Schiavio, Filippo Bonetta, Daniele Binder, Walter 2020 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. application/pdf https://susi.usi.ch/global/documents/322408 https://n2t.net/ark:/12658/srd1322408 https://susi.usi.ch/documents/322408/files/Schiavio_2020_vldb.pdf eng https://doi.org/10.14778/3377369.3377382 info:eu-repo/semantics/altIdentifier/ark/12658/srd1322408 info:eu-repo/semantics/openAccess CC BY-NC-ND Proceedings of the VLDB Endowment. - 2020, vol. 13, no. 5, p. 754-767 info:eu-repo/classification/udc/004 Dynamic speculative optimizations for SQL compilation in Apache Spark http://purl.org/coar/resource_type/c_6501