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.