Fast Access to Columnar, Hierarchically Nested Data via Code Transformation
This addresses a performance bottleneck for researchers in fields like High Energy Physics who rely on nested loops and complex dependencies, though it is an incremental improvement over existing columnar data methods.
The paper tackles the problem of efficiently processing hierarchically nested data in Big Data systems without converting columnar data back to objects, which incurs performance costs. It introduces a code transformation technique that rewrites procedural code to operate directly on columnar data, and presents performance comparisons showing improvements in a High Energy Physics context.
Big Data query systems represent data in a columnar format for fast, selective access, and in some cases (e.g. Apache Drill), perform calculations directly on the columnar data without row materialization, avoiding runtime costs. However, many analysis procedures cannot be easily or efficiently expressed as SQL. In High Energy Physics, the majority of data processing requires nested loops with complex dependencies. When faced with tasks like these, the conventional 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 it operates on hierarchically nested, columnar data natively, without row materialization. It can be viewed as a compiler pass on the typed abstract syntax tree, rewriting references to objects as columnar array lookups. We will also present performance comparisons between transformed code and conventional object-oriented code in a High Energy Physics context.