Horton+

Author:

Sarwat Mohamed1,Elnikety Sameh2,He Yuxiong2,Mokbel Mohamed F.1

Affiliation:

1. Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN

2. Microsoft Research, Redmond, WA

Abstract

Horton+ is a graph query processing system that executes declarative reachability queries on a partitioned attributed multi-graph. It employs a query language, query optimizer, and a distributed execution engine. The query language expresses declarative reachability queries, and supports closures and predicates on node and edge attributes to match graph paths. We introduce three algebraic operators, select, traverse, and join, and a query is compiled into an execution plan containing these operators. As reachability queries access the graph elements in a random access pattern, the graph is therefore maintained in the main memory of a cluster of servers to reduce query execution time. We develop a distributed execution engine that processes a query plan in parallel on the graph servers. Since the query language is declarative, we build a query optimizer that uses graph statistics to estimate predicate selectivity. We experimentally evaluate the system performance on a cluster of 16 graph servers using synthetic graphs as well as a real graph from an application that uses reachability queries. The evaluation shows (1) the efficiency of the optimizer in reducing query execution time, (2) system scalability with the size of the graph and with the number of servers, and (3) the convenience of using declarative queries.

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

Cited by 28 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Minimum Strongly Connected Subgraph Collection in Dynamic Graphs;Proceedings of the VLDB Endowment;2024-02

2. Knowledge Graphs Querying;ACM SIGMOD Record;2023-08-10

3. Scalable Graph Convolutional Network Training on Distributed-Memory Systems;Proceedings of the VLDB Endowment;2022-12

4. Personalized Graph Summarization: Formulation, Scalable Algorithms, and Applications;2022 IEEE 38th International Conference on Data Engineering (ICDE);2022-05

5. Granite: A distributed engine for scalable path queries over temporal property graphs;Journal of Parallel and Distributed Computing;2021-05

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