Journal article

Dynamic speculative optimizations for SQL compilation in Apache Spark

  • 2020
Published in:
  • Proceedings of the VLDB Endowment. - 2020, vol. 13, no. 5, p. 754-767
English Big-data systems have gained significant momentum, and Apache Spark is becoming a de-facto standard for modern data analytics. Spark relies on SQL query compilation to optimize the execution performance of analytical workloads on a variety of data sources. Despite its scalable architecture, Spark's SQL code generation suffers from significant runtime overheads related to data access and de-serialization. Such performance penalty can be significant, especially when applications operate on human-readable data formats such as CSV or JSON. In this paper we present a new approach to query compilation that overcomes these limitations by relying on run-time profiling and dynamic code generation. Our new SQL compiler for Spark produces highly-efficient machine code, leading to speedups of up to 4.4x on the TPC-H benchmark with textual-form data formats such as CSV or JSON.
Language
  • English
Classification
Computer science
Other electronic version

Versione pubblicata

License
CC BY-NC-ND
Open access status
green
Persistent URL
https://susi.usi.ch/usi/documents/322408
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