Overcoming memory limitations in high-throughput event-based applications

The last decade has witnessed the emergence of business critical applications processing streaming data for domains as diverse as credit card fraud detection, real-time recommendation systems, call-center monitoring, ad selection, network monitoring, and more. Most of those applications need to compute hundreds or thousands of metrics continuously while coping with very high event input rates. As a consequence, large amounts of state (i.e., moving windows) need to be maintained, very often exceeding the available memory resources. Nonetheless, current event processing platforms have little or no memory management capabilities, hanging or simply crashing when memory is exhausted. In this paper we report our experience in using secondary storage for solving the performance problems of memory-constrained event processing applications. For that, we propose SlideM, a novel buffer management algorithm that exploits the access pattern of sliding windows in order to efficiently handle memory shortages. The proposed algorithm was implemented in a real stream processing engine and validated through an extensive experimental performance evaluation. Results corroborate the efficacy of the approach: the system was able to sustain very high input rates (up to 300,000 events per second) for very large windows (about 30GB) while consuming small amounts of main memory (few kilobytes).