Controlling Memory Footprint of Stateful Streaming Graph Processing
Explore cutting-edge techniques for controlling memory usage in stateful streaming graph processing systems in this conference talk from USENIX ATC '21. Dive into the challenges of analyzing dynamic graphs and learn about innovative memory-efficient stateful iterative models that significantly reduce memory footprint while maintaining performance. Discover the Selective Stateful Iterative Model and the Minimal Stateful Iterative Model, understanding their implementation strategies and benefits. Examine experimental results demonstrating how these models enable processing of larger graphs that traditional approaches struggle with. Gain insights into the future of efficient streaming graph analysis and its applications in handling ever-growing datasets.