Adding data provenance support to Apache Spark

  • Matteo Interlandi
  • Ari Ekmekji
  • Kshitij Shah
  • Muhammad Ali Gulzar
  • Sai Deep Tetali
  • Miryung Kim
  • Todd Millstein
  • Tyson Condie
Special Issue Paper
  • 229 Downloads

Abstract

Debugging data processing logic in data-intensive scalable computing (DISC) systems is a difficult and time-consuming effort. Today’s DISC systems offer very little tooling for debugging programs, and as a result, programmers spend countless hours collecting evidence (e.g., from log files) and performing trial-and-error debugging. To aid this effort, we built Titian, a library that enables data provenance—tracking data through transformations—in Apache Spark. Data scientists using the Titian Spark extension will be able to quickly identify the input data at the root cause of a potential bug or outlier result. Titian is built directly into the Spark platform and offers data provenance support at interactive speeds—orders of magnitude faster than alternative solutions—while minimally impacting Spark job performance; observed overheads for capturing data lineage rarely exceed 30% above the baseline job execution time.

Keywords

Data provenance Spark Debugging 

Notes

Acknowledgements

Titian is supported through Grants NSF IIS-1302698 and CNS-1351047, and U54EB020404 awarded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative (www.bd2k.nih.gov). We would also like to thank our industry partners at IBM Research Almaden and Intel for their generous gifts in support of this research.

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.MicrosoftRedmondUSA
  2. 2.University of California, Los AngelesLos AngelesUSA
  3. 3.Stanford UniversityStanfordUSA

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