Big Data Analytics: Exploring Graphs with Optimized SQL Queries

Al-Amin, Sikder Tahsin; Ordóñez, Carlos; Bellatreche, Ladjel

doi:10.1007/978-3-319-99133-7_7

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…This work is a continuation of previous research efforts targeting to implement different graph analysis algorithms using queries, among others triangle counting problem [1]. Besides, optimizing linear recursive queries, which has triangles as a particular case of the transitive closure of the input graph [5,6].…”

Section: System Descriptionmentioning

confidence: 92%

PandaSQL

Farouzi

Bellatreche

Ordóñez

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Triangles are an important pattern in large-scale graph analysis for their practical use in many real-life applications. However, with the expansion of networks, maintaining a balanced computational load is challenging especially for problems like triangle computations because of skewed vertices. On the other hand, there is a huge amount of data in database management systems (DBMSs) that can be modeled and analyzed as graphs. With these motivations in mind, we developed PandaSQL, a novel approach using SQL queries to enumerate all the triangles in a given graph based on Randomized Triangle Enumeration Algorithm. Our approach is elegant, abstract, and short compared to traditional languages like C++ or Python. Moreover, our partitioning queries ensures a perfect load balancing. Thus, the triangle enumeration is independent, local, and parallel. CCS CONCEPTS • Information systems → Relational parallel and distributed DBMSs; Structured Query Language.

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Section: System Descriptionmentioning

confidence: 92%

PandaSQL

Farouzi

Bellatreche

Ordóñez

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

Self Cite

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“…Other works like [23,10] stored graphs in relational tables with schema optimized for graph queries by adding a specific layer supporting graph processing on RDBMS. Other interesting work of processing graph in DBMSs can be found at [17,2,9]. Moreover, there exist powerful parallel graph engines in the Hadoop big data ecosystem like Neo4j and Spark GraphX, but they require significant effort to link and integrate information about the graph and, in general, they provide query languages inferior as compared to SQL.…”

Section: Related Workmentioning

confidence: 99%

A Scalable Randomized Algorithm for Triangle Enumeration on Graphs Based on SQL Queries

Farouzi

Bellatreche

Ordóñez

et al. 2020

Big Data Analytics and Knowledge Discovery

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Triangle enumeration is a fundamental problem in large-scale graph analysis. For instance, triangles are used to solve practical problems like community detection and spam filtering. On the other hand, there is a large amount of data stored on database management systems (DBMSs), which can be modeled and analyzed as graphs. Alternatively, graph data can be quickly loaded into a DBMS. Our paper shows how to adapt and optimize a randomized distributed triangle enumeration algorithm with SQL queries, which is a significantly different approach from programming graph algorithms in traditional languages such as Python or C++. We choose a parallel columnar DBMS given its fast query processing, but our solution should work for a row DBMS as well. Our randomized solution provides a balanced workload for parallel query processing, being robust to the existence of skewed degree vertices. We experimentally prove our solution ensures a balanced data distribution, and hence workload, among machines. The key idea behind the algorithm is to evenly partition all possible triplets of vertices among machines, sending edges that may form a triangle to a proxy machine; this edge redistribution eliminates shuffling edges during join computation and therefore triangle enumeration becomes local and fully parallel. In summary, our algorithm exhibits linear speedup with large graphs, including graphs that have high skewness in vertex degree distributions.

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“…Although RDBMSs were not specifically designed to compute queries that "navigate" a graph, they still play a role in managing and processing graphs (SAHU et al, 2017;SAHU et al, 2019). These queries are often represented as recursive queries (ORDONEZ, 2005;PRZYMUS et al, 2010;CORTESI;HALDER, 2013;AL-AMIN;ORDONEZ;BELLATRECHE, 2018), characterized by computing repetitive join operations to perform a graph transversal (GAO et al, 2014;RAJ;PATEL, 2015;JINDAL et al, 2015). Several graph analyses were mapped as recursive queries to execute in RDBMSs (ZHAO; YU, 2017), and most RDBMSs, including DB2, Microsoft SQL Server, Oracle, and PostgreSQL support recursive queries.…”

Section: Introductionmentioning

confidence: 99%

Storage and Navigation Operations on Graphs in Relational DBMS

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Primeiramente agradeço a Deus pelas bênçãos que recebi em minha vida.Aos meus pais, José Roberto e Maria Cristina, à minha irmã Thais e ao meu cunhado Gabriel, por sempre me apoiarem em minhas escolhas, incentivarem meus estudos, e estarem sempre presentes quando precisei. Sou imensamente grato a eles.Ao meu orientador, Prof. Dr. Caetano Traina Junior, por ter me orientado, auxiliado, e ensinado durante todo o projeto. À Prof. a Dr. a Agma Juci Machado Traina, por me apoiar nos mais diversos momentos quando precisei.Ao Prof. Dr. Daniel dos Santos Kaster, por sempre me auxiliar, ensinar e, quando necessário, fazer críticas construtivas em prol de meu crescimento tanto pessoal quanto profissional. Ele foi um verdadeiro companheiro e amigo que espero ter por toda a vida.Ao Grupo de Bases de Dados e Imagens (GBDI), por todas as oportunidades de colaboração e de trocas de experiência. E a todos os amigos que lá fiz:

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Big Data Analytics: Exploring Graphs with Optimized SQL Queries

Cited by 7 publications

References 19 publications

PandaSQL

PandaSQL

A Scalable Randomized Algorithm for Triangle Enumeration on Graphs Based on SQL Queries

Storage and Navigation Operations on Graphs in Relational DBMS

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