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Self-help support groups are voluntary, community-based organizations that provide a platform for individuals with shared concerns to exchange support, insights, and practical strategies for coping with life challenges. Typically led by group members or paraprofessionals, these groups form a cornerstone of mental health care, especially in reaching populations that are underserved by traditional healthcare systems.
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Adding data provenance support to Apache Spark.

Matteo Interlandi1, Ari Ekmekji2, Kshitij Shah3

  • 1Microsoft, Redmond, WA, USA.

The VLDB Journal : Very Large Data Bases : a Publication of the VLDB Endowment
|April 23, 2019
PubMed
Summary
This summary is machine-generated.

Debugging complex data processing in scalable computing systems is challenging. Titian, a new library for Apache Spark, provides fast data provenance tracking to quickly identify bugs and outliers, significantly speeding up debugging.

Keywords:
Data provenanceDebuggingSpark

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Area of Science:

  • Computer Science
  • Software Engineering

Background:

  • Debugging data processing in data-intensive scalable computing (DISC) systems is a significant challenge.
  • Current DISC systems lack adequate debugging tools, leading to time-consuming manual evidence collection and trial-and-error methods.

Purpose of the Study:

  • To develop a library that facilitates efficient debugging of data processing logic in DISC systems.
  • To introduce a data provenance tracking mechanism within Apache Spark.

Main Methods:

  • Developed 'Titian,' a library integrated into Apache Spark for data provenance tracking.
  • Titian tracks data lineage through various transformations within Spark jobs.

Main Results:

  • Titian enables data scientists to rapidly pinpoint the source of bugs or outliers by identifying the relevant input data.
  • The library provides data provenance support at interactive speeds, significantly faster than existing solutions.
  • Observed performance overhead for data lineage capture is minimal, rarely exceeding 30% of the baseline job execution time.

Conclusions:

  • Titian offers an effective solution for debugging data-intensive scalable computing systems.
  • The library enhances the efficiency and speed of identifying data-related issues in Apache Spark applications.
  • Titian's performance and usability make it a valuable tool for data scientists working with large-scale data processing.