A Configurable Framework for High-Performance Graph Storage and Mutation

In the realm of graph processing, efficient storage and update mechanisms are crucial due to the large volume of graphs and their dynamic nature. Traditional data structures such as adjacency lists and matrices, while effective in certain scenarios, often suffer from performance trade-offs such as high memory consumption or slow update capabilities. To address these challenges, we introduce CoreGraph, an advanced graph framework designed to optimize both read and update performance. CoreGraph leverages a novel segmentation method and in-place update techniques, along with configurable memory allocators and synchronization mechanisms, to enhance parallel processing and reduce memory consumption. CoreGraph’s update throughput (with up to 20x) and analytics performance exceed those of several state-of-the-art graph structures such as Teseo, GraphOne and LLAMA, while maintaining low memory consumption when the workload includes updates. This paper details the architecture and benefits of CoreGraph, highlighting its practical application in traffic data management where it seamlessly integrates with existing systems providing a scalable and efficient solution for real-world graph data management challenges.