parallel computations.
  • Debugging these computations is time-consuming and error-prone due to scale and distribution.
  • Traditional debuggers are inefficient for large-scale, distributed data processing.

Purpose of the Study:

  • To design interactive, real-time debugging primitives for big data processing in Apache Spark.
  • To address the challenges of debugging massive parallel computations in data centers.
  • To enable efficient, selective examination of distributed data and error analysis.

Main Methods:

  • Development of simulated breakpoints and on-demand watchpoints for selective data inspection.
  • Implementation of record-level tracing and fine-grained data provenance.
  • Design for pinpointing crash-inducing records and selective sub-computation resumption.

Main Results:

  • BIGDEBUG scales to terabytes with less than 25% overhead on average.
  • Achieves orders of magnitude greater accuracy in identifying crash culprits compared to baseline replay debuggers.
  • Provides up to 100% time saving in debugging processes.

Conclusions:

  • BIGDEBUG enables interactive-speed debugging for big data processing in Apache Spark.
  • Offers significant improvements in accuracy and efficiency over traditional debugging methods.
  • Minimizes performance impact while enhancing the debugging experience for large-scale data analytics.