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Titian: Data Provenance Support in Spark.

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Debugging data processing in Data-Intensive Scalable Computing (DISC) systems is challenging. Titian, a new library for Apache Spark, provides fast data provenance tracking to quickly identify bug origins, improving debugging efficiency.

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Area of Science:

  • Computer Science
  • Software Engineering

Background:

  • Debugging data processing in Data-Intensive Scalable Computing (DISC) systems is complex and time-consuming.
  • Current DISC systems lack adequate debugging tools, leading to extensive manual effort in identifying issues.

Purpose of the Study:

  • To introduce Titian, a library designed to simplify debugging in Apache Spark by enabling data provenance tracking.
  • To provide data scientists with a tool to rapidly pinpoint the source of errors or outliers in their data processing pipelines.

Main Methods:

  • Developed Titian as a library integrated directly into the Apache Spark platform.
  • Implemented data provenance tracking to trace data through various transformations within Spark jobs.

Main Results:

  • Titian enables interactive-speed data provenance, significantly faster than existing solutions.
  • The overhead of Titian's data lineage capture is minimal, rarely exceeding 30% of the baseline job execution time.

Conclusions:

  • Titian offers an efficient solution for debugging data processing in DISC systems.
  • The library enhances the ability of data scientists to identify root causes of issues, improving productivity and reliability.