Columnar objects: improving the performance of analytical applications

Growing volumes of data increase the demand to use it in analytical applications to make informed decisions. Unfortunately, object-oriented runtimes experience performance problems when dealing with large data volumes. Similar problems have been addressed by column-oriented inmemory databases, whose memory layout is tailored to analytical workloads. As a result, data storage and processing are often delegated to such a database. However, the more domain logic is moved to this separate system, the more benefits of object-orientation are lost. We propose modifications to dynamic object-oriented runtimes to store collections of objects in a column-oriented memory layout and leverage a just-in-time compiler (JIT) to take advantage of the adjusted layout by mapping object traversal to array operations. We implemented our concept in PyPy, a Python interpreter equipped with a tracing JIT. Finally, we show that analytical algorithms, expressed through object-oriented code, are up to three t...

• Type: Journal
• Year: 2015
• Where: OOPSLA
• Authors: Toni Mattis, Johannes Henning, Patrick Rein, Robert Hirschfeld, Malte Appeltauer
• Keywords: OOPSLA 2015, Security Privacy