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

TL;DR

This paper introduces gRouting, a novel distributed graph querying framework that decouples storage from query processing, enabling flexible routing, load balancing, and significant performance improvements over traditional partitioned systems.

Contribution

Decouples graph storage from query processing and develops smart routing strategies to improve cache locality and load balancing in distributed graph querying.

Findings

Achieves up to tenfold increase in query throughput.

Reduces dependency on complex graph partitioning and re-partitioning.

Demonstrates effectiveness on large real-world graph datasets.

Abstract

We study online graph queries that retrieve nearby nodes of a query node from a 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 of servers. State-of-the-art distributed graph querying systems place each graph partition on a separate server, where query answering over that partition takes place. This design has two major disadvantages. First, the router needs to maintain a fixed routing table. Hence, these systems are less flexible with respect to query routing, fault tolerance, and graph updates. Second, the graph data must be partitioned such that the workload across the servers is balanced, and the inter-machine communication is minimized. In addition, it is required to update the existing partitions based on workload changes over graph nodes. However, graph partitioning, online monitoring of workloads, and dynamically updating the graph partitions are expensive. In this work, we mitigate both these problems by decoupling graph storage from query processors, and by developing smart routing strategies that improve the cache locality in query processors. Since a query processor is no longer assigned any fixed part of the graph, it is equally capable of handling any request, thus facilitating load balancing and fault tolerance. On the other hand, due to our smart routing strategies, query processors can effectively leverage their cache contents, reducing the overall impact of how the graph is partitioned across storage servers. A detailed experimental evaluation with several real-world, large graph datasets demonstrates that our proposed framework, gRouting - even with simple hash partitioning of the data - achieves up to an order of magnitude better query throughput compared to existing graph querying systems that employ expensive graph partitioning and re-partitioning strategies.