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Performance of graph query languages: comparison of cypher, gremlin and native access in Neo4j

Published: 18 March 2013 Publication History
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  • Abstract

    NoSQL and especially graph databases are constantly gaining popularity among developers of Web 2.0 applications as they promise to deliver superior performance when handling highly interconnected data compared to traditional relational databases. Apache Shindig is the reference implementation for OpenSocial with its highly interconnected data model. However, the default back-end is based on a relational database. In this paper we describe our experiences with a different back-end based on the graph database Neo4j and compare the alternatives for querying data with each other and the JPA-based sample back-end running on MySQL. Moreover, we analyze why the different approaches often may yield such diverging results concerning throughput. The results show that the graph-based back-end can match and even outperform the traditional JPA implementation and that Cypher is a promising candidate for a standard graph query language, but still leaves room for improvements.

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    Published In

    cover image ACM Other conferences
    EDBT '13: Proceedings of the Joint EDBT/ICDT 2013 Workshops
    March 2013
    423 pages
    ISBN:9781450315999
    DOI:10.1145/2457317
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 18 March 2013

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    Author Tags

    1. Neo4j
    2. NoSQL
    3. benchmarks
    4. graph databases
    5. graph query processing and performance optimization
    6. graph query processing for social networks

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    EDBT '13 Paper Acceptance Rate 7 of 10 submissions, 70%;
    Overall Acceptance Rate 7 of 10 submissions, 70%

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    • (2023)ISLKG: The Construction of Island Knowledge Graph and Knowledge ReasoningSustainability10.3390/su15171318915:17(13189)Online publication date: 1-Sep-2023
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