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 code generation to optimize the execution performance of SQL queries on a variety of data sources. Despite its already efficient runtime, Spark’s code generation suffers from significant runtime overheads related to data de-serialization during query execution. Such performance penalty can be significant, especially when applications operate on human-readable data formats such as CSV or JSON. application/pdf https://susi.usi.ch/global/documents/324935 https://localhost:5000/ark:/12658/srd1324935 https://susi.usi.ch/documents/324935/files/Schiavio_2020_ACM_Programming20.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.1145/3397537.3397566 info:eu-repo/semantics/altIdentifier/ark/12658/srd1324935 info:eu-repo/semantics/openAccess License undefined Companion Proceedings of the 4th International Conference on the Art, Science, and Engineering of Programming (<Programming’20> Companion), March 23–26, 2020, Porto, Portugal. - 2020, p. 4 p. Apache Spark SQL SQL Compilation Dynamic Compilation info:eu-repo/classification/udc/004 Towards dynamic SQL compilation in Apache Spark http://purl.org/coar/resource_type/c_5794