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Graph computing has become increasingly crucial in processing large-scalegraph data, with numerous systems developed for this purpose. Two years ago, weintroduced GraphScope as a system addressing a wide array of graph computingneeds, including graph traversal, analytics, and learning in one system. Sinceits inception, GraphScope has achieved significant technological advancementsand gained widespread adoption across various industries. However, one keylesson from this journey has been understanding the limitations of a"one-size-fits-all" approach, especially when dealing with the diversity ofprogramming interfaces, applications, and data storage formats in graphcomputing. In response to these challenges, we present GraphScope Flex, thenext iteration of GraphScope. GraphScope Flex is designed to be bothresource-efficient and cost-effective, while also providing flexibility anduser-friendliness through its LEGO-like modularity. This paper explores thearchitectural innovations and fundamental design principles of GraphScope Flex,all of which are direct outcomes of the lessons learned during our ongoingdevelopment process. We validate the adaptability and efficiency of GraphScopeFlex with extensive evaluations on synthetic and real-world datasets. Theresults show that GraphScope Flex achieves 2.4X throughput and up to 55.7Xspeedup over other systems on the LDBC Social Network and Graphalyticsbenchmarks, respectively. Furthermore, GraphScope Flex accomplishes up to a2,400X performance gain in real-world applications, demonstrating itsproficiency across a wide range of graph computing scenarios with increasedeffectiveness.

GraphScope Flex: LEGO-like Graph Computing Stack