NDLI: All-in-One: Graph Processing in RDBMSs Revisited

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All-in-One: Graph Processing in RDBMSs Revisited

Content Provider	ACM Digital Library
Author	Yu, Jeffrey Xu Zhao, Kangfei
Abstract	To support analytics on massive graphs such as online social networks, RDF, Semantic Web, etc. many new graph algorithms are designed to query graphs for a specific problem, and many distributed graph processing systems are developed to support graph querying by programming. In this paper, we focus on RDBM, which has been well studied over decades to manage large datasets, and we revisit the issue how RDBM can support graph processing at the SQL level. Our work is motivated by the fact that there are many relations stored in RDBM that are closely related to a graph in real applications and need to be used together to query the graph, and RDBM is a system that can query and manage data while data may be updated over time. To support graph processing, in this work, we propose 4 new relational algebra operations, MM-join, MV-join, anti-join, and union-by-update. Here, MM-join and MV-join are join operations between two matrices and between a matrix and a vector, respectively, followed by aggregation computing over groups, given a matrix/vector can be represented by a relation. Both deal with the semiring by which many graph algorithms can be supported. The anti-join removes nodes/edges in a graph when they are unnecessary for the following computing. The union-by-update addresses value updates to compute PageRank, for example. The 4 new relational algebra operations can be defined by the 6 basic relational algebra operations with group-by & aggregation. We revisit SQL recursive queries and show that the 4 operations with others are ensured to have a fixpoint, following the techniques studied in DATALOG, and enhance the recursive WITH clause in SQL'99. We conduct extensive performance studies to test 10 graph algorithms using 9 large real graphs in 3 major RDBMs. We show that RDBMs are capable of dealing with graph processing in reasonable time. The focus of this work is at SQL level. There is high potential to improve the efficiency by main-memory RDBMs, efficient join processing in parallel, and new storage management.
Starting Page	1165
Ending Page	1180
Page Count	16
File Format	PDF
ISBN	9781450341974
DOI	10.1145/3035918.3035943
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-05-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Xy-stratified Datalog Mv-join Graph algorithms Mm-join Relational database systems Sql recursion
Content Type	Text
Resource Type	Article

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