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Big Data query systems represent data in a columnar format for fast,selective access, and in some cases (e.g. Apache Drill), perform calculationsdirectly on the columnar data without row materialization, avoiding runtimecosts.  However, many analysis procedures cannot be easily or efficiently expressedas SQL. In High Energy Physics, the majority of data processing requires nestedloops with complex dependencies. When faced with tasks like these, theconventional approach is to convert the columnar data back into an object form,usually with a performance price.  This paper describes a new technique to transform procedural code so that itoperates on hierarchically nested, columnar data natively, without rowmaterialization. It can be viewed as a compiler pass on the typed abstractsyntax tree, rewriting references to objects as columnar array lookups.  We will also present performance comparisons between transformed code andconventional object-oriented code in a High Energy Physics context.

Fast Access to Columnar, Hierarchically Nested Data via Code Transformation