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We study online graph queries that retrieve nearby nodes of a query node froma large network. To answer such queries with high throughput and low latency,we partition the graph and process the data in parallel across a cluster ofservers. State-of-the-art distributed graph querying systems place each graphpartition on a separate server, where query answering over that partition takesplace. This design has two major disadvantages. First, the router needs tomaintain a fixed routing table. Hence, these systems are less flexible withrespect to query routing, fault tolerance, and graph updates. Second, the graphdata must be partitioned such that the workload across the servers is balanced,and the inter-machine communication is minimized. In addition, it is requiredto update the existing partitions based on workload changes over graph nodes.However, graph partitioning, online monitoring of workloads, and dynamicallyupdating the graph partitions are expensive. In this work, we mitigate boththese problems by decoupling graph storage from query processors, and bydeveloping smart routing strategies that improve the cache locality in queryprocessors. Since a query processor is no longer assigned any fixed part of thegraph, it is equally capable of handling any request, thus facilitating loadbalancing and fault tolerance. On the other hand, due to our smart routingstrategies, query processors can effectively leverage their cache contents,reducing the overall impact of how the graph is partitioned across storageservers. A detailed experimental evaluation with several real-world, largegraph datasets demonstrates that our proposed framework, gRouting - even withsimple hash partitioning of the data - achieves up to an order of magnitudebetter query throughput compared to existing graph querying systems that employexpensive graph partitioning and re-partitioning strategies.

On Smart Query Routing: For Distributed Graph Querying with Decoupled Storage