Towards Interactive Design of Graph Data Structures

Abstract

Graph Frameworks and Databases are critical components of modern software. The need to analyze massive graph datasets have spurred the development of Graph Systems. Graph Frameworks and Libraries with tuned Graph data structures are continuously being developed to handle new workloads and data patterns. This presents a need for a Graph system that has knowledge of its design space and is capable of combining fundamental design constructs to generate optimal graph data structures for a given hardware, data pattern and workload. We propose leveraging non-graph systems with these capabilities and with an overlap in its design space with Graph systems, to bring this intelligence to Graph Systems. As a first step in this process, we have implemented a Key-Value Graph Generator, that demonstrates the use of key-value approach in designing Adjacency List and Compressed Sparse Row (CSR). Our hypothesis is that if we can successfully model Graph data structures using key-value approach then we can leverage learned key-value system and create an interactive and automatic Graph system.