JuliaCon 2017

Data frames are essential tools for data scientists, but existing data frames packages in Julia (and other languages) are sequential and do not scale to large data sets. Alternatively, data frames in distributed frameworks such as Spark are slow and not integrated with other computations flexibly. We propose a novel compiler-based approach where we integrate data frames into the High Performance Analytics Toolkit (HPAT) to build HiFrames. It automatically parallelizes and compiles relational operations along with other array computations in end-to-end data analytics programs, and generates efficient MPI/C++ code. We demonstrate that HiFrames is significantly faster than alternatives such as Spark on clusters, without forcing the programmer to switch to embedded SQL for part of the program. HiFrames is 3.6x to 70x faster than Spark SQL for basic relational operations, and can be up to 20,000x faster for advanced analytics operations, such as weighted moving averages (WMA), that the map-reduce paradigm cannot handle effectively. We will discuss how Julia’s powerful macro and compilation system facilitates developing HiFrames.