A bloat-aware design for big data applications

Abstract

Over the past decade, the increasing demands on data-driven business intelligence have led to the proliferation of large-scale, data-intensive applications that often have huge amounts of data (often at terabyte or petabyte scale) to process. An object-oriented programming language such as Java is often the developer's choice for implementing such applications, primarily due to its quick development cycle and rich community resource. While the use of such languages makes programming easier, significant performance problems can often be seen --- the combination of the inefficiencies inherent in a managed run-time system and the impact of the huge amount of data to be processed in the limited memory space often leads to memory bloat and performance degradation at a surprisingly early stage. This paper proposes a bloat-aware design paradigm towards the development of efficient and scalable Big Data applications in object-oriented GC enabled languages. To motivate this work, we first perform a study on the impact of several typical memory bloat patterns. These patterns are summarized from the user complaints on the mailing lists of two widely-used open-source Big Data applications. Next, we discuss our design paradigm to eliminate bloat. Using examples and real-world experience, we demonstrate that programming under this paradigm does not incur significant programming burden. We have implemented a few common data processing tasks both using this design and using the conventional object-oriented design. Our experimental results show that this new design paradigm is extremely effective in improving performance --- even for the moderate-size data sets processed, we have observed 2.5x+ performance gains, and the improvement grows substantially with the size of the data set.